Consumer protection study 2022: understanding the impacts and resolution of consumer problems
Source: data.gov.uk (2022-05-13)

Consumer protection study 2022. This study aimed to understand consumers’ experiences with purchasing items and services, the type and impact of problems they faced in doing so, and how they managed to resolve these problems.
Detailed analysis tables
The first set of tables contains data on respondents: these can be used to explore what socio-economic demographics (for example age, gender, ethnicity) are associated with given detriment outcomes and patterns.
The second set of tables cover the detriment instances experienced and show in more detail how they vary by type of product, channel of purchase, type of problem, and more.

April 2022
Consumer Protection Study
2022
Understanding the impacts and resolution of
consumer problems
BEIS Research Paper Number 2022/005

Acknowledgements
This independent research report was produced by Gianfranco Addario, Alison Blackwell,
David Hussey, Curtis Jessop, Marta Mezzanzanica, Chujan Sivathasan, Angela Stockman
and Martin Wood from NatCen Social Research.

We are grateful to all survey respondents for their time – this report wouldn’t be possible
without them. We would also like to thank Ingo Hanke and the wider steering group at the
CPP and Which? for the insights and guidance throughout the various phases of the
project.

© Crown copyright 2022
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1
Contents
Executive Summary ______________________________________________________ 7
Overview of consumer detriment in the UK __________________________________ 7
Net monetised detriment ________________________________________________ 8
The perceived impact of COVID-19 on consumer detriment _____________________ 10
Actioned and unactioned consumer detriment ________________________________ 11
Outcomes and resolutions _______________________________________________ 11
Consumers at risk of negative detriment outcomes ____________________________ 12
Consumer detriment in the four UK countries ________________________________ 12
Introduction ____________________________________________________________ 13
Research questions ____________________________________________________ 13
Background __________________________________________________________ 14
The policy context _____________________________________________________ 15
The study context ______________________________________________________ 16
Summary of methodology and definition of key concepts _______________________ 19
1
Levels of consumer detriment in the UK ___________________________________ 23
1.1
Incidence and numbers of consumer detriment_________________________ 23
1.2
Net monetised detriment __________________________________________ 29
1.3
Detriment to wellbeing ____________________________________________ 43
2
Complaints journey ___________________________________________________ 53
2.1
Actioned and unactioned detriment __________________________________ 53
2.2
Detriment resolution _____________________________________________ 58
2.3
Satisfaction with the detriment resolution _____________________________ 63
3
Identifying consumers at risk of detriment __________________________________ 67
3.1
Risk of experiencing detriment _____________________________________ 67
3.2
Facing the most negative consequences of detriment ___________________ 78
3.3
Unactioned consumer detriment ____________________________________ 85
3.4
Satisfaction with the outcome and resolution patterns ___________________ 88
4
Consumer detriment in the four UK countries _______________________________ 89
4.1
Incidence of consumer detriments in the UK countries ___________________ 89
4.2
Value of monetised detriment in the UK countries_______________________ 91
5
Respondent estimated impact of COVID-19 ________________________________ 93
5.1
Impact of COVID-19 on consumer detriment __________________________ 93
5.2
Impact of COVID-19 and elements of consumer detriment ________________ 96
Conclusions ____________________________________________________________ 100

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Appendices ____________________________________________________________ 107
Appendix A. Questionnaire development and testing _________________________ 107
Appendix B. Fieldwork design, response rate and weighting strategy _____________ 111
Appendix C. Comparison of sample types and interview mode __________________ 119
Appendix D. Comparison with previous UK consumer detriment studies __________ 124
Appendix E. Analysis __________________________________________________ 127
Appendix F. Reporting conventions _______________________________________ 141
Appendix G. Questionnaire specification ___________________________________ 142
Glossary _______________________________________________________________ 162
References list __________________________________________________________ 165

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Figures
Figure 1 – The 10 sectors with the highest incidence of detriment ............................................................................ 8
Figure 2 – The 10 sectors with the highest median net monetised detriment ............................................................ 9
Figure 3 – The 10 sectors with the highest proportion of incidents perceived as at least somewhat affected by the
pandemic .......................................................................................................................................................... 10
Figure 4 – Channel of purchase in detrimental experiences .................................................................................... 28
Figure 5 – Detriment type in detrimental experiences .............................................................................................. 28
Figure 6 – Price (initial cost) of the product in detrimental experiences ................................................................... 29
Figure 7 – Net monetised detriment formula and its components ............................................................................ 30
Figure 8 – Distribution of the values of net monetised detriment ............................................................................. 32
Figure 9 – Value of the net monetised detriment components (billion £) ................................................................. 33
Figure 10 – Percentage of detriment incidents by sectors and market clusters (UK total = 100%) ......................... 36
Figure 11 – Percentage of monetised detriment by sectors and market clusters (UK total = 100%)....................... 37
Figure 12 – Total costs and total mitigations by sector (billion £) ............................................................................. 40
Figure 13 – Total net monetised detriment by channel of purchase (billion £) ......................................................... 41
Figure 14 – Median net monetised detriment by channel of purchase .................................................................... 42
Figure 15 – Total net monetised detriment when each detriment type was experienced (billion £) ........................ 43
Figure 16 – Negative impact of the detriment experiences on consumers’ feelings ................................................ 44
Figure 17 – Negative impact of the detriment experiences on consumers’ health and household finances .......... 45
Figure 18 – Proportion of actioned experiences of detriment by market cluster ...................................................... 54
Figure 19 – Proportion of actioned experiences of detriment by purchase channel ................................................ 55
Figure 20 – Proportion of actioned experiences of detriment by detriment type ...................................................... 55
Figure 21 – Reasons why consumer decided not to act after experience of detriment ........................................... 56
Figure 22 – Actions taken by the consumer after experience of detriment .............................................................. 57
Figure 23 – Actions requested by consumers after having experienced detriment ................................................. 58
Figure 24 – Actions taken by sellers after detriment experience .............................................................................. 59
Figure 25 – Consumer’s success in obtaining the resolution wanted for the detriment incident ............................. 60
Figure 26 – Resolution by market cluster ................................................................................................................. 61
Figure 27 – Resolution by product value .................................................................................................................. 62
Figure 28 – Resolution by time spent on problem .................................................................................................... 63
Figure 29 – Satisfaction with the outcome of detriment experiences ....................................................................... 64
Figure 30 – Proportion of concluded experiences of detriment where the consumers were at least somewhat
satisfied with the outcome, by market cluster .................................................................................................. 65
Figure 31 – Whether satisfied by success in obtaining what was wanted ............................................................... 66
Figure 32 – Proportion of consumers who have experienced detriment by their self-assessed financial condition 68
Figure 33 – Proportion of consumers who have experienced detriment by household composition ....................... 69
Figure 34 – Proportion of consumers who have experienced detriment by age group ............................................ 70
Figure 35 – Proportion of consumers who have experienced detriment by ethnic background .............................. 71
Figure 36 – Probability of experiencing detriment (0 = no detriment, 1 = detriment) by number of sectors in which
products were consumed ................................................................................................................................. 73
Figure 37 – Proportion of consumers who faced a total net monetised detriment of £1,500 or more in the 12
months to April 2021 by age group .................................................................................................................. 79
Figure 38 – Proportion of consumers who have lost £1,500 or more in net monetised detriment in the 12 months
to April 2021 by financial situation ................................................................................................................... 80
Figure 39 – Proportion of consumers who reported a very negative effect on their wellbeing at least once by
financial condition ............................................................................................................................................. 81
Figure 40 – Proportion of consumers who reported a very negative effect on their wellbeing at least once by
economic activity .............................................................................................................................................. 81
Figure 41 – Proportion of consumers who mentioned having felt anxious, misled or upset to a great extent, at
least once, in their experiences of detriment by financial situation .................................................................. 84
Figure 42 – Consumers who did not take actions to address at least one experience of detriment by age group .. 85
Figure 43 – Share of consumers who experienced at least one problem and did not take actions on any problem
by propensity to complain ................................................................................................................................ 86
Figure 44 – Reasons for not taking actions to address experiences of detriment mentioned at least once ............ 86
Figure 45 – Incidence of consumer detriment in the four UK countries ................................................................... 90

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Figure 46 – Incidence of consumer detriment in the four UK countries for items and services ............................... 90
Figure 47 – Value of net monetised detriment in the UK by country (billion £) ........................................................ 92
Figure 48 – Views on the extent to which detriment experienced was affected by the COVID-19 pandemic ......... 94
Figure 49 – Median net monetised detriment by whether detriment experienced was affected by the COVID-19
pandemic .......................................................................................................................................................... 95
Figure 50 – Whether felt anxious, helpless, upset or misled as a result of detriment by the extent to which
detriment experienced was affected by the COVID-19 pandemic ................................................................... 95
Figure 51 – Whether consumers felt detriment had a negative effect on health and household finances by the
extent to which detriment experienced was considered to be affected by the COVID-19 pandemic .............. 96
Figure 52 – Percentage of detriment experiences felt to be caused or made worse by the COVID-19 pandemic by
market cluster ................................................................................................................................................... 97
Figure 53 – Sectors in which 50% or more of the experiences detriment were perceived as mostly or fully caused,
or made worse by the pandemic ...................................................................................................................... 98
Figure 54 – Whether consumer felt detriment was caused or made worse by the COVID-19 pandemic by
product’s value ................................................................................................................................................. 99

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Tables
Table 1 – Incidence of purchases and detriment by product type, market clusters and sectors ............................. 26
Table 2 – Total net monetised detriment, its confidence interval and median by product type and by market cluster
......................................................................................................................................................................... 34
Table 3 – Total net monetised detriment, its confidence interval and median by sector.......................................... 38
Table 4: Percentage of detriment experiences that had a negative impact on the seven wellbeing elements
overall, by product type and market clusters ................................................................................................... 46
Table 5 – Percentage of detriment experiences that had a negative impact on the seven wellbeing categories by
channel of purchase, value of the product, satisfaction and resolution status, and whether the consumer took
actions to resolve the detriment ....................................................................................................................... 49
Table 6 – Percentage of detriment experiences that had a negative impact on the seven wellbeing categories by
whether a detriment type was experienced or not ........................................................................................... 51
Table 7 – Median value of the product by reason for not taking action after experiencing detriment ...................... 57
Table 8 – Model with consumer activity variables .................................................................................................... 74
Table 9 – Comparison of the models with age and education variables vs age, education and consumer activity 75
Table 10 – Comparisons of the models with geographical variables vs geographical variables and consumer
activity .............................................................................................................................................................. 76
Table 11 – Comparison of the models with ethnicity, age and urban/rural variables vs ethnicity, age, urban/rural
and consumer activity ...................................................................................................................................... 77
Table 12 – Comparison of the models with internet use, age and education variables vs internet use, age,
education and consumer activity ...................................................................................................................... 78
Table 13 – Proportion of consumers who mentioned having felt anxious or upset to a great extent, at least once,
in their experiences of detriment by age group ................................................................................................ 83
Table 14 - Proportion of consumers who mentioned having felt anxious, misled or upset to a great extent, at least
once, in their experiences of detriment by economic activity........................................................................... 84
Table 15 – Incidence of consumer detriment in the four UK countries in market clusters ....................................... 91
Table 16 – Median of the macro-components of net monetised detriment in the four UK countries (£) .................. 92
Table 17 – Percentage of detriment experiences considered to be caused, or made worse, by the COVID-19
pandemic by detriment type experienced with the product.............................................................................. 99
Table 18 – Cognitive participant characteristics ..................................................................................................... 108
Table 19 – Pilot sample profile ............................................................................................................................... 109
Table 20 – NatCen Panel sample response rates105F105F ................................................................................. 112
Table 21 – Push-to-web survey response rates ..................................................................................................... 113
Table 22 – Respondents profile by sample type (unweighted) .............................................................................. 120
Table 23 – Survey key findings by sample type ..................................................................................................... 121
Table 24 – Respondents profile by mode of completion (unweighted) .................................................................. 122
Table 25 – Survey key findings by mode of completion ......................................................................................... 123
Table 26 – Key methodological differences in the most recent consumer detriment surveys carried out in the UK
....................................................................................................................................................................... 125
Table 27 – Key findings of the 2016 and 2021 consumer detriment studies ......................................................... 126
Table 28 – Classifications of sectors and market clusters ..................................................................................... 128
Table 29 – Analysis labels and survey categories of types of detriment ................................................................ 129
Table 30 – Incidence of consumer detriment in the different sectors ..................................................................... 135
Table 31 – Components of net monetised detriment by sector (million £) ............................................................. 137
Table 32 – Outputs of regression models ............................................................................................................... 139

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Images
Image 1 – Study timeline and key events ........................................................................................... 18
Image 2 – Consumer detriment key figures for the period spanning from April 2020 to April 2021 ................... 24
Image 3 – Two-fifths of the total UK net monetised detriment imputable to four sectors ................................ 35
Image 4 – Exploring the impact of detriment on wellbeing: two levels of analysis ........................................ 44
Image 5 – Number of detriment experiences that had a negative impact on consumers’ mental health, physical
health and household finance every 10 detriment experiences ......................................................... 45
Image 6 – Questionnaire flow ........................................................................................................ 107

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1 Executive Summary
The problems that consumers experience with the products (items and services) they
purchase, and the consequences of consumer detriment, play a key role in consumers’ trust in
the market. The objective of the Consumer Protection Study 1 was to listen to what
consumers could tell us about their experiences of detriment, so that the problems they faced,
and the role played by the consumer protection system 2, could be observed from their point of
view 3.
Overall, this study found that many purchases did not result in problems, most problems were
resolved to the satisfaction of consumers, and most consumers who did face negative
consequences experienced low-value detriment. However, a small number of problems had a
high-value detriment leaving £54 billion of aggregate consumer detriment unaddressed.
This report offers an overview of these findings and highlights how a handful of product types
accounted for a large portion of unresolved detriment and how some purchase channels and
problem types were associated with a higher prevalence of negative consequences. The report
also explores how some groups of consumers were more likely to experience a problem and to
then face more negative consequences as a result.
Overview of consumer detriment in the UK
Between April 2020 and April 2021, 69% of consumers in the UK experienced consumer
detriment 4. Extrapolated to the UK adult population, this means that 36 million consumers
experienced at least one problem with a product (either a service or an item) they bought in
those 12 months, or bought at any time and used in that period, that caused them stress, cost
them money, or took up their time. Four is the median number of incidents per consumer 5,
for a total of 229.8 million problems 6 over a period of 12 months.
Consumers were more likely to experience detriment with services and subscriptions
(55% of consumers who purchased a service or a subscription faced detriment) compared to
items (48%), and the incidence 7 differed across sectors, ranging from 36% for ‘Airline’ to
three percent for ‘Personal care services’ 8. When grouping different sectors together, the

1 The study is based on quantitative data collected through survey questionnaires asked to a probability sample.
2 The concept of the consumer protection system used here encompasses the rights and channels of action afforded to
consumers by consumer protection law as well as the willingness of businesses to mitigate and resolve consumer
detriment problems (either due to market pressure or due to the threat of legal consequences).
3 The subjective dimension of the study is pivotal to the understanding of consumers’ behaviours and actions. While
these are not necessarily driven by what is considered factual detriment, discrepancies between what consumers
report and figures based on transactional data are important in understanding consumers’ behaviour within and trust
of the system.
4 95% Confidence interval: 68% - 70%.
5 95% Confidence interval: 3 - 4.
6 95% Confidence interval: 210.0 - 249.6.
7 The incidence of detriment is the number of consumers who experienced detriment with a product over the number of
consumers who purchased, or used a previously purchased product, in the 12 months covered by the study.
8 Data was collected during the COVID-19 outbreak and many sectors have been at the centre of unusual disruptions
and operating difficulties. This could have influenced the incidence of detriment in some sectors.

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8
highest incidence was found in ‘Fast-moving retail’ (76%) 9, which is characterised by a high
number of purchases and therefore a high probability that something can go wrong, at least
once, often resulting in detriment with products of very small value.
Figure 1 – The 10 sectors with the highest incidence of detriment
Base: UK consumers aged 18+ who made a purchase in the sector or market cluster.
Unweighted: Airline 463, Package holidays and tours 429, Second-hand vehicles 1,011, Internet provision
5,035, Electronic devices and software 3,655, Real estate services 364, Clothing, footwear and accessories
5,321, Furniture and appliances 3,709, Adult care 140, New vehicles 440.
More than one in three experiences of detriment were a consequence of poor-quality
products (36%), followed by having received unusable products (21%), problems with
deliveries (18%) and having never received a purchased item or service (18%) 10.
About two in five problems (42%) happened with products of relatively low cost (up to £100).
Net monetised detriment
Consumer detriment represents a cost for consumers. After taking into account all the costs
consumers faced 11, the compensations they might have received 12, the value given by
consumers to the products they experienced detriment with, and the time they spent dealing
with the problem, consumer detriment was estimated to have had a net monetised cost of
£54.2 billion to UK consumers 13 between April 2020 and April 2021, including £21 billion of
time cost. Indeed, consumers are estimated to have spent 1.5 billion hours dealing with
detriment 14 in the 12 months covered by the study.
Costs and compensations almost offset each other, meaning that most of the incidents had a
low net monetised detriment. A typical consumer lost £28 for each incident of detriment,

9 ‘Fast-moving retail’ includes the following sectors: ‘Groceries and drinks’, ‘Clothing, footwear and accessories’, ‘House
and garden maintenance products’, ‘Stationery, books, magazines and newspapers’, ‘Personal care products’,
‘Prescription and non-prescription medicines’ and ‘Personal care services’.
10 The same incident (i.e. experience of detriment) could have been related to more than one problem. Therefore, these
numbers should not be summed.
11 This includes the initial cost of the product, the cost bared by the consumer for replacing or fixing the product, and
other direct and indirect costs (including the loss of earnings and not being able to use another product the consumer
paid for, such as not being able to use a hotel booking due to problems with the flight).
12 This includes full or partial refunds, the value of having the product fixed, and any other form of compensation (either
monetary or monetised).
13 95% Confidence interval: £40.9 - £67.6 billion.
14 95% Confidence interval: 1.2 - 1.8.
36%
35%
30%
29%
26%
26%
24%
21%
21%
19%
0%
5%
10%
15%
20%
25%
30%
35%
40%
Airline
Package holidays and tours
Second-hand vehicles
Internet provision
Electronic devices and software
Real estate services
Clothing, footwear and accessories
Furniture and appliances
Adult care
New vehicles

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including the value of their time (median of 90 minutes spent dealing with a detriment incident).
However, some detriment incidents had a higher cost for consumers: 18% of incidents had a
net monetised detriment above £100 and three percent above £1,000.
The value of net monetised detriment varied by channel of purchase. A higher net monetised
detriment was found for problems with products purchased in-person from a salesperson
who visited the consumer at home or in the workplace (median of £87), online on social
media platforms (£55) or through auto-renewal processes (£55) compared to problems
with products purchased through other channels (which had a net monetised detriment ranging
between a median of £14 and £32).
The median net monetised detriment was higher for services (£41) compared to items
(£14) and substantially differed across sectors, ranging from over £400 for issues with
‘Second-hand vehicles’ or ‘Renting services’ to £4 for problems with ‘Stationery, books,
magazines and newspapers’.
Figure 2 – The 10 sectors with the highest median net monetised detriment 15
Base: Detriment experiences in the UK in the 12 months to April 2021.
Unweighted: Second-hand vehicles 184, Renting services 88, Childcare 35, Education fees 43, Veterinary 137,
Real estate services 68, Vehicle maintenance and repair 409, Legal and accountancy services 80, House
garden maintenance and repaid 148, Private medical and dental services 106.

When grouping different sectors together, ‘Fast-moving retail’ sectors had a median net
monetised detriment of £9 and accounted for seven percent of the total value of monetised
detriment found in the UK, despite being the group of sectors with the highest incidence of
detriment (76% of those who consumed a product in this market cluster experienced detriment)
and being responsible for 32% of all the problems in the UK. On the other side of the spectrum,
problems with ‘Renting services’, ‘Vehicle maintenance and repair’, ‘Second-hand
vehicles’ and ‘Internet provision’ accounted for a combined 40% of the total value of net
monetised detriment in the UK, due to a high median monetised detriment or a high
incidence, or both.

15 Sectors with unweighted observations equal or below 25 were excluded.
£463
£442
£210
£207
£143
£142
£118
£111
£109
£101
£0
£100
£200
£300
£400
£500
Second-hand vehicles
Renting services
Childcare
Education fees
Veterinary
Real estate services
Vehicle maintenance and repair
Legal and accountacy services
House and garden maintenance and repair
Private medical and dental services

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The perceived impact of COVID-19 on consumer detriment
The data presented in this report was collected during the COVID-19 outbreak. Consumers
felt that 43% of their incidents of detriment had been affected in some way by the
pandemic (13% of incidents were considered mostly or fully caused by it and 30% of incidents
made worse by it). The problems perceived to have been somehow affected by the pandemic
had a volume of net monetised detriment of £26.5 billion. The perceived impact of the
pandemic on consumer detriment differed by sector, and was particularly high in sectors that
offered personal, recreational and transport-related services.
Figure 3 – The 10 sectors with the highest proportion of incidents perceived as at
least somewhat affected by the pandemic 16
Base: Detriment experiences in the UK in the 12 months to April 2021.
Unweighted: Airline 125, Package holiday and tours 116, Sport, cultural and entertainment activities 110, Hotels
and holiday accommodation 180, Private medical and dental services 105, Real estate services 68, Public
transport and trains 126, Education fees 43, Childcare 35, Personal care services 56.
Problems perceived as being caused or made worse by the pandemic were reported as
having been more severe or more time consuming than those that were not considered to
be affected by it. The average time spent to solve a problem considered somehow affected by
the pandemic was two hours, compared to one hour for those not affected by it. Problems
affected by the pandemic also had a higher median net monetised detriment (£34 if mostly or
fully caused by the pandemic, £28 if made worse by it, and £20 if not affected), partly
explained by the additional time cost.
Impact of detriment on wellbeing, general health and finances
Consumer detriment also has a non-financial impact. When looking at dimensions of
wellbeing, emotions and aspects of life, it was found that: 83% of detriment experiences
triggered at least one negative emotion, with feeling upset and misled being the most
common followed by feeling anxious or helpless; and 32% of detriment experiences had a
negative effect on at least one aspect of life, especially on mental health or household’s
finances, but occasionally also on physical health.
Consumers were generally more likely to experience negative consequences on their
wellbeing after having problems with a service, when the product was purchased through a

16 Sectors with unweighted observations equal or below 25 were excluded.
96%
95%
85%
80%
73%
71%
66%
66%
63%
58%
0%
20%
40%
60%
80%
100%
Airline
Package holidays and tours
Sport, cultural and entertainment activities
Hotels and holiday accommodation
Private medical and dental services
Real estate services
Public transport and trains
Education fees
Childcare
Personal care services

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channel that required a personal interaction with the seller or provider (either in-person or over
a phone call), when the value of the product was particularly high, or when they perceived the
problem as being caused or made worse by the COVID-19 outbreak.
Actioned and unactioned consumer detriment
When facing detriment, consumers were likely to take actions to address the problem.
They generally did so by getting directly in contact with the seller or the service provider (this
type of action was taken in 81% of the detriment incidents where consumers sought to address
the problem).
Only a small proportion of incidents remained unactioned by consumers (18%). Inaction
was generally driven by cost-opportunity considerations (comparing the expenditure of
personal time and money, and the likelihood to succeed, with the need to achieve a resolution
to the problem). Common reasons for not taking actions to address detriment were consumers
regarding the problem as not serious enough (22% of the unactioned incidents) or thinking
they would have been unsuccessful in resolving the problem (10%).
Other reasons for consumers not taking action were the intention to do so in the future (20% of
unactioned incidents), or the fact the problem was resolved without the need for them to take
action (9%). Indeed, in many cases, sellers and providers seemed to have taken actions to
address a problem even if it was not reported by the consumer (this happened for 54% of the
unactioned incidents of detriment).
The likelihood of taking action to address an incident of detriment varied by the channels
through which the product was purchased: consumers were less likely to have taken action in
experiences of detriment which related to a product bought via auto-renewal, bought in-person
from a shop, or online from a website where private individuals sell to each other.
Outcomes and resolutions
When they took actions, consumers generally requested sellers and service providers to
replace or fix the product (34% of the actioned incidents of detriment), provide a refund
(33%), apologise (27%) or provide an explanation about the problem (27%).
On average, the actions requested by consumers mirrored what the seller or service providers
did. They generally restored or fixed the product (48%), apologised (42%), offered a
refund – either full (27%) or partial (10%) – as well as provided an explanation (27%).
The patterns of resolution confirmed the general alignment seen between demands made (by
consumers) and solutions offered (by sellers or service providers): 56% of the experiences of
detriment ended with a positive resolution, where consumers generally received what they
asked for, or more. Nineteen per cent of the experiences of detriment led to a negative
resolution (where consumers received nothing or did not receive what they asked for). Nothing
was asked, and nothing was offered, for the remaining 25% experiences of detriment.
The general success consumers had in obtaining the resolutions they sought was, in turn,
reflected in the overall general satisfaction with the outcome of the incident of detriment. Over
half of the detriment incidents (55%) had a satisfactory outcome for consumers.

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Meanwhile, one in five (20%) were neither satisfied nor dissatisfied and 25% were dissatisfied
with the outcome.
Consumers at risk of negative detriment outcomes
No evidence was found that consumers’ demographics and socio-economic elements
influenced the likelihood of a successful resolution. However, consumers’ characteristics
were associated with the likelihood of experiencing detriment, the decision to not take
actions to address an incident, or the prevalence of the most negative consequences of
detriment, either from a financial or from a wellbeing perspective.
Although more research is needed to understand why this happens, this study found younger
consumers (especially those aged 18-39) and consumers who considered their financial
situation difficult 17 were consistently more likely to experience detriment, not to take actions
and to suffer the most negative consequences, compared to other groups.
In some cases, other consumers’ characteristics showed a strong association with the
likelihood of experiencing poor consumer detriment outcomes, but not as consistently as it was
found for younger consumers and those reporting financial difficulties.
Consumer detriment in the four UK countries
The proportion of consumers who experienced detriment was slightly different between the
four UK countries. Scotland and England had a higher incidence of detriment (respectively
72% and 70% of consumers in the country experienced at least one incidence of detriment)
compared to Wales (60%) and Northern Ireland (56%). Only small variations between UK
countries were found when looking at detriment incidents by channel of purchase or at the
percentage of consumers who experienced detriment for goods and services. No statistically
significant variations were identified in other areas 18.

17 This is a subjective measure (The survey question text was: “How well would you say you are managing financially
these days?”). While this measure is highly correlated with household income, the two variables do not perfectly
match: there were respondents in high-income groups who reported finding their situation difficult and respondents in
low income group who reported living comfortably. The analysis carried out in Chapter 3 suggests that subjective
financial wellbeing is a better predictor for consumer detriment outcomes than household income.
18 Significance tests were carried out on the value of the products, type of detriment experienced, whether or not
consumers took any actions to address their experiences of detriment, resolution patterns, satisfaction with the
outcome and net monetised detriment (median and distribution).

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Introduction
In January 2021, a consortium of organisations comprising the Consumer Protection
Partnership 19 (CPP), Ofcom and Which? appointed the National Centre for Social Research
(NatCen) to undertake a project to examine the incidence of consumer detriment in the UK,
estimate its cost and analyse its distribution.
The main aim of this study is to produce robust estimates of consumer detriment and its impact
and cost for society, to inform policymaking, the prioritisation of enforcement activities and
future in-depth studies.
Alongside providing an up-to-date and robust assessment of the scale and value of consumer
detriment in the UK, this study (i) examines how consumer detriment differs by key
demographic groups and product types; (ii) explores the nature of the detriment that
consumers experienced and how they sought to resolve the problems they encountered; (iii)
looks at the emotional effects and impact on wellbeing of experiencing detriment; and (iv)
assesses the extent to which the problems were caused or made worse by what can be
defined as ‘structural detriment’, namely the COVID-19 pandemic 20.
Finally, as specific groups within society may lack the financial resources, the knowledge
and/or the cultural capital to deal with the problems they encounter, the presented analyses
aim at disentangling the effects of different demographics on the experience of detriment.
Research questions
This report comprises five chapters. Each chapter is dedicated to one of the five overarching
research questions the survey aimed to explore:
RQ 1 What is the overall incidence, value and impact on wellbeing of consumer
detriment in the UK?
RQ 2 What is the complaints’ journey? What are the dynamics that the experience of
detriment sets off?
RQ 3 Who are the consumers most vulnerable to detriment?

19 The Consumer Protection Partnership (CPP) was formed in April 2012 as part of the Government’s institutional reform
of the consumer landscape and includes numerous organisations. The main aim of the CPP is to identify and prioritise
areas where there is greatest harm caused to consumers and coordinate action by its’ members. The following CPP
organisations were part of the project’s working group: the Advertising Standards Authority (ASA), the Chartered
Trading Standards Institute (TSI), (the National Association of) Citizens Advice (CitA), Citizens Advice Scotland (CAS),
the Competition and Markets Authority (CMA), the Consumer Council for Northern Ireland (CCNI), COSLA - Trading
Standards Scotland (TSS), the Financial Conduct Authority (FCA), the National Trading Standards Board (NTSB), the
Northern Ireland Department for the Economy (DfE, Irish: An Roinn Geilleagair), the Scottish Government (Scottish
Gaelic: Riaghaltas na h-Alba), and the UK Government Department for Business, Energy & Industrial Strategy (BEIS).
20 The impact of the COVID-19 pandemic on detriment experiences can be studied from two different perspectives: a
macro-level perspective or a micro-level perspective. The former explores how consumers’ attitudes and purchasing
behaviours changed during the pandemic, for example, assessing how many people held off making certain
purchases because they did not want to make purchases online. The latter looks at the extent to which consumers
believe a detriment experience was linked to the pandemic.

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RQ 4 How does consumer detriment vary between the four UK countries?
RQ 5 What was the impact of the COVID-19 outbreak on the consumer experience and
on the incidence of detriment in the UK?
Background
Consumer detriment is defined as the damage suffered by consumers in the marketplace when
they encounter a problem relating to the purchase of an item or service 21. Consumer detriment
can be experienced across all types of items and services, in a variety of forms which may not
always be obvious to the consumer. The detriment can happen for a variety of reasons, for
example because the item or service does not meet the consumer’s expectations, is faulty, is
over-priced, or is otherwise sub-optimal in some way.
While consumer detriment may be a broad concept, not all forms of consumer detriment are
measurable and/or warrant a consumer policy response.
Consumer detriment can be:
• Personal or structural – i.e. affecting an individual only, or all consumers. Although
this study mainly focused on individual detriment, measured through a survey of
consumers, it also collected information about structural detriment (such as the impact
of the COVID-19 pandemic).
• Revealed or hidden – i.e. detriment that is apparent to consumers (e.g. a scam, or a
faulty item) or not apparent (e.g. monopolistic practices). This study only explores
detriment consumers are aware of.
• Monetary or non-monetary – i.e. direct ‘financial detriment’ (such as costs incurred
because of detriment) and ‘monetised detriment’ (such as the time spent solving the
problem, represented in financial terms); or effects on consumers’ wellbeing, emotions
and feelings. This study focused on monetised detriment (the sum of the monetised
value of the time spent solving the problem and the financial detriment), but attention
was also given to the impact of various dynamics of detriment on wellbeing.

For this study, consumer detriment was conceptualised as:

an experience with an item or a service that caused stress to the consumer
(psychological dimension), cost them money (financial dimension), or took up
their time (monetary dimension).

And the concept was presented to respondents as follows 22:

It is possible that problems may have occurred with the things you bought that
caused you stress, cost you money, or took up your time.

For example, you may have:

21 See: OECD. (2014). OECD recommendation on consumer policy decision making.
https://www.oecd.org/sti/consumer/Toolkit-recommendation-booklet.pdf
22 See question ‘CDIntro’ in the questionnaire spec (Appendix G).

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• Bought items or services which were faulty or of lower quality than advertised;
• Experienced problems with the delivery of items or services you ordered;
• Had problems claiming under a warranty, guarantee, or insurance policy;
• Paid more for an item or service than advertised.

For the next set of questions, please think about all problems with the things you
bought that happened in the last 12 months and caused you stress, cost you
money, or took up your time.

The data collected in this study is based on what respondents considered or perceived being
an experience of consumer detriment, and this varies by subjective expectations. What may be
perceived as an experience of consumer detriment for some respondents, may not be viewed
as such by others. Additionally, it is possible that respondents may have reported experiencing
problems, that were not actual cases of detriment - i.e. the fault of providers or sellers – but
instead caused by user error. For example, a product that broke down as the result of a
consumer failing to adhere to necessary instructions. Any quantification of consumer detriment
based on consumer surveys is therefore inherently subjective. As such, the data presented in
this report is not an objective measure of consumer detriment in the UK: perceived
detriment does not always match what is defined as detriment (‘actual’ detriment), and the
other way around.

Glossary of survey concepts and definitions

This report contains a number of complex concepts, refers to specific definitions
and employs relatively technical survey methodology and statistical words. Each
is presented and discussed in the main body of the report or in a footnote the first
time it is introduced, however to help the reader navigate this report a glossary
summarising the key terms that should be kept in mind when reading has been
included after the appendices.
The policy context
Consumer detriment has a significant negative impact on UK consumers. Figures from
previous studies are large but also vary, reflecting the complexity of the topic, variation in the
research methodology and the need for further research. For example, the latest study,
conducted in 2016, estimated that consumer detriment costs UK consumers £22.9 billion a
year 23. Earlier studies reported estimates of £4.15 billion in 2014, £3.08 billion in 2012, and
£6.62 billion in 2008 24.
These highly variable figures were collected in studies that employed different methodologies
and sampling strategies, and have led to uncertainty when reporting estimates of prevalence of

23 Oxford Economics. (2016). Consumer Detriment - Counting the cost of consumer problems. Citizens Advice.
https://www.citizensadvice.org.uk/Global/CitizensAdvice/Consumer%20publications/Final_ConsumerDetriment_OE.pdf
24 Department for Business, Innovation & Skills. (2014). Consumer Engagement and Detriment Survey 2014.
https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/319043/bis-14-881-bis-
consumer-detriment-survey.pdf

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consumer detriment, difficulty in understanding changes in the incidence and the impact of
problems experienced by consumers over time 25.
Protecting consumers, especially ones vulnerable 26 to the impacts of consumer detriment,
from experiencing personal consumer detriment is desirable in itself. In addition, beyond the
initial benefit of reduced detriment, protecting consumers can further improve market
functioning, as consumers who are confident that they have some protection may be more
willing to engage in markets by, for example, switching to new suppliers or signalling problems
through complaints.
People on lower incomes can be more vulnerable consumers and this project asked
respondents about a period when the unemployment rate was at its highest level in the last five
years, even despite the rate starting to fall from its peak from late 2020 onwards, according to
Labour Force Survey (LFS) data 27. Moreover, the number of redundancies reached a record
high in August to October of 2020 (ONS 2020) 28. The lower paid have suffered the worst of the
economic effects of COVID-19 as they are more likely to have lost their job, had reduced
working hours and pay, or to have been furloughed (Cominetti and Slaughter, 2020) 29.
All consumers, but especially the lowest earners, may have had to make changes to their
spending habits at this time of crisis, and may therefore be more susceptible to experiencing,
or vulnerable to the effects of, consumer detriment. It is therefore important to have an
understanding of the level of consumer detriment, how people are being affected by it, how it
can be reduced and where the CPP and the other stakeholders could have the most positive
impact in improving consumers’ living standards.
The study context
This research produces population estimates which, applied consistently going forward, shall
allow tracking on how the incidence of detriment and its impact change over time 30. In addition
to methodological considerations (see Appendices), it is therefore important to note the context
within which this research was carried out. Survey fieldwork took place between the 8th April
and 9th May 2021 and respondents were asked to think about what they purchased, or used,
and experienced detriment with in the 12 months preceding the survey – this is from April 2020
to April 2021.

25 A methodological comparison between this study and the previous studies can be found in Appendix C and Appendix
D respectively.
26 For a definition of consumer vulnerability, see the dedicated paragraph within ‘Summary of methodology’.
27 Office for National Statistics. (2020). UK Labour market overview, UK: December 2020.
https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/bulletins/uklabourmarket/
december2020
28 Office for National Statistics. (2020). UK Labour market overview, UK: December 2020.
https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/bulletins/uklabourmarket/
december2020
29 Cominetti, N., & Slaughter, H. (2020). Low Pay Britain 2020. Resolution Foundation.
https://www.resolutionfoundation.org/publications/low-pay-britain-2020/
30 To date, different survey methodologies have been used to measure consumer detriment in the UK. Where there are
big methodological changes between years, the extent to which it is possible to draw reliable comparisons over time is
limited. One of the main aims of the consortium funding this study was to design a sound methodological approach
that can be used as a reference point to facilitate robust comparisons over time going forward.

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During this time-period there were various events that are worth noting when analysing this
consumer detriment data:
• In early 2020, COVID-19 was reported in the UK, which resulted in various lockdowns
and the imposition of other restrictions for much of the 12 months preceding the survey
as well as during the fieldwork period 31;
• In January 2021 the Brexit transition period ended 32;
• In March 2021 a container ship blocked the Suez Canal.

All these events had a major impact on the supply chain, generated different (for either type or
scale) demand for items and services 33, surging prices on specific goods and services 34, and
may have had a short (or long) term impact on public experiences of consumer detriment.

31 This includes a national lockdown from late March to June 2020 (May 2020 in Scotland and Northern Ireland), as well
as lockdowns in England from November to December 2020 and from January to March 2021; in Scotland from
January to March 2021; in Wales from October to November 2020 and December 2020 to March 2021; and in
Northern Ireland from October to early December 2020 and late December 2020 to April 2021. For more information
on the timeline of UK and devolved government coronavirus lockdowns, see (i) Haddon, C., Sasse, T., & Tetlow, G.
(2021). Lifting lockdown in 2021 - The next phase of the government’s coronavirus strategy. Institute for Government.
[https://www.instituteforgovernment.org.uk/sites/default/files/publications/lifting-lockdown-2021_0.pdf]; (ii) SPICe Spotlight.
(2022). Timeline of Coronavirus (COVID-19) in Scotland. [https://spice-spotlight.scot/2022/01/28/timeline-of-coronavirus-
covid-19-in-scotland/]; (iii) Senedd Research. (2021). Coronavirus timeline: Welsh and UK governments’ response.
[https://research.senedd.wales/research-articles/coronavirus-timeline-welsh-and-uk-governments-response/]; (iv)
Northern Ireland Audit Office. (2021). Second Report – Overview of the Northern Ireland Executive’s Response to the
COVID-19 Pandemic [https://www.niauditoffice.gov.uk/sites/niao/files/media-
files/Overview%20of%20the%20Northern%20Ireland%20Executive%27s%20Response%20to%20the%20COVID-
10%20Pa.._.pdf].
32 From 00.00 (Brussels Time) on the 1st of January 2021, the UK’s relationship with the EU started to be determined by
the new agreement in place. Movements of goods from a customs perspective, for example, have to comply with the
EU or the UK's rules on imports from third countries. This caused some disruptions in distribution chains.
33 For example, since the start of the COVID-19 crisis, demand for broadband communication services has soared. An
Ofcom report revealed that in April 2020, during the height of lockdown, UK adults spent a daily average of four hours
and two minutes online – up from just under three and a half hours in September of the previous year [Source: Ofcom.
(2021). Online Nation 2021 report. https://www.ofcom.org.uk/__data/assets/pdf_file/0013/220414/online-nation-2021-
report.pdf]. An analysis of new consumers trends emerging worldwide following the outbreak of the pandemic was
published by KPMG in November 2020 [KPMG. (2020). Responding to consumer trends in the new reality.
https://assets.kpmg/content/dam/kpmg/xx/pdf/2020/11/consumers-new-reality.pdf]. Insights on consumer spending in the
UK post Brexit, and with an ongoing pandemic, can be found on ONS: Office for National Statistics. (2021). UK
economy latest. Available at:
https://www.ons.gov.uk/economy/economicoutputandproductivity/output/articles/ukeconomylatest/2021-01-25.
34 The Suez Canal Incident, for example, has disrupted the global supply chain and generated an increase in the prices
of oil and gas (Source: Lee, J. M. Y., & Wong, E. Y. C. (2021). Suez Canal blockage: an analysis of legal impact, risks
and liabilities to the global supply chain. MATEC Web of Conferences, 339.
https://doi.org/10.1051/matecconf/202133901019); while the COVID-19 pandemic generated an unprecedented demand,
with associated suspected excessive and unfair pricing, of products like face masks, hand sanitisers and surface
disinfectants (see a guidance on Coronavirus published by the Competition and Markets Authority on the 19th of June
2020: Competition and Markets Authority. (2020). Hand sanitiser products: suspected excessive and unfair pricing.
https://www.gov.uk/cma-cases/hand-sanitiser-products-suspected-excessive-and-unfair-pricing).

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Image 1 – Study timeline and key events
Questionnaire content
Respondents first were asked what types of items and services they had paid for between April
2020 and April 2021, or paid for at any time and used in those 12 months, categorised across
44 sectors.
Respondents were prompted to include things bought and/or used jointly with someone else
(e.g. a Netflix account), but to exclude things that they used or acquired but did not cost any
money (e.g. free trial of a subscription) and things bought while outside the UK.
Respondents were then presented with a list of sectors they had said they had purchased from
and were asked to select all the sectors they experienced detriment in, and how many times
they had experienced detriment in each of them, between April 2020 and April 2021.
Those who experienced detriment in at least one sector were then routed to a section
designed to collect more detailed information about their experiences: the nature of the
purchase, the nature of the detriment, the detriment process, the detriment impact and the
detriment causes.
Respondents were asked about their detriment experiences in each sector they reported
having experienced it. If a participant had experienced detriment in more than three sectors,
three sectors were randomly selected and information was collected about those sectors only.
If a participant had more than one experience of detriment in a given sector, we asked them to
think about the most recent one when answering. 35
Respondents can be therefore classified into four groups:
• Consumers who did not experience any detriment;
• Consumers who experienced detriment in one sector;
• Consumers who experienced detriment in two sectors;
• Consumers who experienced detriment in three or more sectors.
Given that each respondent was asked about three instances of detriment at most, the survey
collected information on 9,416 detriment instances in total.

35 Limiting the number of sectors asked about to three, and only asking about one incident per sector helped ensure that
respondents were still engaged and provided accurate answers in a complex questionnaire. Asking about the most
recent incident made the selection process simpler and helped with the respondent’s ability to recall the event and
provide accurate information.

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Summary of methodology and definition of key concepts
Definition of consumers, consumer detriment and detriment incident
For this study consumers are defined as:

people in the UK who – between April 2020 and April 2021 – purchased an item
or a service, or used an item or a service previously purchased.

Consumer detriment (or ‘detriment’) is defined as:

problems with the products consumers bought in the last 12 months, or bought
at any time and used in the last 12 months, that caused them stress, cost them
money, or took up their time.

The problems consumers faced are often referred to throughout the report as ‘detriment
incidents’, while ‘incidence of detriment’ refers to the proportion of consumers who have
experienced detriment overall, or in a given market/sector.
Definition of consumer vulnerability
For this study we considered consumer vulnerability as dynamic, situational and multi-
dimensional.
Vulnerability is not a static condition. Consumers may move in and out of states of vulnerability
and they may be vulnerable in respect of some categories of transaction but not others. This
perspective acknowledges that vulnerability changes under the influence of the social context.
Yet, in some policy contexts, it may be important to recognise that various personal
characteristics, such as a long-standing disability, can imply that vulnerability remains an
enduring characteristic for particular groups of consumers.
Its situationality means that a consumer can be vulnerable in one situation but not in others
(e.g. a highly educated person with poor vision can find it difficult reading terms and conditions
due to small print but could be less vulnerable than others when it comes to find a resolution
for a detriment encountered) and that some consumers may be more vulnerable than others
(e.g. consumers in a difficult financial situation are generally more likely to be vulnerable in
some indicators compared to other consumers).
Its multidimensionality means the impact of personal characteristics on the likelihood of being
vulnerable as a consumer is multi-dimensional as well: characteristics like age and gender can
increase vulnerability in some indicators, but not in others.
This dynamic, situational and multi-dimensional definition was developed by the European
Commission in 2016 following an extensive research project aiming at operationalising the
concept 36. Five dimensions of consumer vulnerability were distinguished, and a vulnerable
consumer was defined as:

A consumer, who, as a result of socio-demographic characteristics, behavioural
characteristics, personal situation, or market environment:
• is at higher risk of experiencing negative outcomes in the market;
• has limited ability to maximise their well-being;

36 See Consumers, Health, Agriculture and Food Executive Agency. (2016). Consumer vulnerability across key markets
in the European Union. European Commission. https://ec.europa.eu/info/sites/default/files/consumers-approved-
report_en.pdf

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• has difficulty in obtaining or assimilating information;
• is less able to buy, choose or access suitable products; or
• is more susceptible to certain marketing practices.
Definition of products
Respondents were asked to think about problems they might have had with two categories of
products: (i) items 37 and (ii) services or subscriptions. Items and services are two closely
aligned concepts that often overlap 38. Most products have an element of service in them (for
example, a car buyer may buy a comprehensive bundle of service benefits, in addition to the
tangible components of the car), and some services require one or more tangible items (for
example, to use a Wi-Fi service a modem is required). The products investigated for this study
had been assigned to a category (‘items’ or ‘services or subscriptions’) by researchers at the
questionnaire development stage; respondents were asked to think about problems they might
have experienced with each product within the pre-assigned category (for the exact wording of
the questions, see the questionnaire specification in Appendix F).
For this study, items were defined as:

goods that are produced or manufactured for sale and that can typically be
consumed after production. Goods are typically, but not always, tangible,
discernible and re-sellable.

Services were defined as:

work done often by a person for a consumer and are more likely to involve the
consumer in their production. Their benefit is typically intangible, often realised
in parallel with the service being rendered, and cannot generally be returned or
resold.

The classification of the products investigated in this report in either items or
services/subscriptions can be found in the questionnaire specifications in Appendix G (see
variables from ‘PurGoodA’ to ‘PurServiceE’).
Throughout the report the general term ‘product’ is used as a shorthand for “items and services
or subscriptions”. While, the term ‘services’ is used to refer to both “services or subscriptions”.
Classification of sectors and market clusters
Sectors and market clusters were developed in collaboration with BEIS and the CPP and are
based on already existing classifications (European Union, Office for National Statistics, United
Nations).

37 The term ‘items’ was preferred over ‘goods’ as the term ‘goods’ more strongly invokes the notion of ‘ownership’
compared to the concept of ‘usage/consumption’. Since this study aimed at collecting detriment experiences with both
objects ‘owned and used’ and those ‘used but not owned’ by respondents, the term ‘item’ was deemed more
appropriate,
38 Parry G., Newnes L., Huang X. (2011) Goods, Products and Services. In: Macintyre M., Parry G., Angelis J. (eds)
Service Design and Delivery. Service Science: Research and Innovations in the Service Economy. Springer, Boston,
MA. https://doi.org/10.1007/978-1-4419-8321-3_2

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Sectors (the most granular level of classification used in the report) were grouped in market
clusters. While the report generally offers estimates on a sector level, sub-group analysis
needed a greater sample size and was largely carried out on a market-cluster level.
More details about the classification used in the report can be found in Appendix D.
Sample design
To guide the development of legislation and to assess the progress made in tackling detriment,
a high-quality and replicable survey methodology is required to ensure that estimates of the
prevalence of detriment are accurate and can enable comparisons to be made over time with a
known level of confidence. For these reasons, a probability-based sampling approach was
used for this project.
A sample of adults (aged 18 and over) living in the UK was recruited using the NatCen Panel,
a random-probability research panel owned by NatCen, and a push-to-web 39 survey sampled
using a random-probability approach. The push-to-web survey was used to cover Northern
Ireland 40 (not covered by the NatCen Panel), and to boost the sample in Wales to allow for
more precise estimates in that region.
The random-probability approach 41 means that the findings discussed in this report can be
inferred to the general UK population, thus enabling the estimate of the overall economic
impact of consumer detriment for the whole of the UK 42.
More details on the sample design can be found in Appendix B.
Fieldwork and response rates
Data were collected over a four-week fieldwork period with a mixed-mode (Computer-Assisted
Web Interviewing (CAWI) and Computer-Assisted Telephone Interviewing (CATI)) fieldwork
design: all study participants were initially invited to take part online, with those choosing not
to, or unable to, complete online followed up by an interviewer from the NatCen Telephone
Unit.
The survey was completed by 5,101 NatCen Panel members across Great Britain (12% overall
NatCen Panel response rate 43) and 1,481 push-to-web survey participants living in Wales or
Northern Ireland (11% of boost adults invited to participate), resulting in a total of 6,582
interviews. More details on response rates at various stages of recruitment, as well as the
weighting approach, are included in Appendix B.
Analysis
All findings have been tested for statistical significance, and all differences reported are
statistically significant unless stated otherwise. Statistical testing was conducted at the 95%
level.

39 Also referred to as ‘P2W survey’ throughout the report.
40 Also referred to as ‘NI’ throughout the report.
41 Each member of the population has a known and non-zero chance of selection for the study.
42 Moreover, the random-probability approach allows the application of statistical testing to establish where differences
are statistically significant and the provision of confidence intervals around the estimates.
43 The survey achieved a 75% response rate among those panellists invited to participate, However, when accounting
for all stages of non-response, including participation in the recruitment survey and recruitment to the panel, the
overall response rate for the NatCen Panel is 12%. More info available in Appendix B.

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The instances of detriment were analysed both using bivariate and multi-variate analysis. For
more information about the analysis approach, see Appendix D.
Levels of data
The analysis in the report has been carried out using two different levels of data:
• Respondent-level data was weighted to be representative of the UK population
aged 18 or above. The analysis was carried out using information collected in the first
section of the questionnaire (consumer attitudes, having consumed products in the
sectors, having experienced detriment), summary variables from the detriment part of
the questionnaire, and demographics and socio-economic characteristics.
• Detriment-level data was weighted to be representative of all the experiences of
detriment in the UK in the period of interest. The analysis was carried out using
information collected in the detriment section of the questionnaire (variables linked to
single experiences of detriment).
The second and fifth chapters use detriment-level data, while the third chapter’s analysis was
carried out on respondent-level data. Data from both levels was used in the first and the fourth
chapters.
Additional material
In addition to the tables presented in this report, two sets of analysis tables are available. The
base for the first set (“ConsumerProtectionStudy2022_TABLES_RespondentLevel”) are
respondents (N=6,582). Therefore, the 32 tables contained in the first set are useful to explore
what socio-economic demographics are associated with given detriment outcomes and
patterns, looking at the specific experiences reported across all the different detriment
instances experienced by respondents in the 12 months preceding the survey. The base for
the second set (“ConsumerProtectionStudy2022_TABLES_DetrimentLevel”) are all the
detriment instances experienced by respondents in the 12 months preceding the survey
(N=9,416). The 50 tables contained in the second set are therefore particularly useful to
explore how the experiences of detriment vary by predictors such as sector, market cluster and
product type.

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2 Levels of consumer detriment in the UK

This chapter addresses Research Question #1: What is the overall incidence, value and
impact on wellbeing of consumer detriment in the UK?

The chapter examines:
• headline figures of consumer detriment in the UK overall, by market characteristics and
by nature of the detriment;
• the amount of net monetised detriment;
• detriment to wellbeing.
The findings suggest that 36 million UK adult consumers experienced a total of 229.8 million
incidents of detriment between April 2020 to April 2021 and an estimated net monetised
detriment of £54.2 billion 44.
More than one in three experiences of detriment occurred following purchases made online via
the seller’s or trader’s website. And more than one in three experiences of detriment were due
to the poor quality of the products. A little more than two in five detrimental experiences
occurred with products of relatively low value (£100 or less).
Looking at the effect on wellbeing: feeling upset was the most reported emotion across all
experiences of detriment compared to feeling misled, anxious or helpless; and negative effects
on mental health and household finances were more common than those on physical health
across all experiences of detriment.
It is worth reiterating that the data referenced in the following chapter are based on perceived
experiences of detriment by respondents, which may not always match with what is defined as
detriment (‘actual’ detriment).
2.1 Incidence and numbers of consumer detriment
Respondents were shown the list of items and services they said they had bought in the 12
months to April 2021 and asked which, if any, they had experienced problems with.
This study found that, within the UK, 69% [C.I. 68; 70] of consumers experienced at least one
problem with something they bought between April 2020 to April 2021, or bought at any time

44 The figures presented in this sentence are the best estimates of the true value in the population. However, the true
value may be larger or smaller than the point estimate. This can either be due to bias or random error based on the
sample selection, or a combination of both. The confidence interval (CI) around each estimate is the symmetrical
range of values within which values of repeated similar experiments are likely to lie. Statistical testing to determine the
CIs was conducted at the 95% confidence level, meaning that by performing the same experiment 100 times with
different samples of the same population of interest, 95 times the point estimate would lie within the CI presented in
this report. Such a level of confidence is possible because the study is based on a random-probability sample (each
individual in the population of interest has a known and non-zero probability of being selected to this study).

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and used in that period, which caused them stress, cost them money, or took up their time.
The remaining 31% [C.I. 29; 33] did not recall or report experiencing any problems.
Scaling up to the population this means that, overall, 36.0 million [C.I. 34.6; 37.3] UK
consumers experienced a total of 229.8 million [CI 210.0; 250.0] incidents of detriment
between April 2020 to April 2021, with a median of 4.0 [CI 3; 4] detriment experiences per
person and a mean of 6.4 [C.I. 5.9; 6.9].
Image 2 – Consumer detriment key figures for the period spanning from April 2020 to
April 2021
2.1.1 Incidence of consumer detriment by market characteristics
The proportion of consumers who had experienced consumer detriment varied by market
characteristics, such as product type (whether the product was a service or an item), market
clusters and sectors (Table 1) 45.

45 Table 30 in Appendix D presents the incidence of detriment, based on the number of consumers, and the incidence
based on the total population. Both measures are presented for all sectors with 95% confidence intervals.

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The detriment incidence 46 was higher for services and subscriptions than for items: in the 12
months to April 2021 over a half (55%) of consumers who purchased a service or subscription
experienced detriment with it, while just below a half of consumers (48%) who purchased an
item experienced detriment with it.
By market clusters, the highest incidence of detriment by far was in ‘Fast-moving retail’: 76% of
people who purchased or used fast-moving retail products experienced detriment in that
market cluster in the 12 months to April 2021 47. This was followed by ‘Telecoms and other
digital subscriptions’ and ‘(Semi-) durable goods’ which were both just above 30%. The
incidence of detriment in the other market clusters ranged from 21% in ‘Automotive goods and
services’ to 13% in ‘Banking and insurance’.
There was great variation in the detriment incidence by sector. The sectors with the highest
detriment were ‘Airline’ and ‘Package holidays & tours’ (36% and 35% respectively), followed
by ‘Second-hand vehicles’ (30%) and ‘Internet provision’ (29%).
Detriment incidence in second-hand vehicles was higher than in the new vehicles sector (12
percentage points difference). Second-hand vehicles represent a classic case of asymmetric
information with a high-value good as dealers may have better information on important
characteristics of vehicles for sale and may chose not to disclose potential issues to
consumers 48.
The COVID-19 pandemic looks to have played a role in the elevated levels of detriment seen
across some sectors. The high incidence of detriment seen with the ‘Airline’ and ‘Package
holidays & tours’ sectors is likely to be related to the significant level of disruption experienced
by the travel industry as a result of measures implemented to limit the spread of COVID-19.
Others sectors affected by the pandemic were ‘Internet provision’ (29%) and ‘Electronic
devices & software’ (26%) whose consumption and consumers’ expectations increased
markedly as many became reliant on the internet to work, study, make purchases, keep
entertained and stay socially connected 49; ‘Education fees’ (19%) as university students tried
to claim back fees, even appealing to the UK’s competition watchdog 50; ‘Hotels & holiday
accommodation’ (14%) as the travel ban and quarantine measures were enforced; and
‘Funerals’ (10%) as physical distancing limitations had an impact on funeral practices 51.

46 The incidence of detriment within each sector is the proportion between the number of consumers who experienced
detriment in the 12 months to April 2021 and the number of consumers who purchased a product in that period, or
used a product that had been purchased before that period.
47 The high incidence reflects the fact that within this cluster, a very large number of consumers have experienced
detriment in at least one of the fast-moving retail sectors but not necessarily in others. Indeed, with the exception of
‘Clothing’, all fast-moving retail sectors have a relatively low incidence of detriment.
48 A 2014 EU study on the second-hand car market found deficiencies in the provision of information to consumers
[Justice and Consumers. (2014). Study on the second hand cars market. European Commission.
https://ec.europa.eu/info/publications/study-second-hand-cars-market_en].
49 A 2020 study based on the UK Household Longitudinal Study (UKHLS) found that the proportion of people working
exclusively at home rose eight-fold from 5.7% of workers in January/February 2020 to 43.1% in April 2020 and, even
though it had fallen by June 2020, it remained high (36.5%) [Felstead, A., & Reuschke, D. (2020). Homeworking in the
UK: Before and during the 2020 lockdown. WISERD.
https://wiserd.ac.uk/sites/default/files/documents/Homeworking%20in%20the%20UK_Report_Final_3.pdf]
50 See: Hall, R. (2021). UK student unions call for Covid refund process to be simplified. The Guardian.
https://www.theguardian.com/money/2021/apr/22/uk-student-unions-call-for-covid-refund-process-to-be-simplified
51 For more info see: MacNeil, A., Findlay, B., Bimman, R., Hocking, T., Barclay, T., & Ho, J. (2021). Exploring the Use
of Virtual Funerals during the COVID-19 Pandemic: A Scoping Review. OMEGA - Journal of Death and Dying.
https://doi.org/10.1177/00302228211045288

Consumer Protection Study 2022
26

Table 1 – Incidence of purchases and detriment by product type, market clusters and sectors

PURCHASES

DETRIMENT INCIDENCE

Adults who
purchased
(%)

UWTD
base
(N)
Detriment incidence
(%)
Product Type

Services or subscriptions

99%

6,426

Items

99%

6,530

Market clusters and sectors

Fast-moving retail

99%

6,452

Clothing, footwear and accessories

80%

5,321

Groceries and drinks

93%

6,182

Prescription and non-prescription medicines

40%

2,526

House and garden maintenance products

73%

5,040

Personal care products

87%

5,732

Stationery, books, magazines and newspapers

68%

4,666

Personal care services

43%

3,153
Telecoms and other digital subscriptions

94%

6,221
Internet provision

70%

5,035

TV and other digital subscriptions

65%

4,273

Mobile telephone services

82%

5,539

Fixed telephone services

51%

3,976
(Semi-)durable goods

85%

5,662
Electronic devices and software

55%

3,655

Furniture and appliances

55%

3,709

Spectacles and lenses

33%

2,435

Entertainment items

49%

3,034
Automotive goods and services

71%

5,115
Second-hand vehicles

15%

1,011

New vehicles

6%

440

Vehicle maintenance and repair

51%

3,840

Fuel and accessories for vehicles

62%

4,546
Transport

33%

1,819

Airline

7%

463

Public transport and trains

28%

1,430
Vehicle rental

5%

277
Utilities

83%

5,765

Electricity and gas services

81%

5,670
Water services

69%

4,539
Recreational services

80%

5,224

Package holidays and tours

7%

429

Hotels and holiday accommodation

23%

1,659

Sport, cultural and entertainment activities

31%

2,035

Restaurants, cafés and take-away

66%

4,286
Gambling and lottery services

30%

1,963
Housing-related services

48%

3,274

Real estate services

6%

364

Renting services

23%

1,189

Removal and storage

6%

374
Home and garden maintenance and repair

25%

2,096

Table continues on the next page
56%
48%
76%
24%
12%
9%
7%
5%
4%
3%
34%
29%
17%
13%
10%
32%
26%
21%
15%
11%
21%
30%
19%
17%
9%
19%
36%
13%
10%
17%
15%
5%
16%
35%
14%
8%
9%
4%
15%
26%
13%
11%
11%

Consumer Protection Study 2022
27
Table 1 – Incidence of purchase and detriment by product type, market clusters and sectors

PURCHASES

DETRIMENT INCIDENCE

Adults who
purchased
(%)

UWTD
base
(N)
Detriment incidence
(%)
Market clusters and sectors (continues)

Personal services

33%

2,372

Adult care

2%

140

Education fees

7%

369

Childcare

6%

393
Private medical and dental services

23%

1,793
Other services

35%

2,593

Legal and accountancy services

14%

1,056

Veterinary

23%

1,743
Funeral services

4%

327
Pet breeder

2%

149

Banking & Insurance

81%

5,593

Insurance services

57%

4,218

Current accounts, loans and bank services

62%

4,428

Pension funds and investment services

35%

2,514

Base for purchases: All UK adults (18+). Unweighted: 6,571 for all sectors.
Base for detriment incidence: UK consumers who purchased a product in the 12 months to April 2021, or bought at
any time and used in that period, for any given sector or – when looking at market cluster or product type – in at
least one sector within the given market cluster/product type. Unweighted N specified in the table.
Note: Categories are sorted from largest to smallest detriment incidence level by market cluster, and then sectors
within market cluster also ordered from largest to smallest detriment incidence level.

The impact of the pandemic was particularly strong on both business and consumers and will
be discussed in more detail in the final chapter of this report (Chapter 5). When reading this
report, it is important to keep in mind that the COVID-19 outbreak had a large influence on
consumers’ behaviours, habits and experiences as well as businesses’ capacity to adapt and
respond.
Finally, it is worth noting that ‘Clothing’ was the only high-detriment sector within fast-moving
retail (with a detriment incidence of 24%).
2.1.2 The characteristics of consumer detriment
Channel of purchase
Respondents were asked how they originally purchased the product from which they
experienced detriment.
Over three quarters (78%) of detriment experiences occurred following purchases made either
online via the seller’s or trader’s website (36%), in-person from a shop or other outlet (27%), or
over a phone call (15%).
14%
21%
19%
13%
10%
14%
14%
11%
10%
9%
13%
9%
8%
6%

Consumer Protection Study 2022
28
Figure 4 – Channel of purchase in detrimental experiences
Base: All detriment experiences in the UK in the 12 months to April 2021.
Unweighted: 9,388.
Detriment type
Respondents were asked to state the nature of the original problem they experienced with the
product and they were allowed to select more than one option.
More than 1 in 3 experiences of detriment (36%) were a consequence of poor-quality products.
The fact that the product was unsuitable to use generated 21% of detriment experiences, while
problems with delivery and a complete failure to provide were both responsible for 18% of
detriment experiences.
Figure 5 – Detriment type in detrimental experiences
Base: All detriment experiences in the UK in the 12 months to April 2021.
Unweighted: 9,416.

36%
27%
15%
9%
5%
3%
2%
1%
3%
0%
5%
10% 15% 20% 25% 30% 35% 40%
Online (seller website)
In-person from a shop
Over a phone call
Online (third-party marketplace)
Auto-renewal
Online (private seller, e.g. eBay or Airbnb)
In-person from a visiting salesperson
Online (social media platform)
Other
Percentage of detriment experiences
36%
21%
18%
18%
12%
11%
8%
4%
19%
0%
5%
10%
15%
20%
25%
30%
35%
40%
Poor quality
Not usable
Problems with delivery
Complete failure to provide
Misleading price
Misleading information
Unfair or unclear T&C
Guarantees not honoured
Other
Percentage of detriment experiences

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29
A large proportion of detriment problems (19%) fell in the ‘Other’ category. Respondents were
asked to specify what these ‘Other’ problems were. These open-text answers were reviewed
for key themes. This analysis highlighted problems which included:
• Issues with returns/returns being difficult or not an option.
• The item broke down or broke after some time.
• Being unable to get a delivery slot.
• Cancellations/ not being able to use a service due to COVID-19.
• Difficulties leaving a service or cancelling.
• Difficulties altering or changing a service.
• The provider went into administration.
• Fraud/account was hacked.
Price (initial cost of the product)
Respondents were asked to provide the approximate original cost of the item or
service/subscription, or ongoing purchase at the time of the problem.
Most detrimental experiences occurred with products of relatively low value (£100 or less). This
reflects the higher incidence found within the ‘Fast-moving retail’ market-cluster where many of
the products have a lower cost.
Figure 6 – Price (initial cost) of the product in detrimental experiences
Base: All detriment experiences in the UK in the 12 months to April 2021.
Unweighted: 9,416.

2.2 Net monetised detriment
Individual instances of detriment impact consumers in a range of ways. Consumers might:
need to spend additional money on fixing or replacing a substandard product; lose money
because they are unable to use other products that they already paid for; experience a loss of
additional earnings; or face other unforeseen expenses. Detriment can also take up
consumers’ time, which can also be considered an indirect additional cost.
42%
29%
15%
11%
3%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
Up to
£100
£101 to
£500
£501 to
£1,000
£1,001 to
£5,000
Greater
than £5,000
Percentage of detriment experiences

Consumer Protection Study 2022
30
Conversely, this overall cost might be mitigated by actions taken by the provider or seller of the
product, such as refunding, replacing or fixing a substandard product, monetary and non-
monetary compensations for extra costs, one-off or regular discounts or changes in contract
conditions. The cost experienced by society as a result of consumer detriment is, therefore,
multidimensional.
Data collected in the Consumer Protection Study was used to analyse and quantify seven
different monetised components for each experience of detriment. These components were
then combined into an overall net monetised detriment (Figure 7).
Figure 7 – Net monetised detriment formula and its components

Four of the seven components represent costs that consumers face at the initial purchase and
when dealing with detriment (cost components):

1. Original cost. This is the cost of purchasing the service or item. The cost for
subscriptions and ongoing services was standardised for the period that the
detriment lasted for 52.
2. Replacing or fixing the product. The money spent by the consumer to fix or
replace the product.
3. Additional cost. This includes loss of earnings, paying to repair damage caused
by an incident, not being able to use another item or service, direct costs from
contacting the seller or seeking compensation (such as postage or fees paid to a
lawyer) and other costs.
4. Time cost. Each hour spent dealing with detriment was estimated to have a
monetised value of £13.87 53.

52 When the detriment incident was related to a product or service part of a bundle, survey respondents were invited to
give the total cost of the bundle. Although the higher original costs are likely to be offset by other elements in the
compensation-side of the net monetised detriment formula, this is an element that must be kept in mind, especially
when interpreting the values of the individual components of net monetised detriment in sectors where bundles are
particularly common.
53 The cost of time is estimated, that is ‘monetised’, following specific assumptions. See Appendix D for more details.

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The remaining three components are elements that mitigated the overall cost faced by the
consumer (mitigation components) and were subtracted from the sum of the cost components
when calculating the net monetised detriment:

5. Use value. This is the value that was retained by the product. It is a combination
of assumed use value and self-reported subjective value, depending on the
detriment type.
6. Value of the refund/replacement received. The value of having the product
refunded (fully or partly) or replaced by the seller or provider.
7. Other compensations. This includes vouchers or store credit, monetary and
non-monetary compensations for extra costs or inconvenience incurred, one-off
discounts, longer-term price reductions, new contract conditions or other forms of
compensations.

The net monetised detriment presented in this report has been computed for all the detriment
experiences collected in the study, however 19% of these were unresolved at the time of the
survey. This means that net monetised detriment for those incidents of detriment was
computed looking at the situation at the time of the survey; some of the unresolved incidents
might have concluded with a favourable resolution, leading to lower amounts of net monetised
detriment, or might have resulted in additional costs, leading to higher amounts of net
monetised detriment.
Full details on the computation of the components, as well as the management of missing
values and outliers are discussed in Appendix E.
The rest of this section looks at net monetised detriment in the UK by market cluster and sector
and characteristics of detriment.
2.2.1 Amount of net monetised detriment in the UK
In the 12 months to April 2021, the UK experienced an estimated net monetised detriment of
£54.2 billion (95% confidence interval: £40.9 - £67.6 billion ) 54. In the same period (Q3 2020 to
Q2 2021) household consumption expenditure was estimated at £1,293 billion 55.
The cost of detriment varied substantially between experiences of detriment. Figure 8 shows
the weighted distribution of financial detriment for values between -£500 and £1,000.

54 The width of the confidence interval is based on the standard error, which is influenced by factors such as the
standard deviation, the sample size and the survey design (survey weights, stratification and PSU). The confidence
interval discussed in this study was influenced by the presence of heavy outliers and by the weighting strategies
adopted to scale the data collected on detriment incidents up to a population level.
55 Office for National Statistics (2021), UK national and domestic total expenditure, in “Consumer Trends – Publication
Tables”. Available at:
https://www.ons.gov.uk/economy/nationalaccounts/satelliteaccounts/datasets/consumertrendscurrentpricenotseasonal
lyadjusted

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32
Figure 8 – Distribution of the values of net monetised detriment 56

Base: Detriment experiences in the UK in the 12 months to April 2021 with a net monetised
detriment between -£500 and £1,000.
Unweighted: 8,980.

This highlights some key characteristics of net monetised detriment:
• A small number of detriment experiences had extreme values. While 95% of the
detriment experiences fell within a range of -£500 and £1,000, the remaining 5% had
values as low as -£18,500 and as high as £188,000.
• Most of the detriment experiences had a net monetised detriment of small value.
The median net monetised detriment for a detriment experience was £28 57.
• Net monetised detriment can have a negative value. This happens when the use
value, the value of the refund or replacement offered by the seller or provider and the
additional compensations received are higher than all the costs – including the time
cost – faced by the consumer. Nine per cent of the experiences of detriment have a
negative value.
• The distribution of net monetised detriment was skewed. There were more
experiences of detriment which had a positive value (and thereby the detriment had a
cost to the consumer) than had a negative value (and thus the detriment had benefitted
the consumer). Extreme values were also greater on the positive side of the distribution,
compared to the negative side.

56 This figure shows the net monetised detriment values for 95% of the incidents (values between -£500 and £1,000).
‘Extreme’ values were dropped for visualisation purposes but were included in the full analysis.
57 The median has been typically used in the report as it represents a more robust and less biased measure of centrality
given the skewedness of the distribution. Indeed, the mean net monetised detriment was £242, almost ten times
higher than the median.

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33
The components of net monetised detriment should be observed together, and not separately,
as some components heavily influence the values of others. For example, the use value and
the value of the refund/replacement received from the seller or provider are strongly associated
with each other and the original cost.
Nevertheless, there are three insights that can be drawn from observing the values of the
different components (Figure 9). Firstly, the original cost of the service or item alone is higher
than all the values estimated in the mitigation components: the original cost of the products
across all the experiences of detriment is estimated at £158 billion, while the total value of all
the mitigation components (use value, value of the refund/replacement received and other
compensation) is estimated at £143 billion.
Secondly, the cost of time is estimated to be £21 billion. By removing it from the computation, it
is possible to estimate the net financial detriment which is a figure that accounts for direct costs
only. The estimated value of financial detriment amounts to circa £33 billion.
Lastly, the estimates of additional costs (£14 billion), the costs faced by the consumer when
fixing or replacing the product (£5 billion) and other compensation (£7 billion) are relatively
small when compared to the other components. This suggests that net monetised detriment is
primarily driven by refunds, replacements and use value of the product as well as the original
cost and the time spent dealing with the detriment.
Figure 9 – Value of the net monetised detriment components (billion £)

Base: All detriment experiences in UK in the 12 months to April 2021.
Unweighted: 9,416.

2.2.2 Net monetised detriment and market characteristics
The net monetised detriment in the UK varied across items and services and different market
clusters (Table 2). The total net monetised detriment in services equated to £41.2 billion. This
represented 76% of the total monetised detriment in the UK and was more than three times
higher than the monetised detriment estimated for items (£13.1 billion). A similar ratio was
observed for the median net monetised detriment which was £41 for services and £14 for
items. This suggests that the overall difference cannot be attributed to a higher number of
detriment incidents in services but is primarily driven by an experience that was more costly for
people consuming services compared to those consuming items.
Original cost, £158
Replacing or fixing the product, £5
Additional cost, £14
Time
cost,
£21
Use value, £75
Value of the refund/replacement
received, £61
Other compensations, £7
£0
£50
£100
£150
£200
Costs
Mitigations

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34
Table 2 – Total net monetised detriment, its confidence interval and median by
product type and by market cluster
Total
(billion £)
Lower
95% CI
(billion £)
Upper
95% CI
(billion £)
Share
of UK
total
Median
(£)
UWTD
base
(N)
Product type

Services
£41.2
£28.7
£53.6
76%
£41
5,708
Items
£13.1
£9.0
£17.1
24%
£14
3,708
Market cluster

Automotive goods and services
£12.3
£6.5
£18.1
23%
£76
847
Housing-related services
£11.0
£4.3
£17.7
20%
£163
326
Telecoms and other digital
subscriptions
£7.5
£5.6
£9.3
14%
£46
2,237
Banking and insurance
£4.5
-£3.9
£12.8
8%
£30
524
Personal services
£4.4
£1.8
£6.9
8%
£194
205
(Semi-) durable goods
£4.3
£2.5
£6.1
8%
£34
1,568
Fast-moving retail
£3.6
£1.4
£5.8
7%
£9
1,892
Utilities
£2.4
£0.9
£3.9
4%
£28
657
Other services
£2.2
£1.0
£3.5
4%
£111
253
Recreational services
£1.2
£0.5
£1.9
2%
£10
641
Transport
£0.9
£0.5
£1.2
2%
£28
266
Base: All detriment experiences in the UK in the 12 months to April 2021.

Differences could also be seen across market clusters, with the three market clusters with the
highest net monetised detriment (‘Automotive goods and services’, ‘Housing-related services’
and ‘Telecoms and other digital subscriptions’) together accounting for 57% of the total
detriment in the UK.
‘Fast-moving retail’, the market cluster with the highest incidence of detriment (76% compared
to 34% or less in the other market clusters) and with the largest number of detriment incidents
(32% of all detriment experiences in the UK were reported in this cluster), was responsible for
only 7% of the total net monetised detriment in the UK. This is explained by the median value
of £9 per incident – the lowest of any market cluster.
The median was particularly high in three market clusters: ‘Personal services’, ‘Housing-related
services’ and ‘Other services’. These three market clusters did not have a particularly high
incidence of detriment (around one in six people consuming products in these clusters
experienced detriment – 15%, 14% and 14% respectively), nor had a particularly high baseline
of consumers (a minority of the population consumed products in these clusters), but
accounted for 32% of the total net monetised detriment in the UK.
2.2.3 Net monetised detriment and sectors
The sectors with the highest number of incidents were not necessarily the ones with the
highest amounts of net monetised detriment. The largest number of incidents (32% of the UK
total) was recorded in ‘Fast-moving retail’ sectors (especially ‘Clothing, footwear and
accessories’ and ‘Groceries and drinks’) or in ‘Telecoms and other digital subscriptions’ (19%
of the UK total); in this cluster, almost half of the incidents were recorded in ‘Internet provision’
(Figure 10).

Consumer Protection Study 2022
35
The situation is different when we look at the amounts of net monetised detriment (Figure 11).
All the ‘Fast-moving retail’ sectors, combined, accounted for only seven percent of the total UK
net monetised detriment, while the highest amount of net monetised detriment was recorded in
‘Renting services’, ‘Vehicle maintenance and repair’, ‘Second-hand vehicles’ and ‘Internet
provision’ (Image 3). Detriment incidents from those four sectors alone accounted for two-fifths
(40%) of the total net monetised detriment in the UK.
Image 3 – Two-fifths of the total UK net monetised detriment imputable to four sectors

The amount of net monetised detriment in ‘Renting services’ and ‘Second-hand vehicles’ was
driven by a particularly high median detriment; these sectors had the highest median net
monetised detriment (above £440 for both), followed by ‘Childcare’ and ‘Education fees’ (both
above £200). The median was well-above average also for ‘Vehicle maintenance and repair’
(£118) and ‘Internet provision’ (£55) too, but these two sectors also had a relatively large
number of incidents that contributed to the high amount of net monetised detriment.

Consumer Protection Study 2022
36
Figure 10 – Percentage of detriment incidents by sectors and market clusters (UK total = 100%)
Base: All detriment experiences in the UK in the 12 months to April 2021.
(SEMI-) DURABLE
GOODS (13%)
AUTOMOTIVE
GOODS &
SERVICES (8%)
PERSONAL SERV. (2%)
RECREATIONAL SERVICES (7%)
UTILITIES (6%)
TRANSPORT
(3%)
HOUSING-
RELATED
SERVICES
(3%)
BANKING &
INSURANCE (4%)
OTHER
SERVICES
(2%)
New
vehic.
Gambling & lottery
FAST- MOVING RETAIL (32%)
TELECOMS AND
OTHER DIGITAL
SUBSCRIPTIONS
(19%)

Consumer Protection Study 2022
37
Figure 11 – Percentage of monetised detriment by sectors and market clusters (UK total = 100%)
Base: All detriment experiences in the UK in the 12 months to April 2021.
FAST- MOVING
RETAIL (7%)
TELECOMS AND
OTHER DIGITAL
SUBSCRIPTIONS
(14%)
(SEMI-) DURABLE
GOODS (8%)
AUTOMOTIVE
GOODS & SERVICES
(23%)
PERSONAL
SERVICES
(8%)
RECREAT.
SERVICES
(2%)
UTILITIES (4%)
TRANSPORT (2%)
HOUSING-RELATED
SERVICES
(20%)
BANKING &
INSURANCE
(8%)
OTHER
SERVICES
(4%)

Consumer Protection Study 2022
38
Table 3 – Total net monetised detriment, its confidence interval and median by sector

Total
(billion £)
Lower
95% CI
(billion £)
Upper
95% CI
(billion £)
Share
of UK
total
Median
(£)
UWTD
base
(N)
Renting services
£7.4
£1.0
£13.9
14%
£442
88
Vehicle maintenance and repair
£7.0
£2.3
£11.6
13%
£118
409
Second-hand vehicles
£4.1
£1.4
£6.8
7%
£463
184
Internet provision
£3.4
£2.4
£4.5
6%
£55
1,025
Electricity and gas services
£2.2
£0.7
£3.7
4%
£28
522
Education fees
£2.2
-£0.1
£4.4
4%
£207
43
Pension funds and investment services
£2.1
-£6.1
£10.3
4%
£55
71
Real estate services
£2.1
£0.4
£3.8
4%
£142
68
TV and other digital subscriptions
£2.0
£1.0
£3.1
4%
£38
517
Electronic devices and software
£1.9
£0.5
£3.2
3%
£33
593
Furniture and appliances
£1.8
£0.8
£2.9
3%
£42
569
Home and garden maintenance and repair
£1.4
£0.6
£2.2
3%
£109
148
Clothing, footwear and accessories
£1.4
£0.6
£2.1
3%
£9
822
Insurance services
£1.3
£0.1
£2.5
2%
£28
229
Mobile telephone services
£1.2
£0.7
£1.8
2%
£60
418
Veterinary
£1.2
£0.2
£2.2
2%
£143
137
Personal care products
£1.2
-£0.4
£2.7
2%
£28
149
Current accounts, loans and bank services
£1.1
£0.2
£1.9
2%
£28
224
Legal and accountancy services
£0.8
£0.1
£1.6
2%
£111
80
Fixed telephone services
£0.8
£0.0
£1.5
1%
£28
277
Fuel and accessories for vehicles
£0.7
£0.1
£1.3
1%
£28
202
Private medical and dental services
£0.6
£0.1
£1.1
1%
£101
106
New vehicles
£0.6
-£0.1
£1.2
1%
£71
52
Hotels and holiday accommodation
£0.5
£0.1
£1.0
1%
£38
180
Groceries and drinks
£0.5
£0.1
£0.8
1%
£7
426
Public transport and trains
£0.4
£0.1
£0.7
1%
£25
126
Airline
£0.4
£0.2
£0.7
1%
£28
125
Sport, cultural and entertainment activities
£0.4
£0.1
£0.7
1%
£28
110
Spectacles and lenses
£0.4
£0.1
£0.7
1%
£42
224
Childcare
£0.4
£0.1
£0.7
1%
£210
35
House and garden maintenance products
£0.3
£0.2
£0.4
1%
£14
222
Entertainment items
£0.2
£0.1
£0.4
<1%
£14
182
Prescription and non-prescription medicines
£0.2
£0.1
£0.3
<1%
£23
134
Water services
£0.2
£0.0
£0.3
<1%
£25
135
Package holidays and tours
£0.2
-£0.3
£0.6
<1%
£42
116
Stationery, books, magazines and newspapers
£0.1
£0.0
£0.2
<1%
£4
83
Restaurants, cafés and take-away
£0.1
£0.0
£0.1
<1%
£5
191
Personal care services
£0.0
£0.0
£0.1
<1%
£7
56
Gambling and lottery services
£0.0
£0.0
£0.1
<1%
£14
44
Adult care
*
*
*
*
*
21
Pet breeder
*
*
*
*
*
13
Removal and storage
*
*
*
*
*
22
Funeral services
*
*
*
*
*
23
Vehicle rental
*
*
*
*
*
15
Base: All detriment experiences in the UK in the 12 months to April 2021.
* Unweighted count too small for population estimates (n < 30).

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Overall, in all the sectors, the mitigations did not entirely offset the estimated costs suffered by
consumers. However, the ratio between cost and mitigation was not consistent across sectors,
with ‘Renting services’, ‘Second-hand vehicles’, ‘Internet provision’, ‘Vehicle maintenance and
repair’ and ‘Real estate services’ showing patterns that were particularly different from the rest
of the sectors.
The volumes of the components in ‘Real estate services’ were particularly high, but mitigations
and costs were almost aligned (£65.8 billion costs vs £63.7 billion mitigations). The difference
between costs and mitigations were particularly large for ‘Renting services’ (£10.5 billion costs
vs £3.1 billion mitigations), ‘Second-hand vehicles’ (£17.7 billion costs vs £13.6 billion
mitigations), ‘Internet provision’ (£4.5 billion vs £1.1), and ‘Vehicle maintenance and repair’
(£14.4 billion vs £7.4) 58.
The difference between total estimated costs and total estimated mitigations across the
different sectors is shown in Figure 12 (‘Real estate services’ is not shown for visualisation
reasons) along with a symmetry line (an hypothetical line where the total costs are equal to the
total mitigations): the closer the sectors are to the symmetry line, the smaller the gap between
costs and mitigations.

58 A full breakdown of the net monetised detriment components by sector is included in Appendix D.

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Figure 12 – Total costs and total mitigations by sector (billion £)

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2.2.4 Net monetised detriment and characteristics of detriment
Channel of purchase
The level of net monetised detriment was also associated with the channel of purchase of the
product. The highest total net monetised detriment was observed from purchases made in-
person from a shop and from purchasing online directly from the provider (Figure 13). High net
monetised detriment was also observed in purchases made through the phone or through
‘other’ channels 59. It is important to note that the study does not hold information about the
overall number of purchases in the different channels, and cannot explain whether the amount
of net monetised detriment was proportionate, or not, to the activity of consumers.
Figure 13 – Total net monetised detriment by channel of purchase (billion £)

Base: All detriment experiences in the UK in the 12 months to April 2021.
Unweighted: 9,388 (In-person from a shop: 2,268; In-person from salesperson: 188; Over a phone
call: 1,625; Online provider’s website: 3,314; Online third-party marketplace: 755; Online private
individuals: 214; Online social media platform: 84; Auto-renewal: 529; Other 411).

Richer conclusions can be drawn when looking at the median net monetised detriment by
channel of purchase. The median net monetised detriment was estimated to be particularly
high for purchases made through a salesperson who visited the consumer’s home or
workplace, followed by auto-renewal options and online shopping through a social media
platform (Figure 14).

59 Including products purchased from other people on behalf of the consumer, via smartphone apps, through employers,
or where the respondents had difficulty remembering how the product was bought.
£19.9
£11.5
£6.6
£2.5
£2.5
£1.8
£1.6
£0.6
£6.7
£0.0
£5.0
£10.0
£15.0
£20.0
£25.0
In-person from a shop
Online (provider's website)
Over a phone call
Online (private individuals)
In-person from salesperson
Online (third-party marketplace)
Auto-renewal
Online (social media platform)
Other

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Figure 14 – Median net monetised detriment by channel of purchase

Base: All detriment experiences in the UK in the 12 months to April 2021.
Unweighted: 9,388 (In-person from a shop: 2,268; In-person from salesperson: 188; Over a phone
call: 1,625; Online provider’s website: 3,314; Online third-party marketplace: 755; Online private
individuals: 214; Online social media platform: 84; Auto-renewal: 529; Other: 411).

The pattern observed for detriment through online shopping is particularly interesting, as higher
median net monetised detriment was seen across channels that are less regulated or where
peer-to-peer trade is more common. The medians are estimated to be the lowest for purchases
made through third-party marketplaces, where customer satisfaction is generally key for a
seller or provider who wants to keep operating on the third-party platform, and through the
provider’s website. On the other hand, the medians are higher when detriment was
experienced after purchasing online from private individuals or through a social media platform.
The analysis of net monetised detriment by channel of purchase indicates interesting patterns,
however further research is needed to understand the association between channel of
purchase and detriment – for example whether the differences in value are driven by the
nature of the items or services purchased, difficulty in accessing compensation, or other things.
Detriment type
The level of net monetised detriment varied by detriment type, with products identified as being
poor quality having the highest net monetised value, followed by failure to provide the product
and the product not being usable (Figure 15). The median net monetised detriment also varied
between the detriment types experienced. It was £50 when a complete failure to provide the
product was reported (versus £26 when this issue wasn’t reported), £40 for misleading price
(versus £28 when this issue wasn’t reported), £57 for unclear terms and conditions (versus £28
when this issue wasn’t reported) and £84 for warranty and guarantees not honoured (versus
£28 when it wasn’t reported). In all the other cases, the median was statistically the same
when the detriment types were experienced and when they were not.
£87
£55
£55
£32
£31
£28
£17
£14
£28
£0
£20
£40
£60
£80
£100
In-person from salesperson
Auto-renewal
Online (social media platform)
Over a phone call
Online (private individuals)
In-person from a shop
Online (provider's website)
Online (third-party marketplace)
Other

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Figure 15 – Total net monetised detriment when each detriment type was
experienced (billion £)

Base: All detriment experiences in the UK in the 12 months to April 2021.
Unweighted: 9,345 (Experienced the problem - Poor quality: 2,615; Not usable: 1,813; Problems
with delivery: 1,451; Complete failure to provide: 1,404; Misleading price: 854; Misleading
information: 792; Unfair or unclear T&C: 602; Warranty and guarantees not honoured: 291; Other:
2,244).
Note: The question about the type of detriment was a multicode, so respondents could select more
than one option for each detriment experience.

The report looks at detriment types separately and additional analysis to understand whether
specific combinations of detriment types were likely to be associated with higher net monetised
detriment for the consumers is recommended.
2.3 Detriment to wellbeing
This section focuses on the impact that the different incidents of detriment had on consumers’
wellbeing. The analysis presented here looks at the characteristics of the incidents of detriment
and explores if and such incidents’ features were associated with the likelihood of negative or
positive effects on wellbeing.
While this analysis is carried out at a detriment level, the impact of detriment on wellbeing
across a range of consumers’ socio-economic elements and demographics (at the respondent
level) is covered in Chapter 3 (“Identifying consumers at risk of detriment”).
£27.2
£18.5
£18.1
£13.1
£10.3
£8.1
£7.8
£6.5
£10.1
£0.0
£5.0 £10.0 £15.0 £20.0 £25.0 £30.0
Poor quality
Complete failure to provide
Not usable
Problems with delivery
Misleading information
Unfair or unclear T&C
Misleading price
Warranty and guarantees not honoured
Other

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Image 4 – Exploring the impact of detriment on wellbeing: two levels of analysis
2.3.1 Impact of detriment on wellbeing in the UK
The survey asked respondents about seven elements of wellbeing. Respondents were asked
the extent to which each incident of detriment made them feel upset, misled, helpless or
anxious and the extent to which each incident had a negative impact on their household’s
finances, and their mental and physical health. Where a negative emotion was experienced
this was ‘to some’ or ‘to a great’ extent. Where a negative impact was experienced on aspects
of the respondents’ life, this was regarded as ‘somewhat’ or ‘very’ negative.
Overall, 83% of detriment experiences triggered at least one negative emotion with feeling
upset being the most common (Figure 16).

Figure 16 – Negative impact of the detriment experiences on consumers’ feelings
Base: All detriment experiences in the UK in the 12 months to April 2021.
Unweighted: 9,416 (Anxious 9,382; Helpless 9,390; Misled 9,374; Upset 9,375).

16%
24%
24%
27%
18%
17%
18%
19%
42%
36%
37%
35%
24%
23%
21%
19%
0%
20%
40%
60%
80%
100%
Upset
Misled
Helpless
Anxious
Percentage of detriment experiences
Not at all
Hardly at all
To some extent
To a great extent

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A lower proportion of detriment incidents had a negative impact on consumers’ physical or
mental health or their household finances (32%). Overall, nearly one in four incidents (24%)
were deemed to have caused a negative effect on consumers’ mental health; 21% of detriment
experiences had a negative impact on the household finances; and 14% negatively affected
the consumers’ physical health (Figure 17).
Figure 17 – Negative impact of the detriment experiences on consumers’ health and
household finances
Base: All detriment experiences in the UK in the 12 months to April 2021.
Unweighted: 9,416 (Mental health 9,395; Physical health 9,389; Household’s finances 9,392).

The relatively low impact on the household finance is in line with the very low median value of
the overall monetised detriment: as discussed in the previous chapter, half of the consumers
experienced a monetised detriment below £28. The comparatively low incidence of detriment
to physical health is also expected, as most of the product categories presented to survey
respondents do not relate directly to physical health.
However, when looking at this data in absolute terms, the results are quite striking – for every
10 experiences of detriment, 2.4 (24%) affected consumers’ mental health, 2.1 (21%)
negatively affected consumers’ household finance and 1.4 (14%) negatively affected
consumers’ physical health.
Image 5 – Number of detriment experiences that had a negative impact on consumers’
mental health, physical health and household finance every 10 detriment experiences

54%
72%
50%
26%
14%
26%
12%
9%
15%
8%
5%
9%
0%
20%
40%
60%
80%
100%
Household
finance
Physical health
Mental health
Percentage of detriment experiences
No negative
effect
A slightly negative
effect
A negative
effect
A very negative
effect

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2.3.2 Impact of detriment on wellbeing by market characteristics
Product type
Detriment resulting from the consumption of services was more likely to have negatively
impacted consumer wellbeing than detriment related to the purchase or use of items. This was
true across all categories of wellbeing, however for feeling misled and physical health the
difference was not statistically significant (Table 4).

Overall, 85% of detriment experiences with services triggered at least one negative emotion
and 28% had a negative impact on consumers’ physical or mental health or household
finances compared to 73% and 19% respectively of detriment experiences with items.

Table 4: Percentage of detriment experiences that had a negative impact on the seven
wellbeing elements overall, by product type and market clusters

UWTD
base
(N)
Feeling
anxious
(%)
Feeling
helpless
(%)
Feeling
misled
(%)
Feeling
upset
(%)
Negative
effect on
mental
health
(%)
Negative
effect on
physical
health
(%)
Negative
effect on
HH
finances
(%)
Overall

All detriment incidents
9,416
54
58
59
66
24
14
21
Product type

*
*

*
*

*
Items
3,708
46
53
57
62
20
12
16
Services
5,708
60
62
61
70
27
15
25
Market cluster

*
*
*
*
*
*
*
Fast-moving retail
1,892
44
53
57
61
23
13
15
(Semi-) durable goods
1,568
52
52
52
64
15
12
16
Automotive
goods/services
847
53
57
62
68
26
15
34
Telecoms and other
digital subscriptions
2,237
58
66
66
71
25
15
19
Utilities
657
59
54
61
68
20
10
27
Housing-related services
326
76
79
71
83
35
17
35
Banking and insurance
524
65
63
66
67
32
13
28
Transport
266
62
66
59
71
27
12
24
Personal services
205
76
79
65
86
45
33
31
Recreational services
641
47
46
41
56
17
10
16
Other services
253
80
72
71
71
46
20
47
Base: All detriment experiences in the UK in the 12 months to April 2021.
Note: Differences by product type and market cluster are statistically significant (p < 0.05) in the columns marked
with an asterisk 60. For example, the likelihood of feeling helpless statistically varied by market clusters, while the
likelihood of feeling misled did not vary by product type.
Market clusters
The impact on wellbeing varied across market clusters for all the seven elements of wellbeing
(Table 4).

60 For more information on how the statistical tests were conducted and how to interpret them throughout the report, see
Appendix D.

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Across the measures, the ‘Personal Services’, ‘Other services’ 61 and ‘Housing-related
services’ market clusters had the highest proportion of detriment incidents that had a negative
wellbeing impact. Detriment incidents related to ‘Recreational services’, ‘(Semi-) durable
goods’ and ‘Fast-moving retail’ were the three market clusters where detriment experiences
were least likely to result in negative wellbeing impacts.

One third (33%) of detriment experiences associated with ‘Personal Services’ had a negative
impact on consumers’ physical health, making detriment in this market cluster by far the most
damaging to physical health (over twice the overall percentage of 14%). This is perhaps
unsurprising, as this cluster encompasses ‘Private medical & dental services’, as well as ‘Adult
care’ and ‘Childcare services’ while the other market clusters do not relate directly to physical
health. Moreover, it is worth noting these services were likely to have been particularly affected
by the restrictions imposed because of the COVID-19 outbreak.
2.3.3 Impact of detriment on wellbeing by characteristics of detriment
Channel of purchase
The impact on wellbeing also varied by channel of purchase for all the wellbeing categories,
except for feeling misled.
The proportion of incidents that had a negative effect on wellbeing was higher when there was
a personal interaction between the retailer and the customer. Detriment incidents from
purchases made after interacting directly with the seller, either in-person or in a phone call,
were generally more likely to have had negative wellbeing impacts than detriment incidents
from an online purchase or via auto-renewal where the personal interaction was lower (Table
5).
Looking at the channel with the highest level of personal engagement, we see that experiences
of detriment associated with purchases made in-person from a salesperson were more likely to
have resulted in consumers feeling anxious (76%) and suffering negative effects on their
household’s finances (47%) compared to other channels of purchase.
Along with purchases made in-person from a salesperson, detriment resulting from purchases
categorised as ‘Other’ 62 stood out as particularly likely to take a toll on consumers’ wellbeing.
While it is difficult to disentangle the purchases in this category, many of these types of
purchase are likely to be characterised by particularly high cost, complexity, urgency or
personal interactions, which may explain the relatively high proportion of incidents of detriment
in this category that negatively affected consumers’ wellbeing.
Detriment experienced following purchases made via auto-renewal had a comparatively low
percentage incidence for most negative wellbeing impacts, with the exception of feeling
helpless which had the highest incidence at 75%. Whether the automatic renewal was
something the consumer was not aware of or the result of a failure to cancel, it is not surprising

61 The market cluster categorised as ‘Other services’ includes the following sectors: ‘Legal and accountancy services’,
‘Veterinary’, ‘Funeral services’, and ‘Pet breeder’.
62 Other’ was an open text category for participants who did not select any of the channels of purchase presented.
Among the responses from survey participants in this category were purchases made via landlords, councils, financial
providers, medical and veterinary providers, and friends and family.

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consumers were more likely to feel helpless where money is withdrawn automatically on a
rolling contract basis.
Initial cost of the product
The proportion of detriment incidents that made consumers feel anxious, helpless and upset
was greater for products with a higher initial cost than products with a lower initial cost (Table
5). The same pattern is seen for negative effects on the consumer’s mental health and their
household’s finance.
Satisfaction of the outcome and resolution status
Looking at incidents that consumers considered concluded, the likelihood of experiencing
negative consequences on wellbeing decreased as the satisfaction with the outcome increased
(Table 5). However, the rate of change was not consistent across levels of satisfaction with the
outcome.
The incidence of negative emotions was particularly low for those experiences of detriment in
which the consumers were ‘completely satisfied’ with the outcome, while the decrease seen
between the other levels of satisfaction (from completely dissatisfied to somewhat satisfied)
was not as steep.
The incidence of negative consequences on mental health, physical health and household
finances was particularly high for incidents of detriment in which the consumer felt ‘completely
dissatisfied’ with the outcome, while the increase seen between other levels of satisfaction
(from completely satisfied to somehow dissatisfied) was milder.
Whether consumer took actions to resolve the detriment incident
Finally, detriment incidents were also more likely to have negatively affected consumers’
wellbeing if they were not successfully resolved. Across all wellbeing measures, an incident of
detriment was more likely to have resulted in a negative impact on wellbeing if no
compensation was received, in particular if the consumer had asked for something (Table 5).

Interpreting Table 5 – a brief guide

Channel of purchase, value of the product, satisfaction and resolution status, and
whether the consumer took actions to resolve the detriment are all single code
variables. Therefore, respondents could select only one option.
The table – which presents the independent variables in rows and the dependent
variables in columns – comprises four sections (see grey lines), one for each
single-code variable. This way it gives an overview of how the impact on the
seven wellbeing categories vary by each of the four dependent variables (e.g.
how ‘feeling anxious’ varies by channel of purchase) and flags if this variation is
statistically significant or not (e.g. the differences found for feeling anxious by
channel of purchase are significant).

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Table 5 – Percentage of detriment experiences that had a negative impact on the seven wellbeing categories by channel of purchase,
value of the product, satisfaction and resolution status, and whether the consumer took actions to resolve the detriment

UNWTD
base
(N)
Feeling
anxious
(%)
Feeling
helpless
(%)
Feeling
misled
(%)
Feeling
upset
(%)
Negative
effect on
mental
health
(%)
Negative
effect on
physical
health
(%)
Negative
effect on
HH
finances
(%)
Channel of purchase

*
*

*
*
*
*
In-person from a shop or other outlet
2,268
57
56
59
68
28
20
29
In-person from salesperson
188
76
64
75
78
37
10
47
Over a phone call
1,625
63
65
66
73
27
16
20
Online (provider's website)
3,314
49
55
55
61
19
8
13
Online (third-party marketplace)
755
40
50
63
63
18
8
17
Online (private individuals)
214
48
56
60
62
24
10
23
Online (social media platform)
84
57
47
61
71
21
14
24
Auto-renewal
529
47
75
53
58
15
10
14
Other
411
69
73
51
80
40
27
34
Value categories

*
*

*
*

*
Up to £100
2,932
45
51
55
59
18
10
11
£101 to £500
3,076
59
60
63
67
21
12
18
£501 to £1,000
1,316
59
63
61
72
35
21
36
£1,001 to £5,000
1,033
57
72
60
74
26
15
30
Greater than £,5000
303
72
75
68
81
34
11
38
Satisfaction and resolution status

*
*
*
*
*
*
*
The problem is still ongoing
1,728
68
72
70
81
41
28
38
Completely satisfied
2,389
39
34
37
43
14
9
13
Somewhat satisfied
2,248
55
56
58
66
18
9
15
Neither satisfied nor dissatisfied
1,234
44
54
51
58
13
9
18
Somewhat dissatisfied
1,070
61
74
77
82
26
9
16
Completely dissatisfied
730
62
76
80
81
42
22
31
Whether took actions to resolve

*
*
*
*
*
*
*
Did not ask for anything, and received nothing
1,958
55
63
67
69
31
19
29
Did not ask for anything, but received something
1,600
49
58
50
57
21
14
20
Asked for something, but received nothing
774
72
83
81
84
41
19
34
Asked for something, but received less than asked
800
57
65
71
71
18
8
20
Received what asked
1,921
50
45
47
59
18
9
8
Received what asked, plus something else
1,637
45
45
50
63
16
8
14
Base: All detriment experiences in the UK in the 12 months to April 2021.
Note: Differences, for each of the four independent variables, are statistically significant (p < 0.05) in the columns marked with an asterisk.

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Detriment type
The proportion of detriment instances that affected consumers’ wellbeing also varied by types
of detriment (Table 6). While detriment experiences where a warranty or guarantee was not
honoured negatively impacted consumer wellbeing across all wellbeing measures, others only
affected specific areas.
Problems with the delivery of the product or misleading information were more likely to have
triggered a negative emotional impact compared to instances when these problems were not
experienced. However, neither of these detriment types were associated with negative effects
on physical or mental health or household finances.
Incidents where the item or service was poor quality were more likely to have resulted in
consumers feeling misled, but were not associated with any other wellbeing measures, and
whether or not the item or service was usable was not associated with any particular type of
wellbeing effect.

Interpreting Table 6 – a brief guide

Detriment type is a multicode variable. This means that respondents could select
more than one option. The table looks at the likelihood of facing negative impacts
on wellbeing categories when a specific detriment type problem was experienced
compared to when it was not. Asterisks mark statistically significant differences.

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Table 6 – Percentage of detriment experiences that had a negative impact on the seven
wellbeing categories by whether a detriment type was experienced or not

Experienced
Not
experienced
Statistically
significant
Poor quality

UNWTD base (N)
2,615
6,801

Feeling anxious (%)
54
53

Feeling helpless (%)
60
57

Feeling misled (%)
69
53
*
Feeling upset (%)
68
65

Negative effect on mental health (%)
26
23

Negative effect on physical health (%)
14
13

Negative effect on household finances (%)
19
22

Not usable

UNWTD base (N)
1,813
7,603

Feeling anxious (%)
55
53

Feeling helpless (%)
55
59

Feeling misled (%)
58
59

Feeling upset (%)
71
65

Negative effect on mental health (%)
24
24

Negative effect on physical health (%)
11
14

Negative effect on household finances (%)
18
22

Problems with delivery

UNWTD base (N)
1,451
7,965

Feeling anxious (%)
62
52
*
Feeling helpless (%)
66
56
*
Feeling misled (%)
66
57
*
Feeling upset (%)
73
64
*
Complete failure to provide

UNWTD base (N)
1,704
7,712

Feeling anxious (%)
57
53

Feeling helpless (%)
63
57

Feeling misled (%)
59
59

Feeling upset (%)
75
64
*
Negative effect on mental health (%)
33
22
*
Negative effect on physical health (%)
23
11
*
Negative effect on household finances (%)
25
20

Misleading price

UNWTD base (N)
854
8,562

Feeling anxious (%)
65
52
*
Feeling helpless (%)
65
57

Feeling misled (%)
76
57
*
Feeling upset (%)
76
65
*
Negative effect on mental health (%)
31
23

Negative effect on physical health (%)
22
13
*
Negative effect on household finances (%)
40
18
*
Table continues on the next page

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52

Table 6 (continues)
Experienced
Not
experienced
Statistically
significant
Misleading information

UNWTD base (N)
792
8,624

Feeling anxious (%)
67
52
*
Feeling helpless (%)
73
56
*
Feeling misled (%)
83
56
*
Feeling upset (%)
77
65
*
Negative effect on mental health (%)
28
23

Negative effect on physical health (%)
17
13

Negative effect on household finances (%)
27
20

Unfair or unclear T&C

UNWTD base (N)
602
8,814

Feeling anxious (%)
61
53

Feeling helpless (%)
78
56
*
Feeling misled (%)
74
58

Feeling upset (%)
73
65

Negative effect on mental health (%)
37
23
*
Negative effect on physical health (%)
19
13

Negative effect on household finances (%)
37
19
*
Warranty and guarantees not honoured

UNWTD base (N)
291
9,125

Feeling anxious (%)
72
53
*
Feeling helpless (%)
74
57
*
Feeling misled (%)
81
58
*
Feeling upset (%)
82
65
*
Negative effect on mental health (%)
47
23
*
Negative effect on physical health (%)
26
13
*
Negative effect on household finances (%)
43
20
*
Other 63

UNWTD base (N)
2,244
7,172

Feeling anxious (%)
60
52
*
Feeling helpless (%)
60
57

Feeling misled (%)
51
61
*
Feeling upset (%)
66
66

Negative effect on mental health (%)
27
23

Negative effect on physical health (%)
12
14

Negative effect on household finances (%)
22
21

Base: All detriment experiences in the UK in the 12 months to April 2021.
Note 1: Differences are statistically significant (p < 0.05) in the rows marked with an asterisk.
Note 2: The question about the type of detriment was a multicode, so respondents could select more than one option for
each detriment experience. We therefore looked at whether the likelihood of a negative impact on the seven wellbeing
categories was higher or lower depending on whether each detriment type was selected or not.
Note 3: 'Experienced' = detriment type is applicable to the detriment incident (i.e. the specific detriment type was experienced
by the respondent for a given detriment incident); 'Not experienced' = detriment type is not applicable.

63 The category ‘Other’ encompass problems with returns, items breaking after some time, cancellations or issues
accessing a service due to COVID-19, difficulties leaving or cancelling a service, a provider going into administration
and fraud.

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3 Complaints journey

This chapter addresses Research Question 2: What is the complaints journey? What are
the dynamics that the experience of detriment sets off?

This chapter looks at:
• what happens when detriment is experienced;
• how consumers respond and why;
• the resolutions requested and offered;
• and consumers’ satisfaction with those resolutions.
It focuses on how these varied by market cluster and other characteristics relating to the
detriment incident.
The findings suggest that only a small proportion of experiences of detriment were unactioned
by consumers, and when consumers decided not to take actions, elements such as the
severity of the problem and the likelihood of being successful played a major role in this
decision. When consumers decided to take action, by far the most common way was to get in
direct contact with the seller, generally asking for an apology or explanation, a refund, or for
the product to be replaced or fixed. These requests were often met by sellers, producers or
service providers and more than half of the experiences of detriment ended in a positive
resolution and with a satisfactory outcome.
Stages of the complaint journey (from taking the decision to raise a problem, all the way up to
the final resolution and resolution patterns) occasionally varied by product type, detriment type,
channel of purchase, value of the product and time spent by the consumer dealing with the
problem. However, we consistently found differences between market clusters – suggesting
that this is one of the elements that is heavily linked to differences in complaint journeys.
3.1 Actioned and unactioned detriment
The first decision consumers face when experiencing detriment is whether or not they want to
take actions to address it. Taking action generally requires consumers to reach out to the seller
or provider and ask them to make things right, an investment of time and resources from the
consumer and therefore a trade-off on whether it is worth the effort. Consumers took some
form of action in 82% of detriment experiences seen in the UK over the 12 months to April
2021. However, these figures, as well as the reason for not taking actions, varied by market
characteristics and product features.
In this analysis, an incident of detriment was considered actioned by the consumer if:

Consumers took any initiative to address the problem including, but not limited
to: contacting the seller or a consumer rights organisation, claiming under a

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guarantee, writing online reviews, withholding payments, taking legal actions,
using a dispute resolution system, asking family members or friends for help.
3.1.1 Actioned consumer detriment
The likelihood of taking action to address detriment did not vary by whether the detriment
experience related to an item or service or by the value of the product. However, the likelihood
of taking action did vary by the market cluster in which the detriment was encountered (Figure
18). Consumers were most likely to have taken action for detriment experienced relating to
‘Utilities’, ‘(Semi-) durable goods’, ‘Telecoms and other digital subscriptions’, and ‘Housing-
related services’. Whilst they were least likely to have acted after experiencing detriment
related to ‘Personal services’, ‘Transport’ and ‘Other services’.
Figure 18 – Proportion of actioned experiences of detriment by market cluster

Base: All detriment experiences in the UK in the 12 months to April 2021.
Unweighted: 9,404 (Fast-moving retail: 1,889; (Semi-) durable goods: 1,568; Automotive goods and services:
845; Telecoms and other digital subscriptions: 2,234; Utilities: 656; Housing-related services: 325; Banking and
insurance: 524; Transport: 266; Personal services: 205; Recreational services: 640; Other services: 252).

The likelihood of taking action also varied by market sector. Consumers were most likely to
have taken action after experiencing detriment relating to ‘Removal and storage’; ‘Package
holidays and tours’; and ‘Pet breeders’ (action taken in 94% of incidents respectively).
Meanwhile, consumers were least likely to have acted on experiences of detriment when it
related to ‘Personal care products’; ‘Stationery, books, magazines and newspapers’; and
‘Childcare’ (actions taken in 46%, 49% and 50% of incidents respectively).
The proportion of incidents where action was taken also differed by the channel through which
the product was originally bought (Figure 19). Consumers were least likely to have taken
action in experiences of detriment which related to a product bought via auto-renewal, in-
person from a shop or other outlet, or online from a website where individuals sell to each
other. However, they were most likely to have acted after experiencing detriment relating to
purchases made over a phone call, in-person from a salesperson or online via social media.
82%
90%
90%
89%
89%
87%
85%
76%
76%
70%
70%
66%
0%
20%
40%
60%
80%
100%
Overall
Utilities
(Semi-) durable goods
Telecoms and other digital subscriptions
Housing-related services
Recreational services
Banking and insurance
Automotive goods and services
Fast-moving retail
Personal services
Transport
Other services
Percentage of detriment experiences

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Figure 19 – Proportion of actioned experiences of detriment by purchase channel
Base: All detriment experiences in the UK in the 12 months to April 2021.
Unweighted: 9,404 (In-person from a shop or other outlet: 2,265; In-person from salesperson who visited my
home or work: 188; Over a phone call: 1,624; Online from the provider’s/retailer’s/operator’s website: 3,312;
Online from a third-party marketplace website or app: 755; Online from a website where private individuals sell to
each other, such as eBay or Airbnb: 214; Online through a social media platform: 84; Auto-renewal where the
money is automatically taken from your account: 528; Other: 409).

In addition, the probability of taking action also varied for some types of detriment (Figure 20).
Consumers were more likely to have taken action after experiencing detriment relating to
warranties and guarantees not being honoured, the product not being usable and problems
with delivery, compared to experiences of detriment where these types of detriment were not
faced. The likelihood of taking, or not taking, action after experiencing detriment did not
statistically vary for other types of detriment.
Figure 20 – Proportion of actioned experiences of detriment by detriment type

Base: All detriment experiences in the UK in the 12 months to April 2021.
Unweighted: 9,340 (Poor quality: 2,613; Not usable: 1,812; Problems with delivery: 1,451; Complete failure to
provide: 1,703; Misleading price: 852; Misleading information: 792; Unfair or unclear T&Cs: 602; Warranty and
guarantees not honoured: 291; Other: 2,244).
3.1.2 Unactioned consumer detriment
Consumers may decide not to take any action after experiencing an incidence of detriment for
a variety of reasons. The most common reason given for not taking any action after
82%
92%
90%
89%
87%
86%
77%
77%
73%
72%
0%
20%
40%
60%
80%
100%
Overall
Over a phone call
In-person from salesperson
Online (social media platform)
Online (provider's website)
Online (third-party marketplace)
Online (private individuals)
Other
In-person from a shop
Auto-renewal
Percentage of detriment experiences
93%
91%
89%
88%
88%
86%
86%
78%
82%
0%
20%
40%
60%
80%
100%
Warranty and guarantees not honoured
Not usable
Problems with delivery
Complete failure to provide
Misleading information
Poor quality
Misleading price
Unfair or unclear T&Cs
Other
Percentage of detriment experiences

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experiencing detriment was because they felt the problem was not serious enough, alongside
saying they intended to do it in the future (Figure 21).
Figure 21 – Reasons why consumer decided not to act after experience of detriment
Base: Detriment experiences in the UK in the 12 months to April 2021, where no actions were taken by the
consumer.
Unweighted: 1,386.

Two elements emerge from this analysis. Firstly, consumers did not take actions in 18% of
their experiences of detriment, but for a large proportion of incidents this was because they
had not had the chance yet to do so 64, because someone else did it, or because the problem
was somehow fixed without the consumer needing to request anything. Once we remove
these, the net estimate of unactioned consumer detriment falls to 11% of all incidents.
Secondly, it appears that the decision of whether or not to take action was the result of a trade-
off between the need for a resolution of detriment experienced and the expenditure of personal
time and money. Consumers did not act when the investment of their time, efforts and
resources were not worth it (for example, the problem not being serious enough, the
expectation it would be unsuccessful, or the process being difficult or too lengthy).
This second point is reflected in the finding that the median value of the product was higher for
detriment experiences where the consumer took action: £200 for experiences where action
was taken in contrast to £150 for experiences where no action was taken. The difference is
particularly marked when looking at the median value of products in incidents that were
unactioned because the problem was not serious enough (£35), or because the process would
have taken too long (£31).

64 The survey also collected data about detriment experiences that were still ongoing.
22%
20%
10%
9%
7%
6%
6%
5%
5%
9%
0%
5%
10%
15%
20%
25%
Problem not serious enough
Intention to do it in the future
Thought it would be unsuccessful
Problem resolved without taking actions
Process too complicated
Somebody else took action
Process would have taken too long
Uncomfortable with options available
Unclear how to go about complaining
Other reason
Percentage of unactioned detriment experiences

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Table 7 – Median value of the product by reason for not taking action after experiencing
detriment
Reason for not taking action
Median
value
(£)
Unweighted
base
(N)
Intention to do it in the future
655
115
Process too complicated
325
65
Problem resolved without taking actions
200
190
Thought it would be unsuccessful
145
156
Other reason
141
167
Uncomfortable with options available
106
53
Unclear how to go about complaining
67
41
Problem not serious enough
35
324
Process would have taken too long
31
62
Somebody else took action
*
8
Base: Detriment experiences in the UK in the 12 months to April 2021, where no actions were taken by the
consumer.
* Unweighted count too small for population estimates (n < 30).

3.1.3 Actions taken when experiencing detriment
If deciding to take action, consumers can decide to take a range of possible routes. Figure 22
shows that the most common action taken by consumers when experiencing detriment was by
far to directly contact the seller, producer or service provider. On the other hand, consumers
were least likely to have withheld payment, contacted a consumer rights or advice
organisation, used a dispute resolution service or Ombudsman, or taken legal action against
the seller, producer or service provider.
These results suggest that consumers generally took actions that were more accessible, whilst
tending to avoid more serious or formal actions that would require more effort or otherwise
could bring about additional complexity to the detriment resolution.
Figure 22 – Actions taken by the consumer after experience of detriment
Base: Detriment experiences in the UK in the 12 months to April 2021, where actions were taken by the
consumer.
Unweighted: 8,003

81%
12%
12%
8%
4%
4%
3%
1%
7%
0%
50%
100%
Contacted the seller directly
Left a review online
Asked family members/friends for help
Tried to claim under a guarantee/insurance
Withheld payment
Contacted a consumer rights organisation
Used a dispute resolution service
Took legal action against the seller
Other
Percentage of actioned detriment experiences

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3.2 Detriment resolution
3.2.1 What consumers ask and what sellers do
Consumers who decided to take actions to address their experience of detriment were faced
with a second choice: what could be done to make things right? Amongst all the actions that
consumers could ask the seller, vendor or provider to take, they were most likely to have asked
for a replacement or to fix the problem, or otherwise to provide a full or partial refund (Figure
23). Consumers were also quite likely to have requested the seller or supplier apologise or
explain the problem. However, consumers were least likely to have asked the seller or supplier
to provide a voucher or store credit, provide compensation for extra costs or inconvenience
incurred, review or change the contract conditions or request something else. These results
suggest that consumers preferred actions that would result in an immediate resolution to the
problem, rather than other forms of monetary or non-monetary compensations.
Figure 23 – Actions requested by consumers after having experienced
detriment
Base: Detriment experiences in the UK in the 12 months to April 2021, where actions were taken
by the consumer.
Unweighted: 7,997.

We also asked respondents what action sellers actually took. Figure 24 shows that the most
common actions taken by suppliers or sellers were to restore or fix the service or item and
apologise for the inconvenience. Sellers and suppliers were also quite likely to offer a full
refund or explain the problem – these were also the four most commonly requested actions,
although the discrepancy seen between the consumer and the seller estimates indicate that
consumers did not always get what they want.
This is the case for incidents of detriment where consumers requested the seller or supplier
provide a full or partial refund. Requests for a refund were met by the seller or service provider
in 81% of the incidents, while consumers were promised a future refund that had not been
34%
33%
27%
27%
8%
7%
7%
6%
4%
9%
0%
5%
10% 15% 20% 25% 30% 35% 40%
Replace/fix the product
Provide a refund (full or partial)
Apologise
Explain the problem
Provide a discount
Provide a voucher
Provide compensation for extra costs
Review contract conditions
Other
None of these
Percentage of actioned detriment experiences

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provided by the seller at the time of the survey in eight percent of the incidents. Five percent of
the cases of detriment in which the consumers had requested a refund, ended with the
consumers receiving nothing from the seller or service provider.
Figure 24 – Actions taken by sellers after detriment experience
Base: All detriment experiences in the UK in the 12 months to April 2021.
Unweighted: 9,391.
In 18% of detriment experiences the seller or supplier did not do anything – however, these
estimates include instances where consumers did not action their experiences of detriment, so
the seller or provider might not have been aware that a consumer was facing detriment and,
consequently, did not offer anything to resolve the problem. Indeed, sellers and providers took
actions in 88% of the experiences that were actioned by consumers and in 54% of experiences
that were not.
Sellers and providers also took actions in 38% of the incidents of detriment that consumers
reported without making specific requests and in 93% of the incidents of detriment in which
consumers made specific requests.
3.2.2 Whether consumers experienced a positive or negative resolution
Most of the actions requested by consumers had a potential monetary value, except for
offering an apology or explaining the problem. When focusing on those actions, a little more
than half of the experiences of detriment (56%) ended with a positive resolution, with
consumers receiving what they asked for or receiving what they asked for and something else
or receiving some form of compensation even if this was not requested (Figure 25) 65.

65 Further details on how the different sellers’ or vendors’ actions and consumers’ requests were combined in resolution
patterns can be found in the appendix (Appendix E).
48%
42%
27%
27%
10%
10%
7%
6%
5%
5%
5%
18%
0%
5% 10% 15% 20% 25% 30% 35% 40% 45% 50%
Restored/fixed the product
Apologised
Offered a full refund
Explained the problem
Offered a partial refund
Promised a refund or fix (not yet provided it)
Provided a voucher
Provided a discount
Provided compensations for extra costs
Reviewed contract conditions
Other
None
Percentage of detriment experiences

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Nineteen per cent of detriment experiences where the consumer asked the seller to do
something ended in a negative resolution where the consumer did not receive what they asked
for, and 25% of the experiences resulted in a neutral resolution where consumers did not ask
for anything and did not receive anything.
Figure 25 – Consumer’s success in obtaining the resolution wanted for the
detriment incident

Base: Detriment experiences in the UK in the 12 months to April 2021, excluding those where the only
actions sought by the consumer and taken by the seller/provider were apologies, explanations of the
problem, undefined “other” actions, or promises to do something in the future.
Unweighted: 8,690.

Note: This figure shows a derived variable computed to compare what the consumers asked the seller or
service provider to do and what the seller or service provider did. Actions such as apologies and
explanations of the problem, as well as undefined “other” actions and promises to do something in the
future, have been excluded from the computation. Detriment incidents that were not considered concluded
by the consumer are kept in the analysis.

The following sections focus on how positive, neutral, and negative resolution patterns varied by
product elements and detriment features.
3.2.3 Patterns of resolution by market and detriment characteristics
Detriment experiences were more likely to have ended in a positive result where they related to
items rather than services (60% compared to 53%). This may reflect the fact that items can be
more easily replaced or fixed relative to services.
The probability of incidents ending with a positive resolution also varied by market cluster.
Detriment experiences in the ‘Utilities’, ‘Fast-moving retail’, ‘(Semi-) durable goods’ and
‘Recreational services’ market clusters were the most likely to have a positive outcome (Figure
26).
25%
20%
19%
17%
11%
8%
0%
5%
10%
15%
20%
25%
30%
Did not ask for
anything,
received
nothing
Received
what asked
Received what
asked and
something else
Did not ask for
anything,
received
something
Asked for
something,
received
something else
and/or less
than asked
Asked for
something,
received
nothing
Percentage of detriment experiences
Neutral
resolution
Positive
resolution
Negative
resolution

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Interestingly, the likelihood of getting a negative resolution did not statistically vary between
market clusters, suggesting that the lower proportion of positive resolutions seen in some
market clusters was due to a higher proportion of neutral resolutions, rather than negative
ones.
As expected, when neutral and negative resolutions were combined, statistical variation was
seen across market clusters. The clusters most likely to result in a neutral or negative
resolution were ‘Personal services’, ‘Other services’, ‘Housing-related services’ and ‘Transport’
(67%, 66%, 60% and 57% of detriment experiences respectively).
Figure 26 – Resolution by market cluster

Base: Detriment experiences in the UK in the 12 months to April 2021, excluding those where the only
actions sought by the consumer and taken by the seller/provider were apologies, explanations of the
problem, undefined ‘other’ actions, or promises to do something in the future.
Unweighted: 8,690 (Fast-moving retail: 1,770; (Semi-)durable goods: 1,436; Automotive goods and
services: 803; Telecoms and other digital subscriptions: 2,048; Utilities: 573; Housing-related services:
283; Banking and insurance: 493; Transport: 242; Personal services: 183; Recreational services: 580;
Other services: 243).

The likelihood of achieving a positive outcome varied by market sector. The sectors most likely
to have resulted in a positive resolution were ‘Removal and storage’, ‘Personal care services’,
and ‘Package holidays and tours’ (82% 66, 79% and 75% of detriment experiences
respectively).

66 This estimate is based on a particularly small number of observations (n < 25).
64%
61%
61%
59%
55%
50%
48%
43%
40%
34%
33%
19%
23%
15%
24%
20%
34%
31%
37%
33%
51%
43%
17%
16%
23%
17%
25%
16%
21%
19%
27%
14%
24%
0%
20%
40%
60%
80%
100%
Utilities
Fast-moving retail
(Semi-) durable goods
Recreational services
Telecoms and other digital subscriptions
Automotive goods and services
Banking and insurance
Transport
Housing-related services
Other services
Personal services
Percentage of detriment experiences
Positive resolution
Neutral resolution
Negative resolution

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On the flip side of the coin, the likelihood of a negative resolution also varied by sector. The
sectors most likely to result in a negative resolution were ‘Medical and dental services’,
‘Renting services’ and ‘Internet provision’ (38%, 38% and 29% of detriment experiences
respectively).
The likelihood of a positive resolution also varied by the value of the product (Figure 27).
Detriment experiences relating to products of a value below £5,000 were generally more likely
to have resulted in a successful resolution, compared to products of greater value. This may be
due to lower value products being less of a burden for sellers to replace, fix or provide
compensation for. Once below £5,000, the value of the product or service seemingly had little
influence on the final resolution outcome of the detriment experience.
Figure 27 – Resolution by product value
Base: Detriment experiences in the UK in the 12 months to April 2021, excluding those where the
only actions sought by the consumer and taken by the seller/provider were apologies, explanations
of the problem, undefined “other” actions, or promises to do something in the future.
Unweighted: 8,690 (Up to £100: 2,764; £101 to £500: 2,837; £501 to £1,000: 1,220; £1,001 to
£5,000: 936; Greater than £5,000: 266).

In addition, the likelihood of the consumer getting what they sought from the seller varied by
the time they spent dealing with the problem (Figure 28). Experiences where the consumer
spent less time dealing with the problem, were more likely to have had a positive resolution.
56%
59%
48%
57%
35%
26%
20%
29%
20%
35%
18%
21%
24%
22%
31%
0%
20%
40%
60%
80%
100%
Up to
£100
£101 to
£500
£501 to
£1,000
£1,001 to
£5,000
Greater
than £5,000
Percentage of detriment experiences
Positive resolution
Neutral resolution
Negative resolution

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Figure 28 – Resolution by time spent on problem
Base: Detriment experiences in the UK in the 12 months to April 2021, excluding those where the
only actions sought by the consumer and taken by the seller/provider were apologies, explanations
of the problem, undefined ‘other’ actions, or promises to do something in the future.
Unweighted: 8,012 (30 minutes or less: 2,386; Between 31 minutes and 1 hour: 1,568; Between 61
minutes and 2 hours: 1,322; Between 121 minutes and 5 hours: 1,524; More than 5 hours: 1,306).

Although perhaps counter-intuitive, one possible explanation for this pattern might be that
problems which require more time to deal with are also more likely to be more complex and thus
more difficult for the seller to resolve positively. Alternatively, it may be that the association
between longer resolution times and fewer positive resolutions can be explained by sellers or
providers being less willing to solve the problems and/or consumers asking the seller or provider
for more radical monetary actions.
The likelihood of achieving a positive resolution also varied by type of detriment. Experiences
relating to poor quality were less likely to have ended up with a positive resolution, compared
to detriment experiences where this problem was not reported (51% versus 58% of
experiences with other detriment types). The same trend was observed for misleading price
(48% of positive resolutions when this detriment type was experienced, 56% when it was not),
misleading information (42% ended up with a positive resolution, versus 56% of experiences
where this problem was not observed), warranties and guarantees not honoured (40% versus
56%) and other detriment types (50% versus 57%).
Finally, the likelihood of achieving a positive outcome did not vary by the channel of purchase.
3.3 Satisfaction with the detriment resolution
3.3.1 Overall satisfaction
Finally, this section looks at the subjective satisfaction with the outcome, asked in the study for
each experience of detriment that consumers considered concluded. Consumers reported being
satisfied with the outcome in just over half of concluded detriment experiences (55%) and
dissatisfied with the outcome for 25% of experiences (Figure 29).
60%
62%
61%
55%
43%
28%
21%
14%
22%
29%
12%
17%
26%
23%
29%
0%
20%
40%
60%
80%
100%
30 minutes
or less
Between
31 mins
and 1 hour
Between
61 minutes
and 2 hours
Between
121 minutes
and 5 hours
More than
5 hours
Percentage of detriment experiences
Positive resolution
Neutral resolution
Negative resolution

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Figure 29 – Satisfaction with the outcome of detriment experiences
Base: Detriment experiences in the UK in the 12 months to April 2021 considered concluded by the
consumer.
Unweighted: 7,671.
3.3.2 Satisfaction with the outcome by market characteristics
The proportion of detriment incidents in which consumers were either completely or somewhat
satisfied with the outcome did not vary by whether the incident related to an item or service.
However, it did vary by market clusters: consumers reported being satisfied with the outcome of
more than half of their experiences of detriment in seven of the market clusters. Consumers
reported lower levels of satisfaction in the ‘Utilities’, ‘Housing-related services’, ‘Personal
services’ and ‘Transport’ market clusters (Figure 30).
26%
29%
20%
15%
10%
0%
5%
10%
15%
20%
25%
30%
Completely
satisfied
Somewhat
satisfied
Neither
satisfied nor
dissatisfied
Somewhat
dissatisfied
Completely
dissatisfied
Percentage of detriment experiences

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Figure 30 – Proportion of concluded experiences of detriment where the consumers
were at least somewhat satisfied with the outcome, by market cluster

Base: Detriment experiences in the UK in the 12 months to April 2021 considered concluded by the consumer.
Unweighted: 7,671 (Fast-moving retail: 1,668; (Semi-) durable goods: 1,287; Automotive goods and services:
752; Telecoms and other digital subscriptions: 1,749; Utilities: 450; Housing-related services: 246; Banking and
insurance: 407; Transport: 217; Personal services: 143; Recreational services: 535; Other services: 217).
The lower proportion of detriment incidents for which consumers were satisfied with the
outcome in some market clusters might be partly linked to the COVID-19 outbreak, given the
presence of many sectors within those clusters that have faced more severe and unusual
disruptions during the pandemic than those seen in other clusters.
This is the case for ‘Transport’ which includes ‘Airline’ (where 37% of detriment experiences
led to a satisfactory outcome for consumers) and ‘Public transport and trains’ (28%) and for
‘Personal services’, which includes sectors such as ‘Adult care’ (28%), ‘Childcare’ (35%) and
‘Education fees’ (39%). Over 70% of the experiences of detriment in those two market clusters
(‘Transport’ and ‘Personal services’) were considered by consumers to have been fully or
partly caused by the COVID-19 pandemic; this was the highest incidence across all the market
clusters.
This trend seems to be less marked for ‘Telecoms and other digital subscriptions’, despite the
fact that this market cluster was also particularly impacted by the COVID-19 outbreak, due to
the increased pressure on the internet provision infrastructure and related services (even if the
analysis carried out in Chapter 5 suggests that consumers did not generally perceive their
internet problems as being caused by the pandemic). Just over half (58%) of the detriment
incidents in ‘Telecoms and other digital subscriptions’ resulted in consumers being at least
somewhat satisfied; within this cluster, the highest proportion of incidents of detriment with a
satisfactory outcome could be found in “Internet provision” (60% of the experiences of
detriment).
55%
60%
59%
58%
58%
55%
55%
53%
46%
43%
42%
32%
0%
10%
20%
30%
40%
50%
60%
70%
Overall
(Semi-) durable goods
Automotive goods and services
Telecoms and other digital subscriptions
Recreational services
Fast-moving retail
Other services
Banking and insurance
Utilities
Housing-related services
Personal services
Transport
Percentage of detriment experiences

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3.3.3 Satisfaction with the outcome by detriment characteristics
The proportion of incidents in which consumers were satisfied with the outcome did not vary
significantly by the value of the product or the purchase channel through which the affected item
or service was bought. However, the likelihood of being satisfied with the outcome varied by the
type of detriment.
Detriment incidents relating to poor quality or unfair or unclear T&Cs were less likely to have
resulted in a satisfactory outcome relative to other types of detriment. Forty-seven per cent of
experiences of detriment where consumers reported poor quality being one of the problems
concluded with a satisfactory outcome compared to 58% of experiences where poor quality was
not one of the elements of detriment. Similarly, 39% of experiences that related to unfair or
unclear T&Cs concluded with an outcome that left the consumers satisfied compared to 56% of
experiences of detriment where unfair or unclear T&Cs was not listed among the problems.
These may reflect the relatively subjective nature of both these detriment types, making them
harder to be satisfactorily resolved. Satisfaction with the outcome did not vary statistically for the
other types of detriment.
3.3.4 Satisfaction with the outcome by obtaining the resolution sought
As might be expected, satisfaction with the outcome did vary by how successful the consumer
was in obtaining the resolution they wanted. Experiences where consumers received what they
asked for were most likely to end in consumers being satisfied, followed by experiences where
the consumer received something. Meanwhile, experiences where the consumer did not receive
anything were much less likely to have resulted in the consumer being satisfied with the
resolution, irrespective of whether or not the consumer had asked for anything.
Figure 31 – Whether satisfied by success in obtaining what was wanted
Base: Detriment experiences in the UK in the 12 months to April 2021 considered concluded by the consumer.
Unweighted: 7,671 (Did not ask for anything, and received nothing: 1,353; Did not ask for anything, but received
something: 1,513; Asked for something, but received nothing: 364; Asked for something, but received
something else and/or less than asked: 729; Received what asked: 1,827; Received what asked and something
else: 1,598).

74%
68%
63%
58%
23%
23%
0%
20%
40%
60%
80%
Received
what
asked and
something
else
Received
what
asked
Asked for
something,
received
something
else and/or
less than
asked
Did not
ask for
anything,
received
something
Did not
ask for
anything,
received
nothing
Asked for
something,
received
nothing
Percentage of detriment experiences

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4 Identifying consumers at risk of detriment

This chapter addresses Research Question 3: Who are the consumers most vulnerable
to detriment?

The previous two chapters discussed how the likelihood of detriment, and its severity, differ
across products with different features, such as the sector, the market cluster, or how the product
was purchased. This chapter shifts the focus from the incidents of detriment to the characteristics
of consumers, looking at which demographic and socio-economic characteristics are associated
with higher vulnerability to detriment across four measures:
• Risk of experiencing detriment. Which groups were more likely to have experienced
detriment in the 12 months to April 2021?
• Negative consequences of detriment. Which groups were more likely to have
experienced the most negative consequences of detriment in terms of monetised
detriment and impact on wellbeing?
• Not taking actions in response to detriment. Which groups experienced detriment
but did not take any actions to solve the problem? And how do the reasons given for not
taking any actions vary between different groups?
• Unsatisfactory resolutions. Which groups were less likely to have obtained a
satisfactory resolution to their detriment experience?
Whilst there was no evidence that consumer characteristics were associated with unsatisfactory
resolutions [Section 3.4], findings suggest that some subgroups were more likely to have
experienced worse outcomes in the other three forms of vulnerability.
In particular, consumers aged 18-39 and those finding it difficult financially were more likely to:
• have experienced detriment in the 12 months to April 2021 [Section 3.1];
• have reported experiencing higher levels of negative monetised detriment and negative
wellbeing consequences [Section 3.2];
• have not taken actions to address detriment because the process would have taken too
long or because they were not sure who to contact or how to complain [Section 3.3].
Variation of experiences was found also between other groups of the population, but the patterns
were less consistent and changed between different types of vulnerability.
4.1 Risk of experiencing detriment
As discussed in Section 1.1.1, 69% of consumers in the UK suffered detriment at least once in
the 12 months preceding April 2021. This section explores in detail how the incidence of
detriment varies across a range of socio-economic and demographic characteristics of UK
consumers.

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4.1.1 Overview of consumers experiencing detriment
The likelihood of having experienced detriment in the 12 months covered by the study varied by
socio-economic status; statistical differences were found by equivalised household income, self-
assessed financial condition and household composition.
Consumers with higher equivalised monthly household incomes 67 were more likely to have
encountered detriment (73% of those with equivalised monthly household incomes of more than
£2,500, decreasing to 66% of those with incomes of £1000 or less). However, consumers finding
it more difficult to manage financially were more likely to have experienced detriment (86% of
those finding it very difficult to manage financially and 78% of those finding it quite difficult
compared to 66% of those living comfortably) 68 (Figure 32).
Figure 32 – Proportion of consumers who have experienced detriment by their
self-assessed financial condition

Base: UK consumers aged 18+.
Unweighted: 6,520 (Living comfortably: 1,841; Doing alright: 2,756; Just about getting by: 1,398;
Finding it quite difficult: 336; Finding it very difficult: 178).

Households with children were more likely to have experienced detriment compared to
households without children (Figure 33), with lone parent households the most likely to have
encountered detriment 69. Having children in the household did not seem to be associated with
a higher incidence of detriment in households with three or more adults.

67 Equivalised income is a measure of income that accounts for the number of people in the household.
68 When comparing the trends of equivalised household income and self-reported financial condition, it is important to
remember that the first represents a nominal value and does not account for a series of factors that might impact
financial wellbeing (such as debt, savings or specific needs); the respondent might consider those factors when
reporting how well they are doing financially.
69 This result may reflect the fact that those in lone-parent households were more likely to be finding it difficult to
manage financially (17% of this group were finding it quite difficult and 9% finding it very difficult, compared to 8% and
7% of households with 2 adults and children respectively).
66%
67%
69%
78%
86%
0%
20%
40%
60%
80%
100%
Living
comfortably
Doing
alright
Just about
getting by
Finding it
quite difficult
Finding it
very difficult
Percentage of consumers

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Figure 33 – Proportion of consumers who have experienced detriment by
household composition

Base: UK consumers aged 18+.
Unweighted: 6,520 (Single person household: 1,452; Lone parent: 250; 2 adults no children: 2,527;
2 adults with children: 1,281; 3+ adults no children: 650; 3+ adults with children: 325).

Looking at demographics, differences were seen across age and ethnic groups. These
differences were also reflected in other characteristics that are associated with age (such as
frequency of internet use, level of education achieved, or economic activity) and ethnicity
(whether living in an urban or rural area). There was no variation in the experience of detriment
by gender.
Younger consumers were more likely to have experienced detriment (Figure 34), peaking for
consumers aged 30 to 39 and then decreasing with age. The relatively lower likelihood of having
experienced detriment for consumers in later life is reflected in the analysis of detriment by
economic activity. Retired consumers were less likely to have experienced detriment (59%)
relative to those in paid work or unemployed consumers (74% in both groups).
83%
75%
71%
62%
67%
69%
0%
20%
40%
60%
80%
100%
1 adult
2 adults
3 adults
or more
Percentage of consumers
Children in household
No children in household

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Figure 34 – Proportion of consumers who have experienced detriment by age group

Base: UK consumers aged 18+.
Unweighted: 6,520 (18-29: 474; 30-39: 899; 40-49: 1,140; 50-59: 1,288; 60-69: 1,335; 70+: 1,377).

A greater likelihood of having experienced detriment was also seen for higher levels of formal
education. 76% of those with a degree had experienced detriment, compared to 71% of those
with A-levels and 66% of those with qualifications below A-levels. The least likely to have faced
detriment were those without any qualification (56%) and those with other qualifications (55%).
Higher levels of education are correlated with more frequent use of the internet 70. Indeed,
increased frequency of internet use was also associated with a greater likelihood of having
experienced detriment (74% of those who access the internet several times a day, compared to
61% of consumers who access the internet daily and 50% of those who do so weekly or less).
There was significant variation in the likelihood of having experienced detriment by ethnic group,
with White consumers from any background less likely to have experienced detriment than
consumers in other ethnic groups (68% compared to 76%). However, the proportion of
consumers experiencing detriment differed amongst White consumers with 67% of White British
consumers 71 reporting experiencing detriment in the 12 months covered by the study compared
to 80% of consumers from any other White background.

70 80% of people with a degree or above reported using the internet several times a day. This proportion decreases with
education, reaching 39% amongst those without qualifications.
71 The White British group presented in this report includes respondents who have self-identified as White English,
White Scottish, White Welsh, White Northern Irish as well as White British.
72%
80%
74%
69%
63%
56%
0%
20%
40%
60%
80%
100%
18-29
30-39
40-49
50-59
60-69
70+
Percentage of consumers

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Figure 35 – Proportion of consumers who have experienced detriment by ethnic
background

Base: UK consumers aged 18+.
Unweighted: 6,520 (White British: 5,529; Other white background: 504; Black, Asian, Mixed or Other
ethnic background: 401).

As people belonging to Black, Asian, other White, Mixed or other ethnic groups are more likely
than White British people to live in urban areas 72, it is not surprising there is an association
between likelihood of detriment and level of urbanisation; consumers living in urban areas were
more likely to have experienced detriment than those in rural areas (70% compared to 65%).
4.1.2 Economic characteristics in the experience of detriment
The previous section shows that the likelihood of experiencing detriment in the 12 months to
April 2021 varied by consumers’ socio-economic and demographic characteristics. However, the
likelihood of experiencing detriment is likely to be primarily influenced by the type of consumer
activity (quality and quantity of products consumed, as well as the amount of money consumers
have to spend on purchases). Hence, the higher incidence of detriment observed in some
subgroups of the population might be the consequence of consumer activity, in turn a result of
their socio-economic or demographic characteristics.
In order to explore this further, a model of UK consumers was created using a binary logistic
regression which enables us to see how the probability of experiencing detriment changes by
key characteristics, while controlling for other factors.
The first model measures patterns of consumer activity, and the following four models look at
different combinations of socio-economic and demographic characteristics. These four models
explore how the association between the characteristic of interest and the likelihood of having
experienced detriment change after controlling for consumer activity. The full output for these
models, as well as the output of a model including all the socio-economic and demographic
characteristics used in this analysis, is included in the Appendix D.

72 According to the 2011 census of England and Wales, 79% of the White ethnic groups live in urban areas, compared
to 92% Mixed, 97% Asian or Other and 98% Black [Office for National Statistics. (2018). Regional ethnic diversity.
https://www.ethnicity-facts-figures.service.gov.uk/uk-population-by-ethnicity/national-and-regional-
populations/regional-ethnic-diversity/latest]
67%
80%
76%
0%
20%
40%
60%
80%
100%
White British
Other White
background
Black, Asian,
Mixed or Other
ethnic background
Percentage of consumers

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Interpreting the models – a brief guide

The likelihood of experiencing detriment (dependent variable) was further
investigated in this report using a binary logistic regression (also known as binary
logit).
The experience of detriment is a dichotomous (binary) event; while some consumers
have experienced it, some others have not. Binary logistic regression converts the
binary dependent variable (detriment experienced/detriment not experienced) in a
probability and allows exploration on how different socio-economic characteristics
and attitudes (independent variables) change the probability of experiencing the
outcome of interest (experience of detriment), and how their relationship with the
outcome of interest changes when other variables are taken into account.
The probability can assume two forms: logarithmic and odds. This report presents the
regression coefficients in the form of odds and occasionally discusses probabilities
modelled in a 0 to 1 form (where 0 means that the probability of experiencing
detriment is 0% and 1 indicating a probability of 100%).
The regression coefficients should be interpreted in relation to the reference category
for each variable. For example, looking at the coefficients for financial condition in
Table 8, consumers who find their financial condition difficult are 3.7 times more
likely to have experienced detriment, compared to those who live comfortably (living
comfortably is the reference category), other things included in the model remaining
equal (i.e. the same number of sectors where consumed products, the same
household income group and irrespective of whether the consumer compares prices
before making the purchase).
If the coefficient is greater than 1 the likelihood of experiencing detriment increases
for that category in comparison to the reference category; if it is smaller than 1,
consumers in that group have a lower probability of experiencing detriment compared
to the reference category.
The analysis identified groups of independent variables that are generally correlated,
and a binary logistic regression allowed checking of how strong their association with
the likelihood of experiencing detriment is when controlling for the effect on each
other (how strongly ethnicity influences the likelihood of experiencing detriment when
the effect of age is taken into account ). After creating models with these groups of
independent variables, consumer activity variables were added to each model; this
allowed checking as to whether the effects on the likelihood of experiencing detriment
seen in some socio-economic characteristics are reduced once the differences in
how consumers behave and for varying levels of disposable income are controlled
for.
An important measure presented in the tables is the McFadden’s pseudo R-squared.
This parameter can be used to interpret which models are better at predicting the
likelihood of experiencing detriment (the higher the value, the better the model).
Table 9 shows that the model using only age and education to explore the likelihood
of experiencing detriment has a R-squared of 0.03; this parameter increases to 0.12
once we add consumer activity variable. It can therefore be assumed that a model
with consumer activity, age and education variables is better at explaining the
likelihood of experiencing detriment than a model with age and education variables
only.

Consumer activity and economic characteristics in the experience of detriment
The first model (Table 8) includes four variables that were likely to influence the quality and
quantity of products people could consume, the amount of money they could spend, as well as
the attitudes they had when making a purchase:

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• Number of sectors in which the respondent has purchased or used a product in the last
12 months.
• Monthly household income.
• Self-assessed financial condition.
• Whether or not the consumer compares prices before making a purchase 73.
The model with consumer activity variables suggests that, when keeping the other variables
consistent, the number of sectors where the consumer made a purchase or used a product was
strongly associated with the likelihood of having experienced detriment.
Indeed, a person living in a household with an income between £1,411 and £2,560 pm, doing
alright financially, and who reported doing due diligence before making a purchase, had a
predicted probability of experiencing detriment in the 12 months to April 2021 that ranged from
0.27 if they had consumed products in a single sector, to 0.98 if they had consumed products in
all of the 44 sectors listed in the study 74 (Figure 36).
Figure 36 – Probability of experiencing detriment (0 = no detriment, 1 = detriment) by
number of sectors in which products were consumed
Reference group: household income between £1,411 and £2,560, financially doing alright, consumer who makes
price comparisons

Base: UK consumers aged 18+.
Unweighted: 6,246.

One possible explanation for this trend, isolated from the effect of income, might be the fact
that the higher the number of sectors, the higher the likelihood the consumer is moving away

73 Consumers considered to compare prices before making a purchase are those who strongly agreed or agreed with
the statement “before I buy something, I always compare the price and quality of similar items or services”.
74 Probabilities have a value included between 0 and 1, with 0 indicating that detriment does not happen and 1 that it
always happens.
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1
4
7
10
13
16
19
22
25
28
31
34
37
40
43
Probability of experiencing detriment
Number of sectors where consumed products

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from their comfort zone of well-known products and trusted providers that are part of the
regular consumers’ habits, increasing the likelihood of experiencing detriment at least once.
While household income does not seem to have a statistically significant effect on the likelihood
of experiencing detriment when controlling for the other predictors of the model, consumers who
were finding it difficult financially were still more likely to have experienced detriment in the 12
months preceding April 2021. When keeping all the other elements of the model equal,
consumers finding it difficult financially were 3.7 times more likely to have experienced detriment
than those who were living comfortably. The likelihood decreases as the financial situation
improves, with consumers who were getting by being 1.6 times more likely to have experienced
detriment than those living comfortably, whilst there was no statistically significant difference
between those who were living comfortably and those who were doing alright.
Several elements might explain why consumers in a more difficult financial situation had a higher
risk of experiencing detriment. One of the possible explanations could be in the compromises
they make on their consumer activities; having a limited disposable income could force
consumers to consume products of lower quality or with poorer standards, compared to the
products that those who are living more comfortably can afford. We would recommend additional
research to shed light on this element.
People who reported always comparing price and quality of products before proceeding with a
purchase, were 1.5 times more likely to have faced detriment at least once, compared to
consumers who reported not doing it.
Table 8 – Model with consumer activity variables
Predictors
Odds ratio
(Intercept)
0.2*
Sectors where consumed products (numeric)

Number of sectors
1.1*
Sectors where consumed products (category)

10 or less (reference category)
-
11-15
1.0
16-20
1.2
21-25
1.2
26 or more
1.2
Household income
£1,410 pm or less
-
£1,411 - 2,560 pm
1.1
£2,561 - 4,350 pm
1.2
£4,351 or more pm
1.3
Financial condition
Living comfortably
-
Doing alright
1.2
Just about getting by*
1.6*
Finding it difficult*
3.7*
Whether or not the consumer compares prices before making a purchase
Does not make price comparisons
-
Makes price comparisons
1.5*
Unweighted N
6,246
Base: UK consumers aged 18+.
* Statistically significant (p < 0.05).

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Age and education in the experience of detriment
The second model aimed to understand how the relationship between age and level of education
and consumer detriment varied when controlling for consumer activity. Age and education were
added together in the model because they are strongly associated, with the level of formal
education attained varying across generations.
Models including age and level of education confirmed the findings of the descriptive analysis,
with younger and more educated consumers being more likely to have experienced detriment.
The association of age and education with the likelihood of experiencing detriment was slightly
weakened when consumer activity was added to the model, suggesting that differences by age
and education on the probability of having faced detriment were partly associated with, but not
fully explained by, differences in consumer activity (Table 9).
Table 9 – Comparison of the models with age and education variables vs
age, education and consumer activity
Predictors
Models
Age and
education
only
Age and
education
controlling for
consumer
activity

Odds ratio
(Intercept)
1.1
0.1*
Age groups
70 and above (reference category)
-
-
60-69
1.3*
1.1
50-59
1.7*
1.3
40-49
2.0*
1.5*
30-39
2.9*
2.1*
18-29
1.9*
2.1*
Level of education
Below university degree (reference category)
-
-
University degree or above
1.6*
1.4*
Unweighted N
6,504
6,241
McFadden’s pseudo R-squared
0.03
0.12
Base: UK consumers aged 18+.
* Statistically significant (p < 0.05).

When keeping education and consumer activity constant, the likelihood of experiencing
detriment decreased as age increased. Consumers below the age of 40 were 2.1 times more
likely to have experienced detriment than consumers aged 70 or above. Those in their 40s were
1.5 times more likely than consumers aged 70 or above to have experienced detriment, while
there were no statistically significant differences in the likelihood of having experienced detriment
between consumers aged 50 to 69 and consumers aged 70 or above.
The impact of having a degree on the likelihood of having faced detriment was smaller.
Consumers who held a university degree were 1.4 times more likely than consumers with lower
levels of formal education to have experienced detriment at least once.
There are many possible explanations for the variation in the likelihood of experiencing detriment
between age groups. It might be explained by older consumers having more experience when
purchasing products, having more conservative purchasing behaviours, or consuming different
types of products. Differences in the types of products purchased might also be a possible

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explanation for differences seen between consumers who have a level of education equal or
above university degree and those who don’t.
Differences between the UK countries in the experience of detriment
The third model looked at the differences in the likelihood of having experienced detriment for
consumers living in the four countries that form the United Kingdom and for consumers living in
urban or rural areas, when controlling for consumer activity (initial model presented in Table 8,
without age and education variables). Differences between the four UK countries are discussed
in more detail in the next chapter (Chapter 4).
The variation in the likelihood of experiencing detriment between different geographies does not
change significantly when consumer activity predictors are added to the model, suggesting that
levels of consumer activities are relatively uniform across people living in urban or rural areas
and in the four UK Countries. As with the bi-variate analysis, the model suggests that when
consumer activity and the UK country of residence are controlled for, people living in rural areas
were 1.3 times 75 less likely to have experienced detriment than people living in urban areas.
Looking at the likelihood of experiencing detriment across the different countries, when the other
elements in the model were kept equal, the likelihood of experiencing detriment did not differ for
people living in England or in Scotland. However, Welsh consumers were 1.3 times 76 less likely
to have experienced detriment than English consumers. Consumers from Northern Ireland were
also less likely to have experienced detriment compared to people living in England, by a factor
of 1.7 77.
Table 10 – Comparisons of the models with geographical variables vs
geographical variables and consumer activity
Predictors
Models
Geographical
variables only
Geographical
variables
controlling for
consumer
activity

Odds ratio
(Intercept)
2.4*
0.2*
Urban/Rural areas
Urban area (reference category)
-
-
Rural area
0.8*
0.8*
UK Country
England (reference category)
-
-
Scotland
1.1
1.4
Wales
0.7*
0.8*
Northern Ireland
0.6*
0.6*
Unweighted N
6,520
6,246
McFadden’s pseudo R-squared
0.00
0.10
Base: UK consumers aged 18+.
* Statistically significant (p < 0.05).

75 This is obtained by dividing 1 by the coefficient for rural consumers (0.8).
76 This is obtained dividing 1 by the coefficient for Welsh consumers (0.8).
77 This is obtained dividing 1 by the coefficient for Northern Irish consumers (0.6).

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The role of ethnicity in the experience of detriment
The model looking at the association between ethnic identity and the likelihood of having
experienced detriment (Table 11) was built with the addition of two control variables which are
associated with both ethnicity and the likelihood of experiencing detriment:
• Age. People who identify themselves as White are significantly older than people from
other ethnic backgrounds 78 and older consumers are also less vulnerable to detriment.
• Whether the consumer lived in an urban or rural area. People identifying as ethnic
minorities in the UK are concentrated in urban centres 79 where the proportion of people
experiencing detriment is higher.
The results of the model suggest that consumers from other ethnic groups are still more likely to
have experienced detriment when controlling for these factors. Consumers from Black, Asian,
Mixed, or other ethnic groups, as well as consumers from ‘other’ White backgrounds, were 1.6
times more likely to have experienced detriment compared to White British people.
Table 11 – Comparison of the models with ethnicity, age and urban/rural
variables vs ethnicity, age, urban/rural and consumer activity
Predictors
Models
Ethnicity, age
and
Urban/Rural
only
Ethnicity, age
and Urban/Rural
controlling for
consumer
activity

Odds ratio
(Intercept)
1.3*
0.1*
Age groups
70 and above (reference category)
-
-
60-69
1.3*
1.1
50-59
1.7*
1.2
40-49
2.1*
1.5*
30-39
3.0*
1.9*
18-29
1.9*
2.0*
Urban/Rural areas
Urban area (reference category)
-
-
Rural area
0.9
0.8*
Ethnic groups

White British (reference category)
-
-
Any other White background
1.6*
1.6*
Black, Asian, Mixed or other ethnic groups
1.3
1.6*
Unweighted N
6,428
6,168
McFadden’s pseudo R-squared
0.04
0.13
Base: UK consumers aged 18+.
* Statistically significant (p < 0.5).

78 The 2011 census indicates that the median age of White people in England and Wales was 41, while it was 30 for
Black and Other, 29 for Asian and 18 for Mixed ethnic groups [Office for National Statistics. (2018). Age groups.
https://www.ethnicity-facts-figures.service.gov.uk/uk-population-by-ethnicity/demographics/age-groups/latest].
79 According to the 2011 census of England and Wales, 79% of the White ethnic groups live in urban areas, compared
to 92% Mixed, 97% Asian or Other and 98% Black [Office for National Statistics. (2018). Regional ethnic diversity.
https://www.ethnicity-facts-figures.service.gov.uk/uk-population-by-ethnicity/national-and-regional-
populations/regional-ethnic-diversity/latest].

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Further research is needed to understand the higher likelihood of consumers in ethnic minority
groups experiencing detriment. One possible explanation is the language barrier that people
from ethnic minority groups might face when consuming a product which could lead to difficulties
in accessing a service or selecting an item. Another aspect that could influence the risk of
detriment is whether the consumer was born in the UK or not, as it may be associated with a
weaker knowledge of the consumer protection system and of the different markets for items and
services, creating a condition of vulnerability. There is a large amount of heterogeneity within
ethnic minority groups, and different populations may have different experiences.
Internet use and experience of detriment
The association between use of the internet and the likelihood of having experienced detriment
was modelled controlling for variables that are associated both with higher internet use and
likelihood of experiencing detriment: the age of the consumer and their educational attainment.
The analysis suggests that the differences in the likelihood of experiencing detriment by
frequency of internet use is largely explained by patterns of consumer activity. When consumer
activity, age and level of education were controlled for in the model, those using the internet
more frequently were not significantly more or less likely than people accessing the internet less
frequently to have experienced detriment.
Table 12 – Comparison of the models with internet use, age and education
variables vs internet use, age, education and consumer activity
Predictors
Models
Internet, age
and Education
only
Internet, age
and Education
controlling for
consumer
activity

Odds ratio
(Intercept)
0.8
0.1*
Age groups
70 and above (reference category)
-
-
60-69
1.2
1.0
50-59
1.5*
1.2
40-49
1.7*
1.4*
30-39
2.3*
1.9*
18-29
1.4*
1.8*
Level of education
Below university degree (reference category)
-
-
University degree or above
1.5*
1.3*
Frequency of internet use

Weekly or less (reference category)
-
-
Daily
1.4
1.1
Several times a day
2.0*
1.4
Unweighted N
6,503
6,240
McFadden’s pseudo R-squared
0.04
0.12
Base: UK consumers aged 18+.
* Statistically significant (p < 0.5).

4.2 Facing the most negative consequences of detriment
This section focuses on the outcomes of detriment and examines the profile of those who
experienced the most negative outcomes, including: the total value of the monetised detriment;

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the impact on household finances, physical and mental health; and the likelihood of having
experienced negative emotional consequences.
4.2.1 Experience of the largest value of monetised detriment
Fourteen per cent of consumers in the UK who experienced detriment in the 12 months to April
2021 faced a total net monetised detriment value of £1,500 or more (this is the sum of the net
monetised detriment in all the experiences of detriment consumers had in the 12 months covered
by the study).
This figure varied between age groups, with younger consumers more likely to have suffered a
monetised detriment of £1,500 or more (Figure 37). This pattern is reflected in economic activity,
with just eight percent of retired consumers having experienced a total net monetised detriment
of more than £1,500 compared to 14% of those in paid work, 18% of those in full-time education,
18% of those in other activities and 20% of those unemployed.
Figure 37 – Proportion of consumers who faced a total net monetised
detriment of £1,500 or more in the 12 months to April 2021 by age group

Base: UK consumers aged 18+ who have experienced detriment in the 12 months to April 2021.
Unweighted: 4,405 (18-29: 335; 30-39: 689; 40-49: 832; 50-59: 882; 60-69: 852; 70+: 810).

Consumers finding things more difficult financially were also more likely to have experienced net
monetised detriment of £1,500 or more (Figure 38).
19%
15%
15%
14%
12%
8%
14%
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
20%
18-29
30-39
40-49
50-59
60-69
70+
Overall
Percentage of consumers who
experienced detriment

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80

Figure 38 – Proportion of consumers who have lost £1,500 or more in net
monetised detriment in the 12 months to April 2021 by financial situation

Base: UK consumers aged 18+ who have experienced detriment in the 12 months to April 2021.
Unweighted: 4,405 (Living comfortably: 1,185; Doing alright: 1,851; Just about getting by: 957;
Finding it quite difficult: 261; Finding it very difficult: 144).

There were small, but significant, differences in the proportion of consumers who have
experienced a higher 12-month net monetised detriment by level of education: 16% of those with
a level of education equivalent to a university degree or above have faced a 12-month monetised
detriment of £1,500 or higher, compared to 12% of those with an education below university
degree.
Differences observed by gender, ethnicity, whether living in an urban or rural area, and frequency
of internet use were not statistically significant.
The higher likelihood of younger consumers and consumers finding things difficult financially to
have experienced higher values of total net monetised detriment may be partly explained by
these groups being more likely to have faced more experiences of detriment 80 and/or more likely
not to take any actions to address the detriment. Over a period of 12 months, these two factors
can lead to the accumulation of a large sum of net monetised detriment.
4.2.2 Experience of the most negative wellbeing consequences
As seen in Section 1.3, the survey asked participants to report whether their experiences of
detriment had a negative effect on three aspect of life – their physical health, mental health and
household finances – and how severe this effect was. Ten percent of consumers reported a very
negative effect on their mental health for at least one of their experiences of detriment, nine
percent a very negative effect on their household finances and five percent a very negative effect
on their physical health.
These three measures of negative consequences of detriment on wellbeing had different
incidences in different subgroups of the population. Firstly, consumers finding it difficult

80 The median number of incidents for consumers who experienced detriment below the age of 49 was 4, while it was 3
for consumers aged 50 or above. Those finding it very or quite difficult had, respectively, 4 and 5 median experiences
of detriment, compared to 3 of consumers living comfortably or of those doing alright.
12%
14%
12%
23%
22%
14%
0%
5%
10%
15%
20%
25%
Living
comfortably
Doing
alright
Just about
getting by
Finding it
quite
difficult
Finding it
very difficult
Overall
Percentage of consumers who
experienced detriment

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81

financially were more likely to report at least one of their experiences of detriment had had a very
negative impact on their physical health, their mental health or their household finances
compared to those with a more comfortable financial situation (Figure 39).
Figure 39 – Proportion of consumers who reported a very negative effect on their
wellbeing at least once by financial condition

Base: UK consumers aged 18+ who have experienced detriment in the 12 months to April 2021.
Unweighted: 4,401 (Living comfortably: 1,184; Doing alright: 1,851; Just about getting by: 955; Finding it quite
difficult: 261; Finding it very difficult: 144).

This pattern is reflected in the analysis by economic activity (Figure 40). Unemployed
consumers who experienced detriment were more likely to mention, at least once, negative
effects on their mental health, physical health or household finances than any other group of
economic activity.
Figure 40 – Proportion of consumers who reported a very negative effect on their
wellbeing at least once by economic activity

Base: UK consumers aged 18+ who have experienced detriment in the 12 months to April 2021.
Unweighted: 4,399 (Full-time education: 97; Paid work: 2,259; Unemployed: 149; Retired: 1,129; Other:
763).

5%
6%
11%
26%
28%
10%
4%
3%
7%
13%
13%
5%
4%
5%
11%
20%
31%
9%
0%
5%
10%
15%
20%
25%
30%
35%
Living
comfortably
Doing
alright
Just about
getting by
Finding it
quite difficult
Finding it
very difficult
Overall
Percentage of consumers who
experienced detriment
Very negative effect on mental health
Very negative effect on physical health
Very negative effect on household finances
10%
8%
27%
7%
13%
10%
5%
4%
16%
4%
7%
5%
8%
8%
25%
4%
13%
9%
0%
5%
10%
15%
20%
25%
30%
35%
Full-time
education
Paid work
Unemployed
Retired
Other
Overall
Percentage of consumers who
experienced detriment
Very negative effect on mental health
Very negative effect on physical health
Very negative effect on household finances

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The likelihood of reporting a very negative effect on mental or physical health was also
associated with the frequency of internet use. Nineteen percent of consumers who accessed the
internet less frequently (weekly or less) reported a detriment incident having a very negative
effect on their mental health, compared to 11% of those using the internet daily and 8% of those
using it several times a day. The same pattern can be seen for physical health: 14% of
consumers who used the internet less frequently (weekly or less) reported a detriment incident
having a very negative effect, compared to 7% of those using the internet daily and 4% of those
who accessed it several times a day. The likelihood of having experienced a very negative effect
on the household finances did not vary significantly by frequency of internet use.
While it was found that the risk of experiencing detriment increased with frequency of internet
use [Section 3.1], it seems that those who used the internet less frequently were more likely to
have faced the most negative consequences on their mental and physical health. In other terms,
people who used the internet less frequently were less likely to have experienced detriment, but,
when they did, they were more likely to have faced greater consequences on their mental and
physical wellbeing. It is important to remember that people who access the internet less
frequently are generally older and more likely to have pre-existing health problems and long-
term conditions 81.
Consumers living in urban areas were also more likely to have reported a detriment incident that
had a very negative impact on their mental health and household finances at least once in the
12 months to April 2021, compared to people living in rural areas. These consequences were
mentioned at least once by 10% of urban consumers, compared to 6% of rural consumers.
The likelihood of having experienced a very negative impact on household finances as a result
of an incident of detriment differed across several other socio-economic subgroups. Over one in
ten (14%) consumers with an Asian, Black, Mixed or other ethnic background reported
experiencing this, compared to eight percent of consumers from any White background. A
difference was also observed between people with different levels of formal education: 11% of
consumers with a level of education below university degree who experienced detriment
reported a very negative impact on their household finances, compared to seven percent of
those with a degree. Finally, consumers in their 30s and 40s were more likely to report having
experienced an incident of detriment that had a very negative impact on their household finances
compared to other age bands (seven percent of consumers aged 18-29, 12% of those aged
between 30-49, 10% of those aged 50-59, seven percent of those aged 60-69 or 18-29 and five
percent of those aged 70 or above).
The likelihood of a consumer who experienced detriment reporting an incident that very
negatively affected their physical or mental health did not vary significantly by age, ethnicity, or
education.

4.2.3 Experience of the most negative emotional outcomes
Respondents were asked to what extent they felt anxious, helpless, misled or upset as a result
of each of their (up to three) reported experiences of consumer detriment. One in three

81 Office for National Statistics (2019), Exploring the UK’s digital divide, “Home internet and social media usage”.
Available at
https://www.ons.gov.uk/peoplepopulationandcommunity/householdcharacteristics/homeinternetandsocialmediausage/
articles/exploringtheuksdigitaldivide/2019-03-04

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consumers who experienced detriment reported feeling misled (31%), upset (31%) or helpless
(29%) ‘to a great extent’ at least once, and 25% felt anxious ‘to a great extent’ at least once 82.
This proportion did not vary significantly by frequency of internet use or highest educational
attainment achieved, but there were some differences seen across other socio-economic and
demographic characteristics.
Gender
Female consumers were more likely to report having felt anxious, helpless or upset ‘to a great
extent’ because of an incident of consumer detriment than male consumers. Specifically, 28%
of female consumers felt anxious ‘to a great extent’ as a result of one of their reported detriment
experiences (compared to 21% of male consumers), 32% felt helpless ‘to a great extent’ (27%
of male consumers) and 34% reported having felt upset ‘to a great extent’ (28% of male
consumers).
Age group
Significant differences by age group were only observed in the proportion of consumers who had
felt either anxious or upset ‘to a great extent’ (Table 13). In both cases the proportion having
experienced negative emotional consequences decreased with age, with older respondents
being particularly unlikely to have felt anxious or upset ‘to a great extent’ compared to younger
consumers.
Table 13 – Proportion of consumers who mentioned having felt anxious or
upset to a great extent, at least once, in their experiences of detriment by
age group
Age group
UWTD
base
(N)
Feeling anxious
to a great extent
(%)
Feeling upset
to a great extent
(%)
18-29
335
28
34
30-39
688
26
35
40-49
830
25
30
50-59
880
26
32
60-69
847
20
26
70+
804
18
25
Base: UK consumers aged 18+ who have experienced detriment in the 12 months to April 2021.

Economic activity
Unemployed consumers who experienced detriment were more likely to have felt a negative
emotion ‘to a great extent’ as a result of an experience of detriment, although consumers in full-
time education and those engaging in other economic activities were similarly likely to have felt
upset ‘to a great extent’ (Table 14).
Conversely, reflecting differences by age group, retired consumers were less likely than
consumers in other groups to have felt anxious, misled, or upset ‘to a great extent’ as a result of
an experience of detriment.

82 These figures are different from the ones discussed in the second chapter of this report. Whilst Chapter 2 used a
sample of experiences of detriment, and reported on the number of experiences of detriment which led to negative
emotional outcomes, this chapter used a sample of respondents, and looked at the number of consumers who
experienced the most negative outcomes, at least once, across all their experiences of detriment.

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Table 14 - Proportion of consumers who mentioned having felt anxious, misled or
upset to a great extent, at least once, in their experiences of detriment by economic
activity
Economic activity
UWTD
base
(N)
Feeling anxious
to a great extent
(%)
Feeling misled to
a great extent
(%)
Feeling upset to a
great extent
(%)
Full-time education
96
32
26
43
Paid work
2,258
23
31
29
Unemployed
149
44
46
42
Retired
1,122
18
26
25
Other
762
28
37
40
Base: UK consumers aged 18+ who have experienced detriment in the 12 months to April 2021.

Ethnicity
Three in ten (31%) consumers from Black, Asian, Mixed or other ethnic groups reported having
felt anxious ‘to a great extent’ in at least one of their experiences of detriment faced in the 12
months covered by the study, while only 23% of consumers with any White background
reported having felt so.
The proportion of consumers who felt upset ‘to a great extent’ because of detriment was also
higher among consumers with any ethnic minority background (40%), compared to consumers
from any White background (29%).
Financial situation
Detriment was higher for consumers finding things more difficult financially. A greater proportion
of consumers finding things more difficult financially reported having felt anxious, misled or upset
‘to a great extent’ due to an experience of consumer detriment, compared to those who were
better off (Figure 41). The proportion of people who felt helpless ‘to a great extent’ did not vary
by the financial situation of the consumer.
Figure 41 – Proportion of consumers who mentioned having felt anxious, misled or
upset to a great extent, at least once, in their experiences of detriment by financial
situation

Base: UK consumers aged 18+ who have experienced detriment in the 12 months to April 2021.
Unweighted: 4,394 (Living comfortably: 1,181; Doing alright: 1,850; Just about getting by: 953; Finding it quite
difficult: 260; Finding it very difficult: 144).

19%
20%
31%
36%
39%
25%
28%
27%
36%
48%
42%
32%
24%
28%
35%
46%
41%
31%
0%
10%
20%
30%
40%
50%
Living
comfortably
Doing
alright
Just about
getting by
Finding it
quite difficult
Finding it
very difficult
Overall
Percentage of consumers who
experienced detriment
Feeling anxious
to a great extent
Feeling misled
to a great extent
Feeling upset
to a great extent

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85

Whether living in an urban or in a rural area
Twenty-six per cent of consumers living in urban areas reported having felt anxious ‘to a great
extent’ as a result of an experience of detriment, compared to 19% of consumers living in rural
areas. The proportion of people reporting feeling helpless, misled or upset ‘to a great extent’ did
not vary significantly by urbanity.
4.3 Unactioned consumer detriment
When detriment is experienced, consumers can either take action to address the detriment and
seek a resolution, or not. A number of factors come into play when taking this decision, and while
Chapter 2 focused on the influence of product and market characteristics, this section looks at
unreported detriment and the reasons for not reporting it by socio-economic and demographic
characteristics.
4.3.1 Overview of consumers who did not take action to address their experiences of
detriment
In the 12 months up to April 2021, 25% of consumers who experienced detriment in the UK
reported not having taken actions to address at least one of their reported incidents of detriment.
This estimate did not vary significantly across many socio-economic (economic activity, highest
level of formal education achieved, financial situation, frequency of internet use, and whether
living in an urban or rural area) and demographic characteristics (gender and ethnicity).
However, there were significant variations by age (Figure 42), with younger participants being
more likely to have not taken actions to address at least one experience of detriment compared
to older participants.
Figure 42 – Consumers who did not take actions to address at least one
experience of detriment by age group

Base: UK consumers aged 18+ who have experienced detriment in the 12 months to April 2021.
Unweighted: 4,405 (18-29: 335; 30-39: 689; 40-49: 832; 50-59: 882; 60-69: 852; 70+ :810).

While the low levels of variation seen by socio-economic and demographic characteristics
suggest that market and product characteristics might play a larger role in whether a consumer
decides to take actions to address detriment or not (as discussed in Chapter 2), the decision is
also linked to an attitudinal factor.
Respondents were asked to what extent they agreed with the statement: ‘if I am unhappy with
something I have bought, I normally make a complaint’. Consumers who disagreed with this
30%
28%
22%
19%
23%
23%
25%
0%
5%
10%
15%
20%
25%
30%
35%
18-29
30-39
40-49
50-59
60-69
70+
Overall
Percentage of consumers
who experienced detriment

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86

statement, or felt neutral, were more likely to have not actioned at least one experience of
detriment in the 12 months up to April 2021 (Figure 43).
Figure 43 – Share of consumers who experienced at least one problem and
did not take actions on any problem by propensity to complain

Base: UK consumers aged 18+ who have experienced detriment in the 12 months to April 2021.
Unweighted: 4,405 (Strongly agree: 965; Agree: 2,167; Neither agree nor disagree: 700; Disagree:
492; Strongly disagree: 81).

4.3.2 Reasons consumers did not take actions to address their experiences of
detriment
Understanding the reason why consumers decided not to follow-up with the seller or provider is
key to understanding the dynamics of unactioned detriment. Across all consumers who did not
take action in at least one of their reported detriment incidents, the most common reason given
was because the problem was not serious enough (Figure 44).
Figure 44 – Reasons for not taking actions to address experiences of detriment
mentioned at least once 83

Base: UK consumers aged 18+ who have not taken action to address at least one experience of detriment,
encountered in the 12 months to April 2021.
Unweighted: 1,077.

83 Other reasons include limitations imposed by COVID-19 restrictions, considering COVID-19 as the root cause of the
problem and not the vendor, having bought the products overseas and being unable to reach out to the provider, lack
of time, the small value of the product, not wanting anything to do with the seller or provider, or being afraid of extra
charges and costs.
20%
21%
31%
33%
31%
25%
0%
5%
10%
15%
20%
25%
30%
35%
Strongly
agree
Agree
Neither
agree nor
disagree
Disagree
Strongly
disagree
Overall
Percentage of consumers
who experienced detriment
30%
17%
15%
13%
9%
9%
5%
5%
1%
14%
0%
5%
10%
15%
20%
25%
30%
35%
Problem not serious enough
Problem resolved without taking actions
Thought it would be unsuccessful
Intention to do it in the future
Process would have taken too long
Process too complicated
Unclear how to go about complaining
Uncomfortable with options available
Somebody else took action
Other reasons
Percentage of consumers with unactioned detriment

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The prevalence of some of the reasons given for not taking actions differed by consumers’ age.
Younger consumers were more likely to have listed amongst the reasons that the process would
have taken too long: 15% of consumers aged 18-29 and 11% aged 30-39 gave this reason at
least once, compared to 6% of those aged 40-49, 9% aged 50-59, 5% aged 60-69 and 2% aged
70 or above. The likelihood of giving a reason different to the ones listed in the questionnaire
(‘Other reason’ category) increased with age. Consumers above the age of 60 specified another
reason more frequently (27% of those aged 70 or above and 21% of those aged 60- 69),
compared to younger consumers (15% aged 50-59, 14% aged 40-49, 8% aged 30-39 and 9%
aged 18-29). Many of the ‘Other reasons’ given in the questionnaire were linked to the COVID-
19 outbreak – people in later life were subject to greater caution during the pandemic due to
higher vulnerability to the virus 84, and may have wanted to avoid taking actions that involved in-
person interactions.
The trend seen by age was also found when looking by frequency of internet use. This is perhaps
driven by the fact younger people are more likely to be active internet users 85. Frequent internet
users were more likely to have mentioned that the process would have taken too long (11% of
consumers who use the internet several times a day, compared to 4% of those using the internet
daily or less) and less likely to have given other reasons (12% of those using the internet several
times a day, 15% of consumers using the internet daily and 35% of those using the internet
weekly or less gave another reason).
Looking by ethnicity, consumers from Black, Asian, Mixed or other ethnic groups were more
likely to have mentioned the process would have taken too long (18% compared to 8% of people
from any White background). This probably reflects the relatively younger age of people from
ethnic minority groups compared to White British people 86.
Looking by self-reported household financial situation, consumers finding things difficult
financially were more likely to have not taken any actions because the process was expected to
take too long: 19% of consumers finding their financial situation quite difficult and 14% of those
finding it very difficult gave this reason at least once, whilst the proportion was lower amongst
those who were getting by (5%), doing alright (9%) or living comfortably (8%). Consumers finding
things difficult financially were also more likely to have reported they did not take any actions
because they were not sure who to contact, or how to go about complaining. This reason was
given by 19% of those finding things very difficult and 12% of those finding it quite difficult, while
it was less common amongst better-off people (3% of consumers getting by or doing alright, 5%
of those living comfortably).
One of the questions raised by this is to what extent people with a more difficult financial situation
are less knowledgeable about the consumer protection system and to what extent they are more
likely to rely on sellers and providers, who may offer less transparent support channels to their
customers. More research is needed to explore this pattern.
Differences observed by consumers’ level of formal education, whether the consumer lived in an
urban or rural area and gender were not statistically significant. There were some differences by

84 Steptoe, A., & Steel N. (2020). The experience of older people instructed to shield or self-isolate during the COVID-19
pandemic, ELSA. https://www.elsa-project.ac.uk/covid-19-reports
85 Office for National Statistics. (2021). Internet users, UK: 2020.
https://www.ons.gov.uk/businessindustryandtrade/itandinternetindustry/bulletins/internetusers/2020.
86 Office for National Statistics. (2018). Age groups. https://www.ethnicity-facts-figures.service.gov.uk/uk-population-by-
ethnicity/demographics/age-groups/latest.

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economic activity – however, the lack of a clear pattern and the small sample size for some
groups invite caution with these estimates.
4.4 Satisfaction with the outcome and resolution patterns
Fifteen per cent of consumers in the UK who experienced detriment reported being completely
dissatisfied with the final outcome of at least one of their concluded detriment incidents. This
proportion did not vary significantly by socio-economic or demographic characteristics,
suggesting that the likelihood of being completely dissatisfied with the outcome was better
explained by product features, detriment and market characteristics (see Chapter 2).
Looking at resolution patterns, the worst resolution (that is: having asked the seller or provider
to do or offer something to resolve the incident but having received nothing) was experienced in
at least one of the reported detriment incidents by 15% of consumers who experienced
detriment. As for the satisfaction with the final outcome, the likelihood of having experienced that
resolution scenario did not vary significantly by consumers’ characteristics.

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5 Consumer detriment in the four UK
countries

This chapter addresses Research Question 4: How does consumer detriment vary
between the four UK countries?

Building on the headline findings discussed in the previous chapters, this section looks at if and
how those findings differ between the four UK countries (England, Wales, Scotland and
Northern Ireland).
This chapter:
• describes the incidence of detriment, the nature of the detriment and its outcomes by
country;
• looks at how the amount of monetised detriment varies across the UK.
This study finds that the incidence of detriment varied in the four UK countries, with consumers
in England and Scotland more likely to have experienced detriment than those living in Wales
and Northern Ireland. However, other elements of detriment did not differ, suggesting that the
general patterns discussed for the whole of the UK in the previous chapters apply in each of
the UK countries.
The total amount of net monetised detriment was £46.1 billion in England, £4.7 billion in
Scotland, £2.5 billion in Wales and £0.8 billion in Northern Ireland, reflecting differences in their
population sizes. Other elements of net monetised detriment, including median and
distribution, did not vary significantly between countries.
5.1 Incidence of consumer detriments in the UK countries
The overall incidence of detriment varied between UK countries: it was higher in Scotland and
England, and lower in Wales and Northern Ireland (Figure 45). This variance was also found in
Chapter 3 (“Identifying consumers at risk of detriment”), where the differences between
countries were analysed while controlling for consumer activity and whether the consumers
lived in an urban or in a rural area 87.

87 The multivariate analysis presented in chapter 3 (see ‘Model 3b’) found that the likelihood of experiencing detriment
was lower amongst consumers living in Wales or Northern Ireland compared to consumers living in England, but did
not find statistical differences between consumers living in England and those living in Scotland. The full regression
model (see ‘Model 6’ in Appendix D) suggested that – when controlling for age, level of education, ethnic groups and
frequency of internet use – the likelihood of experiencing detriment in Scotland was significantly higher than England.
This might indicate that the distribution of these four socio-demographic variables varies across the four UK countries.

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Figure 45 – Incidence of consumer detriment in the four UK countries

Base: UK consumers aged 18+.
Unweighted: 6,520 (England: 4,467; Scotland: 364; Wales: 915; Northern Ireland: 774).

Estimates scaled to the UK population indicate that, in the 12 months to April 2021, 198.8
million incidents of detriment were experienced by consumers in England, 18.6 million in
Scotland, 7.9 million in Wales and 4.3 million in Northern Ireland. Consumers in England also
faced a higher number of detriment experiences per person, compared to those living in
Scotland, Wales and Northern Ireland. Excluding those who did not suffer any detriment,
consumers in England had a median of four detriments per person, while it was three for
consumers living in Scotland, Wales and Northern Ireland.
Two additional elements emerge when analysing the incidence of detriment in the UK
countries by product type. Firstly, consistent with the overall analysis (Chapter 1 – Levels of
consumer detriment in the UK), consumers in each country were more likely to have
experienced detriment related to services than items (Figure 46). This difference was greater
for consumers living in Scotland than those living in Northern Ireland. Secondly, the patterns of
detriment incidence by country are confirmed across both product types: consumers living in
England and Scotland were more likely to have experienced detriment with both items and
services than consumers in Wales and Northern Ireland.
Figure 46 – Incidence of consumer detriment in the four UK countries for items and
services

Base: UK consumers aged 18+.
Unweighted: Items: 6,530 (England: 4,472; Scotland: 362; Wales: 919; Northern Ireland: 777); Services: 6,426
(England: 4,411; Scotland: 358; Wales: 898; Northern Ireland: 759).

72%
70%
60%
56%
0%
20%
40%
60%
80%
Scotland
England
Wales
Northern
Ireland
Percntage of consumers
49%
46%
40%
38%
57%
60%
49%
44%
0%
20%
40%
60%
80%
England
Scotland
Wales
Northern
Ireland
Percentage of consumers
Items
Services

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Differences in the incidence of detriment between the four UK countries also emerged within
market clusters. Table 15 shows that consumers in England and Scotland were significantly
more likely than consumers in Northern Ireland and Wales to report experiencing detriment in
the ‘(Semi-) durable goods’, ‘Automotive goods and services’, ‘Telecoms and other digital
subscriptions’, ‘Utilities’, ‘Banking and insurance’ and ‘Recreational services’ market clusters.
In fact, there were no market clusters where incidence of detriment was significantly higher in
Wales or Northern Ireland, versus the respective levels in England or Scotland.
Table 15 – Incidence of consumer detriment in the four UK countries in market clusters
Market Cluster
UWTD
base
(N)
England
Scotland
Wales
Northern
Ireland
Fast-moving retail
6,452
77%
75%
75%
73%
(Semi-) durable goods*
5,662
33%
32%
26%
25%
Automotive goods and services*
5,115
21%
25%
16%
14%
Telecoms and other digital subscriptions*
6,221
35%
35%
28%
28%
Utilities*
5,765
18%
17%
15%
7%
Housing-related services
3,274
15%
18%
11%
12%
Banking and insurance*
5,593
13%
17%
9%
12%
Transport
1,819
18%
28%
16%
20%
Personal services
2,372
14%
16%
15%
9%
Recreational services*
5,224
17%
16%
12%
12%
Other services
2,593
14%
12%
13%
9%

Base: UK consumers aged 18+.
*Statistically significant differences (p < 0.05)

5.1.1 The characteristics of consumer detriment in the UK countries
The differences seen in the incidence of consumer detriment in the four UK countries – either
overall, by product type or by market clusters – were not reflected in differences in the
characteristics of detriment. Specifically, we did not find any significant differences between the
four UK countries when looking at: the value of the product, type of detriment experienced, or
whether or not consumers took any actions to address their experiences of detriment. We also
did not find any statistically significant differences by satisfaction with the outcome or resolution
patterns. The general trends discussed in Chapter 2 (Complaints journey) can therefore be
inferred for each of the four countries that form the UK.
The only exception is the proportion of experiences of detriment related to in-person purchases
(either from a shop or from a salesperson visiting home or work): 37% of the experiences of
detriment in Northern Ireland followed an in-person purchase (35% from a shop, 2% from a
salesperson), while the proportion of experiences of detriment from in-person purchases were
29% in England (27% from a shop, 2% from a salesperson), 27% in Scotland (26% from a
shop, 1% salesperson) and 24% in Wales (23% from a shop, 1% salesperson).
5.2 Value of monetised detriment in the UK countries
In the 12 months to April 2021 covered by the study, experiences of detriment cost consumers
£46.1 billion in England (85% of the UK total monetised detriment), £4.7 billion to those based
in Scotland (9% of the UK total), £2.5 billion in Wales (5%) and £0.8 billion in Northern Ireland
(2% of the total monetised detriment in the UK) (Figure 47).

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Figure 47 – Value of net monetised detriment in the UK by country (billion £)

Base: All detriment experiences in the UK in the 12 months to April 2021.
Unweighted: 9,416 (England: 6,732; Scotland: 557; Wales: 1,197; Northern Ireland: 930).

The different values of net monetised detriment across the countries reflect both their
population size (84% of the total population in the UK live in England, 8% in Scotland, 5% in
Wales and 3% in Northern Ireland 88) and the number of incidents of detriment (87% of the
experiences of detriment in the UK happened in England, 8% in Scotland, 3% in Wales and
2% in Northern Ireland). This suggests that, on average, net monetised detriment varied very
little between the four UK countries. Indeed, the median value of net monetised detriment did
not vary significantly between countries: £28 in England, Scotland and Wales and £24 in
Northern Ireland. Similarly, the distribution of net monetised detriment was not statistically
different between countries.
No statistical differences were found between the four UK countries across the different market
clusters. Although there were some variations for ‘Other services’ and ‘Recreational services’,
the number of observations was too small to produce robust statistical evidence. In addition, no
significant differences were seen in the value of the different components of monetised
detriment: cost components (initial cost of the product and any other costs borne by the
consumer, including fixing or replacing a product), mitigation components (the value of having
the product replaced or fixed, the use value or other compensations) and time cost (Table 16).
Table 16 – Median of the macro-components of net monetised detriment in the four
UK countries (£)
Country
UWTD base
(N)
Cost
components
Mitigation
components
Time
cost
Monetised
detriment
England
6,732
£55
£40
£21
£28
Scotland
557
£69
£48
£28
£28
Wales
1,197
£64
£40
£21
£28
Northern Ireland
930
£60
£43
£14
£24
UK
9,416
£55
£40
£21
£28
Base: All detriment experiences in the UK in the 12 months to April 2021.

88 Office for National Statistics. (2021). Population estimates for the UK, England and Wales, Scotland and Northern
Ireland: mid-2020.
https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/bulletins/annual
midyearpopulationestimates/mid2020.
England
£46.1
Scotland
£4.7
Wales
£2.5
Northern
Ireland
£0.8
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Percentage of UK Total detriment

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6 Respondent estimated impact of COVID-19

This chapter addresses Research Question 5: What was the impact of the COVID-19
outbreak on the consumer experience and on the incidence of detriment in the UK?

The following chapter looks at the extent to which respondents felt that the incidents of consumer
detriment they experienced between April 2020 and April 2021 were caused or made worse by
the COVID-19 pandemic. The chapter focuses on the:
• respondent-estimated impact of the COVID-19 outbreak on consumer detriment, looking
firstly at the overall impact, before exploring the impact on monetised detriment and
wellbeing.
• respondent-estimated impact of the COVID-19 pandemic and detriment by market
characteristics as well as the characteristics of detriment.
The analysis suggests respondents thought that 43% of their experiences of detriment, with a
cost of £26.5 billion in net monetised detriment for UK consumers, were caused or made worse
by the COVID-19 pandemic. Some market clusters were more affected than others, with
‘Transport’, ‘Recreational services’ and ‘Personal services’ being the ones with the highest
proportion of detriment incidents linked to the pandemic. Consumer detriment incidents affected
by COVID-19 were more likely to be associated with problems with delivery (either having
experienced problematic deliveries or not having received the product at all), and with misleading
prices. Consumers were also more likely to report that incidents caused or made worse by
COVID-19 were more likely to have had negative effects on their physical and mental health,
and their household’s finances, and to have made them feel anxious, helpless, upset or misled.
6.1 Impact of COVID-19 on consumer detriment
Since its emergence in early 2020, the COVID-19 pandemic has caused significant social and
economic disruption across the world, with businesses forced to close, borders closed, personal
restrictions imposed and supply chains interrupted 89. Consumers were also affected, directly or
indirectly, by this disruption.
As part of being asked about their experiences of detriment, respondents were asked to what
extent they thought the problem was caused or made worse by the COVID-19 pandemic. The
outbreak of the COVID-19 pandemic was considered irrelevant by consumers for almost half of
the experiences of detriment (47%), while over 2 in 5 (43%) detriment experiences were felt by
consumers to be at least somewhat the result of the COVID-19 pandemic (Figure 48).

89 See: International Labour Organisation. (2020). The effects of COVID‑19 on trade and global supply chains.
https://www.ilo.org/global/research/publications/WCMS_746917/lang--en/index.htm

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Figure 48 – Views on the extent to which detriment experienced was affected by the
COVID-19 pandemic

Base: All detriment experiences in the UK in the 12 months to April 2021.
Unweighted: 9,407.

6.1.1 The impact of COVID-19 on monetised detriment
The COVID-19 outbreak had an impact on the total monetised detriment estimated in the UK in
the 12 months covered by the study. Overall, detriment affected, at least to some extent, by the
pandemic cost £26.5 billion to UK consumers (detriment mostly or fully caused by the
pandemic cost £7.7 billion, while £18.8 billion was linked to detriment made worse by the
pandemic). In the same period, detriment unrelated to the pandemic had a net monetised
value of £23 billion and detriment for which consumers were unsure of the pandemic’s role had
a net monetised value of £4.5 billion.
These proportions in value broadly reflect the proportions of incidents that were affected. This
suggests that, although the pandemic caused additional problems and exacerbated others,
detriment incidents associated with the pandemic were not more or less costly than those
incidents that were not affected by the pandemic. Nevertheless, the median net monetised
detriment varied between incidents caused, made worse or not affected by the pandemic
(Figure 49) 90. This was mostly driven by monetised time costs; the median time consumers
spent on a problem affected by the pandemic was two hours, compared to one hour for
problems not affected by the pandemic or where the role of the pandemic was unclear (the
monetised cost of one hour is £13.87).

90 The significance test returned a borderline p-value of 0.052 (the commonly accepted threshold is 0.05).
13%
30%
47%
11%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
Mostly or
fully caused
Made worse
Not affected
Don't know
Percentage of detriment experiences

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Figure 49 – Median net monetised detriment by whether detriment experienced was
affected by the COVID-19 pandemic
Base: All detriment experiences in the UK in the 12 months to April 2021.
Unweighted: 9,407.
6.1.2 The impact of COVID-19 on the wellbeing of consumers affected by detriment
Consumers were more likely to have reported an adverse impact on their wellbeing as a result
of the detriment for experiences that they thought were caused or made worse by the COVID-
19 outbreak.
Where the pandemic was considered the cause or at least partly to blame for detriment
experienced, consumers were more likely to have felt to at least some extent upset, helpless,
misled or anxious (Figure 50).
Figure 50 – Whether felt anxious, helpless, upset or misled as a result of detriment by
the extent to which detriment experienced was affected by the COVID-19 pandemic
Base: All detriment experiences in the UK in the 12 months to April 2021.
Unweighted: Mostly or fully caused 1,171; Made worse 2,290; Not affected 5,108; Don't know 838.

The association between the impact of detriment on wellbeing and the extent to which it was
affected by the pandemic was weaker for feeling misled. A possible explanation for this may be
£34
£28
£20
£28
£0
£5
£10
£15
£20
£25
£30
£35
£40
Mostly or
fully caused
Made worse
Not affected
Don't know
63%
63%
75%
57%
68%
73%
77%
68%
44%
47%
58%
55%
42%
60%
57%
52%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
Anxious
Helpless
Upset
Misled
Percentage of detriment experiences
Mostly or fully caused
Made worse
Not affected
Don't know

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that COVID-19-related detriment could be considered structural (caused by major events outside
of the seller’s control, such as border closures or broken supply chains) as opposed to mistakes,
damages, malpractices and errors made by businesses. Additional research is needed to
confirm whether structural detriment is less likely to cause consumers to feel misled.
Thinking the COVID-19 pandemic played a role in detriment experienced was also associated
with whether consumers felt it had had a ‘very negative’ or ‘negative’ effect on their mental health,
physical health and their household’s finances. The proportion of detriment experiences where
consumers reported negative effects on health and finances was consistently higher for incidents
considered to have been caused or made worse by the pandemic, than those considered to not
have been affected (Figure 51).
Figure 51 – Whether consumers felt detriment had a negative effect on health and
household finances by the extent to which detriment experienced was considered to
be affected by the COVID-19 pandemic
Base: All detriment experiences in the UK in the 12 months to April 2021.
Unweighted: Mostly or fully caused 1,171; Made worse 2,290; Not affected 5,108; Don't know 838.
6.2 Impact of COVID-19 and elements of consumer detriment
As discussed, the COVID-19 outbreak had an impact on UK consumers, by either being the
cause of experiences of detriment or making them worse. However, not all sectors and market
clusters were affected in the same way.
6.2.1 Perceived impact of COVID-19 and market characteristics
There was no significant difference in the likelihood of thinking that detriment experienced was
caused or made worse by the COVID-19 pandemic by whether the detriment was experienced
whilst buying a service or an item. However, the likelihood of thinking the COVID-19 pandemic
played a role in detriment experienced did vary by market cluster. Experiences of detriment with
37%
23%
34%
33%
19%
27%
15%
7%
14%
20%
17%
18%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
Mental health
Physical health
Household's finances
Percentage of detriment experiences
Mostly or fully caused
Made worse
Not affected
Don't know

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‘Transport’, ‘Personal services’ or ‘Recreational services’ 91 were significantly more likely to have
been considered to be caused or made worse by the COVID-19 pandemic than other market
clusters (Figure 52). This finding likely reflects the higher levels of disruption experienced among
these market clusters as a result of lockdown and social distancing measures imposed by the
Westminster and devolved governments since early 2020 92.
Figure 52 – Percentage of detriment experiences felt to be caused or made worse by
the COVID-19 pandemic by market cluster

Base: All detriment experiences in the UK in the 12 months to April 2021.
Unweighted: Fast-moving retail 1,891; (Semi-)durable goods 1,567; Automotive goods and services 845;
Telecoms and other digital subscriptions 2,237; Utilities 656; Housing-related services 326; Banking and
insurance 523; Transport 266; Personal services 204; Recreational services 641; Other services 251.
Interestingly, the perceived impact of COVID-19 on experiences of detriment in ‘Telecoms and
other digital subscriptions’ was considered low (34% of the experiences of detriment in this
market cluster were considered to have been caused or made worse by the pandemic), despite
the increased pressure that the COVID-19 outbreak put on internet provision. This suggests that
the impact on internet provision was less tangible than it was on other sectors, where the role of
the pandemic appeared to be more obvious to consumers.
Detriment relating to ‘Airline’; ‘Package holidays and tours’; and ‘Adult care’ were most likely to
have been felt as caused or made worse by the COVID-19 pandemic relative to other sectors
(96%, 95% and 91% 93 respectively), followed by ‘Sport, cultural and entertainment activities’
(85%), ‘Hotels and holiday accommodation’ (80%), ‘Private medical and dental services’ (73%)
and ‘Real estate services’ (71%). Sectors in which at least 50% of the experiences of detriment
were considered to have been affected by the pandemic are presented in Figure 53.

91 ‘Transport’ includes: airline, public transport, trains and vehicle rental; ‘Personal services’ includes: adult care,
education fees and childcare; ‘Recreational services’ includes: hotels and holiday accommodation, package holidays
and tours, restaurants, cafés and take-away, sport, cultural and entertainment activities, gambling and lottery.
92 Senedd Research. (2020). Coronavirus timeline: Welsh and UK governments’ response.
https://research.senedd.wales/research-articles/coronavirus-timeline-welsh-and-uk-governments-response/
93 This estimate is based on a particularly small number of observations (n < 25).
43%
74%
71%
55%
45%
44%
43%
41%
40%
37%
36%
34%
0%
10%
20%
30%
40%
50%
60%
70%
80%
Overall
Transport
Personal services
Recreational services
Fast-moving retail
Automotive goods and services
Other services
Housing-related services
Utilities
(Semi-) durable goods
Banking and insurance
Telecoms and other digital subscriptions
Percentage of detriment experiences

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In contrast, consumers were least likely to think that experiences of detriment with ‘Removal and
storage’ (12% 94); ‘Pension funds and investment services’ (13%); and ‘Stationery, books,
magazines and newspapers’ (17%) were the result of the pandemic.
Figure 53 – Sectors in which 50% or more of the experiences detriment were perceived
as mostly or fully caused, or made worse by the pandemic

Base: All detriment experiences in the UK in the 12 months to April 2021.
Unweighted: Airline 125; Package holidays and tours 116; Sport, cultural and entertainment activities 110;
Hotels and holiday accommodation 180; Private medical and dental services 105; Real estate services 68;
Public transport and trains 126; Education fees 43; Childcare 35; Personal care services 56; Vehicle
maintenance and repair 409; Clothing, footwear and accessories 822; New vehicles 52.
Note: sectors with unweighted count equal or below 25 are excluded.
6.2.2 Impact of COVID-19 and characteristics of detriment
Detriment type
Feelings around whether the COVID-19 pandemic had an effect on detriment experienced also
varied significantly by type of detriment. Experiences of detriment relating to problems with
delivery or a complete failure to provide the item or service were more likely to have been thought
to be caused or made worse by the pandemic (respectively 57% and 58% of the experiences of
detriment where the detriment type was reported, compared to 39% and 38% of the experiences
where it was not). These results likely reflect the significant disruption the pandemic caused to
supply chains and distribution 95.
Consumers were also more likely to have thought that detriment experiences which related to a
misleading price were caused or made worse by COVID-19 (52% compared to 41% of the
detriment experiences where misleading price was not mentioned).

94 This estimate is based on a particularly small number of observations (n < 25).
95 See: International Labour Organisation. (2020). The effects of COVID‑19 on trade and global supply chains.
https://www.ilo.org/global/research/publications/WCMS_746917/lang--en/index.htm
96%
95%
85%
80%
73%
71%
66%
66%
63%
58%
53%
52%
50%
0%
20%
40%
60%
80%
100%
120%
Airline
Package holidays and tours
Sport, cultural and entertainment activities
Hotels and holiday accommodation
Private medical and dental services
Real estate services
Public transport and trains
Education fees
Childcare
Personal care services
Vehicle maintenance and repair
Clothing, footwear and accessories
New vehicles
Percentage of detriment experiences

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Table 17 – Percentage of detriment experiences considered to be caused, or made
worse, by the COVID-19 pandemic by detriment type experienced with the product
Type of detriment
Detriment type
experienced
Detriment type not
experienced
Statistically
significant
Complete failure to provide
58%
38%
*
Problems with delivery
57%
39%
*
Misleading price
52%
41%
*
Warranty and guarantees not honoured
52%
42%

Misleading information
49%
41%

Poor quality
41%
43%

Not usable
41%
42%

Unfair or unclear T&Cs
37%
43%

Other
43%
42%

Base: All detriment experiences in the UK in the 12 months to April 2021.
Unweighted: 9339; Poor quality (yes: 2615, no: 6724); Not usable (yes: 1812, no: 7527); Problems with
delivery (yes: 1451, no: 7888); Complete failure to provide (yes: 1704, no: 7635); Misleading price (yes: 853,
no: 8486); Misleading information (yes: 791, no: 8548); Unfair or unclear T&Cs (yes: 602, no: 8737); Warranty
and guarantees not honoured (yes: 290, no: 9049); Other (yes: 2242, no: 7097).
*Statistically significant differences (p<0.05).
Channel of purchase
There was no variation in the extent to which an experience of detriment was considered caused
or made worse by COVID-19 by the channel through which respondents purchased the item or
service.
Initial cost of the product
Generally, the higher the initial cost of the product with which detriment was experienced, the
greater the likelihood of the respondent feeling the detriment was caused or made worse by
the COVID-19 pandemic (Error! Reference source not found.). This trend holds true even after
taking into account the fact that the initial cost of the products varies between sectors and that
some sectors with relatively high-value products have been perceived by consumers as
particularly affected by the pandemic.
Figure 54 – Whether consumer felt detriment was caused or made worse by the COVID-
19 pandemic by product’s value

Base: All detriment experiences in the UK in the 12 months to April 2021.
Unweighted: 9,407 (Up to £100: 2,931; £101 to £500: 3,075; £501 to £1,000: 1316; £1,001 to £5,000: 1,032;
Greater than £5,000: 303).
43%
37%
45%
50%
55%
0%
10%
20%
30%
40%
50%
60%
Up to
£100
£101 to
£500
£501 to
£1,000
£1,001 to
£5,000
Greater
than £5,000
Percentage of detriment
experiences

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Conclusions
Consumer detriment is an endemic problem in market economies. Whilst it cannot be entirely
eliminated, if its incidence and negative consequence becomes too severe, it can undermine
consumers’ trust in the market.
The objective of this study was to listen to what consumers could tell us about their
experiences, in order to investigate detriment from the point of view of consumers operating in
the market, and measure perceived inefficiencies and strengths in the customer protection
system.
The study itself represents only a piece of the puzzle; while there are trackers and research
based on factual data, the conclusions in this report are based on what consumers reported:
their subjective perceptions, considerations and expectations play a key role in the findings.
Nevertheless, the subjective dimension of consumer detriment should not be disregarded as
secondary or unimportant. After all, trust in the market is a subjective dimension itself, and
perceived detriment is as important as factual detriment in determining consumers’ trust and
behaviours.
The report does not discuss objective data, neither does it engage in such considerations in
the conclusions. However, if there are clear discrepancies between what consumers told us
and what analyses based on transactional data have found, understanding and investigating
these incongruities could represent a first action point for policy teams and consumer
protection organisations.
This chapter details a series of conclusions and presents in short summaries the data that led
to the formulation of those statements. When reading these conclusions, it is important to
remember that the study was carried out during the COVID-19 outbreak – with consumers’
perceptions and behaviours likely affected by the pandemic. Similarly, some sectors have
faced unforeseen disruptions, such as travel restrictions, the need to implement smart working
solutions, sick leave of personnel, sudden decreases/increases in demand, changes in
consumers’ expectations, additional pressures on logistics or interruption of supply chains.
These had an impact on the type of problems experienced, the incidence of detriment and the
capacity of sellers and service providers to respond to cases of consumer detriment.

The findings of this study suggest that the consumer protection system 96 in the UK
can be considered generally effective and the response to detriment offered by
sellers and service providers is on average adequate.

Between April 2020 and April 2021, 69% of consumers have reported experiences of
detriment. Where detriment happened, the financial costs were generally offset by several
mitigating factors, such as compensations received and use value of the product.

96 The concept of consumer protection system used here encompasses the rights and channel of action afforded to
consumers by consumer protection law as well as the willingness of businesses to mitigate and resolve consumer
detriment problems (either due to market pressure or due to threat of legal consequences).

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• Consumers who experienced detriment had a median of four incidents between
April 2020 and April 2021; this averages out to one incident each quarter.
• The median net monetised detriment was £28 per incident.
• Nine percent of the experiences of detriment resulted in consumers receiving a
higher amount in compensations, compared to all the costs they faced.
When comparing what consumers asked and what sellers or service providers did in
response to the detriment, resolutions were generally favourable for consumers.
• 56% of the experiences of detriment ended with a positive resolution, where
consumers received what they asked for, received what they asked for and
something else, or did not ask for anything and still received some form of
compensation for the problem.
• Neutral resolutions (where consumers did not ask for anything and did not receive
anything) were seen in 25% of the experiences of detriment.
• Only 19% of the experiences of detriment led to a negative resolution (where
consumers asked for something and received nothing, or did not receive what they
asked for).
This is reflected in the overall satisfaction with the detriment outcome: the majority of the
experiences of detriment were resolved in a form that was considered at least somewhat
satisfactory by consumers.
• Consumers reported being satisfied with the outcome in 55% of the concluded
experiences of detriment.
• Dissatisfaction with the outcome was reported in 25% of the experiences of
detriment.
• Consumers were neither satisfied nor dissatisfied with the outcome in 20% of the
experiences of detriment.

While the consumer protection system mitigated the presence of negative
outcomes for most consumers, some incidents of detriment led to high levels of net
monetised detriment. Interventions should focus on areas where detriment
resulting in a relatively higher financial loss for consumers was more common.

Taking into consideration only experiences of detriment that the consumers considered
concluded:
• Eighteen percent of all the experiences of detriment had a net monetised detriment
above £100, 6% had a value above £500 and 3% above £1000.
Strong variations could be seen between sectors, but also by characteristics of the
detriment experience such as the channel through which the product was purchased.

The median net monetised detriment per incident was higher for some purchase
channels than others. Actions targeting these channels can represent a step
towards the reduction of high levels of net monetised detriment. However, such
actions might not be sufficient on their own, as they would be directed to a
relatively small number of problems.

• Incidents of detriment resulting from products purchased from a social media
platform had a higher median net monetised detriment (£55), compared to those

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resulting from other online purchases (£31 when purchasing from private
individuals, £17 from a provider’s website and £14 from a third-party marketplace).
• The median net monetised detriment resulting from purchases requiring a personal
interaction between the seller/service provider and the consumer was higher for
those made in-person from a salesperson (£87), compared to over a phone call
(£32) and in-person from a shop (£28).
• The median net monetised detriment for purchases made through auto-renewal
processes was £55.
While problems with products purchased in-person from a salesman, online on a social
media platform or through auto-renewal processes resulted in higher-than-average net
monetised detriment (the overall median net monetised detriment was £28), only a small
number of detriment incidents originated in these channels. Products from these three
channels, combined, accounted for less than 10% of all the experiences of detriment and
for 9% of the total net monetised detriment.

Sectors differed by the proportion of consumers who experienced detriment and
median values of net monetised detriment per incident. The mitigation of negative
financial consequences of consumer detriment should focus on those sectors with
a larger baseline of consumers where negative outcomes were more likely.

Policy actions and initiatives are more likely to have a wider impact if targeted towards
sectors in which a larger number of consumers purchase products (87% of consumers
bought ‘Personal care products while only two percent accessed ‘Pet breeder services’).
Sectors with a high incidence of detriment and high median net monetised detriment,
accessed by more than 50% of consumers, were:
• ‘Internet provision’ (29% incidence of detriment, £55 median net monetised
detriment).
• ‘Vehicle maintenance and repair’ (17% incidence of detriment, £118 median net
monetised detriment).
• ‘Mobile telephone services’ (13% incidence, £59 median net monetised detriment).
Although they are accessed by a lower number of consumers (between 6% and 25% of
consumers purchased in these sectors), a high incidence of detriment and net monetised
detriment was also observed in:
• ‘Second-hand vehicles’ (30% incidence of detriment, £463 median net monetised
detriment).
• ‘Real estate services’ (26% incidence of detriment, £142 median net monetised
detriment).
• ‘New vehicles’ (19% incidence of detriment, £71 median net monetised detriment).
• ‘Education fees’ (19% incidence of detriment, £207 median net monetised
detriment).
• ‘Legal and accountancy services’ (14% incidence of detriment, £110 median net
monetised detriment).
• ‘Childcare services’ (13% incidence of detriment, £210 median net monetised
detriment).
• ‘Home and garden maintenance and repair’ (11% incidence of detriment, £109
median net monetised detriment).
• ‘Veterinary’ (11% incidence of detriment, £142 median net monetised detriment).

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• ‘Private medical and dental services’ (10% incidence of detriment, £101 median net
monetised detriment).
Compared to the first group of three sectors, policies and other initiatives directed at this
second group of sectors would have an impact on a smaller number of consumers.
Problems in other sectors not listed here had a lower incidence, or a lower median net
monetised detriment, or both.
It is important to remember the data was collected during the COVID-19 outbreak. Many
sectors have been at the centre of unusual disruptions and operating difficulties – which
could be the reason for both the high incidence of detriment and the high median net
monetised detriment.
The negative consequences of consumer detriment differed not only by market features
and problem elements, but also by consumer characteristics. Any initiatives aiming at
mitigating detriment, would need to take into account the unequal factor of risks and the
varying effects between subgroups of the general population.

The impact of detriment differed by key socio-economic elements and demographic
characteristics of consumers. Younger people and those facing financial difficulties
were particularly vulnerable to different forms of negative outcomes. However,
more research is needed to explore why this happens.

This report identified three negative factors of consumer detriment that varied by
consumers’ characteristics:
• Risk of experiencing detriment.
• Risk of facing the most negative consequences of consumer detriment.
• The decision to not take actions to address an experience of detriment.
Younger consumers (especially consumers aged 18-39) and those considering their
financial situation difficult were consistently more likely to have faced the most negative
outcomes in all those areas. Occasionally, other consumers’ characteristics showed a
strong association with the likelihood of experiencing negative outcomes (households with
children, smaller households, ethnic groups other than White British, consumers living in
urban areas, and unemployed consumers).
Although younger consumers and those living in difficult financial conditions would benefit
from initiatives directed to mitigate their vulnerability towards the effects and risks of
consumer detriment, the data available in this study cannot tell what direction such
initiatives should take and the reasons why the likelihood of negative outcomes are so
high in some subgroups of the population, but not in others. The development of a policy
aimed at reducing the negative effects of detriment among some subgroups of consumers
requires a deep understanding of causes and effects, which can be achieved with
additional research and analysis.
Additional research is also needed to explore the impact of consumer detriment on
wellbeing. This was one of the most critical, yet somehow unexpected, consequences of
detriment on individuals. More research should be carried out to understand specific
consequences and how they develop.

While feeling anxious, upset, misled or helpless after experiencing detriment is
expected, the study found a substantial number of consumers who reported very
negative consequences on their mental and physical health, and on their household
finances.

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The proportion of consumers who reported having had, at least once in the 12 months
between April 2020 and April 2021, a very negative effect on their wellbeing because of
consumer detriment was particularly high:
• Ten percent of consumers reported a very negative effect on their mental health.
• Five percent of consumers reported a very negative effect on their physical health.
• Nine percent of consumers reported a very negative effect on their household
finances.
The incidence triples (29%, 15% and 27%) if both negative and very negative effects are
considered. The study found that experiences of detriment resulting in negative
consequences on wellbeing were generally associated with several characteristics of
detriment, including: product type, market cluster, channel of purchase, value of the
product, and several types of detriment.
Very negative consequences on wellbeing were also more likely to have been reported by
consumers with a difficult financial situation, unemployed people, frequent internet users,
consumers living in urban areas, those with a Black, Asian, Mixed or other ethnic
background as well as consumers in their 30s and 40s.
Variations by socio-economic elements and demographic characteristics were also found
in the likelihood of taking action to address an experience of detriment, and in the reasons
given for not taking actions by some subgroups of consumers.

Only a minority of incidents were unactioned by consumers and their decision was
generally motivated by cost-opportunity considerations. However, there were some
exceptions, with some subgroups being more likely to have unactioned detriment
because they did not know who to contact or how to complain.

• Consumers did not take actions in 18% of their experiences of detriment.
• Many experiences of detriment were unactioned at the time of the survey because
the consumer had not had the chance yet to do so, because someone else did it, or
because the problem was somehow fixed without the consumer needing to do
anything. Once these incidents of detriment were removed, the proportion of
unactioned consumer detriment fell to 11%.
• Consumers did not act when the investment of their time, efforts and resources
were not worth it (for example, the problem not being serious enough, the
expectation it would be unsuccessful, or the process being difficult or too lengthy).
• The median value of the product was £200 for experiences where action was taken
and £150 where no action was taken. However, the median value was lower when
detriment was unactioned because the problem was not serious enough (£35), or
because the process would have taken too long (£31).
Whilst cost-opportunity was generally the key consideration for unactioned detriment, the
findings of the study suggest that this is not always the case, as some reasons for not
taking actions differed by consumers’ characteristics.
• Consumers finding their financial situation difficult were more likely to not have
taken any actions because the process was not clear (they were not sure who to
contact, or how to go about complaining).
The data collected in this study cannot explain why this happens. Those with a more difficult
financial situation might be less knowledgeable about the consumer protection system or

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might be more likely to rely on sellers and providers, who may offer less transparent support
channels to their customers.
Encouraging consumers who experienced detriment to take actions should represent a
priority in policy development. However, unless other data sources and analyses can offer
strong evidence on a causal relationship for the development of specific policies and
initiatives, additional research might be necessary to shed light on this trend.
In addition to investigating how consumer detriment, and its consequences, varied
between sectors, type of consumers and different typologies of incidents, this study also
focused on differences between UK countries.

Although there were some variations in the incidence of consumer detriment
between England, Scotland, Wales and Northern Ireland, the study did not find
evidence of substantial differences. Consequently, the findings of this report do not
support the need for country-specific policy approaches or initiatives.

The data collected in this study did not have sufficient sample size for an in-depth analysis
of all the trends within and between the four UK countries. Nevertheless, it was possible to
compare top level estimates and conduct subgroup analysis in several macro-groups.
• Scotland and England showed a higher incidence of consumer detriment
compared to Wales and Northern Ireland. The trend was confirmed when
controlling for levels of urbanisation and patterns of consumer activity.
• The total net monetised detriment and the count of incidents in each country
broadly reflected their different population sizes.
• There were some differences between countries in the incidence of detriment in
some market clusters or by product type, but they always mirrored the general
trend (England and Scotland showed a higher incidence than Wales and Northern
Ireland).
• No statistically significant variations were identified by value of the product, type of
detriment experienced, whether or not consumers took any actions to address their
experiences of detriment, satisfaction with the outcome or resolution patterns.
• The proportion of experiences of detriment from in-person purchases was slightly
higher in Northern Ireland, compared to the other three UK countries.
Finally, the study investigated the perceived role that the COVID-19 outbreak had on
consumer detriment.

On average, consumers perceived the COVID-19 outbreak as having been a major
cause of their experiences of detriment. Detriment incidents perceived as affected
by the pandemic resulted in higher median net monetised detriment and took
longer to be resolved.

The disruptions caused by the COVID-19 outbreak were generally perceived by
consumers as having played an important role in the problems they incurred:
• Consumers thought that 43% of their experiences of detriment were caused or
made worse by the COVID-19 pandemic. The net monetised detriment of problems
perceived as affected by the pandemic totalled up to £26.5 billion.

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Consumers acknowledged the disruptions caused by the pandemic to have been a key
reason for the detriment they faced in some sectors, but they did not recognise its role in
others.
• Consumers were more likely to have considered their detriment as affected by the
pandemic in sectors such as ‘Airlines’, ‘Package holidays and tours’, ‘Adult care’,
‘Sport, cultural and entertainment activities’, ‘Hotels and holiday accommodation’,
‘Private medical and dental services’ and ‘Real estate services’.
• They were less likely to consider detriment in ‘Removal and storage’, ‘Pension
funds and investment services’, and ‘Stationery, books, magazines and
newspapers’ to have been affected by the COVID-19 outbreak.
• Despite the significant disruptions brought by the pandemic upon ‘Telecoms and
other digital subscriptions’, only 34% of the experiences in this market cluster were
considered affected by the pandemic. Within this market cluster, 33% of ‘Internet
provision’ problems were perceived as linked to the COVID-19 outbreak.
Consumers were more likely to have perceived detriment instances as caused by the
pandemic when the problem was related to not having received a product they purchased,
or having received it later than expected.
Detriment incidents perceived as being caused or made worse by the pandemic were
reported as being more severe than those that were considered not to have been affected
by it.
• The average time spent to resolve a problem perceived as caused or made worse
by the pandemic was 2 hours, compared to 1 hour for problems perceived as not
affected by the pandemic or where the role of the pandemic was unclear.
• A problem caused or made worse by the pandemic resulted in a higher net
monetised detriment, compared to a problem not affected by the disruptions of the
COVID-19 outbreak. This is partly explained by the additional time cost.

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Appendices
Appendix A. Questionnaire development and testing
A key challenge for this project was the questionnaire design.
‘Consumer detriment’ is a complex concept with a precise definition that may not be
understood consistently by policymakers, researchers, and members of the public. In addition,
collecting data on detriment requires respondents to accurately recall detailed information
about events that may have happened a long time in the past and they may not have given
extensive consideration to at the time. In order to minimise risks of measurement error and
maximise the quality of the final data and analysis, the questionnaire underwent a thorough
pre-testing stage, which included a pilot with 178 respondents recruited from the NatCen Panel
and 12 cognitive interviews with people purposefully recruited from the general population.
The questionnaire was developed to be administered both online and over the phone to
include people without internet access. A unimodal questionnaire design approach was
therefore adopted to minimise measurement differences between modes and between different
internet enabled devices (large screens such as laptops and tablets, versus small screens
such as mobiles).
Questionnaire content and structure
The questionnaire was developed in collaboration between researchers from NatCen, BEIS,
and the CPP. While the questionnaire content builds on the questionnaire used in previous
waves, there have been substantial changes to content and definitions used. This should be
considered when comparing estimates from this wave of the study to previous estimates.
The questionnaire starts broadly, asking people about their purchasing behaviour before
introducing a definition of consumer detriment and asking respondents if they had experienced
detriment in the last 12 months in sectors they had made a purchase in (or previously made a
purchase in, but used in the last 12 months). It then asks for more detail about up to three
instances of detriment the respondent experienced. Image 6 gives an overview of the
questionnaire content by section.
Image 6 – Questionnaire flow

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The flow of the section investigating detriment incidents was designed such that:
• where people had experienced detriment in more than three sectors, the interview
programme randomly selected three sectors to ask about the experience in more detail.
This was done to limit the burden on respondents, and thus reduce the risk of survey
dropouts or decline in data quality 97.
• where people have experienced multiple instances of detriment in a sector, they were
asked only about the most recent instance within that given sector. This was done to
ease recall. However, this approach risks biasing the data if, on aggregate, the ‘most
recent’ experience is not representative of all experiences over the year 98.
The detriment incident questionnaire loops asked about a number of elements of the detriment
experience, including the nature of the purchase (i.e. the cost of the product and the channel
through which it was bought), the nature of the detriment (i.e. the type of problem encountered
and its status), as well as the detriment process, its impact and its causes.
The full questionnaire specification is available in Appendix G.
Cognitive Testing
Cognitive interviews were used to test the questionnaire ahead of fieldwork.
Cognitive interviewing uses ‘think aloud’ and probing techniques to give insight into the thought
processes respondents go through when answering survey questions. This approach helps
researchers identify problems with question wording and questionnaire design by exploring, for
example: comprehension of key terms within the questions; whether respondents were able to
select a suitable response option; or sensitivity of questions.
A total of 12 interviews 99 were carried out by NatCen researchers and expert interviewers.
Participants were sampled purposively to cover both consumers who experienced detriment
and those who didn’t, as well as a range of sexes, ages, incomes, and education levels (Table
18). Participants were given a £30 voucher as a thank you for their time and help.
Table 18 – Cognitive participant characteristics
Characteristics
Number
achieved
Gender
Male
6
Female
6
Age
Up to 40
6
Over 40
6
Highest education
A-levels or above
8
GCSE or below
4
Whether experienced detriment in the 12
months preceding the cognitive interview
Yes
9
No
3

Interviews were recorded and summarised in a thematic matrix alongside participants’ answers
to questions and probes, and interviewers’ observations, allowing for the systematic analysis of

97 For details on how the detriment incidents reported by respondents but not fully investigated were taken into
account in the net monetised detriment formula, see Appendix D.
98 See Appendix B for more information on how this risk of bias was dealt with.
99 Interviews were undertaken by video-call interviews, or phone interviews if the recruited participant was unable to
access the internet.

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the qualitative data. Once all interviews had been completed and analysed, the findings were
discussed in depth with BEIS and the CPP alongside findings from the pilot, from which
recommendations for the mainstage survey were made.
Piloting
Piloting the questionnaire aimed to: (i) provide indicative estimates of the proportion of the
population who experienced detriment in a given 12 month period, as well as the distribution of
detriment across the various sectors; (ii) test whether the questionnaire was working, creating
clean data and running to the correct length; and (iii) provide insights into how the
questionnaire was being answered.
The pilot questionnaire carried the latest version of the questions intended for the mainstage
fieldwork, as well as a small number of follow-up probes that asked participants how easy or
difficult they found specific questions and for suggestions for improvements, as well as for
general feedback on any questions or the questionnaire as a whole.
The pilot sample was drawn from the NatCen Panel, specifically active panel members aged
25+ recruited from the BSA 2020 wave that had email addresses 100. A total of 263 panel
members were invited to take part, with 178 doing so. Table 19 summarises the sample
profile.
Table 19 – Pilot sample profile
Characteristics
Number and %
achieved
Gender
Male
80 (45%)
Female
97 (55%)
Other
1 (1%)
Age
18-24
0 (0%)
25-34
23 (13%)
35-44
36 (20%)
45-54
32 (18%)
55-64
32 (18%)
65+
55 (31%)
Household
income
Less than £1,201
15 (8%)
£1,201 - £2,200
13 (7%)
£2,201 - £3,300
37 (21%)
£3,301 or more
59 (33%)
Don’t
know/Refused/Missing
54 (30%)

Total
178 (100%)

Fieldwork lasted for five days, running from the 10th March to 14th March 2021, and was
conducted online-only. This approach reflected the time constraints before the start of the main
fieldwork and that the pilot data would not be used for robust population reporting. The pilot
approach differs from the mainstage design, where telephone fieldwork and a longer fieldwork
period were used to reduce the risk of bias in the sample.
The analysis of the pilot data included estimating the number of experiences of detriment, the
questionnaire length, use of ‘other’ answer options, use of help links, use of ‘Don’t know’ and

100 Panel members invited to take part in the pilot were not invited to take part in the mainstage fieldwork. Only those
aged 18-24 were recruited from BSA 2020 for the mainstage fieldwork.

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‘Refusal’ codes, the selection of ‘invalid’ answers, distributions of answers on scales, and
participant feedback in probe questions.
The findings were written into a short report and discussed in depth with the CPP and the other
stakeholders alongside findings from the cognitive testing, from which recommendations for
the mainstage survey were made.

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Appendix B. Fieldwork design, response rate and weighting strategy
Fieldwork design and response rates
Mainstage fieldwork was conducted using sample from the random-probability NatCen Panel 101
and a push-to-web survey. Overall, the survey was completed by 6,582 UK adults (18+).
NatCen Panel survey (main sample)
The NatCen Panel is a panel of people recruited from the British Social Attitudes 102 (BSA)
survey, a high-quality, random probability face-to-face 103 survey.
Fieldwork was conducted using a sequential mixed-mode web/telephone design over a three-
week fieldwork period. Respondents were initially invited to take part online, and web fieldwork
ran from the 8th April to 9th May 2021. Those not taking part online were issued to telephone
fieldwork which ran from the 15th April to 9th May 2021 104. A total of 5,101 people took part in
the survey, of whom 4,616 (90%) completed online and 485 (10%) completed on the phone.
An incentive was offered to participants as a thank you for their time 105.
Response rates are a simple indicator of quality for surveys based on probability samples and
are summarised in Table 20. For the NatCen Panel sample (main sample), this survey
achieved a 75% response rate among those panellists invited to participate. When taking
account of non-response at the BSA interview and then also at the point of recruitment to the
Panel, the overall response rate was 12%.
Various checks are routinely conducted on the NatCen Panel to ensure accuracy, consistency
and non-duplication of the data. Panel members’ survey-taking behaviour is monitored and
tracked across all surveys with the aim of removing suspect and inactive panellists from
eligible sampling pools. After validation checks conducted for this study (see paragraph on
data validation procedures), 96 interviews were removed.

101 More information on the design of the NatCen Panel can be found here:
Jessop, C. (2018). The NatCen Panel: developing an open probability-based mixed-mode panel in Great Britain. Social
Research Practice. 4(Summer 2018). Available at: https://the-
sra.org.uk/Common/Uploaded%20files/Social%20Research%20Practice%20Journal/social-research-practice-journal-
issue-06-summer-2018.pdf
102 The BSA survey is designed to yield a representative sample of adults aged 18 or over living in Great Britain. For
practical reasons, the BSA sample is confined to those living in private households. People living in institutions
(though not in private households at such institutions) are excluded, as are households whose addresses were not on
the Postcode Address File (PAF). The PAF is a list of addresses (or postal delivery points) compiled by the Post Office
which is used as the sample frame.
103 Due to COVID-19 the 2020 wave of BSA was conducted using a ‘push-to-web’ design. Only participants aged 18-24
recruited from BSA 2020 were issued to the survey in order to refresh the sample.
104 Using telephone fieldwork allows for people who are unwilling or unable to take part online to do so.
105 Incentives varied between £5 and £20 depending on the respondent’s characteristics: for example, whether or not
they received a longer interview.

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Table 20 – NatCen Panel sample response rates 106
Response to the Consumer Protection Study survey

Number of issued panel members
6,796
Number of deadwoods 107 at the time of the survey launch
8
Number of suspected fraudulent cases
96
Number of ‘Consumer Protection Study’ productive and validated cases
5,101
Survey response rate (%)
75%
Overall response

Number of issued BSA cases
46,860
Number of deadwoods 108 addresses at BSA stage
4,409
Number of BSA productive and validated cases
18,597
Number of cases recruited to the NatCen Panel
11,480
BSA response rate
44%
NatCen Panel recruitment rate (%)
62%
Number of deadwoods 109 at the NatCen Panel recruitment stage
114
Overall survey response rate (%)
12%

Push-to-web survey (boost sample)
The push-to-web survey was used to cover Northern Ireland (not covered by the NatCen
Panel), and to boost the sample in Wales to allow for more precise estimates in that region.
An unclustered sample of addresses in Northern Ireland and Wales was drawn from the
Postcode Address File (PAF), a list of addresses (or postal delivery points) compiled by the
Post Office which was used as the sample frame. All PAF addresses within Northern Ireland
and Wales were sorted by: (i) population density at Local Authority level, (ii) tenure profile (%
owner occupation) at Output Area (OA) level, (iii) postcodes within OAs and (iv) addresses
within postcodes. A systematic (1 in N) random sample of addresses was then drawn to select
3,900 addresses in each country.
Every issued address was sent an invitation letter and up to two reminder letters with
instructions on how to take part online, but also on how to contact NatCen to schedule a
telephone interview if preferred. Where a selected household contained more than one person
aged 18 and over, it was decided not to attempt to select at random one person to be
interviewed. Although it is possible to provide participants with instructions to randomly select

106 For participants aged 18-24 recruited from BSA 2020 the same response rate as for other age groups was assumed.
107 Deadwoods for the NatCen Panel at the time of the survey launch are individuals (i.e. panel members) who, during
fieldwork, were found to be ineligible (e.g. deceased, moved outside of the UK, etc.).
108 Deadwoods at BSA stage are sample points (addresses) which were ineligible at the time when the interviewer
visited the address. These comprised both residential addresses that were not eligible at the time when the BSA
survey was carried out (for example: unoccupied/demolished premises, premises not yet built/under construction,
second homes) and non-residential addresses (e.g. solely business, school, office, factory, institution, etc.).
109 Deadwoods at the NatCen Panel recruitment stage are individuals (i.e. panel members) who, before the start of any
specific Panel fieldwork, were classified as ineligible and therefore were not issued to the specific panel survey (e.g.
deceased, moved outside of the UK, etc.).

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one person per household, studies 110 have shown that respondent compliance with the
instructions is poor. Instead, the invitation and reminder letters contained two unique access
codes to be used by any two adults aged 18 or over living within the household to log in and
complete the questionnaire.
The survey response rate for the push-to-web survey (boost sample) was 11%.
The response rate (Table 21) was computed making assumptions on (i) the number of
ineligible addresses (‘deadwood’) and (ii) the number of eligible adults per eligible address. In
push-to-web surveys it is not possible to record accurately the number of selected addresses
which were not eligible because, for example, they were unoccupied or not a main residence.
For this study it was assumed the proportion of ineligible addresses was similar to figures
found in previous face-to-face surveys (10%). It was assumed there were 1.9 adults eligible to
respond per eligible household, based on the ONS Labour Force Survey (ONS, 2021).
As a result of the use of incentives and the availability of multiple log-ins at each address
(survey invitations letters contained log-in details for up to two household members), there was
a possibility of respondents fraudulently completing interviews in order to claim the incentive.
The data processing therefore included procedures to identify and remove potentially
fraudulent cases (61 in total).
Table 21 – Push-to-web survey response rates

NI
Wales
Total
Number of serials 111 issued
7,800
7,800
15,600
Number of addresses issued
3,900
3,900
7,800
Estimated number of deadwoods 112 addresses
390
390
780
Estimated number of adults issued
6,669
6,669
13,338
Number of suspected fraudulent cases
38
23
61
Number of productive and validated cases
789
692
1,481
Response rate (%)
12%
10%
11%
Note: response rate computed assuming 11% deadwood addresses and 1.9 adults per eligible household.

110 See results of Kantar’s methodological work conducted between 2012 and 2015 on the Community Life Survey
(Hamlyn, B., Fitzpatrick, A., & Williams, J. (2015). Community Life Survey - Investigating the viability of moving from a
face-to-face to an online/postal mode: evidence from a series of methodological studies 2012-2015. TNS. Available at:
https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/466921/Investigatin
g_the_the_viability_of_moving_from_a_face-to-face_to_an_online_postal_mode_FINAL.pdf) and the trial NatCen did
in 2012 on the European Social Survey (ESS) (abstract and PowerPoint presentation: Park, A., Humphrey, A., & Agur,
M. (2013). Mixed-mode and the European Social Survey (ESS): evidence from the UK [Conference Presentation].
ESRA 2013, Ljubljana, Slovenia. Available at:
http://www.websm.org/db/12/17147/Web%20Survey%20Bibliography/Mixedmode_and_the_European_Social_Survey
_ESS_evidence_from_the_UK_/ )
111 Serials are the unique access codes needed to access the survey. Each invitation letter contained two serials.
112 Deadwoods for the P2W survey are sample points (addresses) which were not eligible to complete the survey, such
as second homes, vacant properties, or business addresses. The number of deadwoods was estimated considering
the rate of deadwoods usually obtained in face-to-face surveys.

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Data validation
To identify potentially falsified cases, statistical outliers were reviewed to identify problematic
cases.
This was done by creating measures for different potential indicators of falsification (e.g. speed
of completion), and then examining the proportion of outliers 113 on each measure. This allows
outliers to be excluded rather than making subjective judgements about individual cases.
The statistical definition of an outlier is itself arbitrary, but it is a widely used and accepted
standard. The following definitions were used:
• UQ = Upper Quartile (The upper quartile is the median of the upper half of a data set.
This is located by dividing the data set with the median and then dividing the upper half
that remains with the median again)
• LQ = Lower Quartile
• IQR = Inter-Quartile Range (UQ-LQ)
• Low Threshold = LQ - 1.5*IQR
• High Threshold = UQ + 1.5*IQR
The responses falling below the Low Threshold or above the High Threshold were deemed to
be statistical outliers, and the formulae (LQ - 1.5*IQR and UQ + 1.5*IQR) are the accepted
standards for doing so.
For this survey, checks for (i) age eligibility (ii) speeders, (iii) duplicates and (iv) invalid answers
were conducted. While straight-lining and high use of ‘Don’t know’ (DK) & ‘Prefer not to say’
(PNTS) responses are often used in these types of checks, they were not included in our final
parameters as the characteristics of this survey meant they were not useful 114.
Age eligibility verification 115
It was possible for a respondent to enter that their age was under 18, then be shown a screen
which said that they were ineligible, and subsequently go back and change their age so that
they could complete the questionnaire. Questionnaire paradata were used to identify such
cases and they were reviewed alongside other data.
Speeders
If people answer questions too quickly, it may indicate they have not read or answered the
questions properly. The approach used to identify speeders was to compare people's overall
questionnaire time 116 to an estimate of how long they should have taken given their route

113 A statistical outlier is a value that is much smaller or larger than most of the values in a distribution. An accepted
convention is to treat values that fall more than 1.5 times the interquartile range above the upper quartile or below the
lower quartile as outliers.
114 Checks of straight-lining were not carried out because the survey presented almost each question on a separate
page (one survey question = one web page), and only included a small number of grid questions whose design
usually favours negative respondent behaviour by rushing through a survey clicking on the same response every time
easier. Checks on DK and PNTS were not implemented because all item nonresponse codes were not displayed
upfront to the respondents.
115 This check was only applied to push-to-web participants. NatCen Panel members are all adults (18+), and the
majority of them have been recruited face-to-face by a NatCen interviewer.
116 Capped to account for outliers where a respondent may have left and returned to a single question.

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through the questionnaire, had they been a ‘median length respondent’ 117 for each of those
specific questions. This is a better approach than looking at overall questionnaire length
because it accounts for the actual route through the questionnaire taken by each respondent,
which varied substantially for this study. Statistical outliers on this speeding measure were
identified and reviewed in combination with other data.
Invalid answers
A number of questions had ‘invalid’ answers which were soft-checked but allowed. An invalid
answer may indicate a participant was not paying full attention when answering, and therefore
potentially fraudulent. A set of rules were created to identify with a flag potentially fraudulent
cases due to invalid answers. These flags were then analysed alongside the results for the
other quality indicator measures.
Duplicates
Checks for duplication, within a household, were undertaken to identify any potential duplicated
cases. Note that it was impossible to distinguish between people who had disguised the
duplication and genuinely different people within the same household. Checking for duplicates
was undertaken based on observing matches on the following variables: (i) name and
surname; (ii) sex and age within one year 118; and (iii) contact details such as mobile numbers
and email addresses.
It was also checked whether household-level variables corroborated with individuals’ answers
to identify where participants might not have answered properly, or when individuals were
made up. A record was flagged as potentially fraudulent if:
• two people in a household took part, but one of the participants identified only one adult
(18+) living in the household.
• the answers of the two persons taking part for questions about the number of adults,
number of children, tenure type, and household income did not match.
Flags for researchers to review were created if there was an exact match of the variables listed
above. None of the flags were used on their own to determine whether a case was fraudulent
or not as, for example, people within a household can share email accounts. The duplicates
flags were reviewed together with speeders and invalid answers flags.
Weighting and scaling
The Consumer Protection Study uses two different weights in its analysis: (i) a respondent-
level weight that adjusts for design, recruitment and non-response bias of the panel sample
and for the push-to-web component of the study, and (ii) a detriment-level weight used for the
analysis of detriment-level data. Both weights were also ‘grossed’ (i.e. scaled up) to produce
counts at population-level i.e. estimates for numbers of people or of detriment experiences in
the UK population, rather than in the responding sample.
Detriment-level analysis was carried out using the individual serial number of study participants
included in the dataset (CDS_ID) as the primary sampling unit parameter. The use of CDS_ID
as PSU ensures a more robust computation of standard errors and accounts for the fact that
incidents of detriments were sampled within study participants.

117 The median time taken for each question was used, as the mean would be distorted by high outliers.
118 This check was conducted on NatCen Panel members only.

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This section offers an overview on the methodological approach followed in the production of
these different weights.
NatCen Panel (main sample)
Non-response for NatCen’s probability panel surveys can occur at three stages: (i) non-
response at the survey used for recruitment (BSA), (ii) refusal to join the panel at the end of
that interview and (iii) non-response in the survey of panel members itself. A weight to account
for non-response at each of these three stages was computed. The final weight is the product
of these three weights. This three-stage system was used because the variables underlying
non-response could be different at each stage. This system also allows for maximising the use
of all the information available from the BSA.
These are the weights computed:
• BSA survey weight: the panel members were recruited from the BSA 2015 to BSA
2019, plus panel members aged 18-24 from BSA 2020. The BSA survey weights for
each year were produced in three stages. Firstly, selection weights account for unequal
chances of selection in the BSA sampling. Secondly, a non-response model is used to
produce a non-response weight. This weight adjusts for address level non-response at
the BSA survey using: region, type of dwelling, whether there were entry barriers to the
selected address, the relative condition of the immediate local area, the relative
condition of the address, the percentage of owner-occupied properties in quintiles and
population density. Finally, the selection weights and non-response weights were
combined and adjusted using calibration weighting to make the sample of BSA
respondents representative of the general British population in terms of gender, age and
Government Office Region (GOR) 119.
• Panel weight: this weight accounts for non-response at the panel recruitment stage
where some people interviewed as part of the BSA survey chose not to join the panel. A
logistic regression model (weighted by the BSA weights) was used to derive the
probability of response of each panel member; the panel weight non-response weight is
computed as the inverse of the probabilities of response. This weight adjusts the panel
for non-response using the following variables: age and sex groups, GOR, BSA year,
household type, household income, education level, internet access, ethnicity, tenure,
social class group, economic activity, political party identification, and interest in
politics 120. The resulting panel weight has been multiplied by the BSA weights, so the
panel is representative of the population.
• Survey weight: this weight is to adjust the bias caused by non-response to this
particular panel survey. A logistic regression model was used to compute the
probabilities of response of each participant. The panel survey non-response weight is
equal to the inverse of the probabilities of response. The initial set of predictors used to
build the model was the same as for the panel weight; and at this wave the final set of
variables used was also the same. The final survey weight is the result of multiplying the
survey non-response weight by the panel weight.

119 More details on the BSA weight can be found at http://bsa.natcen.ac.uk/
120 The characteristics that are likely to change with time for an individual and whose distribution differed between 2017
and 2018 BSA sample have been entered into the model in interaction with BSA year.

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Push-to-web (boost sample)
Weights were constructed to adjust for differential selection probabilities (between countries)
and differential non-response. The “push-to-web” methodology meant that non-response could
occur within households as well as at the level of the address.
As described above, addresses were selected with equal probability within Wales and NI, and
up to two people in one household at each address were invited to take part. Weighting was
therefore required to adjust for (i) differential selection probabilities in Wales and NI 121, (ii) non-
response amongst households at selected addresses, and (iii) non-response within
households. Separate non-response models were constructed to deal with each of these
elements of non-response.
The first model (address level non-response) was weighted by the selection weight and
included area-level covariates 122 (plus a binary country indicator). The predicted probability
from this model was used to produce a non-response weight to adjust for non-response at the
level of the address.
The second (within household) model was weighted by the product of the selection weight and
the address-level non-response weight. The model was used to predict the expected number
of completed surveys in responding households and included the number of adults in the
household and an indicator for whether (or not) someone has a degree (plus a binary country
indicator). From this, a within household non-response weight was calculated as the ratio of
the number of adults in the household (capped at 4) divided by the expected number of
responses. The final step was to produce a composite non-response weight, calculated as the
product of the selection weight and the two non-response weights described above.
Final respondent-level weight
Following non-response weighting, the “push-to-web” survey respondents were combined with
the panel respondents. The panel weights (for panellists) and the composite non-response
weight (for “push-to-web” respondents) were combined to produce a pre-calibration weight.
Calibration weighting was then used to adjust the weighted profile of the (combined)
responding sample so that it matched the profile of the UK population in terms of age and sex
(within country) and (separately) by region/country 123.
Respondent-level grossing weight
The final survey weight (combined panel weight and push-to-web composite weight, calibrated
to match the profile of the UK population) is scaled to the responding sample size (mean
weight = 1; sum of the weight = 6,582). The grossing weight is the survey weight scaled up so
that the weighted total matches the UK population (i.e. the survey weight was multiplied by
8,003). The grossing weight therefore produces estimates for numbers of people in the UK
aged 18 or above, rather than numbers in the sample (mean weight = 8,003; sum of the weight
= 52,673,433).

121 The same number of addresses were selected in each country, but Wales is larger than NI, hence the probability of
selection was greater in NI.
122 Deprivation quintiles (within country), Output Areas Classification (OAC) and percent in professional occupations by
LSOA,
123 Based on ONS mid-2019 population estimates.

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Detriment-level weight
The survey collected information about the most recent incident of consumer detriment across
a random sample of up to three sectors where participants reported having experienced
detriment in the 12 months covered by the study.
The experiences of detriment were scaled-up to make them representative of all the incidents
of detriment experienced by study participants in the 12 months (scaling factor). The scaling
factor was the product of:
• Sector scaling: the number of sectors where the participant experienced detriment
divided by the number of sectors the participant was asked about in the survey. The
resulting values were trimmed at the 99th percentile (93 values were trimmed at 4.3
from a max value of 9.6).
• Detriment scaling: the number of independent incidents of detriment experienced in the
sector by the study participant. Missing values were replaced with the median number
of incidents in the sector across all study participants. The resulting values were
trimmed, removing – within each sector – outliers and extreme values (37 values were
trimmed: one value in 12 sectors, two values in seven sectors, three values in one
sector and four values in two sectors).
The resulting scaling factor was multiplied by the survey weight to make the data
representative of all the incidents of detriment experienced by UK consumers (detriment
weight).
This approach relies on the assumption that the most recent experience of detriment in each
sector can be considered representative of all detriment experiences occurring within the 12
months covered by the study. This assumption was a necessary trade-off within the Total
Survey Error framework 124: measurement errors linked to this assumption were considered
less harmful to data integrity than measurement errors caused by a complex questionnaire
design and by difficulties in recalling correctly events that happened up to 12 months before
the data collection process.

124 Biemer, P. (2010). Total Survey Error: Design, Implementation and Evaluation, The Public Opinion Quarterly, 74(5),
817-848.

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Appendix C. Comparison of sample types and interview mode
This appendix presents and discusses the distribution of respondents, as well as headline
figures of the survey, by sample type and mode of completion.
Comparison of the NatCen Panel and Push-to-web (P2W) samples
Table 22 presents the unweighted profile of respondents by sample type; the table includes
socio-economic and demographic characteristics found to be important predictors for
consumer detriment outcomes in the analysis. Four elements must be noted:
• The P2W sample and the Panel sample cover different populations. Both the
samples cover adults, that is people aged 18 or over, but they cover different
geographical areas: the NatCen Panel is a panel of adults living in Great Britain, while
the P2W sample was only employed in Wales (‘boost sample’ to allow for more precise
estimates in that region) and NI (which is not covered by the NatCen Panel).
• The P2W sample is younger than the Panel sample. Just under 1 in 5 (19%) Panel
respondents are less than 40 years old, while 28% of P2W respondents are below that
age. The NatCen Panel profile is slightly older than the P2W profile due to design
elements: the minimum age for recruitment in the Panel sample was 18 at the time of
the BSA interview and, due to natural ageing, earlier cohorts of BSA participants are
relatively older compared to later cohorts 125. Although younger participants are
incorporated in the NatCen Panel sample each year from new BSA studies, the
youngest age group (18-24 years) is under-represented in the unweighted data.
Additionally, for the P2W sample up to two people per household were invited to take
part in the study while within the Panel sample 126 only one person per household was
interviewed – given that larger households tend to include younger residents, the P2W
sample is more likely to include younger study participants.
• The P2W sample is more likely to use the internet frequently. As might be
expected, respondents recruited from the P2W survey were more likely to use the
internet several times a day compared to panellists (75% vs. 68%). This is correlated
with the different age profile of the two samples and with a different operationalisation of
the telephone (CATI) fieldwork. On the latter: for the Panel sample, all participants who
did not take part online in the first two weeks of fieldwork and for whom a telephone
number was held on record, were contacted by an interviewer to be offered a telephone
interview; for the P2W sample, the telephone option was only an opt-in one as
telephone numbers weren’t available in the sample – thus, it was impossible to actively
cover the offline population and those who simply prefer not to take part online within
the P2W sample.
• The P2W sample is ‘better off’ than the Panel sample. When asked how well they
would say they are managing financially these days, almost 1 in 3 (32%) P2W
respondents answered they are ‘living comfortably’ compared to 27% of panellists.
Looking at the other end of the scale, 8% of panellists said they are finding it quite or

125 Respondents who were aged 18 at the time of the BSA baseline interview, when interviewed for the Consumer
Detriment 2021 survey were: 23-24 years if recruited from BSA 2015; 22-23 years if recruited from BSA 2016; 21-22
years if recruited from BSA 2017; 20-21 years if recruited from BSA 2018, 19-20 years if recruited from BSA 2019.
126 Those recruited from the 2020 wave of BSA which, due to COVID-19 was conducted using a ‘push-to-web’ design.
Only participants aged 18-24 recruited from BSA 2020 were issued at this wave in order to refresh the sample.

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very difficult compared to 6% of respondents in the P2W sample. Similar patterns were
found when looking at other variables such as tenure or household income. This
difference is probably related to the different recruitment approach: the NatCen Panel
sample is almost entirely 127 recruited via a face-to-face survey which – thanks to the
presence of an interviewer – are generally more effective in achieving higher response
rates within economically deprived groups compared to recruitment approaches that are
not led by interviewers.
Table 22 – Respondents profile by sample type (unweighted)

NatCen Panel
P2W
Sex

Female
57%
56%
Male
43%
44%
Age

18-29
7%
9%
30-39
12%
18%
40-49
17%
18%
50-59
20%
18%
60-69
21%
19%
70+
22%
17%
Education level

Degree or equivalent, and above
49%
44%
Below degree or no qualification
51%
56%
Internet

Several times a day
68%
75%
Daily
25%
20%
Weekly or less
7%
5%
Subjective financial situation

Living comfortably
27%
32%
Doing alright
42%
43%
Just about getting by
22%
18%
Finding it quite difficult
5%
4%
Finding it very difficult
3%
2%
Economic activity

Full-time education
2%
2%
Paid work
47%
54%
Unemployed
3%
3%
Retired
30%
24%
Other
17%
17%
Country

England
88%
-
Scotland
7%
-
Wales
5%
47%
Northern Ireland
-
53%
Unweighted base NatCen Panel (5,084 - 5,098); Push-to-web (1,478 - 1,481)

Table 23 summarises the weighted and unweighted survey headline figures by sample type. In
the P2W sample a larger proportion of respondents did not experience any detriment from April
2020 to April 2021, and when they experienced it, they did so in a more limited number of
sectors.

127 This is true for all the Panel members invited to take part in the Consumer Detriment survey except for those aged
18-24 recruited from BSA 2020 (a very small part of the Panel sample). Due to COVID-19 the 2020 wave of BSA was
conducted using a ‘push-to-web’ design, so up to two people per household are in the panel sample for that cohort.

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The study also found that the incidence of detriment was lower in Wales and Northern Ireland
compared to England and Scotland. Given the Northern Ireland participants were entirely
recruited via the P2W survey and the Wales ones were boosted via it, additional analysis was
conducted to assess whether the sample of origin could explain the difference found. These
analyses were carried out on the Welsh respondents only as they were the only ones present
in both the Panel and P2W sample 128. The results suggest that the difference seen in the
unweighted figures (68% Panel vs. 61% P2W) was mitigated by the weights (61% Panel vs.
60% P2W). The data available therefore suggests that the differences in detriment incidence
by country does not seem to be affected by the sample type.
Table 23 – Survey key findings by sample type

Unweighted
Weighted

NatCen
Panel
P2W
NatCen
Panel
P2W
Overall detriment incidence

Experienced at least one detriment incident
70%
60%
70%
58%
No detriment
30%
40%
30%
42%
Number of sectors experienced detriment
in

One
31%
35%
31%
34%
Two
23%
24%
22%
25%
Three or more
46%
41%
47%
41%
Number of experiences of detriment per
person

Median
2
1
2
1
Net monetised detriment per incident

Mean
£251
£216
£239
£316
Median
£28
£28
£28
£28
Most common detriment type

Poor quality
Poor quality
Poor quality
Poor quality
Percentage of experiences of detriment
where the consumers did not take actions

15%
17%
18%
20%
Detriment incidence by country

England
70%
-
70%
-
Scotland
72%
-
72%
-
Wales
68%
61%
61%
60%
Northern Ireland
-
59%
-
56%
Comparison of web (CAWI) and phone (CATI) interview modes
Table 24 presents the profile of respondents by mode of completion. As might be expected
older participants, those who are retired, those who do not use the internet regularly and those
with a lower educational level were more likely to take part over the phone rather than online.

128 Of the total Welsh respondents, 25% were part of the Panel sample and 75% of the P2W sample (weighted
percentage: 27% vs. 73%).

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Table 24 – Respondents profile by mode of completion (unweighted)

Phone
Web
Sex

Female
57%
57%
Male
43%
43%
Age

18-29
4%
8%
30-39
7%
14%
40-49
10%
18%
50-59
12%
20%
60-69
20%
21%
70+
48%
19%
Education level

Degree or equivalent, and above
36%
49%
Below degree or no qualification
64%
51%
Internet

Several times a day
38%
72%
Daily
23%
24%
Weekly or less
39%
4%
Subjective financial situation

Living comfortably
31%
28%
Doing alright
38%
43%
Just about getting by
25%
21%
Finding it quite difficult
4%
5%
Finding it very difficult
2%
3%
Economic activity

Full-time education
1%
2%
Paid work
27%
51%
Unemployed
4%
3%
Retired
52%
26%
Unweighted base Phone (522 - 524); Web (6,039 - 6,054)
Table 25 summarises the survey headline figures by mode of completion. Over the phone a
larger proportion of respondents did not experience any detriment from April 2020 to April
2021, and when they experienced it, they did so in a more limited number of sectors. Some
differences were observed for the average net monetised detriment, but they were mitigated in
the weighted data. As per the analysis by sample type, no differences were found for the most
common type of detriment and the rate of ‘unactioned’ incidents by mode of completion.

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Table 25 – Survey key findings by mode of completion

Unweighted
Weighted

Phone
Web
Phone
Web
Overall detriment incidence

Detriment experienced
60%
68%
60%
70%
No detriment
40%
32%
40%
30%
Number of sectors experienced detriment in

One
37%
31%
35%
31%
Two
20%
24%
21%
22%
Three or more
44%
45%
44%
47%
Number of experiences of detriment per
person

Median
1
2
1
2
Net monetised detriment per incident

Mean
£335
£238
£248
£242
Median
£28
£28
£26
£28
Most common detriment type

Poor quality
Poor quality
Poor quality
Poor quality
Percentage of unactioned detriment

Percentage of experiences of detriment where the
consumers did not take actions
14%
15%
13%
18%

Differences between samples
The estimates presented in Table 24 and Table 25 do not indicate that the differences in
sampling strategies (Panel or P2W) or the operationalisation of a multi-mode design (web and
phone) have introduced significant elements of bias in the analysis and the findings of the
study. Variations in the estimates are particularly small and likely to be influenced by the
different socio-economic and demographic profiles in the samples (as shown in Table 22 and
Table 24, web respondents had a different socio-demographic profile compared to those who
took part on the phone, and the P2W approach covered a geographical area that was partly
different to the area covered by the NatCen Panel).

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Appendix D. Comparison with previous UK consumer detriment studies
The Consumer Protection Study presented in this report is not the first of its kind to be carried
out in the UK; indeed, other studies investigating consumer detriment in the UK through
quantitative surveys have taken place between 2008 and 2016. Key estimates vary
substantially between studies, but differences in methodology mean direct comparisons should
not be made.
Differences in the methodological approach
While the study presented in this report built upon the methodology of the two most recent
ones (2016 129 and 2014 130), there are significant methodological and analysis divergences
that should be kept in mind when comparing the figures presented in this report, even just with
those of the two most recent studies. Some key differences between these studies are
summarised in Table 26.
The main difference in terms of survey methodology is represented by the sampling strategy
adopted. While probability-based samples offer data with a known margin of error and the
findings can be inferred to the population of interest, quota samples have unknown statistical
error and the generalisation of the findings to the population of interest is tentative. Other
differences between these three studies (e.g. fieldwork length, survey mode) can be explained
by the different methodological approaches needed when working with probability-based
samples compared to convenience and nonprobability samples.
There are many differences also in the study design. To name some of them: the studies
collected information in very different ways (most recent or most serious problem; different
number of loops); used different classifications of sectors; included different types of sectors
(such as business services, which were not covered in this study); and used different
conceptualisations of consumer detriment (in 2021 it was expanded to include problems that
caused stress to the consumers, while previous studies focused on financial aspects only or on
having genuine cause for complaint).
The analysis design also differed between studies. The studies differed by denominators
used to calculate the incidence of detriment; followed different weighting strategies for scaling
up detriment to the UK population (some of the data used to build the detriment-level weights
in this study was not collected in previous studies); dealt in different ways with missing values
and outliers; and, finally, used different computations for net monetised detriment.

129 Oxford Economics. (2016). Consumer Detriment - Counting the cost of consumer problems. Citizens Advice.
https://www.citizensadvice.org.uk/Global/CitizensAdvice/Consumer%20publications/Final_ConsumerDetriment_OE.pd
f
130 Department for Business, Innovation & Skills. (2014). Consumer Engagement and Detriment Survey 2014.
https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/319043/bis-14-881-
bis-consumer-detriment-survey.pdf

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Table 26 – Key methodological differences in the most recent consumer detriment
surveys carried out in the UK

2021
2016
2014
Survey methodology
Sample type

Probability-based samples
(NatCen Panel and push-
to-web)
Quota samples (two
independently recruited
samples)
Quota sample
Survey mode
Sequential Web-CATI
Omnibus CAPI; Web
Omnibus CAPI
Fieldwork length
4.5 weeks
3 weeks (CAPI); 2 weeks
(Web)
2 weeks
Fieldwork geographical
coverage
United Kingdom
Great Britain
United Kingdom
Study design
Number of sectors
44 sectors
43 sectors
70 sectors
Survey loops about
specific detriment
incidents
Up to 3 sectors
Up to 5 sectors
Up to 2 sectors
Whether the
occurrence of multiple
incidents within sectors
was accounted for
Yes – respondents
reported the total number
of detriment incidents in
each sector
Yes – respondents
reported the total number
of detriment incidents in
each sector
No – respondents only
reported whether they
experienced at least an
incident in each sector
Selection criteria of the
detriment experiences
investigated in the
survey loops (if multiple
problems experienced
within one sector)
Most recent
Most recent
Most serious
Conceptualisation of
consumer detriment
Problems which caused
stress to the consumer,
cost them money, or took
up their time.
Problems which cost the
consumers money, or took
up their time, or both.
Problems where the
consumers felt they have
had a genuine cause for
complaint.
Differences in the key findings
The key findings show a strong variation across the consumer detriment studies carried out in
the UK. However, given the data does not allow differentiation between changes in the
experiences of UK consumers and changes influenced by varying methodological approaches,
one should not assume that the differences are a result of real change over time in the
population.
A clear example of why figures are incomparable is given by the estimated net monetised
detriment in the UK across the different studies. This study found that the cost of consumer
detriment in the UK, between April 2020 and April 2021, totalled to £54.2 billion, while previous
studies had all reported lower volumes of net monetised detriment 131. However, it is impossible
to say whether net monetised detriment in the UK has changed since 2016. Other than the
differences in survey methodology (primarily, sampling approach), the estimates of monetised
detriment are particularly affected by varying study design and analysis design.
Table 27 summarises the key findings for this study and the one carried out in 2016. It is worth
noting that the 2016 study found significant differences also between the two different samples

131 The difference is particularly marked between earlier studies (which estimated a monetised detriment of £4.15 billion
in 2014, £3.08 billion in 2012, and £6.62 billion in 2008) and later studies (the 2016 study estimated monetised
detriment to be as high as £22.9 billion a year, yet lower than this 2021 study, at £54.2 billion).

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used (the key findings strongly diverged between the Omnibus CAPI and the Web samples,
thus they were kept separated in the report).
Table 27 – Key findings of the 2016 and 2021 consumer detriment studies

2021
2016
(Omnibus
CAPI)
2016
(Web)
Estimated number of detriment incidents
229.8 million
123 million
380 million
Percentage of consumers who experienced at
least one detriment incident
69%
35%
67%
Number of detriment incidents per person
Average: 6.4
Median: 4
Average: 2.4
Average: 7.4
Average/median monetised cost per detriment
incident
Average: £242
Median: £28
Average: £159
Not reported
Total net monetised detriment
£54.2 billion
£22.9 billion
£42.9 billion
Most common problem experienced with the
product (item/service)
Poor quality
Poor quality
Poor quality
Note: The data of the two studies does not allow analysis to differentiate between changes in the experiences
of UK consumers from changes influenced by varying methodological approaches – thus from this table it
should not be concluded that differences between the two studies are a result of real change over time in the
population.

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Appendix E. Analysis
Levels of data
The analysis in the report has been carried out using two different levels of data:
• Respondent-level data that was weighted to be representative of the UK population
aged 18 or above. The analysis was carried out using information collected in the first
section of the questionnaire (consumer attitudes, having consumed products in the
sectors, having experienced detriment), summary variables from the detriment part of
the questionnaire, and demographics and socio-economic characteristics.
• Detriment-level data that was weighted to be representative of all the experiences
of detriment in the UK in the period of interest. The analysis was carried out using
information collected in the detriment section of the questionnaire (variables linked to
single experiences of detriment).
The second and fifth chapters were written using detriment-level data, while the third chapter’s
analysis was carried out on respondent-level data. Data from both levels was used in the first
and the fourth chapters.
The two levels of data were kept separated in this report (e.g. consumers’ demographics and
socio-economic characteristics were not used in the analysis of detriment-level data), in order
to avoid the misestimation of standard errors. Between-level analysis would have required the
use of methods that can account for clustering and nested observations (such as multilevel
models), which were deemed to be too complex for a descriptive report and outside of the
study’s scope.
Classification of sectors and market clusters
The classification of the sectors used in this study primarily followed the methodology used in
the EU monitor of consumer markets 132 but was also influenced by the classification used in
the previous study 133 and by the COICOP classification used by the UN 134 and adopted by the
ONS 135. Finally, it was adapted following input from BEIS and the CPP group.
In particular, this report follows the hierarchical system employed by the EU (sectors are
grouped in market clusters) but differently from the EU system, market clusters are not divided
into services vs. items (product type); in this report, such classification happens at a sector
level (a market cluster can include both services and items, while each sector includes either
services or items, but not both).

132 See Consumers, Health, Agriculture and Food Executive Agency. (2018). Monitoring consumer markets in the
European Union 2017 – Final report Part 1. European Commission. Available at: https://op.europa.eu/en/publication-
detail/-/publication/92a1589a-fc2f-11e8-a96d-01aa75ed71a1 Previous editions available at:
https://ec.europa.eu/info/publications/market-monitoring-survey-previous-editions_en
133 Oxford Economics. (2016). Consumer Detriment - Counting the cost of consumer problems. Citizens Advice.
Available at:
https://www.citizensadvice.org.uk/Global/CitizensAdvice/Consumer%20publications/Final_ConsumerDetriment_OE.pd
f
134 Department of Economic and Social Affairs. (2018). Classification of Individual Consumption
According to Purpose (COICOP) 2018. UN. Available at:
https://unstats.un.org/unsd/classifications/unsdclassifications/COICOP_2018_-_pre-edited_white_cover_version_-
_2018-12-26.pdf
135 Office for National Statistics. (2021, Sep 30). User guide to consumer trends. Available at:
https://www.ons.gov.uk/economy/nationalaccounts/satelliteaccounts/methodologies/consumertrendsuk

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Overall, the study classification included 11 market clusters and 44 sectors. Of the latter, 12
were item sectors and 32 service sectors (see Table 28).
Table 28 – Classifications of sectors and market clusters
Market cluster
Survey category
Analysis label
Product
type
Automotive
goods and
services
New cars or other new vehicles
New vehicles
Items
Second-hand cars or other second-hand
vehicles
Second-hand vehicles
Fuel, accessories, and maintenance
equipment for vehicles
Fuel and accessories for vehicles
Vehicle maintenance and repair services
Vehicle maintenance and repair
Services
Banking and
insurance
Current accounts, loans and bank
services
Current accounts, loans and bank
services
Services
Pension funds and investment services
Pension funds and investment services
Insurance services
Insurance services
Fast-moving
retail
Food and drink, including alcohol, for
consumption at home (not takeaways)
Groceries and drinks
Items
Clothing, footwear and fashion
accessories
Clothing, footwear and accessories
Cleaning or maintenance items and tools
for the home or garden
House and garden maintenance
products
Stationery, books, magazines and
newspapers
Stationery, books, magazines and
newspapers
Toiletries, cosmetics, hair products and
beauty appliances
Personal care products
Prescription and non-prescription
medicines
Prescription and non-prescription
medicines
Services
Hair, beauty, and wellness services
Personal care services
Housing-related
services
Real estate purchases and related
services
Real estate services
Services
Renting a home and associated services
Renting services
Home and garden maintenance and
repair services
Home and garden maintenance and
repair
Removal and storage services
Removal and storage
Other services
Legal, financial advice, and accountancy
services
Legal and accountancy services
Services
Funeral services
Funeral services
Veterinary services
Veterinary
Pet breeder
Pet breeder
Personal
services
Private medical services and dental
services
Private medical and dental services
Services
Carers, nursing homes and other adult
care services
Adult care
Private and higher education fees and
services
Education fees
Childcare services
Childcare
Table continues on the next page

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Table 28 – Classifications of sectors and market clusters
Market cluster
Survey category
Analysis label
Product
type
Recreational
services
Hotels and holiday accommodation
Hotels and holiday accommodation
Services
Package holidays and tours
Package holidays and tours
Cafés, fast-food, restaurants and take-
away services, bars, pubs and nightclubs
Restaurants, cafés and take-away
Sport, cultural and entertainment activities,
memberships or events
Sport, cultural and entertainment
activities
Gambling and lottery services
Gambling and lottery services
(Semi-) durable
goods
Glasses (spectacles) and lenses
Spectacles and lenses
Items
Furniture, furnishings, domestic appliances
and fixtures
Furniture and appliances
Electronic devices and software, including
computers, phones, media devices
Electronic devices and software
Entertainment items including musical
instruments, toys, sporting and hobby
equipment
Entertainment items
Telecoms and
other digital
subscriptions
Mobile telephone services and data plans
Mobile telephone services
Services
Landline telephone services
Fixed telephone services
Internet provision services
(excluding mobile phone data plans)
Internet provision
Satellite, cable or streaming TV or other
digital subscriptions (music, gaming, etc.)
TV and other digital subscriptions
Transport
Vehicle rental services
Vehicle rental
Services
Public transport and train services
Public transport and trains
Airline services
Airline
Utilities
Water services
Water services
Services
Electricity and gas services
Electricity and gas services
Detriment types
In the detriment incident section of the questionnaire, study participants were asked to select
the original cause of the detriment from a list of nine options (detriment types). The categories
were worded in different ways, depending if the reported incident occurred in an item sector or
a service sector. The wording used in the survey and the corresponding analysis labels are
summarised in the table below (Table 29).
Table 29 – Analysis labels and survey categories of types of detriment
Analysis label
Detriment type (item)
Detriment type (service)
Poor quality
The item was of a lower quality or didn’t
function/look as advertised
The service was of a lower quality or didn’t do
what was advertised
Not usable
The item was faulty, unsafe or broken
The service was unsafe or didn’t work
Problems with delivery
The item arrived late or there were other
problems with the delivery
The service was provided late or took longer
than expected
Complete failure to
provide
I never received the item
The service was not provided / available when I
needed it
Misleading pricing
The price charged was more than
advertised
The price charged was more than advertised
Misleading information
I was not provided with all relevant
information about the item before
purchasing
I was not provided with all relevant information
about the service before purchasing

Tables continues on the next page

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130

Table 29 – Analysis labels and survey categories of types of detriment
Analysis label Detriment type (item)
Detriment type (service)
Unfair or unclear T&C
The terms & conditions of the purchase
were unclear or unfair
The terms & conditions of the purchase were
unclear or unfair
Warranty and
guarantees not
honoured
The seller or manufacturer did not honour
a warranty or guarantee
The service provider did not honour a warranty
or guarantee
Other
Other problem
Other problem
Net monetised detriment
The net monetised detriment used in this report is a composite variable resulting from the sum
of four cost-bearing elements (initial cost of the product, cost of replacing or fixing the product,
other cost and monetised time cost) minus the sum of mitigating factors (value of having the
product refunded or fixed, the use value of the product and other compensations received):
𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀
= (𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼+ 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅+ 𝑂𝑂𝑂𝑂ℎ𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒+ 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇) −(𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅
+ 𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈+ 𝑂𝑂𝑂𝑂ℎ𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒)
The seven components used in the formula were complex constructs derived using detriment-
level data collected in the survey. Each component was derived using continuous data, which
often included a large number of missing values and a small number of heavy and influential
outliers.
Several methodologies were considered for the management of missing values. Multiple
imputation was deemed inappropriate given the conditions and patterns of missingness and
the budget for the study (the demand of resources would not have justified the very small
expected improvement of data quality); the prediction of missing values using other
multivariate approaches, such as median regressions and multilevel median regressions, were
found to return results that did not significantly differ from approaches that did not require
statistical modelling; eventually, the approach employed in the study was the replacement of
missing values with measures of centrality by key subgroups.
Medians were calculated for each purchase type (ongoing or one-off) within each sector (or
only by sector when the sample size was too small) and these values were used to replace the
missing values, so that all responses could be included in the analysis. This approach was
taken both for some of the source variables of the components and for some components, as
discussed in detail in the following sections. This solution was proportionate to the level of
observed error in the data and to what was possible within the scope of the study.
Outliers were also an issue in the 2016 consumer detriment study 136. Similar to the previous
study, the assumption was that the most extreme values seen in each market cluster were
caused by measurement errors, but a more conservative and cautious approach was taken for
this study.
Given the research objectives of the study, it was not possible to code outliers as missing
values, as it would have systematically removed extreme observations from the estimation of
net monetised detriment. Trimming (i.e. recoding values above a specific threshold to be equal

136 Oxford Economics. (2016). Consumer Detriment - Counting the cost of consumer problems. Citizens Advice.
Available at:
https://www.citizensadvice.org.uk/Global/CitizensAdvice/Consumer%20publications/Final_ConsumerDetriment_OE.pdf

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to the threshold) was tested at different percentiles of the data (99.9th, 99.5th, 99th and 95th
percentile) and a sensitivity analysis was carried out on the estimates and the distribution
resulting from each test. The 99.9th threshold, by market cluster, was found to reduce the
range of the final distribution, while maintaining the integrity of the original data.
Five of the components of monetised detriment were trimmed at the 99.9th percentile within
each market cluster (that is, any values above the 99.9th percentile were recoded to be equal
to the 99.9th percentile). The other two components, UseValue and RefundFix, were computed
using the already-trimmed version of InitialCost. This meant that trimming was performed on
both sides of the net monetised detriment formula, reducing the range of the net monetised
detriment distribution above and below zero.
Within each component, the number of trimmed cases was c. 15 (generally 1 case in each
market cluster – up to 2 or 3 cases in the largest clusters). In total, the trimming affected 78
cases across all components (0.8% of all the observations).
Initial cost of the product (InitialCost)
InitialCost was created by combining Value (derived variable which included the value of
products from one-off purchases and the annualised value of products from ongoing
purchases 137) and Detln (length of detriment). Missing values for Value and Detln were
replaced with the median measurements across the mentioned key subgroups (sector and
purchase type) ahead of the derivation:
• If the product has an ongoing cost and the length of the detriment is greater than 0
(Detln > 0), InitialCost is the Value for the weeks of detriment ((Value/52)* Detln).
• If the product has an ongoing cost and the length of the detriment is 0 weeks (Detln =
0), InitialCost is the Value for one week (Value/52).
• If the product has a one-off cost, InitialCost is the Value (Value).
The small number of missing values in the InitialCost variable were replaced with the median
measurement across sectors; the variable was subsequently trimmed at the 99.9th percentile.
Cost of replacing or fixing the product (RepFixBuyer)
Questions about replacing or fixing the product were only asked for incidents of detriment
where the need to get a replacement or pay to fix the problem was considered feasible, i.e. if
the participants reported problems with the quality of the product, a faulty product, that the
product was never received, or when the ‘other’ option was selected.
In the derivation of RepFixBuyer, account was taken for the fact that the replacement or refund
could also be offered by the seller after the consumer had already paid for replacing or fixing
the problem by themselves. When a replacement or a refund was offered by the seller, the
costs experienced by the consumer for the replacement or fixing of a product themselves – if
any – were considered equal to 0. This is because the study design could not compute the
additional marginal value of having fixed/replaced the product on top of the
refund/replacement/fix received by the seller and it was preferred to consider the consumer
action as a separate event from the original detriment.
Therefore, RepFixBuyer was derived using data from Payfixrepl (whether the consumer fixed
or replaced the product at their own expenses), Payfixreplcost (the cost of doing so),

137 Based on apr21_ongoingoneoffloop_ongoingoneoff_q, apr21_oneoffcostloop_oneoffcost_q,
apr21_ongoingcostmqyloop_ongoingcostmqy_q and apr21_ongoingcostloop_ongoingcost_q.

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Refsummary (information about having received a refund by the seller), Replfix (having
received a replacement of having had the product fixed by the seller) and the type of detriment
problems experienced by the consumer 138. Missing values on Payfixreplcost were replaced
with the median measurements across key subgroups (sector and purchase type) ahead of the
derivation:
• If the consumer paid to fix or replace the product (Payfixrepl = 1), did not receive any
refund from the seller (Refsummary = 3,4) and the seller did not replace or fix the
product (Replfix = 2,3), the value of RepFixBuyer is the amount of money spent by the
study participant to fix or replace the product (Payfixreplcost).
• If the only types of detriments were late deliveries/problems with deliveries (DetType03
= 1) or price problems (DetType05 = 1) or information asymmetry (DetType06 = 1) or
problems with T&C/warranty (DetType07 = 1, DetType08 = 1), the value of RepFixBuyer
is set to 0.
• If the consumer received a full or partial refund (Refsummary = 1,2) and/or the product
was replaced or fixed by the seller (Replfix = 1), the value of RepFixBuyer is set to 0.
• If the consumer did not pay to fix or replace the product (Payfixrepl = 2,3), the value of
RepFixBuyer is set to 0.
The small number of missing values in the RepFixBuyer variable were replaced with the
median measurement across sectors; the variable was subsequently trimmed at the 99.9th
percentile.
Other consumer costs (OtherCost)
OtherCost was derived from Costestimate (cost consumers reported having faced as a
consequence of the incident of detriment) and Costexp_q6 (whether they have experienced
any costs other than paying for replacing or fixing the product as a consequence of detriment).
Missing values on Costestimate were replaced with the median measurements across key
subgroups (sector and purchase type) ahead of the derivation:
• If the respondent faced additional costs (Costexp_q6 = 0), OtherCost is the reported
cost (Costestimate),
• If the respondent did not incur any additional costs (Costexp_q6 = 1), OtherCost is set
to 0.
The small number of missing values in the OtherCost variable were replaced with the median
measurement across sectors; the variable was subsequently trimmed at the 99.9th percentile.
Monetised time cost (TimeCost)
The variable TimeCost was the product of the standardised time in minutes (TimeSpentM 139)
and the 1/60th of the hourly monetised cost coefficient, equal to £13.87. Missing values on
TimeSpentM were replaced with the median measurements across key subgroups (sector and
purchase type) ahead of the derivation:
• The product of time in minutes (TimeSpentM) and the monetised coefficient (£13.87/60).
The small number of missing values in the TimeCost variable were replaced with the median
measurement across sectors; the variable was subsequently trimmed at the 99.9th percentile.

138 Based on the DetType variables, derived from Dettypegd and Dettypeser.
139 Derived from Timemh and Time.

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The hourly time coefficient was computed starting from the value estimated by HMRC in
2010 140, which was subsequently updated to account for changes in employment rates 141 and
inflation.
Value of having the product refunded or fixed (RefundFix)
RefundFix was computed using InitialCost (the trimmed and imputed component of monetised
detriment representing the initial cost of the product or service) as well as Replfix (having
received a replacement or having had the product fixed by the seller), Refsummary
(information about having received a refund by the seller) and Partialref (value of the partial
refund received by the consumer). Missing values on Partialref were replaced with the median
measurements across key subgroups (sector and purchase type) ahead of the derivation:
• If received a full refund (Refsummary = 1), the RefundFix value is the cost of the product
(InitialCost).
• If the product was fixed or replaced by the seller (Replfix = 1) the RefundFix value is the
cost of the product (InitialCost).
• If received a partial refund (Refsummary = 2) of the same value of the product or greater
(Partialref >= InitialCost), the RefundFix value is the cost of the product (InitialCost).
• If received a partial refund (Refsummary = 2) of less than the initial value of the product
(Partialref < InitialCost), the RefundFix value is the value of the partial refund
(Partialref).
• If the product was not fixed or replaced (Replfix = 2, 3) and did not receive a refund
(Refsummary = 3, 4), RefundFix is 0.
The small number of missing values in the RefundFix variable were replaced with the median
measurement across sectors.
Use value of the product (UseValue)
Similar to RefundFix, UseValue also used InitialCost (the trimmed and imputed component of
monetised detriment). Its derivation was also based on the type of detriment problems
experienced by the consumer 142, Subjectivevalue (how much the consumer thought the
product was worth), Replfix (having received a replacement or having had the product fixed by
the seller) and Refsummary (information about having received a refund by the seller). Missing
values on Subjectivevalue were replaced with the median measurements across key
subgroups (sector and purchase type) ahead of the derivation:
• If the detriment type is problem of low quality (DetType01 = 1) or a faulty product
(DetType02 = 1) or Other (DetType09 = 1), and the product was received (DetType04 =
0), the UseValue is the subjective value assigned to the product (SubjectiveValue).
• If the only detriment types were problems with delivery (DetType03 = 1) or misleading
price (DetType05 = 1) or misleading information (DetType06 = 1) or unfair/unclear T&C
(DetType07 = 1) or warranty and guarantees not honoured (DetType08 = 1), the
UseValue is the value of the product (InitialCost).
• If the product was never received (DetType04 = 1), the UseValue is set to 0.

140 Hill, A., & Noti, J. (2010). Costing Customer Time - Research Paper. HM Revenue & Customs. Available at:
https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/330346/cost-of-
time.pdf
141 The coefficient computed using this methodology is weighted to account for the proportion of in-work and non-
working individuals.
142 Based on the DetType variables, derived from Dettypegd and Dettypeser.

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• If the consumer received a full refund (Refsummary = 1) or the problem was fixed or
replaced by the trader (Replfix = 1), the UseValue is set to 0.
The small number of missing values in the UseValue variable were replaced with the median
measurement across sectors.
Other compensations received (OtherComp)
OtherComp was derived from Refreceivedapprox (value of additional compensations received)
and Compobt (lists of monetary and non-monetary compensations, and whether they were
received by the consumer). Missing values on Refreceivedapprox were replaced with the
median measurements across key subgroups (sector and purchase type) ahead of the
derivation:
• If received a monetary compensation (CompObt1 = 1 or CompObt2 = 1 or CompObt3 =
1 or CompObt4 = 1 or CompObt8 = 1), OtherComp is the reported compensation
received (RefReceivedApprox).
• If did not receive a monetary compensation (CompObt1 = 0 and CompObt2 = 0 and
CompObt3 = 0 and CompObt4 = 0 and CompObt8 = 0) or did not receive any
compensation (CombtObt9 = 1), OtherComp is set to 0.
• If the compensation was not obtained (CompObt9 = 1), the OtherComp is 0.
The small number of missing values in the OtherComp variable were replaced with the median
measurement across sectors; the variable was subsequently trimmed at the 99.9th percentile.
Detriment resolution
Resolution patterns were discussed in the report comparing what the consumers asked the
sellers or service providers to do, and what the sellers or service providers did in response.
The variables used in the analysis (SuccessRes) were computed looking at all the actions, with
the exception of apologies, providing an explanation for the problem and undefined ‘other’
actions. Apologies and providing an explanation were excluded because they were the only
actions without potential monetary impact, and they were commonly asked by the consumers
and generally offered by the sellers or service providers. ‘Other’ undefined actions were
excluded because it was not possible to appropriately match undefined actions requested by
the consumers against what was offered by the sellers or service providers and undefined
actions offered by the sellers or service providers against what was requested by the
consumers.
The derivation of SuccessRes was computed in two stages. Firstly, temporary variables were
computed for each of the actions below:
• Refund (partial or full).
• Product replacement or fix.
• Voucher or store credit.
• Compensation for extra costs or inconvenience incurred.
• One-off or longer-term discount.
• Review or change of terms and conditions.
For each action, the temporary variable reported the status of the consumer request and of the
seller/provider action:
• Did not ask for it.

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• Asked for it and did not obtain it.
• Asked for it and obtained it.
• Did not ask for it but obtained it.
Actions offered to be delivered in the future were not considered in the computation (e.g.
promises of future refunds were coded as missing values).
Secondly, these six temporary variables were combined in the final variable (SuccessRes),
which included the categories below:
• Did not ask for anything, received nothing.
• Received what asked.
• Received what asked and something else.
• Did not ask for anything, received something.
• Asked for something, received something else and/or less than asked.
• Asked for something, received nothing.
Incidence of consumer detriment in the different sectors
Table 30 summarises the incidence of consumer detriment in the different sectors covered in
this study, its 95% confidence interval (lower and upper bounds) and the unweighted number
of observations in the data. The incidence in the table has been computed using two different
denominators; the first set of columns presents the incidence of detriment over the number of
consumers in the sector (this is the measure discussed in the report); the second set reports
the incidence based on the total population. For example, 19% of consumers who purchased,
or used, new vehicles experienced detriment, but only 1% of the UK adults experienced
detriment with new vehicles.
Table 30 – Incidence of consumer detriment in the different sectors
Sector
Denominator = consumers
Denominator = population
%
Low
CI
Upp.
CI
Uwtd
n
%
Low
CI
Upp
CI
Uwtd
N
Airline
36%
28%
44%
463
3%
2%
3%
6,582
Package holidays and tours
35%
28%
43%
429
2%
2%
3%
6,582
Second-hand vehicles
30%
26%
35%
1011
5%
4%
6%
6,582
Internet provision
29%
27%
31%
5,035
20%
18%
21%
6,582
Real estate services
26%
20%
32%
364
1%
1%
2%
6,582
Electronic devices and software
26%
23%
28%
3,655
14%
13%
16%
6,582
Clothing, footwear and accessories
24%
22%
26%
5,321
19%
17%
21%
6,582
Adult care
21%
12%
30%
140
0%
0%
1%
6,582
Furniture and appliances
21%
19%
23%
3,709
11%
10%
13%
6,582
New vehicles
19%
13%
24%
440
1%
1%
1%
6,582
Education fees
19%
13%
24%
369
1%
1%
2%
6,582
Vehicle maintenance and repair
17%
15%
19%
3,840
9%
8%
10%
6,582
TV and other digital subscriptions
17%
15%
18%
4,273
11%
10%
12%
6,582
Electricity and gas services
15%
14%
17%
5,670
12%
11%
14%
6,582
Spectacles and lenses
15%
12%
17%
2435
5%
4%
6%
6,582
Hotels and holiday accommodation
14%
12%
17%
1,659
3%
3%
4%
6,582
Legal and accountancy services
14%
10%
17%
1,056
2%
1%
2%
6,582
Childcare
13%
9%
18%
393
1%
1%
1%
6,582
Table continues on the next page

Consumer Protection Study 2022
136

Table 30 – Incidence of consumer detriment in the different sectors
Sector
Denominator = consumers
Denominator = population
%
Low
CI
Upp.
CI
Uwtd
n
%
Low
CI
Upp
CI
Uwtd
N
Renting services
13%
11%
16%
1,189
3%
2%
4%
6,582
Mobile telephone services
13%
11%
14%
5,539
10%
9%
12%
6,582
Public transport and trains
13%
10%
15%
1,430
4%
3%
4%
6,582
Groceries and drinks
12%
10%
13%
6,182
11%
10%
12%
6,582
Home and garden maintenance and repair
11%
9%
13%
2,096
3%
2%
3%
6,582
Veterinary
11%
9%
13%
1,743
3%
2%
3%
6,582
Entertainment items
11%
9%
13%
3,034
5%
4%
6%
6,582
Removal and storage
11%
6%
15%
374
1%
0%
1%
6,582
Private medical and dental services
10%
8%
13%
1,793
2%
2%
3%
6,582
Fixed telephone services
10%
9%
12%
3,976
5%
4%
6%
6,582
Vehicle rental
10%
5%
15%
277
0%
0%
1%
6,582
Funeral services
10%
6%
14%
327
0%
0%
1%
6,582
Pet breeder
9%
3%
16%
149
0%
0%
0%
6,582
Insurance services
9%
8%
11%
4,218
5%
4%
6%
6,582
Fuel and accessories for vehicles
9%
8%
10%
4,546
6%
5%
6%
6,582
Prescription and non-prescription medicines
9%
7%
11%
2,526
4%
3%
4%
6,582
Restaurants, cafés and take-away
9%
7%
10%
4,286
6%
5%
7%
6,582
Current accounts, loans and bank services
8%
7%
10%
4,428
5%
4%
6%
6,582
Sport, cultural and entertainment activities
8%
6%
10%
2,035
3%
2%
3%
6,582
House and garden maintenance products
7%
6%
8%
5,040
5%
5%
6%
6,582
Pension funds and investment services
6%
4%
8%
2,514
2%
1%
3%
6,582
Water services
5%
4%
6%
4,539
4%
3%
4%
6,582
Personal care products
5%
4%
6%
5,732
4%
3%
5%
6,582
Stationery, books, magazines and newspapers
4%
3%
5%
4,666
3%
2%
3%
6,582
Gambling and lottery services
4%
2%
5%
1,963
1%
1%
2%
6,582
Personal care services
3%
2%
5%
3,153
1%
1%
2%
6,582

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137

Components of net monetised detriment by sector
The monetised detriment formula relies on components that offset each other’s value (the strongest interdependency can be found
between original cost, use value and value of the refund/replacement received). Therefore, any interpretations and comparisons of the
values of the individual components should take into account this element. Table 31 offers a comparison of the values of the different
components by sector (figures in million £).
Table 31 – Components of net monetised detriment by sector (million £)
Sector
UWTD
base
(N)
Net
monetised
detriment
Original
cost
Replacing or
fixing the
product
Additional
cost
Time
cost
Use
value
Value of the
refund/
replacement
received
Other
compensations
Renting services
88
7,422
9,296
4
547
655
1,945
1,124
10
Vehicle maintenance and repair
409
6,967
8,969
1,537
2,244
1,623
2,629
4,685
93
Second-hand vehicles
184
4,061
14,247
875
1,011
1,569
5,972
7,212
457
Internet provision
1025
3,434
1,900
85
683
1,847
150
726
206
Electricity and gas services
522
2,219
1,860
26
375
1,765
766
851
190
Education fees
43
2,188
2,674
108
751
162
1,138
369
1
Pension funds and investment services
71
2,098
6,049
0
325
250
1,300
358
2,868
Real estate services
68
2,089
64,106
18
1,562
149
44,143
19,561
43
TV and other digital subscriptions
517
2,040
1,061
23
638
1,175
101
647
109
Electronic devices and software
593
1,865
3,076
109
343
1,320
1,232
1,621
130
Furniture and appliances
569
1,839
5,314
118
391
1,248
1,997
3,040
195
Home and garden maintenance and repair
148
1,365
4,637
301
272
355
3,322
749
129
Clothing, footwear and accessories
822
1,362
2,172
141
106
921
254
1,548
176
Insurance services
229
1,309
955
462
598
313
377
347
294
Mobile telephone services
418
1,231
664
27
310
784
138
302
113
Veterinary
137
1,164
976
59
574
452
464
320
113
Personal care products
149
1,158
186
6
18
1,145
96
91
10
Current accounts, loans and bank services
224
1,075
2,282
131
742
298
261
2,015
103
Legal and accountancy services
80
843
1,162
25
136
588
714
316
38
Fixed telephone services
277
765
272
115
97
499
25
143
50
Fuel and accessories for vehicles
202
721
1,780
52
70
485
325
1,290
50
Private medical and dental services
106
639
676
88
191
281
424
140
34
New vehicles
52
577
7,164
28
124
112
3,304
3,478
69
Hotels and holiday accommodation
180
523
3,990
28
245
118
556
3,043
259
Groceries and drinks
426
455
665
16
100
355
176
398
107
Public transport and trains
126
420
299
4
110
362
119
148
87
Table continues on the next page

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Table 31 – Components of net monetised detriment by sector (million £)
Sector
UWTD
base
(N)
Net
monetised
detriment
Original
cost
Replacing or
fixing the
product
Additional
cost
Time
cost
Use
value
Value of the
refund/
replacement
received
Other
compensations
Airline
125
409
1,438
47
196
100
268
1,050
53
Sport, cultural and entertainment activities
110
396
243
11
105
182
21
102
22
Spectacles and lenses
224
392
576
75
163
163
161
394
30
Childcare
35
384
580
26
98
175
252
219
23
House and garden maintenance products
222
300
431
22
51
204
176
219
13
Entertainment items
182
238
553
6
70
154
132
346
66
Prescription and non-prescription medicines
134
190
85
2
22
137
17
28
11
Water services
135
189
134
23
123
119
37
69
104
Package holidays and tours
116
162
4,221
32
88
120
998
2,878
423
Stationery, books, magazines and newspapers
83
90
30
-
3
83
14
8
4
Restaurants, cafés and take-away
191
70
171
2
15
49
39
104
24
Personal care services
56
39
78
1
6
21
4
56
6
Gambling and lottery services
44
34
274
1
8
21
264
5
0
Funeral services
23
*
*
*
*
*
*
*
*
Removal and storage
22
*
*
*
*
*
*
*
*
Adult care
21
*
*
*
*
*
*
*
*
Vehicle rental
15
*
*
*
*
*
*
*
*
Pet breeder
13
*
*
*
*
*
*
*
*
Base: All detriment experiences in the UK in the 12 months to April 2021.
* Unweighted count too small for population estimates (n < 25).
Output of regression models
Multivariate analysis was carried out in the third chapter of this report, where the likelihood of having experienced detriment at least once
in the 12 months of the study was analysed across a range of consumer activity measures, socio-economic elements and demographic
characteristics. The table below (Table 32) reports the odds ratio, significance level, unweighted count and pseudo-R2 for the 10 models
used in the analysis.

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Table 32 – Outputs of regression models

Model 1
Model
2a
Model
2b
Model
3a
Model
3b
Model
4a
Model
4b
Model
5a
Model
5b
Model 6
A
B
A+B
C
A+C
D
A+D
E
A+E
A+B+C+D+E
Constant
0.16*
1.1
0.11*
2.38*
0.16*
1.27*
0.11*
0.77*
0.1*
0.09*
Sectors where consumed products (numeric)

Number of sectors
1.11*

1.12*

1.12*

1.13*

1.12*
1.12*
Sectors where consumed products (category)

10 or less (reference category)
-

-

-

-

-
-
11-15
1.02

1.03

0.99

1.05

1.01
1.03
16-20
1.16

1.11

1.12

1.14

1.07
1.07
21-25
1.2

1.05

1.15

1.11

1.01
1.02
26 or more
1.24

1.01

1.16

1.19

0.99
1.09
Household income

£1,410 pm or less (reference category)
-

-

-

-

-
-
£1,411 - 2,560 pm
1.09

0.99

1.09

1.02

0.96
0.96
£2,561 - 4,350 pm
1.15

1

1.15

1.05

0.95
0.94
£4,351 or more pm
1.26

1

1.27

1.1

0.96
0.96
Financial condition

Living comfortably (reference category)
-

-

-

-

-
-
Doing alright
1.24

1.15

1.23

1.16

1.16
1.17
Just about getting by
1.6*

1.41*

1.56*

1.42*

1.45*
1.49*
Finding it difficult
3.73*

3.08*

3.62*

2.92*

3.12*
3.05*
Whether compares prices before making a purchase

Does not make price comparisons (reference category)
-

-

-

-

-
-
Makes price comparisons
1.52*

1.53*

1.54*

1.53*

1.49*
1.49*
Age groups

70 and above (reference category)

60-69

1.27*
1.09

1.28
1.05
1.17
1.04
1.03
50-59

1.72*
1.28*

1.74*
1.23
1.46*
1.18
1.12
40-49

2.02*
1.53*

2.12*
1.45*
1.66*
1.4*
1.31
30-39

2.87*
2.11*

2.99*
1.89*
2.34*
1.91*
1.63*
18-29

1.86*
2.09*

1.88*
1.98*
1.44*
1.81*
1.68*
Level of education

Below university degree (reference category)

-
-

-
-
-
University degree or above

1.61*
1.35*

1.48*
1.32*
1.27*
Urban/Rural areas

Urban area (reference category)

-
-
-
-

-
Rural area

0.82*
0.75*
0.9
0.82*

0.85
Table continues on the next page

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Table 32 – Outputs of regression models

Model 1
Model
2a
Model
2b
Model
3a
Model
3b
Model
4a
Model
4b
Model
5a
Model
5b
Model 6
UK Country

England (reference category)

-
-

-
Scotland

1.14
1.37

1.49*
Wales

0.69*
0.75*

0.75*
Northern Ireland

0.58*
0.63*

0.53*

Ethnic groups

White British (reference category)

-
-

-
Any other White background

1.64*
1.56*

1.64*
Black, Asian, Mixed or other ethnic groups

1.26
1.58*

1.51*
Frequency of internet use

Weekly or less (reference category)

Daily

1.35
1.13
1.14
Several times a day

2.07*
1.44
1.52*
Unweighted observations
6,246
6,504
6,241
6,520
6,246
6,428
6,168
6,503
6,240
6,165
Pseudo-R² (McFadden)
0.1
0.03
0.12
0
0.1
0.04
0.13
0.04
0.12
0.14
* Statistically significant (p < 0.5).

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Appendix F. Reporting conventions
The analysis and this report applied the following conventions:
Rounding
To improve readability, and because differences smaller than one percentage point will not be
meaningful, percentages are presented to zero decimal points. As a result, figures may not
sum to 100%.
Bases
All reported base sizes (i.e. the number of cases on which the analysis is based) are
unweighted and exclude those who refused to answer or selected the option ‘Don’t Know’
(unless these options were presented up-front). Small sample sizes reduce the reliability of
estimates, and it is indicated where results should be treated with caution. Figures based on a
sample size of 25-50 cases are marked with an asterisk. Figures based on a sample size of
less than 25 are not presented.
Significance testing
All findings have been tested for statistical significance, and all differences reported are
statistically significant unless stated otherwise. Statistical testing was conducted at the 95%
confidence level 143.
Percentages
Some tables and figures in the report relate to questions with mutually exclusive responses. In
these tables, percentages will generally sum to 100; however, some percentages will not sum
exactly to 100% because of rounding. In addition, percentages will not sum to 100% for
questions where respondents could choose multiple responses.

143 This means that, if new samples were drawn from our population of interest, 19 out of 20 times the results of the
analysis would be consistent with the results presented in this report and that our findings are unlikely to be caused by
random variations in the sample.

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Appendix G. Questionnaire specification
A unimodal questionnaire design approach was adopted for the development of the survey
instrument. This means that differences between the web and telephone survey have been
actively minimised. Differences between the two modes, when unavoidable, are marked in the
questionnaire specification presented below.

START SURVEY.
Attitudes
{ASK ALL}
ConsAtt [GRID: RANDOMISE, FLIP SCALE]
{WEB: “Below are”; TEL: “I will now read out”} some statements about buying things.

To what extent do you agree or disagree that each of the following apply to you when buying something
substantial?

{WEB: “Please select one answer on every row”}
INTERVIEWER: READ OUT EACH STATEMENT AND THE ANSWER CODES. REPEAT ANSWER
CODES AS REQUIRED
GRID ROWS
1. If I am unhappy with something I have bought, I normally make a complaint
2. Before I buy something I always compare the price and quality of similar items or services

GRID COLUMNS
1. Strongly agree
2. Agree
3. Neither agree nor disagree
4. Disagree
5. Strongly disagree
Purchase and detriment instances
Purchase instances
{ASK ALL}
PurIntro
Our next few questions will ask you about all of the items, subscriptions and services that you have
bought yourself while in the UK.

We would like to know about things that you have bought in the last 12 months, or you bought at any
time and have used in the last 12 months.

{HELPLINK HS: More information on what to include/exclude
•
Please include things bought jointly with someone else
•
Please do not include items, services or subscriptions that you used or acquired but did
not cost any money.
•
Please do not include things bought while you were outside the UK}

DISPLAY

LAYOUT: RANDOMISE WHETHER PurGoodX OR PurServiceX ARE PRESENTED FIRST, AND
THE ORDER A…E APPEAR, ADJUSTING ‘THINKING FIRSTLY…’ ETC. AS APPROPRIATE.

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143

{ASK ALL}
PurGoodA [MULTICODE: RANDOMISE 1…9]
{Thinking firstly/And still thinking/And now thinking} about items you have bought in the last 12
months, or you bought previously but used in the last 12 months…

Which, if any, of the following types of items did you buy?

{WEB: “Please select all that apply”}
INTERVIEWER: READ OUT EACH OPTION AND CODE ALL THAT APPLY

1. Food and drink, including alcohol, for consumption at home (not takeaways) {HELPLINK:
“Excluding food and drink from cafés, bars, restaurants, or take-aways.
Include all groceries, for example fruit and vegetables, meat, dairy products, bread and cereals,
pre-prepared meals, alcoholic and non-alcoholic drinks, food boxes delivered to you.”}
2. Clothing, footwear and fashion accessories {HELPLINK: “Clothing (including tailor-made goods)
and footwear, sportswear, hats, clothing material, furs, protective clothing, jewellery, handbags
and accessories”}
3. Cleaning or maintenance items and tools for the home or garden {HELPLINK: “Including
cleaning products, products bought to take care of the home and garden, plants, DIY and
building products and tools”}
4. Stationery, books, magazines and newspapers {HELPLINK: “Including books, magazines,
newspapers, stationery, periodicals (excluding postal delivery). Including subscriptions to
newspapers or magazines, whether print or digital.”}.
5. Toiletries, cosmetics, hair products and beauty appliances {HELPLINK: “Personal care items,
including cosmetics, toiletries (including nappies), wigs, hair care products, perfumes, electric
razors and hair trimmers, hair dryers, curling tongs and styling combs”}
6. Glasses (spectacles) and lenses {HELPLINK: “Including spectacles, glasses, lenses,
sunglasses”}
7. Furniture, furnishings, domestic appliances and fixtures {HELPLINK: “Furnishings and furniture
including beds and mattresses, garden furniture, floor coverings, bedding, cushions, curtains
and blinds, glassware, tableware and household utensils.
Large and small domestic appliances including electronic cookers, fridges and freezers,
washing machines, tumble dryers, dishwashers, heaters, vacuum cleaners, microwave ovens,
sewing machines, food-processing appliances, coffee machines, irons, toasters, or grills.”}
8. Electronic devices and software, including computers, phones, media devices {HELPLINK:
“Including computers, laptops, tablets, and software and accessories, monitors, routers, printers
and scanners, smartphones and other phones. Including televisions, games consoles and
games, DVD players and DVDs, CD players and CDs, radios, and cameras.”}
9. Entertainment items including musical instruments, toys, sporting and hobby equipment
{HELPLINK: “Not including electronic devices. Including exercise and leisure equipment e.g.
bicycles, rackets, weights, camping, hobby items like model cars.”}
10. None of these [EXCLUSIVE]

{ASK ALL}
PurGoodB [MULTICODE: RANDOMISE 1…3]
{Thinking firstly/And still thinking/And now thinking} about items you have bought in the last 12
months, or you bought previously but used in the last 12 months…

Which, if any, of the following types of items did you buy?

{WEB: “Please select all that apply”}
INTERVIEWER: READ OUT EACH OPTION AND CODE ALL THAT APPLY
1. New cars or other new vehicles {HELPLINK: “Including cars, vans, motorcycles, caravans or
boats”}
2. Second-hand cars or other second-hand vehicles {HELPLINK: “Including cars, vans,
motorcycles, caravans or boats”}

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3. Fuel, accessories, and maintenance equipment for vehicles {HELPLINK: “Fuel for vehicles
(petrol, diesel, electric, lpg), accessories for vehicles, products for routine maintenance of the
vehicles (motor oil, water, cleaning products)”}
4. None of these [EXCLUSIVE]

{ASK ALL}
PurServiceA [MULTICODE: RANDOMISE 1…10]
{Thinking firstly/And still thinking/And now thinking} about services or subscriptions you have
paid for in the last 12 months, or you paid for previously but used in the last 12 months…

Which, if any, of the following types of services or subscriptions did you pay for?

{WEB: “Please select all that apply”}
INTERVIEWER: READ OUT EACH OPTION AND CODE ALL THAT APPLY

1. Mobile telephone services and data plans {HELPLINK: “Including mobile telephone services and
smartphone data plans”}
2. Landline telephone services {HELPLINK: “Including landline telephone services and telecom
provision. Excluding line rental for internet broadband services”}
3. Internet provision services (excluding mobile phone data plans) {HELPLINK: “Excluding
smartphone data plans. Including home broadband, dial-up and mobile internet plans (using
dongles)”}
4. Satellite, cable or streaming TV or other digital subscriptions (music, gaming, etc.) {HELPLINK:
“Including satellite or cable TV subscriptions (not TV licence fees), cable TV network
subscriptions, digital video subscriptions such as Netflix or Now TV, online gaming subscriptions
and on-demand music providers such as Spotify, iTunes or YouTube Music.”}
5. Water services {HELPLINK: “Including supply but excluding repair services”}
6. Electricity and gas services {HELPLINK: “Including supply but excluding repair services”}
7. Real estate purchases and related services {HELPLINK: “Buying or selling a home or another
property. Services related to real estate purchases, such as conveyancers and searches.”}
8. Renting a home and associated services {HELPLINK: “Renting a home and associated services
such as a deposit scheme.”}
9. Home and garden maintenance and repair services {HELPLINK: “Home maintenance, repair
and improvement services including cleaning, roofing, decorator services, plumbers and
plumbing, floor covering/fitting, central heating (installation and service), electrical services and
installations, bricklayers, glaziers, architects, carpenters, gardeners, tree-surgeons, paving,
fitted kitchens, insulation, burglar alarms, damp proofing, solar heating, guttering, chimney
sweeps, replacing doors, fitting bathrooms, swimming pools, etc."}
10. Removal and storage services
11. None of these [EXCLUSIVE]

{ASK ALL}
PurServiceB [MULTICODE: RANDOMISE 1…5]
{Thinking firstly/And still thinking/And now thinking} about services or subscriptions you have
paid for in the last 12 months, or you paid for previously but used in the last 12 months…

Which, if any, of the following types of services or subscriptions did you pay for or put your money into?

{WEB: “Please select all that apply”}
INTERVIEWER: READ OUT EACH OPTION AND CODE ALL THAT APPLY
1. Current accounts, loans and bank services {HELPLINK: “Any current accounts, debit & credit
cards, loans, mortgages, store cards, consumer credit, revolving credit, peer-to-peer lending,
such as Funding Circle and Zopa, and other non-bank lending”}
2. Pension funds and investment services {HELPLINK: “Banking investments, private pensions
and securities, packaged investments, portfolio and fund management, private personal
pensions, stock broking and derivatives.”}

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3. Insurance services {HELPLINK: “Including transport (car and other vehicles), dwelling
insurance, private life-insurance, endowment insurance and annuities.”}
4. Legal, financial advice, and accountancy services
5. Funeral services
6. None of these [EXCLUSIVE]

{ASK ALL}
PurServiceC [MULTICODE: RANDOMISE 1…4]
{Thinking firstly/And still thinking/And now thinking} about services or subscriptions you have
paid for in the last 12 months, or you paid for previously but used in the last 12 months…

Which, if any, of the following types of services or subscriptions did you pay for?

{WEB: “Please select all that apply”}
INTERVIEWER: READ OUT EACH OPTION AND CODE ALL THAT APPLY

1. Vehicle rental services {HELPLINK: “Including car, motorcycle, van, caravan and boat rental.”}
2. Public transport and train services {HELPLINK: “Including trams, buses, taxis, boats, metro,
underground and railways.”}
3. Airline services
4. Vehicle maintenance and repair services {HELPLINK: “Maintenance and repair of vehicles and
other transport, including independent and franchise garages or dealers and road assistance”}.
5. None of these [EXCLUSIVE]

{ASK ALL}
PurServiceD [MULTICODE: RANDOMISE 1…6]
{Thinking firstly/And still thinking/And now thinking} about services or subscriptions you have
paid for in the last 12 months, or you paid for previously but used in the last 12 months…

Which, if any, of the following types of services or subscriptions did you pay for?

{WEB: “Please select all that apply”}
INTERVIEWER: READ OUT EACH OPTION AND CODE ALL THAT APPLY

1. Private medical services and dental services
2. Prescription and non-prescription medicines
3. Carers, nursing homes and other adult care services
4. Private and higher education fees and services
5. Childcare services {HELPLINK: “Including nurseries, childminders and nannies”}
6. Veterinary services
7. None of these [EXCLUSIVE]

{ASK ALL}
PurServiceE [MULTICODE: RANDOMISE 1…7]
{Thinking firstly/And still thinking/And now thinking} about services or subscriptions you have
paid for in the last 12 months, or you paid for previously but used in the last 12 months…

Which, if any, of the following types of services or subscriptions did you pay for?

{WEB: “Please select all that apply”}
INTERVIEWER: READ OUT EACH OPTION AND CODE ALL THAT APPLY

1. Hair, beauty, and wellness services {HELPLINK: “Including hairdressers, diet clubs, beauty and
cosmetic treatments, nail shop services, spas, saunas, etc.”}
2. Hotels and holiday accommodation {HELPLINK: “Including hotels, bed & breakfasts, hostels,
caravan sites and camp sites.”}
3. Package holidays and tours

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146

4. Cafés, fast-food, restaurants and take-away services, bars, pubs and night clubs {HELPLINK:
“Including restaurants, cafés, caterers, takeaways, mobile food vendors, night clubs, bars and
pubs”}
5. Sport, cultural and entertainment activities, memberships or events {HELPLINK: “Including
health clubs and gyms, sports facilities, sports instructors (not including ‘not-for-profit’ sports
clubs or activities), theatres, cinemas, festivals, museums, zoos, amusement parks, and other
ticket-selling services”}.
6. Gambling and lottery services {HELPLINK: “Online, in-person, and other gambling and betting
involving monetary value including lotteries, casino games, poker, bingo and sports betting
(including horse and dog racing)”}
7. Pet breeder {HELPLINK: Someone who has supplied you with a pet dog, cat etc}
8. None of these [EXCLUSIVE]
Detriment instances
{IF PurGoodA=1…9 OR PurGoodB=1…3 OR PurServiceA = 1…10 OR PurServiceB = 1…5 OR
PurServiceC = 1…4 OR PurServiceD = 1…6 OR PurServiceE = 1…7}
CDIntro
It is possible that problems may have occurred with the things you bought that caused you stress, cost
you money, or took up your time.

For example, you may have:
•
Bought items or services which were faulty or of lower quality than advertised.
•
Experienced problems with the delivery of items or services you ordered.
•
Had problems claiming under a warranty, guarantee, or insurance policy.
•
Paid more for an item or service than advertised.

For the next set of questions, please think about all problems with the things you bought that happened
in the last 12 months and caused you stress, cost you money, or took up your time.

DISPLAY

{IF PurGoodA=1…9 OR PurGoodB=1…3}
DMGood [MULTICODE: RANDOMISE 1…12]
{WEB: “Below are”; TEL: “I will now read out”} the types of items you said you have bought in
the last 12 months, or bought previously but used in the last 12 months.

For each type of item, did you experience any problems in the last 12 months which caused you stress,
cost you money, or took up your time?

{WEB: “Please select all that apply”}
INTERVIEWER: READ OUT EACH OPTION AND CODE ALL THAT APPLY

1. {IF PurGoodA = 1: “Food and drink, including alcohol for consumption at home (not takeaways)
{HELPLINK: “Excluding food and drink from cafés, bars restaurants, or take-aways.
Include all groceries, for example fruit and vegetables, meat, dairy products, bread and cereals,
pre-prepared meals, alcoholic and non-alcoholic drinks, food boxes delivered to you.”}”}
2. Etc.
.
.
.
[..] None of these [EXCLUSIVE]

{IF DMGood = 1…12}
DMGoodCount [GRID: RANDOMISE ROWS]
And for each of these types of item…

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How many different items did you experience problems with which caused you stress, cost you
money, or took up your time?

Please count multiple experiences of stress caused, money lost, or time lost as a result of the same
item as one instance.

{WEB: “Please enter one answer on every row”}
INTERVIEWER: READ OUT EACH OPTION AND ENTER ONE ANSWER ON EVERY ROW

GRID ROWS
1. {IF DMGood = 1: “Food and drink, including alcohol for consumption at home (not takeaways)
{HELPLINK: “Excluding food and drink from cafés, bars restaurants, or take-aways.
Include all groceries, for example fruit and vegetables, meat, dairy products, bread and cereals,
pre-prepared meals, alcoholic and non-alcoholic drinks, food boxes delivered to you.”}”}
2. Etc.
.
.
.
[..] None of these [EXCLUSIVE]

GRID COLUMNS
RANGE 1…99

SOFTCHECK: IF DMGoodCount GT 5: “You have said that you have experienced six or more
problems with a type of item. Remember that multiple issues with the same item should be counted as
one instance. For example, if an item broke multiple times, this should count as one instance.”

SOFTCHECK: IF DMGoodCount = DK: “Your best estimate would be fine here, but otherwise please
continue. Thanks!”

{PurServiceA =1…10 OR PurServiceB = 1…5}
DMServiceA [MULTICODE: RANDOMISE 1…15]
{WEB: “Below are”; TEL: “I will now read out”} some of the types of services and subscriptions
you said you paid for in the last 12 months, or you paid for previously but used in the last 12 months.

For each type of service or subscription, did you experience any problems in the last 12 months which
caused you stress, cost you money, or took up your time?

{WEB: “Please select all that apply”}
INTERVIEWER: READ OUT EACH OPTION AND CODE ALL THAT APPLY

1. {IF PurServiceA = 1: “Mobile telephone services and data plans {HELPLINK: “Including mobile
telephone services and smartphone data plans”}”}
2. Etc.
.
.
.
[..] None of these [EXCLUSIVE]

{IF DMServiceA = 1…15}
DMServiceCountA [GRID: RANDOMISE ROWS]
And for each of those types of service or subscription …

How many different services or subscriptions did you experience problems with which caused
you stress, cost you money, or took up your time?

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148

Please count multiple experiences of stress caused, money lost, or time cost as a result of the same
service or subscription as one instance.

{WEB: “Please enter one answer on every row”}
INTERVIEWER: READ OUT EACH OPTION AND ENTER ONE ANSWER ON EVERY ROW

GRID ROWS
1. {IF DMServiceA = 1: “Mobile telephone services and data plans {HELPLINK: “Including mobile
telephone services and smartphone data plans”}”}
2. Etc.

GRID COLUMNS
RANGE 1…99

SOFTCHECK: IF DMServiceCountA GT 5: “You have said that you have experienced six or more
problems with a type of service or subscription. Remember that multiple issues with the same service or
subscription should be counted as one instance. For example, if your gas company has been sending
you the wrong bill for months, this should be counted as one single incident. Please check your
answers before continuing.”

SOFTCHECK: IF DMServiceCountA = DK: “Your best estimate would be fine here, but otherwise
please continue. Thanks!”

{IF PurServiceC = 1…4 OR PurServiceD = 1…6 OR PurServiceE = 1…7}
DMServiceB [MULTICODE: RANDOMISE 1…17]
{WEB: “Below are”; TEL: “I will now read out”} some of the types of services and subscriptions
you said you paid for in the last 12 months, or you paid for previously but used in the last 12 months.…

For each type of service or subscription, did you experience any problems in the last 12 months which
caused you stress, cost you money, or took up your time?

{WEB: “Please select all that apply”}
INTERVIEWER: READ OUT EACH OPTION AND CODE ALL THAT APPLY

1. {IF PurServiceC = 1: “Vehicle rental services {HELPLINK: “Including car, motorcycle, van,
caravan and boat rental.”}”}
2. Etc.
.
.
.
[..] None of these [EXCLUSIVE]

{IF DMServiceB = 1…17}
DMServiceCountB [GRID: RANDOMISE ROWS]
And for each of those types of service or subscription…

How many different services or subscriptions did you experience problems with which caused
you stress, cost you money, or took up your time?

Please count multiple experiences of stress caused, money lost, or time cost as a result of the same
service or subscription as one instance.

{WEB: “Please enter one answer on every row”}
INTERVIEWER: READ OUT EACH OPTION AND ENTER ONE ANSWER ON EVERY ROW

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GRID ROWS
1. {IF DMServiceB = 1: ““Vehicle rental services {HELPLINK: “Including car, motorcycle, van,
caravan and boat rental.”}”}
2. Etc.
.
.
.
[..] None of these [EXCLUSIVE]

GRID COLUMNS
RANGE 1…99

SOFTCHECK: IF DMServiceCountB GT 5: “You have said that you have experienced six or more
problems with a type of service or subscription. Remember that multiple issues with the same service or
subscription should be counted as one instance. For example, if your gas company has been sending
you the wrong bill for months, this should be counted as one single incident. Please check your
answers before continuing.”

SOFTCHECK: IF DMServiceCountB = DK: “Your best estimate would be fine here, but otherwise
please continue. Thanks!”

{COMPUTE FOR ALL}
DetSectCount
COUNT of number of selected options at DMGood, DMServiceA, DMServiceB

{COMPUTE FOR ALL}
DetIncidentCount
SUM of answers given at DMGoodCount, DMServiceCountA, DMServiceCountB

{IF DetSectCount >= 1}
Loop1Sector
Randomly select one of selected options at DMGood, DMServiceA, DMServiceB (1….44)

{IF DetSectCount >= 2}
Loop2Sector
Randomly select one of selected options at DMGood, DMServiceA, DMServiceB (1…44), excluding option
selected for Loop1Sector

{IF DetSectCount >= 3}
Loop3Sector
Randomly select one of selected options at DMGood, DMServiceA, DMServiceB (1…44), excluding
options selected for Loop1Sector and Loop2Sector

{IF DetSectCount >= 1}
Sector_Text
IF LoopXSector =
1
LoopXSector_Text = “food or drink, including alcohol for consumption at
home (not takeaways)”.
IF LoopXSector =
2
LoopXSector_Text = “clothing, footwear or fashion accessories”.
IF LoopXSector =
3
LoopXSector_Text = “cleaning or maintenance items or tools for the home
or garden”.
IF LoopXSector =
4
LoopXSector_Text = “stationery, books, magazines or newspapers”.
IF LoopXSector =
5
LoopXSector_Text = “toiletries, cosmetics, hair products or beauty
appliances”.
IF LoopXSector =
6
LoopXSector_Text = “glasses (spectacles) or lenses”.
IF LoopXSector =
7
LoopXSector_Text = “furniture, furnishings, domestic appliances or
fixtures”.
IF LoopXSector =
8
LoopXSector_Text = “electronic devices or software, including computers,
phones and media devices”.
IF LoopXSector =
9
LoopXSector_Text = “entertainment items including musical instruments,
toys, sporting or hobby equipment”.
IF LoopXSector =
10
LoopXSector_Text = “new cars or other new vehicles”.

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IF LoopXSector =
11
LoopXSector_Text = “second-hand cars or other second-hand vehicles”.
IF LoopXSector =
12
LoopXSector_Text = “fuel, accessories, or maintenance equipment for
vehicles”.
IF LoopXSector =
13
LoopXSector_Text = “mobile telephone services or data plans”.
IF LoopXSector =
14
LoopXSector_Text = “landline telephone services”.
IF LoopXSector =
15
LoopXSector_Text = “internet provision services”.
IF LoopXSector =
16
LoopXSector_Text = “satellite, cable or streaming TV or other digital
subscriptions (music, gaming, etc.)”.
IF LoopXSector =
17
LoopXSector_Text = “water services”.
IF LoopXSector =
18
LoopXSector_Text = “electricity or gas services”.
IF LoopXSector =
19
LoopXSector_Text = “real estate or related services”.
IF LoopXSector =
20
LoopXSector_Text = “home rental or associated services”.
IF LoopXSector =
21
LoopXSector_Text = “home or garden maintenance or repair services”.
IF LoopXSector =
22
LoopXSector_Text = “removal or storage services”.
IF LoopXSector =
23
LoopXSector_Text = “current accounts, loans or bank services”.
IF LoopXSector =
24
LoopXSector_Text = “pension funds or investment services”.
IF LoopXSector =
25
LoopXSector_Text = “insurance services”.
IF LoopXSector =
26
LoopXSector_Text = “legal, financial advice or accountancy services”.
IF LoopXSector =
27
LoopXSector_Text = “funeral services”.
IF LoopXSector =
28
LoopXSector_Text = “vehicle rental services”.
IF LoopXSector =
29
LoopXSector_Text = “public transport or train services”.
IF LoopXSector =
30
LoopXSector_Text = “airline services”.
IF LoopXSector =
31
LoopXSector_Text = “vehicle maintenance or repair services”.
IF LoopXSector =
32
LoopXSector_Text = “private medical services or dental services”.
IF LoopXSector =
33
LoopXSector_Text = “prescription or non-prescription medicines”.
IF LoopXSector =
34
LoopXSector_Text = “carers, nursing homes or other adult care services”.
IF LoopXSector =
35
LoopXSector_Text = “private or higher education services”.
IF LoopXSector =
36
LoopXSector_Text = “childcare services”.
IF LoopXSector =
37
LoopXSector_Text = “veterinary services”.
IF LoopXSector =
38
LoopXSector_Text = “hair, beauty or wellness services”.
IF LoopXSector =
39
LoopXSector_Text = “hotels or holiday accommodation”.
IF LoopXSector =
40
LoopXSector_Text = “package holidays or tours”.
IF LoopXSector =
41
LoopXSector_Text = “café, bar, pub, fast-food, restaurant or take-away
services”.
IF LoopXSector =
42
LoopXSector_Text = “sport, cultural or entertainment facilities,
memberships or events”.
IF LoopXSector =
43
LoopXSector_Text = “gambling or lottery services”.
IF LoopXSector =
44
LoopXSector_Text = “pet breeder services”.

START FILTER: IF DMGood = 1…12 OR DMServiceA = 1…15 OR DMServiceB = 1…17

{IF DetSectCount>=3}
LongInt
“As you have experienced several different types of problem as a consumer, we would like to ask you
some extra questions and this survey may take a little longer than normal – around 25 to 30 minutes.

It is really important that your experiences are represented, and as a thank you for your extra time, we
will now send you a {IF VouchType = 5: “£10”; IF VouchType = 10: “£20”} voucher when you
complete the questionnaire.

We hope that you would like to continue.”

DISPLAY

{ASK ALL}
IntroLoop
We would now like to understand more about the problems which caused you stress, cost you money,
or took up your time when using or buying {IF DetSectCount=1: {LOOP1SECTOR_TEXT}; IF
DetSectCount>=2:
•
{LOOP1SECTOR_TEXT}

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•
{LOOP2SECTOR_TEXT};
•
IF DetSectCount>=3: {LOOP3SECTOR_TEXT}}.

{IF DetIncidentCount GT DetSectCount: “If you have had multiple problems with {IF DetSectCount=1:
“this type”; IF DetSectCount>=2: “these types”} of item, subscription or service, please think about the
problem that started most recently.”}

DISPLAY

START LOOP: IF DetSectCount=1 Loop once; IF DetSectCount=2 Loop twice; IF
DetSectCount>=3 Loop thrice
Detriment experience loops – nature of purchase
Purchase channel
{ASK ALL}
DetChan1 … DetChan3
{IF LOOP = 1: “Thinking firstly about the problem you had when using or buying
{LOOP1SECTOR_TEXT}”; IF LOOP = 2: “And now thinking about the problem you had when
using or buying {LOOP2SECTOR_TEXT}”; IF LOOP = 3: “And finally thinking about the
problem you had when using or buying {LOOP3SECTOR_TEXT}”}} that started most
recently…

How were these {IF LOOP = 1: {LOOP1SECTOR_TEXT}; IF LOOP = 2: {LOOP2SECTOR_TEXT}; IF
LOOP = 3: {LOOP3SECTOR_TEXT}} originally bought?

INTERVIEWER: READ OUT

1. In-person from a shop or other outlet
2. In-person from salesperson who visited my home or work
3. Over a phone call
4. Online, from the provider’s/retailer’s/operator’s website {HELPLINK: “For example the Argos or
Matalan website or app”}
5. Online, from a third-party marketplace website or app {HELPLINK: “For example Amazon,
Expedia or Deliveroo”}
6. Online, from a website where private individuals sell to each other, such as eBay or Airbnb
7. Online through a social media platform {HELPLINK: “For example Facebook marketplace”}
8. ‘Auto-renewal’ where the money is automatically taken from your account
9. Other (Please describe)

Cost of good/service at purchase
{ASK ALL}
OngoingOneoff1… OngoingOneoff3
Did the problem you had relate to something that was paid for through a subscription or on an ongoing
basis, or a ‘one-off’ purchase?

1. Subscription or ongoing purchase
2. One-off purchase

{ASK IF OngoingOneoff1… OngoingOneoff3 = 1}
OngoingCostMQY1…OngoingCostMQY3
“We would like to know the approximate cost of the subscription or ongoing purchase at the time of the
problem.

Would you be best able to estimate the cost per month, per quarter or per year?

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1. Month
2. Quarter
3. Year

{ASK IF OngoingCostMQY1…OngoingCostMQY3 = 1…3}
OngoingCost1…OngoingCost3
“What was the approximate {IF OngoingCostMQY1…OngoingCostMQY3 = 1: “monthly”; IF
OngoingCostMQY1…OngoingCostMQY3 = 2: “quarterly”; IF OngoingCostMQY1…OngoingCostMQY3
= 3: “yearly”} cost of the subscription or ongoing purchase at the time of the problem?

{HELPLINK: “What if it was paid for as part of a bundle?”
“If the item, service or subscription was paid for as part of a bundle, please give the total cost of the
bundle.”}

Please give your best estimate to the nearest pound.

RANGE £0….1000000
1. It did not cost anything
2. Don’t know

SOFTCHECK: IF OngoingCostMQY1…OngoingCostMQY3 = 1 AND OngoingCost1…OngoingCost3
GT 999: “You have said that the approximate monthly cost of the subscription or ongoing purchase was
£1,000 or more. Please check your answers before continuing.”

SOFTCHECK: IF OngoingCostMQY1…OngoingCostMQY3 = 2,3 AND OngoingCost1…OngoingCost3
GT 9999: “You have said that the approximate {IF OngoingCostMQY1…OngoingCostMQY3 = 2:
“quarterly”; IF OngoingCostMQY1…OngoingCostMQY3 = 3: “yearly”} cost of the subscription or
ongoing purchase was £10,000 or more. Please check your answers before continuing.”

SOFTCHECK: IF OngoingCost1…OngoingCost3 = DK: “Your best estimate would be fine here, but
otherwise please continue. Thanks!”

SOFTCHECK: IF OngoingCost1…OngoingCost3 = 0 OR ‘It did not cost anything’: “You have said that it
did not cost you anything. Please check your answer before continuing.”

{COMPUTE FOR ALL}
OngoingCost1_Year…OngoingCost3_Year
IF OngoingCostMQY1…OngoingCostMQY3 = 1 OngoingCost1_Year…OngoingCost3_Year =
OngoingCost1…OngoingCost3 x 12
IF OngoingCostMQY1…OngoingCostMQY3 = 2 OngoingCost1_Year…OngoingCost3_Year =
OngoingCost1…OngoingCost3 x 4
IF OngoingCostMQY1…OngoingCostMQY3 = 3 OngoingCost1_Year…OngoingCost3_Year =
OngoingCost1…OngoingCost3

{ASK IF OngoingOneoff1… OngoingOneoff3 = 2}
OneoffCost1… OneoffCost3
“What was the approximate original cost of the item or service?

{HELPLINK: “What if it was paid for as part of a bundle?”
“If the item, service or subscription was paid for as part of a bundle, please give the total cost of the
bundle.”}

Please give your best estimate to the nearest pound.”

RANGE £0….10,000,000
1. It did not cost anything

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2. Don’t know

SOFTCHECK: IF OneoffCost1… OneoffCost3 GT 9999: “You have said that the approximate cost of
the item or service was £10,000 or more. Please check your answers before continuing.”

SOFTCHECK: IF OneoffCost1… OneoffCost3 = DK: “Your best estimate would be fine here, but
otherwise please continue. Thanks!”

SOFTCHECK: IF OneoffCost1… OneoffCost3= 0 OR ‘It did not cost anything’: “You have said that it
did not cost you anything. Please check your answer before continuing”
Detriment experience loops – nature of detriment
Initial problem type
{IF LOOP ABOUT A GOOD}
DetTypeGd1 … DetTypeGd3 [MULTICODE: RANDOMISE 1…8]
And still thinking about the problem you had when using or buying {IF LOOP = 1:
“LOOP1SECTOR_TEXT ”; IF LOOP = 2: “LOOP2SECTOR_TEXT ”; IF LOOP = 3:
“LOOP3SECTOR_TEXT ”} that started most recently…

Which of the following best describes the nature of the original problem you experienced?

{HELPLINK HS: More information on what to include/exclude
•
If this problem has led to further issues, please tell us about the original problem here.
•
Please do not include any problems you experienced if you tried to get compensation.}

{WEB: “Please select all that apply”}
INTERVIEWER: READ OUT EACH OPTION AND CODE ALL THAT APPLY

1. The item was of a lower quality or didn’t function/look as advertised {HELPLINK: “Including
missing parts and items not fitting”}
2. The item was faulty, unsafe or broken
3. The item arrived late or there were other problems with the delivery
4. I never received the item
5. The price charged was more than advertised
6. I was not provided with all relevant information about the item before purchasing
7. The terms & conditions of the purchase were unclear or unfair
8. The seller or manufacturer did not honour a warranty or guarantee
9. Other problem (Please specify)

{IF LOOP ABOUT A SERVICE}
DetTypeSer1 … DetTypeSer3 [MULTICODE: RANDOMISE 1…8]
And still thinking about the problem you had when using or buying {IF LOOP = 1:
“LOOP1SECTOR_TEXT ”; IF LOOP = 2: “LOOP2SECTOR_TEXT ”; IF LOOP = 3:
“LOOP3SECTOR_TEXT ”} that started most recently…

Which of the following best describes the nature of the problem you experienced?

{HELPLINK HS: More information on what to include/exclude
•
If this problem has led to further issues, please tell us about the original problem here.
•
Please do not include any problems you experienced if you tried to get compensation.}

{WEB: “Please select all that apply”}
INTERVIEWER: READ OUT EACH OPTION AND CODE ALL THAT APPLY

1. The service was of a lower quality or didn’t do what was advertised

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2. The service was unsafe or didn’t work
3. The service was provided late or took longer than expected
4. The service was not provided / available when I needed it
5. The price charged was more than advertised
6. I was not provided with all relevant information about the service before purchasing
7. The terms & conditions of the purchase were unclear or unfair
8. The service provider did not honour a warranty or guarantee
9. Other problem (Please specify)

{IF (DetTypeSer1… DetTypeSer3 = 1,2,3,4 OR DetTypeGd1… DetTypeGd3 = 1,2,3,4) AND
OngoingOneoff1… OngoingOneoff3 = 1}
DetLn1 … DetLn3
You said that you experienced the following problems:
{IF DetTypeGd1… DetTypeGd3=1: “- The item was of a lower quality or didn’t function/look as
advertised”}
{IF DetTypeGd1… DetTypeGd3=2: “- The item was faulty, unsafe or broken”}
{IF DetTypeGd1… DetTypeGd3=3: “- The item arrived late or there were other problems with the
delivery”}
{IF DetTypeGd1… DetTypeGd3=4: “- I never received the item”}
{IF DetTypeSer1… DetTypeSer3=1: “- The service was of a lower quality or didn’t do what was
advertised”}
{IF DetTypeSer1… DetTypeSer3=2: “- The service was unsafe or didn’t work”}
{IF DetTypeSer1… DetTypeSer3=3: “- The service was provided late or took longer than expected”}
{IF DetTypeSer1… DetTypeSer3=4: “- The service was not provided / available when I needed it”}

For how many weeks did this problem/these problems last during the last 12 months?

If the problem is still ongoing, please say how many weeks the problem has lasted so far

RANGE 1..52 weeks
1. Less than a week
Current status
{ASK ALL}
DetStatus1 … DetStatus3 [FLIP SCALE]
And which of the following best describes the current status of the problem?

If you do not expect yourself or anyone else to take any further action related to the problem {WEB:
“please select that”; TEL: “then”} it is closed, irrespective of whether or not the problem was resolved to
your satisfaction.

INTERVIEWER: READ OUT

1. The problem is closed
2. The problem is still ongoing
Detriment experience loops – process
Actions taken and what obtained
{ASK ALL}
ActTake1 … ActTake3 [MULTICODE: RANDOMISE 1…8]
And still thinking about the problem you had when using or buying {IF LOOP = 1:
“LOOP1SECTOR_TEXT ”; IF LOOP = 2: “LOOP2SECTOR_TEXT ”; IF LOOP = 3:
“LOOP3SECTOR_TEXT ”} that started most recently…

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Which, if any, of the following actions {IF DetStatus1…DetStatus3 = 1: “did you take”; IF
DetStatus1…DetStatus3 = DK, REF, 2: “have you taken so far”}?

{WEB: “Please select all that apply”}
INTERVIEWER: READ OUT EACH OPTION AND CODE ALL THAT APPLY

1. Contacted the seller, producer or service provider directly
2. Tried to claim under a guarantee, warranty or insurance policy
3. Contacted a consumer rights/advice organisation
4. Left a review on a website or social media platform
5. Withheld payment
6. Used a dispute resolution service or Ombudsman {HELPLINK: “For example the Financial
Ombudsman Service and Ombudsman Services, the Centre for Effective Dispute Resolution
(CEDR) or Consumer Dispute Resolution Limited (CDRL)”}
7. Took legal action against the seller, producer or service provider
8. Asked family members or friends for help with the problem
9. Other (Please describe)
10. None of these [EXCLUSIVE]

{IF ActTake1…ActTake3 = 1…9}
ActClaim1 … ActClaim3 [MULTICODE: RANDOMISE 1…8]
And which, if any, of the following did you ask the seller, producer or service provider to do?

{WEB: “Please select all that apply”}
INTERVIEWER: READ OUT EACH OPTION AND CODE ALL THAT APPLY

1. Provide a refund (full or partial)
2. Provide a voucher or store credit
3. Provide a replacement or fix the problem (e.g. by repairing an item or restoring a service)
4. Provide compensation for extra costs or inconvenience incurred {HELPLINK: “Please include
both monetary and non-monetary compensation, for example, a voucher, a free night at the
hotel, a new broadband router, etc.”}
5. Provide a one-off discount or longer-term price reduction
6. Review/change the contract conditions
7. Apologise for the inconvenience caused
8. Explain the problem
9. Other (Please describe)
10. None of these [EXCLUSIVE]
{IF ActTake1…ActTake3 = 10}
WhyNoAct1…WhyNoAct3 [RANDOMISE 1…6]
What {IF DetStatus1…DetStatus3 = 1: “was the main reason you did not ask for anything,
make a complaint, or take any other action”; IF DetStatus1…DetStatus3 = DK, REF, 2: “is the main
reason you have not asked for anything, made a complaint, or taken any other action so far”}?

INTERVIEWER: READ OUT

1. The problem was not serious enough
2. It was not clear who to contact, or how to go about complaining
3. I did not think it would be successful
4. The process would have taken too long
5. The process would have been too complicated
6. There wasn’t a way to take action I felt comfortable using
7. The issue was resolved without me having to take action
8. Somebody else took action (on my behalf)
9. I will do in the future
10. Other reason (Please describe)

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{ASK ALL}
RefSummary1…RefSummary3
Apart from a voucher or store credit that you may have been given, have you received a refund
for the item or service{IF DetStatus1…DetStatus3 = DK, REF, 2: “ so far”}?

INTERVIEWER: READ OUT

1. Yes – full refund
2. Yes – partial refund
3. No
4. Not yet, but have been promised one

{ASK IF RefSummary1…RefSummary3 = 2}
PartialRef1…PartialRef3
How much was the refund you received?

RANGE £0…. {IF OngoingOneoff1… OngoingOneoff3 = 1: 1,000,000; ELSE: 10,000,000}
1. It did not have any value
2. Don’t know

SOFTCHECK: IF PartialRef1…PartialRef3 = DK: “Your best estimate would be fine here, but otherwise
please continue. Thanks!”

SOFTCHECK: IF PartialRef1…PartialRef3 GT OngoingCost1…OngoingCost3 AND
OngoingCost1…OngoingCost3 GT 0: “You have said that the refund you received was greater than the
approximate {IF OngoingCostMQY1…OngoingCostMQY3 = 1: “monthly”; IF
OngoingCostMQY1…OngoingCostMQY3 = 2: “quarterly”; IF OngoingCostMQY1…OngoingCostMQY3
= 3: “yearly”} cost. Please check your answers before continuing.”

SOFTCHECK: IF PartialRef1…PartialRef3 GT OneoffCost1… OneoffCost3 AND OneoffCost1…
OneoffCost3 GT 0: “You have said that the refund you received was greater than the approximate
original cost. Please check your answers before continuing.”

SOFTCHECK: IF PartialRef1…PartialRef3 = 0 OR ‘It did not have any value: “You have said that the
refund did not have any value. Please check your answer before continuing.”

{ASK ALL}
ReplFix1…ReplFix3
And{IF DetStatus1…DetStatus3 = DK, REF, 2: “, so far,”} has the item or service been replaced,
fixed or restored without additional charge?

INTERVIEWER: READ OUT

1. Yes
2. No
3. Not yet, but have been promised it will be

{ASK IF OngoingOneOff1… OngoingOneOff3=1 AND ReplFix1… ReplFix3 = 1} [1 item]
ReplFixTime1…ReplFixTime3
How many weeks did it take for it to be replaced, fixed or restored after the problem started?
RANGE 1...52 weeks
1. Less than a week

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{ASK ALL}
CompObt1…CompObt3 [MULTICODE: RANDOMISE 1…6]
And which, if any, of the following other things has the seller, producer or service provider done {IF
DetStatus1…DetStatus3 = DK, REF, 2: “ so far”}?

{WEB: “Please select all that apply”}
INTERVIEWER: READ OUT EACH OPTION AND CODE ALL THAT APPLY

1. Provided a voucher or store credit
2. Provided compensation for extra costs or inconvenience incurred {HELPLINK: “Please include
both monetary and non-monetary compensation, for example, a free night at the hotel, a new
broadband router, etc.”}
3. Provided a one-off discount or longer-term price reduction
4. Reviewed/changed the contract conditions
5. Apologised for the inconvenience caused
6. Explained the problem
7. Promised something, but not yet provided it
8. Other (Please describe)
9. None of these [EXCLUSIVE]
Compensation value
{ASK IF CompObt1…CompObt3 = 1,2,3,4,8}
RefReceivedApprox1… RefReceivedApprox3
{IF RefSummary1…RefSummary3 = 1,2 OR ReplFix1…ReplFix3 = 1: “Apart from the {IF
RefSummary1…RefSummary3 = 1,2: “refund”}{IF RefSummary1…RefSummary3 = 1,2 AND
ReplFix1…ReplFix3 = 1: “ and ”}{IF ReplFix1…ReplFix3 = 1: “replacement”}, approximately”; ELSE
“Approximately”} what was the total value of the {IF RefSummary = 1,2 OR ReplFix = 1: “other ”
ELSE: “”}monetary or other types of compensation that you received?

Please include your best estimate of the value of both monetary and non-monetary compensation you
received

Please give your best estimate to the nearest pound.

RANGE £0….10000000
1. It did not have any value
2. Don’t know

SOFTCHECK: IF RefReceivedApprox1… RefReceivedApprox3 = 0: “You have said that the total value
of the monetary and other compensation that you received was £0 – is this right?”

SOFTCHECK: IF RefReceivedApprox1… RefReceivedApprox3 GT 9999: “You have said that the total
value of the monetary and other compensation that you received was £10,000 or more. Please check
your answers before continuing.”

SOFTCHECK: IF RefReceivedApprox1… RefReceivedApprox3 = DK: “Your best estimate would be
fine here, but otherwise please continue. Thanks!”
Detriment experience loops – impact
Financial detriment experienced
{ASK IF DetTypeSer1… DetTypeSer3 = 1,2,4,9 OR DetTypeGd1… DetTypeGd3 = 1,2,4,9}
PayFixRepl1… PayFixRepl3
{“IF ReplFix=1: Before the item or service was replaced, fixed or restored by the seller, d”; ELSE “D”}id
you pay to replace, fix or restore the item or service at your own expense?

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INTERVIEWER: READ OUT

1. Yes
2. No
3. Not yet

{ASK IF PayFixRepl1… PayFixRepl3 = 1)
PayFixReplCost1… PayFixReplCost3
How much did you pay to replace, fix or restore the item or service?

RANGE £0…. 10000000
1. It did not cost anything
2. Don’t know

SOFTCHECK: PayFixReplCost1… PayFixReplCost3 = DK: “Your best estimate would be fine here, but
otherwise please continue. Thanks!”

SOFTCHECK: IF PayFixReplCost1… PayFixReplCost3 GT OngoingCost1…OngoingCost3 AND
OngoingCost1…OngoingCost3 GT 0: “You have said that the cost to replace, fix, or restore the item or
service was greater than the approximate original {IF OngoingCostMQY1…OngoingCostMQY3 = 1:
“monthly”; IF OngoingCostMQY1…OngoingCostMQY3 = 2: “quarterly”; IF
OngoingCostMQY1…OngoingCostMQY3 = 3: “yearly”} cost. Please check your answers before
continuing.”

SOFTCHECK: IF PayFixReplCost1… PayFixReplCost3 GT OneoffCost1… OneoffCost3 AND
OneoffCost1… OneoffCost3 GT 0: “You have said that the cost to replace, fix, or restore the item or
service was greater than the approximate original cost. Please check your answers before continuing.”

SOFTCHECK: IF PayFixReplCost1… PayFixReplCost3=0 OR ‘It did not cost anything’: “You have said
that it did not cost you anything to replace, fix or restore the item. Please check your answer before
continuing”

{ASK ALL}
CostExp1… CostExp3 [MULTICODE: RANDOMISE 1…4]
{IF PayFixRepl=1: “Apart from what you have already mentioned, in”; ELSE “In”} which, if any, of the
following ways did you incur additional costs as a result of your problem?

By “additional costs” we mean any financial costs you experienced in addition to the original
cost of the item or service.

{WEB: “Please select all that apply”}
INTERVIEWER: READ OUT EACH OPTION AND CODE ALL THAT APPLY

1. Loss of earnings
2. Paying to repair damage caused by an incident {HELPLINK: “For example to repair or replace
items damaged by a leak”}
3. Not being able to use another item or service that you paid for {HELPLINK: “For example not
being able to use tickets for an event you missed because of the problem}
4. Costs from contacting the seller or seeking compensation {HELPLINK: “For example fees paid
to a lawyer or specialist adviser, costs of using a help line, postage costs to return an item, or
the cost of travel to the seller’s premises”}
5. Other (Please describe)
6. I did not incur any additional costs [EXCLUSIVE]

{IF CostExp1… CostExp3 = 1…5}
CostEstimate1… CostEstimate3
And thinking about those additional financial costs…

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What has been the approximate total additional financial cost to you so far?

Please give your best estimate of all the costs to the nearest pound.”

RANGE £0….1000000
1. No additional financial costs
2. Don’t know

SOFTCHECK: IF CostEstimate1… CostEstimate3 = 0 OR ‘No additional financial costs’: “You have
said that the additional cost to you so far was £0. Please check your answers before continuing.”

SOFTCHECK: IF CostEstimate1… CostEstimate3 GT 9999: “You have said that the total additional
financial cost to you so far was £10,000 or more. Please check your answers before continuing.”

SOFTCHECK: IF CostEstimate1… CostEstimate3 = DK: “Your best estimate would be fine here, but
otherwise please continue. Thanks!”

{IF OneoffCost1…OneoffCost3 = RESPONSE OR OngoingCost1…OngoingCost3 = RESPONSE}
SubjectiveValue1…SubjectiveValue3
You said that you originally paid £{IF OneoffCost1…OneoffCost3 = RESPONSE:
“{OneOffCost1…OneoffCost3}”; IF OngoingCost1…OngoingCost3 = RESPONSE
“{OngoingCost1…OngoingCost3} per {IF OngoingCostMQY1…OngoingCostMQY3 = 1: “month”; IF
OngoingCostMQY1…OngoingCostMQY3 = 2: “quarter”; IF OngoingCostMQY1…OngoingCostMQY3 =
3: “year”}”} for the item or service.

{IF ReplFix1…ReplFix3 =1 OR PayFixRepl1… PayFixRepl3 = 1: “Before it was replaced, fixed or
restored, w”; ELSE “W”}hat would you say was the actual value of the item or service to you{“
per {year/quarter/month}”}?”

RANGE £ 0…. {IF OngoingOneoff1… OngoingOneoff3 = 1: 1,000,000; ELSE: 10,000,000}
1. It had no value to me
2. Don’t know

SOFTCHECK: SubjectiveValue1…SubjectiveValue3 = DK: “Your best estimate would be fine here, but
otherwise please continue. Thanks!”

SOFTCHECK: IF SubjectiveValue1…SubjectiveValue3 GT OngoingCost1…OngoingCost3 AND
OngoingCost1…OngoingCost3 GT 0: “You have said that the value was greater than the approximate
{IF OngoingCostMQY1…OngoingCostMQY3 = 1: “monthly”; IF
OngoingCostMQY1…OngoingCostMQY3 = 2: “quarterly”; IF OngoingCostMQY1…OngoingCostMQY3
= 3: “yearly”} cost. Please check your answers before continuing.”

SOFTCHECK: IF SubjectiveValue1…SubjectiveValue3 GT OneoffCost1… OneoffCost3 AND
OneoffCost1… OneoffCost3 GT 0: “You have said that the value was greater than the approximate
original cost. Please check your answers before continuing.”
Time spent on the problem
{ASK ALL}
TimeMH1… TimeMH3 [FLIP SCALE]
Experiencing problems with items, services or subscriptions, and efforts to resolve them or seek
compensation, can take up people’s time.

We would like to know approximately how much time you have personally spent on the
problem so far, including any time loss caused by the problem itself, any time spent trying to resolve the
problem, and any time spent pursuing compensation.

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Would you be best able to estimate the time in minutes or hours?

1. Minutes
2. Hours

{ASK IF TimeMH1… TimeMH3 = 1,2}
Time1… Time3
In total, approximately how many {IF TimeMH1… TimeMH3 = 1: “minutes”; IF TimeMH1… TimeMH3 =
2: “hours”} did you personally spend on the problem?

Please include any time loss caused by the problem itself, any time spent trying to resolve the problem,
and any time spent trying to get compensation.

Please give your best estimate.

RANGE 0….999 {IF TimeMH1… TimeMH3 = 1: “minutes”; IF TimeMH1… TimeMH3 = 2: “hours”}
1. I did not spend any time on it
2. Don’t know

SOFTCHECK: IF Time1… Time3 GT 99: “You have said that you have personally spent 100
{IF TimeMH1… TimeMH3 = 2: “hours”; IF TimeMH1… TimeMH3 = 1: “minutes”} or more on the
problem. Please check your answers before continuing.”

SOFTCHECK: IF Time1… Time3 = DK: “Your best estimate would be fine here, but otherwise please
continue. Thanks!”

{COMPUTE FOR ALL}
Time1_Hrs… Time3Hrs
IF TimeMH1… TimeMH3 = 1 Time1_Hrs… Time3Hrs = Time1… Time3 / 60
IF TimeMH1… TimeMH3 = 2 Time1_Hrs… Time3Hrs = Time1… Time3
Satisfaction with outcome
{IF DetStatus1 … DetStatus3 = 1}
DetOCSat1 … DetOCSat3 [FLIP SCALE]
And how satisfied or dissatisfied are you with the outcome?

INTERVIEWER: READ OUT

1. Completely satisfied
2. Somewhat satisfied
3. Neither satisfied nor dissatisfied
4. Somewhat dissatisfied
5. Completely dissatisfied
Other detriment experienced
{ASK ALL}
Feeling [GRID: RANDOMISE, FLIP SCALE]
And to what extent, if at all, did your experience make you feel…?

{WEB: “Please select one answer on every row”}
INTERVIEWER: READ OUT EACH STATEMENT AND THE ANSWER CODES. REPEAT ANSWER
CODES AS REQUIRED

GRID ROWS
1. Anxious
2. Helpless
3. Misled

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4. Upset

GRID COLS
1. To a great extent
2. To some extent
3. Hardly at all
4. Not at all

{ASK ALL}
WellBeing [GRID: RANDOMISE ROWS, FLIP SCALE]
Overall, to what extent, if at all, has this problem had a negative effect on each of the following?

{WEB: “Please select one answer on every row”}
INTERVIEWER: READ OUT EACH STATEMENT AND THE ANSWER CODES. REPEAT ANSWER
CODES AS REQUIRED

GRID ROWS
1. Your mental health
2. Your physical health
3. Your household's finances

GRID COLS
1. A very negative effect
2. A negative effect
3. A slightly negative effect
4. No negative effect
Detriment experience loops – causes
Pandemic
{ASK ALL}
Pandemic [FLIP SCALE 1…3]
To what extent, if at all, do you think this problem was caused or made worse by the COVID-19
pandemic?
INTERVIEWER: READ OUT

1. The problem was mostly or fully caused by the COVID-19 pandemic
2. The problem was made worse by the COVID-19 pandemic
3. The problem was not affected by the COVID-19 pandemic
4. Don’t know

END LOOP: IF DetSectCount=1 Loop once; IF DetSectCount=2 Loop twice; IF DetSectCount>=3
Loop thrice
END FILTER: IF DMGood = 1…12 OR DMServiceA = 1…15 OR DMServiceB = 1…17

END SURVEY.

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Glossary
Term
Definition
Action(s) taken to
address an
experience of
detriment
A situation where a consumer has taken any initiative to address a
detriment problem (including, amongst other things: contacting the
seller or a consumer rights organisation, claiming under a
guarantee, writing online reviews, withholding payments, taking
legal actions, using a dispute resolution system, asking family
members or friends for help).
Complaints journey
The journey taken by a consumer following an experience of
detriment towards a potential resolution.
Confidence interval
(CI)
The confidence interval (CI) around each estimate is the
symmetrical range of values within which values of repeated similar
experiments are likely to lie. Statistical testing to determine the CIs
was conducted at the 95% confidence level. This means that, by
performing the same experiment 100 times with different samples of
the same population of interest, 95 times the point estimate would
lie within the CI presented. Such a level of confidence is possible
because the study is based on a random-probability sample (each
individual in the population of interest has a known and non-zero
probability of being selected to this study).
Consumer detriment The monetised, emotional and wellbeing impacts/consequences of
detriment incidents experienced by consumers.
Consumer
protection system
The rights and channels of action afforded to consumers by
consumer protection law as well as the willingness of businesses to
mitigate and resolve consumer detriment problems (either due to
market pressure or due to threat of legal consequences).
Consumer
vulnerability
The vulnerabilities faced by certain consumers as a result of socio-
demographic characteristics, behavioural characteristics, personal
situation, or market environment – including: a higher risk of
experiencing negative outcomes in the market; a limited ability to
maximise their well-being; an increased difficulty in obtaining or
assimilating information; a reduced ability to buy, choose or access
suitable products; and an increased susceptibility to certain
marketing practices.
Consumers
People in the UK who – between April 2020 and April 2021 –
purchased an item or a service, or used an item or a service
previously purchased. When discussing a specific sector or market
cluster, consumers are considered as those who purchased an item
or a service, or used an item or a service previously purchased, in
the sector, or in at least one of the sectors included in the market
cluster.

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Costs
The costs faced by a consumer after experiencing detriment,
including the initial cost of the product and any other costs borne by
the consumer, including fixing or replacing a product.
Detriment
characteristics
Characteristics which relate to detriment, including value of the
product, purchase channel, and type of detriment (e.g. poor quality,
misleading pricing etc.).
Detriment incident
(or experience of
detriment)
Problems with the products consumers bought in the last 12
months, or bought at any time and used in the last 12 months, that
caused them stress, cost them money, or took up their time
Detriment resolution The comparison of the actions requested by consumers and those
taken by sellers or service providers to achieve a resolution to an
incidence of detriment.
Impact
The study focused on perceptions and subjective views. Study
participants were asked to indicate to what extent they believed the
incident of detriment had an impact on aspects such as their
finances and well-being, as well as to estimate to what extent they
believed the COVID-19 outbreak had an impact on their detriment
experience. As such, impact in this report should be considered as a
self-reported perception, as opposed to an assessment of direct
causality.
Incidence of
consumer detriment
The proportion of consumers who have experienced detriment
overall, or in a given market cluster or sector, calculated over the
total number of consumers overall, or in a given market cluster or
sector.
Items
Goods that are produced or manufactured for sale and that can
typically be consumed after production. Goods are typically, but not
always, tangible, discernible and re-sellable.
Market
characteristics
Characteristics which relate to the market, including product type
(whether the product was a service or an item), market clusters and
sectors.
Market clusters
Groups of thematically linked sectors. Each market cluster can
include both items and services. For more info see Appendix E.
Median
Value lying in the midpoint of a frequency distribution (50% of the
other values would fall above it, and 50% below it).
Mitigations
Elements which mitigated the overall cost faced by a consumer after
experiencing detriment, including the value of having the product
replaced or fixed, the use value and other compensations.
Net monetised
detriment
The difference between the costs faced by the consumer as a result
of detriment (including the monetised value of the time spent solving
the problem) and the mitigations (such as having the value of having
product fixed or replaced).
Products
Items, and services or subscriptions.
Sectors
Categories of items or services/subscriptions for which the
consumers reported making a purchase or using a product
previously purchased in the 12 months covered by the study. For
more info see Appendix E.

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Services or
subscriptions
Work that is done often by a person for a consumer and is more
likely to involve the consumer in its production. Their benefit is
typically intangible, often realised in parallel with the service being
rendered, and cannot generally be returned or resold. Throughout
the report ‘services’ is used as a summary term for both ‘services
and subscriptions’.
Use value of a
product
The value that is retained by a product. It is a combination of
assumed and self-reported subjective value, depending on the
detriment type (e.g. if the only problem was a late delivery of an
item, the use value is assumed to be the same as the cost of the
item; self-reported subjective values are used, for example, when
the consumer purchased or used a substandard product).

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