Platform data access and secondary data sources : analytical paper 1.
Date: 2021-05-04
Institutions: Directorate-General for Communications Networks, Content and Technology
Subjects: Internet site, cloud computing, consumer behaviour, data collection, data protection, digital economy, digital single market, electronic commerce, goods and services, provision of services, search engine, trade intermediary

 Platform data access and 
secondary data sources  

 Analytical paper 1 

[Written by] 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
This analytical paper was produced for the Study on "Support to the Observatory 
for  the  Online  Platform  Economy",  Contract  No  LC-01109125  –  SMART 
2018/0034:  

www.platformobservatory.eu 

The study was implemented in 2019-2021 by 

PPMI (the lead partner)  

Open Evidence 

Rand Europe and 

German Economic Institute (IW) 

Analytical  paper  1:  Platform  data  access  and  secondary 
data sources 

Vaida  Gineikytė  (PPMI),  Egidijus  Barcevičius  (PPMI),  Loreta 
Matulevič (PPMI) 

EUROPEAN COMMISSION 

Directorate-General for Communications Networks, Content and Technology 
Digital Transformation 
Digital services and Platforms  

Contact: cnect-f2@ec.europa.eu 
European Commission 
B-1049 Brussels  

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Title of the document 

Directorate-General for Communications Networks, Content and Technology 2021 

 EN 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Platform data access and secondary 
data sources 

Analytical paper 1 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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CONTENTS 

Executive summary .............................................................................................................................. 3 
Setting the context ........................................................................................................................................... 3 
Sources and methodology ................................................................................................................................4 
Business users: data needs and access to data ..................................................................................................4 
The role of data companies .............................................................................................................................. 6 
Obliging platforms to share data .......................................................................................................................8 
Further research................................................................................................................................................8 

1.

2.
2.1. 
2.2. 

3.
3.1. 
3.2. 

3.3. 

4.
4.1. 
4.2. 
4.3. 
4.4. 
4.5. 

5.
5.1. 
5.2. 

6.
6.1. 

6.2. 
6.3. 

7.

Introduction ........................................................................................................................ 10 

Scope and methodology ....................................................................................................... 13 
Scope of the analytical paper ........................................................................................................... 13 
Methodology of data collection and analysis.................................................................................... 14 

Platform business user data needs ........................................................................................ 17 
Types of data for platform business users ........................................................................................ 17 
Sector-specific data needs ............................................................................................................... 21 
E-commerce sellers .......................................................................................................... 22 
3.2.1. 
App developers ................................................................................................................. 24 
3.2.2. 
Accommodation and hospitality industry ......................................................................... 25 
3.2.3. 

General data needs of platform business users ................................................................................ 26 

Platform data and business user access ................................................................................. 28 
Amazon Marketplace ....................................................................................................................... 29 
eBay ................................................................................................................................................ 35 
Google Play app store ...................................................................................................................... 37 
Booking.com ................................................................................................................................... 40 
Reasons for providing data or restricting the access ......................................................................... 44 

Business user perspective on data access ............................................................................... 47 
Business users’ perspective on platform data access ........................................................................ 47 
Data alternatives for business users ................................................................................................. 50 

Data companies ................................................................................................................... 55 
Types of data supplied by data brokers ............................................................................................60 
E-commerce .....................................................................................................................64 
6.1.1. 
Software development ..................................................................................................... 65 
6.1.2. 
Accommodation and hospitality .......................................................................................66 
6.1.3. 
Non sector-specific data providers ....................................................................................66 
6.1.4. 

Data sources and collection methods ............................................................................................... 67 
Data management and protection ................................................................................................... 71 

Conclusions and discussion ................................................................................................... 75 

List of interviewees ............................................................................................................................ 78 

Reference list ..................................................................................................................................... 80 

1 

 
 
 
 
 
 
 
 
 
 
 
 
LIST OF FIGURES 

Figure 1. Total completed interviews according to their type on the left and sector on the right (N=66) .................... 14 
Figure 2. Country distribution .................................................................................................................................... 15 
Figure 3. Company size .............................................................................................................................................. 15 
Figure 4. Industry category ....................................................................................................................................... 16 
Figure 5. Data value chain ......................................................................................................................................... 19 
Figure 6. Levels of data from the platform business user perspective ........................................................................ 19 
Figure 7. Data sources and creation of the value of data ............................................................................................. 27 
Figure 8. Amazon Retail Analytics: Basic vs Premium package .................................................................................. 30 
Figure 9. My business can easily access data collected by the platform that is important to my business ...................47 
Figure 10. Do you have access to the following types of data held by the Main online platform? ................................47 
Figure 11. How much processing does the Main platform carry out on data it provides to your business? .................. 48 
Figure 12. Needed but inaccessible data held by the online platform ......................................................................... 48 
Figure 13. Difficulties experienced in accessing data from the platform ..................................................................... 49 
Figure 14. Business user survey: Does your business analyse raw data provided by the platform, by third-party 
sources (data brokers) or collected by itself? ............................................................................................................. 50 
Figure 15. Data collection by businesses themselves, by data type ............................................................................. 51 
Figure 16. Use of third-party sources to access any types of data .............................................................................. 52 

LIST OF TABLES 

Table 1. Amazon Brand Analytics, as accessed by a seller ........................................................................................... 31 
Table 2. Types of data and analytics accessible by Amazon Marketplace sellers ........................................................ 32 
Table 3. Types of data and analytics accessible by eBay sellers .................................................................................. 36 
Table 4. Google Play Console: key metrics for app developers ................................................................................... 37 
Table 5. Types of data and analytics accessible by Google Play app developers on the platform ............................... 39 
Table 6. Analytics for Booking.com business users .................................................................................................... 40 
Table 7. Types of data and analytics accessible by properties listed on Booking.com ................................................ 42 
Table 8. Cooperative platform as a way to generate data ........................................................................................... 51 
Table 9. Data brokers’ ownership status and locations .............................................................................................. 56 
Table 10. Data types collected and supplied by data brokers ..................................................................................... 60 
Table 11. Data collection sources and methods of analysed data brokers .................................................................. 68 
Table 12. Analysed data companies' compliance with GDPR ...................................................................................... 72 

LIST OF BOXES 

Box 1. Key definitions of technical data collection methods ...................................................................................... 65 
Box 2. Issues with data protection and privacy ........................................................................................................... 71 

2 

      
 
 
 
 
 
Executive summary 

Setting the context  

Online  platforms  create  value  by  using  data  to  facilitate  interactions  (for  example,  commercial 
transactions)  between users. This means  that data is  at the  core of the  platforms’  business model and 
they  use it to provide  and  improve  their  services. When a platform  acts  as a marketplace to  business 
users,  some  data  sharing  will  be  part  of  the  service offered.  Business  users  can  draw  on  such  data  in 
order  to  reach,  understand,  and  maintain  their  clients,  although  the  scope  and  conditions  of  data 
sharing may also be subject to disputes between platforms and business users.  

Platforms collect and process a variety of data that can be volunteered or observed, individual-level or 
aggregated, personal or non-personal1. The data in their possession allows the platforms to understand 
the  preferences  of  customers  and  their  reactions  to  market  signals,  including  changes  in  prices  and 
product characteristics. This puts online platforms in a unique position as they are able to observe the 
functioning  of  the  market  in  real  time2.  Data  is  thus  a  key  source  of  market  power.  In  other  words, 
platforms’ decisions on what data to share, with whom, and under which conditions have far-reaching 
consequences to all the participants in the market.   

When taking decisions on data sharing, platforms perform a balancing act between several competing 
pressures3. From the platforms’ perspective, they have an interest in facilitating as many transactions as 
possible, which is an argument for sharing enough data so that businesses could attract their customers 
better.  Yet  providing  individual-level  or  personally  identifiable  information  to  business  users  may 
undermine a platforms’ business model as business users may draw on this data to bypass the platform 
in the future. Platforms may also use data to ensure their own competitive advantage in the market vis-
à-vis other platforms or, in the case of vertically integrated platforms, in relation to their own business 
users.  Finally,  collection  and  sharing  of  personal  information  may  conflict  with  privacy  concerns  and  
has  therefore  become  subject  to  increasingly  thorough  regulation  by  the  public  sector  (e.g.  GDPR), 
which means that platforms have a legal obligation not to collect or share some data, or should ask for 
explicit consent  from data subjects before collecting or sharing data.  

From  the  perspective  of  business  users,  it  would  be  useful  for  them  to  receive  data  about  the 
competition  in  the  market,  in  particular  about  competing  businesses,  products  or  services.  Yet,  they 
would also not want that  detailed information about them  was shared with their  competitors. Again, 
platforms must balance these needs by allowing the business users to understand the market without 
breaching  their  trust.  Given  that  business  users  themselves  are  very  different  in  terms  of  their  size, 
market power and analytical capacities, their data needs are also very different: while some ask for raw, 
granular  data,  others  can  only  make  use  of  highly-processed  data  and  prefer  to  get  actionable  data-
based insights. 

1 See Chapter 3 of the paper where we discuss the types of data 
2 Crémer, J., de Montjoye, Y.-A., & Schweitzer, H. (2019). Competition policy for the digital era. European Commission. Retrieved from 
https://ec.europa.eu/competition/publications/reports/kd0419345enn.pdf 

3 Any data sharing decision is indeed a private decision of a platform as only few areas benefit from rules on mandatory data sharing or industry 
codes of conduct, such as automotive, financial services, agriculture. 

3 

      
 
 
                                                           
Sources and methodology  

The paper draws on extensive desk research,  business user  survey and interviews.  The desk research 
focussed  on  an  overview  of  the  research  literature  (both  grey  and  academic)  as  well  as  various  news 
articles,  blogs  and  forums.  Further,  we  carried  out  over  60  interviews  with  online  platforms,  their 
business  users,  data  brokers  and  non-governmental  organisations  representing  the  key  stakeholders. 
The  business  user  survey  was  conducted  in  November-December  2019; it  covered  nine  EU  countries, 
with the sample size of 1,667. Given that the data needs and sharing practices are sector and platform-
specific,  we  focussed  our  research  on  three  sectors:  e-commerce,  dissemination  of  apps  and  software, 
and accommodation/hospitality. For the analysis of data companies, we analysed 15 companies, serving 
the data needs of the three sectors investigated in this paper.  

Business users: data needs and access to data 

In the paper we identified three general dimensions of data relevant to platform business users: 

 

the  type  of  data  by  object  (customers,  businesses,  user  behaviour,  markets,  transactions, 
etc.);  

  whether  the  data  relates  to  an  individual  business,  or  to  other  businesses  (competitors)on 

the platform / whole marketplace.  

  by the level of data processing and its value, from raw datasets to insights guiding business 

decision-making. 

The  paper  shows  that  the  kinds  of  data  provided  or  not  provided  by  the  platforms  (Amazon,  eBay, 
Google  Play  and  Booking.com)  are  rather  similar.  Access  to  data  as  well  as  advanced  analytics  are 
granted  to  the  extent  that  it  could  generate  more  income  for  the  platform  as  well  as  for  the  business 
users. In such a case the key question is whether the business users can take full advantage of the data 
provided  to  them.  Further,  a  significant  share  of  businesses  signal  that  they  are  experiencing  data 
access problems. This was very visible in interviews where businesses, especially the bigger or stronger 
ones, felt strongly about the data access. Their key concern was getting access to data so that they could 
use  it  to  innovate  and  keep  up  with  the  competition.  A  recurring  issue  was  also  the  power  of  the 
vertically  integrated  platforms  and  especially  the  extent  to  which  such  platforms  may  use  data  to 
develop their own competing products. In the next two sections we briefly explore, firstly, the findings 
concerning taking advantage of the available data and, secondly, the evidence concerning data that is 
not shared with the business users. 

Making use of the available data 

In order to understand to what extent business users take advantage of data that is available to them, 
we  reviewed  the  types  of  data  collected  by  the  platforms.  The  platforms  collect  data  on  customers, 
businesses, user behaviour, markets and transactions, and other dimensions. When asked, the business 
users usually report that all such data is of interest to them. Most of the businesses use (or would like to 
use) both, data on their individual performance, as well as data on their competitors and overall market 
trends. Nevertheless, our study also shows that business users tend to think about data in very different 
ways. Further, the interviews with, for example, Amazon sellers showed that businesses do not always 
know what data is collected by platforms and what is accessible and useful to them.  

A key dimension in discussing the usefulness of data is access to raw vs. processed data. Access to raw 
data  is  mainly  of  interest  to  the  bigger  business  users,  who  have  the  infrastructure  and  specialised 

4 

      
 
 
analytical skills to generate market insights. Yet, the majority of businesses do not have the capacity to 
derive  value  from  unprocessed  data.  Therefore,  as  corroborated  by  the  interview  programme,  the 
majority  of  business  users  need  processed  data  or  data  analytics  that  offer  insights  on  how  to  better 
position or market their products and services, appear more frequently in search results, communicate 
with clients, etc.  In fact, platforms such as Amazon, eBay, Etsy, Booking, Allegro and others provide 
data-based  guidelines,  tips,  seasonal  advice,  etc.    Some  examples  of  such  advice  include:  to  optimise 
product titles, based on the search behaviour data;  to develop a new product based on predictions  on 
what  consumers  will  buy;  add  tea  kettles  to  hotel  rooms  to  attract  Asian  tourists;  change  the 
monetisation model of an app based on user engagement trends. This is appreciated by business users. 

Finally, some  data may  be collected by platforms and provided in  both raw  and processed  form,  but 
only as part  of other services and for  a price. eBay offers  the  Terapeak Research product to its sellers 
with  a  Basic,  Premium,  Anchor  or  Enterprise  Store  account;  the  sellers  with  the  Starter  account  may 
access  data  for  a  yearly  or  monthly  subscription4.  Platforms  such  as  Amazon,  Etsy  and  others  offer 
advertising services that generate data, which is made available specifically to businesses subscribing to 
such services.  

Data that is not available to business users  

Our research showed that a significant share of business users express dissatisfaction with regard to the 
level of data access provided to them by online platforms. The business user survey showed that access 
to data possessed by online platforms is of concern to around a third of surveyed business users who 
reported  that  they  cannot  access  at  least  some  data  that  is  essential  to  their  business.  Generally, 
although  the  platforms  collect  and  analyse  loads  of  data,  only  a  fraction  of  this  is  provided  to  other 
players. The platforms do not share the raw big data on day-to-day activities, as well as detailed data 
on customers and competitors.  

Our paper identified three groups of concerns that business users express with regard to data sharing.   

The first is related to lack of access to personal data, such as customers’ e-mail address. Some business 
users,  especially  in  the  hospitality  and  e-commerce  sectors  consider  such  data  of  key  importance  to 
them  so  that  they  could  establish  a  more  direct  client  relationship.  Other  personal  data  collected  by 
platforms, but usually not provided to business users include, for example: telephone, address, credit-
card data. As confirmed by our desk research and interviews with the platforms themselves, this data is 
not  provided  for  a  number  of  reasons.  Firstly,  this  is  not  considered  compatible  with  the  platform’s 
business  model  as  business  users  may  use  direct  communication  to  bypass  platforms  in  the  future. 
Secondly, platforms consider that a consistent client relationship and data protection is part of the client 
experience.  They  are  wary  that  direct  access  to  the  clients  by  business  users  may  result  in  a  surge  of 
unwanted marketing messages (this argument was not supported by the interviewed business users). 
Finally,  the  personal  data  protection  regulation  (including  GDPR)  puts  obligations  on  platforms  in 
terms  of  data  sharing  and  management,  including  the  obligation  of  getting  explicit  consent  from 
consumers  to  collect  and  share  their  data.  According  to  the  business  user  survey,  legislative  or 
regulatory restrictions is indeed acknowledged as the key reason for not getting access to data.  

Secondly,  business  users  need  data  that  help  them  to  stay  competitive,  innovate  and  develop  their 
products and services. Partly this is related to data on, for example, search keywords, search volumes, 
consumer  behaviour  in  reaction  to  different  price  signals.  Platforms  do  share  such  data  to  a  certain 
extent  (e.g.  three  most  important  key  words),  however  some  interviewed  business  users  felt  that  this 

4 eBay Customer Service. (2020). Terapeak Research. Retrieved from https://www.ebay.com/help/selling/selling-tools/terapeak-research?id=4853  

5 

      
 
 
                                                           
does not give them sufficient level of detail. Partly,  this is also related  to data  about competitors  and 
their  products  and  services.  In  this  case  however,  both  the  interviewed  platforms  as  well  as  business 
users expressed understanding that the level of detail is naturally limited as businesses would not want 
their individual business performance information to be made available to others.  

The third concern is that platforms are taking advantage of data to promote their own products that are 
very similar to those offered by their business users. This is primarily pertinent to vertically integrated 
platforms with significant market power. So, in the business user survey, 58% of respondents reported 
that the platform itself offers the same (or very similar) goods or services to those that their businesses 
offer on the platform. Among these respondents, 55% argued that online platforms are favouring their 
own goods or services vis-à-vis the same (or very similar) goods or services offered by their businesses. 
Unique and comprehensive datasets on all the firms and their consumers operating in the marketplace 
can  give  a  huge  business  advantage  to  the  platform  operators.  The  key  ways  of  favouring  include 
ranking, placement of advertisement, pricing and other – all of these are enabled by the data collected 
by platforms. Some interviewed business users argued that platforms (specifically – Amazon) are using 
data  to  monitor  which  goods  have  the  best  margins  in  the  market  and  then  move  into  offering  such 
goods  themselves.  The  interviewees  did  not  want  to  present  specific  examples,  in  fear  of  possible 
repercussions. However, media articles provide stories on unfair competition between Amazon private 
labels  and  third-party  Marketplace  sellers5.  Amazon’s  practices  of  sharing  seller  data  between  Retail 
and Marketplace, as well as sophisticated use of metrics to boost its own sales were also corroborated 
by the platform’s former employees6.  

The role of data companies 

The businesses that need more data than they can get from platforms pursue two broad strategies: (1) 
collect and analyse  data  themselves,  from sources  available  to them;  (2) rely on third party providers 
(data  brokers).  Four-fifths  of  the  business  user  survey  respondents  (81%)  indicated  that  they  collect 
some data themselves; the most prevalent data type is identification details of own customers (57% of 
respondents  collect  this  data),  followed  by  business  performance  data  (55%)  and  analysis  of  market 
trends/  developments (55%). Further, a third of  the  surveyed  companies (33%) reported that they use 
third-party  sources  (data  brokers).  Most  of  the  interviewees  -  especially  in  the  e-commerce  sector  - 
reported that they use the services of third-party data and analytics providers. 

Companies specialising in data and insights fill important data gaps, especially  with regard to data on 
competition  and  actionable  business  insights.  According  to  PwC  estimations,  data  companies  earned 
$21  billion  in  20187.  Some  data  brokers  specialise  to  cover  specific  sectors,  such  as  applications  (App 
Annie,  App  figures,  Apptopia,  Mobile  Action,  Sensor  Tower),  e-commerce  (Algopix,  Jungle  Scout, 
Sellics, Teikametrics, Terapeak) or hospitality (AirDNA, Beyond Pricing, Uplisting, Wheelhouse, Skift). 
Other data brokers, such as Similar Web and Zirra provide data on multiple sectors.  

Data brokers use highly advanced technical methods to extract data, or they buy data from online and 
offline  sources.  A  lot  of  data  is  scraped  from  the  platforms.  According  to  a  blogger  and  speaker  on 

5 The Washington Post. (n.d.). Amazon sellers say online retail giant is trying help itself not consumers. Retrieved from 
https://www.washingtonpost.com/technology/2019/10/01/amazon-sellers-say-online-retail-giant-is-trying-help-itself-not-
consumers/?arc404=true;   Creswell, J. (2018). How Amazon steers shoppers to its own products. The New York Times. Retrieved from 
https://www.nytimes.com/2018/06/23/business/amazon-the-brand-buster.html  
6 Hu, K. (2019). Revealed: how Amazon uses seller data to build a private label juggernaut. Yahoo! Finance. Retrieved from 
https://finance.yahoo.com/news/amazon-uses-thirdparty-sellers-data-to-build-private-labels 

7 Gröne, F., Péladeau, P., & Samad, R. A. (2019). Tomorrow’s data heroes. Strategy+business. Retrieved from https://www.strategy-
business.com/article/Tomorrows-Data-Heroes?gko=5f270  

6 

      
 
 
                                                           
Amazon, the platform’s approach to the data companies is “to let them ride” by leaving “the backdoor 
open”.  Yet  Amazon  also  banned  several  entities  that  were  acquiring  user  data  from  the  platform  in 
violation of its privacy policies8. Some other platforms do not permit scraping for commercial purposes, 
but that does not always prevent data companies from collecting data this way.  

Another key source is crowdsourcing business user account data. Some third-party data providers ask 
online  sellers  to  share  their  marketplace  information,  and  then  link  the  data  of  thousands  of  users  to 
draw  market  insights.  For  example,  Jungle  Scout  collects  data  from  a  large  number  of  sellers  (over 
225,000) who have opted in to share their sales information9. When merged with the data gathered by 
scraping  the  platform’s  front-end  (e.g.,  Best  Seller  rank  on  Amazon),  this  can  yield  quite  precise 
estimations and extrapolations. Similarly, if AirDNA users wish to receive performance analytics, they 
will be asked to upload their Airbnb host IDs. After doing this, they can see their performance trends, 
comparative and financial analysis on all vacation rental listings. 

The  key  value  proposition  of  the  data  brokers  lies  in  their  ability  to  bring  together  a  combination  of 
sources  as  well  as  superior  technical  and  analytical  capacities,  innovative  tools  and  approaches.  Data 
brokers  allow  their  users  to  learn  about  their  competitors,  get  a  detailed  market  overview,  obtain 
actionable  insights.  According  to  the  analysis  presented  in  this  paper,  this  is  the  kind  of  information 
that  is  most  in  demand  by  the  business  users  and/or  platforms  do  not  provide  to  a  sufficient  extent. 
Further,  business  users  themselves  do  not  need  to  invest  into  any  analytics  or  IT,  but  rather  buy 
products tailored to their needs.  

For  instance,  as  explained  by  several  interviewed  Amazon  sellers,  Jungle  Scout  and  other  providers, 
such as Helium10, AMZScout and Unicorn Smasher, supply them with comprehensive market insights 
and competitor overviews. Obviously, these data brokers provide estimations based on what data they 
could  gather  rather  than  exact  information.  Nevertheless,  the  estimations  are  said  to  be  “spookily 
accurate”10.  Similarly,  data  providers  for  app  developers,  such  as  AppAnnie  and  SensorTower,  offer 
comprehensive app market data, including performance of specific apps and markets. Interviewed app 
developers  mentioned  that  they  use  the  sources  together  with  the  app  store  data  extensively.  In  the 
accommodation/ hospitality sector, companies such as AirDNA provide insights based on data that the 
OTAs  do  not  share.  For  example,  in  late  2015  Airbnb  stopped  providing  the  overall  real-time 
reservation data. AirDNA, in turn, uses an algorithm based on 16 indicators picked up in historical data 
to determine the reservation status for each listing. They argue that their algorithm has an error margin 
of only 5%.  

The data companies’ market is very dynamic and fast-paced. This paper identified a number of issues, 
illustrating  the  key  challenges  and  limitations  of  data  brokers.  Firstly,  the  data  companies  remain 
highly dependent on data sharing policies of platforms. For example, Amazon until recently provided 
exact  and  broad  match  search  volume  and  product  relevance  data  via  one  of its  APIs.  It  was  feeding 
several  third-party  software  providers  such  as  Viral  Launch  and  Helium10  until  late  2018,  when  the 
platform removed these metrics from the API. Another platform, Allegro made significant investment 
to  develop  new  data  products  (Allegro  Statistics)  that  are  now  provided  to  its  sellers;  this  is 
endangering the business model of third-party analytics providers. 

8 Matsakis, L. (2019). Amazon cracks down on third=-party apps over privacy violations. Wired. Retrieved from 
https://www.wired.com/story/amazon-marketplace-apps-privacy/ 

9 Rohler, M. (2019). How are Estimated Sales and Revenue Calculated? What are AccuSales? Jungle Scout Help Center. Retrieved from 
https://support.junglescout.com/hc/en-us/articles/360008616814-How-are-Estimated-Sales-and-Revenue-Calculated-What-are-AccuSales-
#:~:targetText=We%20gather%20it%20from%20a,their%20sales%20info%20with%20us.&targetText=This%20means%20that%20as%20we,analyze%2
0and%20test%20it%20daily.  

10 Gerber, S. (2018). Want to sell on Amazon? 15 strategies for success. The Next Web (TNV). Retrieved from 
https://thenextweb.com/contributors/2018/01/19/want-sell-amazon-15-strategies-success/  

7 

      
 
 
                                                           
Secondly, the data needs of platform business users are often very specific and concern platforms that 
they  use.  Such  data  cannot  be  easily  scraped  or  estimated  by  the  third-party  data  providers11.  It 
includes information on real-time of activities on the platform (e.g. X currently has product Y added to 
the  shopping  cart),  which  would  allow  to  effectively  address  the  customer;  transaction-related  data 
about the customers, sales activities and listings of specific business user. 

Thirdly,  the  huge  amounts  of  data  that  data  brokers  collect,  store,  possibly  re-personalise  and 
disseminate are of interest from the regulatory perspective, first and foremost due to privacy concerns. 
Most individuals or companies are unaware of what information data brokers collect on them or even 
that  they  collect  information  at  all12.  Due  to  this  asymmetry,  the  data  broker  industry  has  been  often 
characterised  as  opaque,  non-transparent,  arbitrary,  biased,  unfair  and  unaccountable13.  Interviewees 
from  the  data  brokers  argued  that  they  are  taking  actions  to  make  sure  they  are  compliant  with  data 
protection and privacy laws, such as the GDPR. However, other sources show that such compliance has 
not always been properly ensured. For example, a few months after the GDPR came into force, Privacy 
International  filed  a  complaint  against  seven  data  brokers:  Acxiom,  Oracle,  Criteo,  Quantcast,  Tapad, 
Equifax, and Experian14. The main argument was their failure to comply with data protection principles 
(such as acquiring consent, providing detailed and transparent information for the data subject access 
requests) and exploitation of data in unknown ways. 

As  a  final  point  we  also  observe  that  some  business  users  are  exploring  innovative  approaches  that 
would  allow  them  to  joint  forces  and  be  less  dependent  on  big  platform  companies.  One  example 
includes  cooperative  marketplaces,  such  as  Fairmondo.de,  which  belongs  to  its  business  users  and 
employees. Through a cooperative structure, the users can share the platform as a resource for mutual 
benefit and decide on the rules for data sharing and access. 

Reflexions on mandatory data sharing by platforms  

The  question  whether  there  should  be  horizontal  obligations  for  platforms  to  share  more  data  is 
complicated and our research did not lead to unanimous results. Firstly, providing everyone with the 
same  access  to  data  may  be  detrimental  to  the  business  users  themselves,  whose  financial,  client  and 
performance  information  could  be  disclosed  to  competitors.  Secondly,  the  impact  of  data  sharing 
should  also  be  assessed  from  a  consumer  perspective.  Platforms  invest  a  lot  in  customer  satisfaction, 
and  data  protection  is  part  of  the  consumer  experience.  Consumers  prefer  to  deal  with  centralised 
services rather than with multiple actors asking for consent, and they are not willing to be flooded with 
marketing  messages.  Finally,  mandatory  data  sharing  may  have  a  negative  impact  on  platforms’ 
business and profits and might act as a disincentive for innovation15.  

Further research 

This analytical paper presents an overview on the state of the art of data access for platforms’ business 
users. It also raises new questions for further research. The analysis revealed concrete issues in specific 

11 Graef, I. (2016). EU Competition Law, Data Protection and Online Platforms: Data as Essential Facility. Kluwer Law International BV.  

12 Christl, W. (2017). How companies use personal data against people. Working paper by Cracked Labs, Institute for Critical Digital Culture. 

13 Christl, W. (2017). How companies use personal data against people. Working paper by Cracked Labs, Institute for Critical Digital Culture.  

14 Privacy International. (2018). Why we’ve filed complaints against companies that most people have never heard of – and what needs to happen 
next. Retrieved from https://privacyinternational.org/advocacy/2434/why-weve-filed-complaints-against-companies-most-people-have-never-
heard-and-what    
15 Easley, D., Huang, S., Yang, L., & Zhong, Z. (2018). The Economics of Data. Available at SSRN 3252870 

8 

      
 
 
                                                           
segments  of  the  platform  economy.  Firstly,  the  most  problematic  cases  concern  vertically  integrated 
platforms with significant market power, who can use data to their advantage, while not sharing it with 
their  own  competing  business  users.  Secondly,  this  paper  looked  into  three  sectors  of  the  platform 
economy:  e-commerce,  app  development  and  accommodation/  hospitality.  In  order  to  get  further 
insights  on  data  access,  further  investigation  into  other  sectors  not  covered  in  this  paper  is  needed. 
Thirdly, further research would be useful into the legal and technical aspects  of the platform data and 
sharing, including interoperability.  

9 

      
 
 
 
  
1.

 Introduction 

Due  to  technological  innovations  and  penetration  of  technologies  into  new  areas,  many  entities  from 
different industries have the tools to collect extensive and diverse types of data on products, processes, 
customers  and  internet  users  more  generally.  Some  sources  estimate  that  as  of  2019,  the  nearly  4.5 
billion Internet  users generate  about 2.5 exabytes16 of data each day17. A very significant share of this 
enormous  amount  of  data  comes  namely  from  online  platforms,  that  collect  data  on  all  the  activities 
taking  place  on  them,  both  actively  and  passively.  To  illustrate,  as  of  January  2019,  out  of  20  most 
visited websites on internet, 11 were what we classify as online platforms18, also taking four top places 
(Google,  YouTube,  Facebook  and  Baidu)19.  Google  alone  processes  over  3.5  billion  searches20  and  20 
petabytes of data every day21. Another four petabytes of data are created on Facebook daily22.  

Together with the increase in the amounts of data collected, its importance for business success has also 
increased  dramatically  in  recent  decades.  In  many  cases,  data  is  no  longer  the  mere  accessory  of  a 
process or product, but often an integral part of it. It has also become a key commodity of many market 
players interested to improve their services and products, expand the market, understand competition, 
and  apply  many  other  data-driven  solutions  for  their  business  growth.  The  existing  body  of  research 
emphasises data as a key input for optimisation of business processes and innovation23. Some studies 
suggest  that  companies  using  data-driven  innovation  benefit,  on  average,  from  5%  to  10%  faster 
productivity  growth  than  similar  companies  that  do  not  apply  data-driven  innovation24.  Basically, 
having the most accurate and comprehensive dataset ensures market power25. 

From the economic perspective, as a commodity, data has two important characteristics. First, it is non-
rivalry  in  consumption.  This  means  that  the  same  data  record  can  be  used  by  different  actors  for 
different  purposes  at  the  same  time,  because  it  is  easily  reproduced  and  distributed  almost  free  of 
charge.  This  also  means  that,  theoretically,  different  companies  and  individuals  can  exploit  the  same 
data,  thus  providing  opportunities  in  markets  for  more  participants,  and  allowing  for  dynamic 
entrepreneurial  activity.  This  characteristic  distinguishes  data  from  many  other  assets  and  tangible 
goods, which are available in limited quantities and at a significant cost. 

However,  while  the  non-rivalry  of  data  in  consumption  implies  that  it  is  not  a  scarce  resource,  the 
excludability  characteristic  of  data  works  in  the  opposite  direction.  That  means  that  the  owner  of  a 

16 1 EB = 1,0006 bytes = 1018 bytes = 1,000,000,000,000,000,000 B = 1,000 petabytes = 1 million terabytes = 1 billion gigabytes. 

17 Domo. (n.d.). Data never sleeps. 6.0. How much data is generated every minute? Retrieved from 
https://www.domo.com/assets/downloads/18_domo_data-never-sleeps-6+verticals.pdf 

18 Similarweb.  

19 Similarweb & Alexa.  

20 Internet Live Stats (n.d.). Google Search Statistics. Retrieved from https://www.internetlivestats.com/google-search-statistics/  

21 Marr, B. (2018). How much data do we create every day? The mind-blowing stats everyone should read. Forbes. Retrieved from 
https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-
read/#27e03f2960ba  

22 Desjardins, J. (2019). How much data is generated each day? World Economic Forum. Retrieved from 
https://www.weforum.org/agenda/2019/04/how-much-data-is-generated-each-day-cf4bddf29f/  

23 OECD. (2019). Digital Innovation: Seizing Policy Opportunities, OECD Publishing, Paris. Retrieved from https://doi.org/10.1787/a298dc87-en  

24 OECD. (2016). Big data: bringing competition policy to the digital era. Retrieved from https://one.oecd.org/document/DAF/COMP(2016)14/en/pdf  

25 Di Porto, F., & Ghidini, G. (2018). Big data between privacy and competition: dominance by exploitation? Which remedies? ASCOLA. 

10 

      
 
 
                                                           
dataset  may  exclude  other  users  from  consuming  by  denying  them  access.  In  this  way  data  can  be 
monetized, and collecting it becomes economically attractive26. In fact, the recent dynamics in the data 
markets rather show that companies - platforms and their business users alike - are striving to acquire a 
'data advantage' over rivals27.  

Currently,  as  illustrated  above,  most  of  the  global  data  is  collected  and  processed  by  several  large 
players.  Mediating  millions  of  transactions  every  day,  the  largest  online  platforms  have  access  to  the 
data  of  all  their  user  groups.  They  collect  and  aggregate  vast  amounts  of  data  in  their  day-to-day 
operations28,  allowing  them  to  have  a  comprehensive  market  overview  that  is  more  valuable  than 
separate  data  on  individual  transactions29.  This  leads  to  a  substantial  data  asymmetry  between  firms 
and  the  platform  which  they  use  to  market  their  products30.  Such  massive  amounts  of  data  are 
considered  to  place  the  platforms  in  a  position  of  almost  unassailable  dominance  among  economic 
actors31.  

Most business users of online platforms heavily rely on data and analytics32. These have become a key 
tool  for  the  entities  conducting  business  on  online  platforms  to  determine  which  product,  price  and 
advertising  is  best  to  maximise  their  profits,  understand  their  customers  and  sales,  and  manage  the 
supply  chain  efficiently33.  All  platforms  provide  them  with  at  least  some  access  to  such  information: 
granting  access  to  certain  types  of  data  spurs  the  development  of  new  products,  services  or 
complementary  applications  that  enhance  the  value  of  the  platform  itself.  However,  many  earlier 
investigations show that online platforms quite often refuse to share some of the crucial data with their 
business users34, preventing these firms from providing products or services that require this data as an 
input. 

At  the  same  time,  the  largest  online  platforms  become  the  focus  of  many  third-party  data  providers, 
offering  the  platform  business  users  an  alternative  source  of  data  and  insights.  The  growth  of  online 
platforms and numbers of internet players more generally goes hand in hand with data accumulation 
by third parties, who use these data for analytics and value creation. The importance of data companies 
also  seems  to  be  growing  rapidly.  European  Commission  forecasts  that  the  overall  data  market  in 
Europe could be worth as much as €106.8 billion by 202035. Similarly, PwC expects that the global data 
economy will be worth more than USD 400 billion by 202536. Some authors even argue that most of the 
data is actively traded and accessible to anyone willing to pay for it, indicating a healthy data market. 

26 Demary, V., & Rusche, C. (2019). Data sharing im e-commerce. Rechtliche und ökonomische Grundlagen. The German Economic Institute. 
Retrieved from https://www.iwkoeln.de/fileadmin/user_upload/Studien/Gutachten/PDF/2019/Gutachten_Data_Sharing_Final.pdf  

27 Stucke, Maurice E., & Grunes, Allen P. (2016). Introduction: Big Data and Competition Policy. Big Data and Competition Policy, Oxford University 
Press (2016). Available at SSRN https://ssrn.com/abstract=2849074 

28 Chen, H., Chiang, R. H. L., Storey, & V. C. (2012). Business intelligence and analytics: from big data to big impact. MIS Quarterly, 36(4), 1165-1188. 
Retrieved from https://pdfs.semanticscholar.org/f5fe/b79e04b2e7b61d17a6df79a44faf358e60cd.pdf%3E. 

29 Martens, B. (2018). The impact of data access regimes on artificial intelligence and machine learning. JRC Working Papers on Digital Economy 
2018-09, Joint Research Centre (Seville site). 

30 Martens, B. (2016). An economic policy perspective on online platforms. Institute for prospective technological studies digital economy working 
paper 2016/05, JRC Technical Reports. Retrieved from https://ec.europa.eu/jrc/sites/jrcsh/files/JRC101501.pdf  

31 Competition Policy International. (2018). Retrieved from https://www.competitionpolicyinternational.com/wp-content/uploads/2018/05/CPI-
Garces.pdf  

32 Graef, I., Wahyuningtyas, S. Y., & Valcke, P. (2015). Assessing data access issues in online platforms. Telecommunications policy, 39(5), 375-387. 

33 Li, B., Ch’ng, E., Chong, A. Y.-L., & Bao, H. (2016). Predicting online e-marketplace sales performances: A big data approach. Computers & 
Industrial Engineering, 101, 565-571. 

34 Erickson, K. (2018). In their own words: why sellers are fed up with Amazon. Medium. Retrieved from https://medium.com/public-market/in-
their-own-words-why-sellers-are-fed-up-with-amazon-e97da44f7f18 

35 European Commission (2017), Final results of the European Data Market study measuring the size and trends of the EU data economy. Available 
at https://ec.europa.eu/digital- single-market/en/news/final-results-european-data-market-study-measuring-size-and-trends-eu-data-economy  

36 Hazelwood, G. (2019). Sell your data. Earn passive income. What could go wrong? Slate. Retrieved from 
https://slate.com/technology/2019/05/data-exchanges-streamr-ubdi-ocean-protocol-privacy.html  

11 

      
 
 
                                                           
Often  many  substitutable  sources  exist  to  acquire  data,  and  having  large  in-house  datasets  does  not 
necessarily confer an entrenched advantage37. 

Nonetheless,  there  is  little  knowledge  whether  such  tradable  data  sources,  third-party  analytics 
providers  or  data  brokers  can  indeed  provide  businesses  with  sufficient  information  to  overcome  the 
data disadvantages in the platform economy. Moreover, so far, the discussion about the data needs of 
business users has been rather vague. In this context, the aim of this paper is to investigate and provide 
comprehensive insights on: 

o  The general data needs of platform business users, including a data typology, allowing to specify 

the discussion on platform business user data demand.  

o  Platform data collection and sharing practices, the current limitations in accessing data collected 
and  held  by  platforms,  and  rationale  for  not  sharing  the  data,  considering  potential  trade-offs 
between the interests of business users, consumers and those of the online intermediaries. 

o  The  ability  of  business  users  to  collect  data  relevant  for  their  businesses  from  alternative  data 
sources, allowing to  meet  their  data demand,  limit the  dependency  on  the  online  platforms, or 
triangulate data sources and build more valuable insights.  

Exploration of these topics will address several knowledge gaps and research questions, identified by 
the Expert group in their report on data in platform economy, including: 

o  Review  of  the  actual  data  sharing  practices  in  a  range  of  online  platforms  through  case 

studies.  

o  Review of sector-specific data needs by platform business users, and purposes of data use. 
o  Analysis of how businesses and 3rd parties obtain and exploit data, considering elements of 

digital skills and contractual constraints. 

o  Research  into  the  role  of  data  brokers  in  the  ecosystem  for  facilitating  data  sharing  and 

making its benefits available to players of all sizes. 

The  following  Chapter  2  of  this  paper  presents  the  scope  of  this  analysis  and  methodology  of  data 
collection applied in answering these questions. The third chapter starts the analysis with an overview 
of the main data types and characteristics that platform business users need to make informed business 
decisions  as  well  as  successfully  compete  and  scale  their  operations.  The  fourth  chapter  reviews  the 
data  collection  and  sharing  practices  of  four  selected  platforms  from  different  sectors  of  the  platform 
economy, vertically integrated and non-integrated. After identifying the gaps in data access, Chapter 5 
focuses  on  the  availability  of  alternative  sources  for  platform  business  users  to  acquire  data.  Finally, 
Chapter 6 looks in-depth into actors providing alternatives to platform data - third-party data brokers. 
Finally, Chapter 7 provides a summary of the main findings as well as directions for future research.  

37 Lambrecht, A., & Tucker, C. E. (2015). Can Big Data Protect a Firm from Competition. Retrieved from 
http://ec.europa.eu/information_society/newsroom/image/document/2016-6/computer_and_communications_industry_association_-
_can_big_data_protect_a_firm_from_competition_13846.pdf 

12 

      
 
 
                                                           
2.

  Scope and methodology 

2.1.  Scope of the analytical paper 

platform 

economy: 

e-commerce, 

To investigate  the  business  user  data  needs,  this  analytical  paper  focuses  on  three  broad  sectors  with 
the 
and 
accommodation/hospitality.  These  were  selected  as  the  sectors  and  economic  activities  most  and 
increasingly  reliant  on  online  platform  intermediation  services.  To  illustrate,  between  2014  and  2018 
online  hotel  distribution  in  Europe  grew  by  46.7%38,  while  the  growing  e-commerce  sector  in  Europe 
was estimated to reach sales of 621 billion euros in 201939. Meanwhile, only a miniscule share of mobile 
application  distribution  takes  place  outside  app  stores.  The  following  parts  of  the  paper,  therefore, 
looks into the platforms, business users and third-party data providers operating in these three sectors.  

dissemination 

software, 

apps 

and 

of 

For  the  in-depth  analysis  of  the  platform  data  collection  and  sharing  practices,  we  selected  four 
platforms to represent each sector. The specific platforms were selected as the most used ones globally 
and  in  Europe:  Amazon  and  eBay  for  e-commerce;  Google  Play  app  store  for  app  developers,  and 
Booking.com for hospitality sector. This selection allowed us to limit the scope of our focus and get a 
quite comprehensive view of the broader data-sharing practices in the platform economy, because:  

 

 

 

 

The selection covers platforms that intermediate commercial transactions between businesses 
and  their  customers.  This  multi-sided  nature  allows  them  generate  loads  of  data  on  these 
activities,  which  becomes  important  for  business  users  to  further  develop  their  business 
models, strategies and products. 
VVA  study40  indicated  that  these  three  groups  of  business  users  have  quite  different  data 
needs and levels of access to data. The selection, therefore, will allow us to cover the existing 
variety and look into the issue comprehensively.  
The platform selection allows comparisons between vertically integrated and non-integrated 
platforms. More specifically, most existing major app stores are vertically integrated (i.e., they 
offer  own  apps  on  their  stores),  while  most  OTAs  are  not  (they  do  not  run  own  hotels). 
Meanwhile,  among  major  e-commerce  marketplaces,  we  can  find  both  vertically  integrated 
and  non-integrated.  Our  case  selection  therefore  includes  two  marketplaces  to  cover  both 
models.  
These  are  the  largest/  most  successful  platforms,  covering  most  business  users.  Many 
interviewees  agree  that  business  “must”  be  on  them  to  be  successful  in  their  respective 
businesses. For example, (although e-commerce is the most diverse area of the three selected) 
Amazon  is  the  major  global  and  EU  player,  with  a  wide  variety  of  products,  as  well  as 
attempts to copy Amazon  model by other platforms.  Google  Play is the  largest  app  store in 
Europe  and  globally,  with 3.3  million  apps  in  2019. Meanwhile,  Booking.com  has  66%  OTA 

38 D-edge. (2019). How online hotel distribution is changing in Europe. A deep-dive into European Hotel distribution trends 2014 – 2018. Retrieved 
from https://www.d-edge.com/how-online-hotel-distribution-is-changing-in-europe/ 

39 Nielsen., M.-L. (2019). Trends and key figures in European e-commerce. PrestaShop. Retrieved from https://www.prestashop.com/en/blog/2019-
european-e-commerce-report 

40 Hausemer, P., Rabuel L., & Graux, H. (2017). Study on data in platform-to-business relations. VVA. Retrieved from 
https://op.europa.eu/en/publication-detail/-/publication/4af6cec1-48fb-11e8-be1d-01aa75ed71a1/language-en 

13 

      
 
 
                                                           
market  share  in  Europe  in  201741,  with  most  European  accommodation  properties  quite 
dependent on it42. 

Further, to investigate the alternatives for business user data access and third-party data brokers more 
specifically, we broadly overview a selection of data companies, servicing the businesses from the three 
investigated sectors (app development, e-commerce and hospitality). 

2.2.  Methodology of data collection and analysis 

Generally,  the  data  and  evidence  used  in  this  analytical  paper  was  collected  through  desk  research, 
interviews and an online  business user  survey. Given that  most of the  data on the  research  questions 
was  collected  from  actors  involved  directly  in  the  platform  economy  and  representing  particular 
interests, the research team triangulated and fact-checked the information feeding into this report.  

Desk research included the overview of the research literature (both grey and academic) on the issues 
of data in the platform economy. Given the existing literature focusing on the specific questions of this 
analytical  paper  was  rather  scarce,  the  research  team  also  reviewed  various  news  articles,  blogs  and 
forums, some of them also serving as primary data sources. The researchers used this information for 
two purposes: as a source factual information, after fact-checking and triangulating with other sources 
(e.g.,  we  investigated  whether  some  types  of  data  are  provided  by  platform  or  not,  by  triangulating 
business user impressions, platform’s own presentation of their policies and interview data).  The full 
list of reviewed sources is provided at the end of this report.  

Furthermore, the analysis presented in this paper is based on over 60 interviews with online platforms, 
their  business  users  and  associations  representing  each  of  these  groups.  The  lists  of  main  interview 
topics  and  interviewees  is  provided  in  at  the  end  of  this  document.  The  figure  below  shows  a 
breakdown  of  the  total  completed  interviews,  including  all  stakeholders,  according  to  their  types 
(business  users,  online  platforms,  and  data  brokers)  and  sectors  (e-commerce,  accommodation/ 
hospitality, apps and software, or multiple sectors).  

FIGURE 1. TOTAL COMPLETED INTERVIEWS ACCORDING TO THEIR TYPE ON THE LEFT AND SECTOR 
ON THE RIGHT (N=66) 

6 

19 

Business users

Online platforms

41 

Data brokers

22 

8 

24 

12 

E-commerce

Accommodation/
hospitality
Apps and software

Multiple sectors

The  interview transcripts were coded using NVivo software for  qualitative  data analysis, using codes 
corresponding to the main research questions of this paper. This allowed us to employ all the evidence 
from interviews in a comprehensive and systematic manner.  

41 Statista. 

42 Martin-Fuentes, E., & Mellinas, J. P. (2018). Hotels that most rely on Booking. com – online travel agencies (OTAs) and hotel distribution channels. 
Tourism Review, 73(4), 465-479. 

14 

      
 
 
 
 
                                                           
Finally, the analytical paper also presents the results of a quantitative online business user survey. The 
survey  was  administered  using  SurveyGizmo  survey  software  and  disseminated  to  respondents  of 
commercial  online  B2B  panels43.    The  final  sample  size  of  the  business  user  survey  is  1,667  responses 
from nine EU Member States (see the figure below). 

 FIGURE 2. COUNTRY DISTRIBUTION 

[VALUE] (13%) 

[VALUE] (13%) 

[VALUE] (10%) 

[VALUE] (7%) 

[VALUE] (13%) 

[VALUE] (15%) 

[VALUE] (12%) 

[VALUE] (9%) 

[VALUE] (8%) 

300
250
200
150
100
50
0

Belgium

Czech
Republic

France

Germany

Ireland

Lithuania

The
Netherlands

Spain

Sweden

Source: PPMI; n=1,667 

The  majority  of  the  sample  (55%)  is  comprised  of  micro  and  small  companies  (with  less  than  50 
employees). Large enterprises account for 19% of the sample, and medium – 26% (see Figure 3).   

FIGURE 3. COMPANY SIZE 

[VALUE] (322) 

[VALUE] (424) 

[VALUE] (921) 

Source: PPMI; n=1,667 

Small (1-49)

Medium (50-249)

Large (250+)

In terms of sectorial distribution, a quarter of enterprises in the survey sample are from wholesale and 
retail  trade,  a  fifth  from  industry  (manufacturing  and  construction),  see  Figure  4  below.  It  must  be 
noted  that  the  business  user  survey  targeted  the  business  users  of  all  internet  platforms  that  fit  the 
study  definition,  not  only  e-commerce  sellers,  accommodation  providers  and  app  developers.  More 
specifically,  the  enterprises  that  participated  in  the  survey  use  platform  for  one  or  several  of  the 
listing 
following  purposes:  selling  goods,  selling  services,  distributing  apps  or  software, 
accommodation  and  advertising.  Therefore,  the  survey  results  reflect  a  broader  overview  of  data 
sharing in P2B relationships. 

43 A detailed methodological report of the survey is presented in a separate document, attached to the First Interim Report.  

15 

      
 
 
 
 
                                                           
FIGURE 4. INDUSTRY CATEGORY 

Wholesale and retail trade

Professional, scientific and technical activities

Information and Communication

Personal services

Arts, Entertainment & Recreation

Human healthcare services

0

50

100

150

200

250

300

350

400

450

500

[VALUE] (25%) 

[VALUE] (21%) 

[VALUE] (10%) 

[VALUE] (8%) 

[VALUE] (7%) 

[VALUE] (5%) 
[VALUE] (5%) 
[VALUE] (5%) 

[VALUE] (4%) 
[VALUE] (3%) 
[VALUE] (3%) 
[VALUE] (2%) 

Non-profit/charity organisation

[VALUE] (1%) 

Source: PPMI; n=1,667 

The synthetic analysis of all the collected data, focusing of the main research questions of this paper, is 
presented throughout the paper. The interpretation of these results should take into account the limited 
scope of this paper, not allowing to generalise them to the overall platform economy or all platforms in 
the specific analysed segment. In addition to this, the researchers did not have the same level of access 
to platform and data broker data as their registered or paying users do. Therefore, we had to rely on the 
information  on  the  data  access  as  it  was  reported  by  the  interviewees  or  collected  from  secondary 
internet sources.  

16 

      
 
 
 
3.

 Platform business user data needs 

This  section  focuses  on  the  data  needs  from  the  platform  business  users’  perspective.  We  start  the 
analysis on the  assumption that the  need for  data by platform business users is driven by the  goal to 
succeed  and  maximise  profits  from  activities  conducted  on  the  platforms.  Data  (of  various  types  and 
levels  of  processing),  therefore,  should  be  available  and  used  instrumentally  to  achieve  this  goal.  We 
first provide  an overview  of the  most relevant classifications of data in the  view of platform business 
user needs, as well as propose a way to approach data for the further analysis. Second, we review the 
sector-specific  platform  business  user  needs  of  data.  We  end  the  chapter  with  a  summary  of  insights 
into the existing evidence of business data needs and the limitations to acquire this data.  

3.1.  Types of data for platform business users 

Many ways to classify data exist, and they often differ very significantly content-wise and in the choice 
of  dimensions.  For  example,  a  report  by  DG  Competition  provides  several  typologies  by  source 
(volunteered,  observed,  inferred)  and  form  (individual  level,  bundled  (anonymous)  individual-level, 
aggregated-level,  contextual)  of  data,  as  well  as  its  personal  (which  can  be  further  distinguished 
between volunteered, observed and inferred44) or non-personal45 character. Meanwhile, OECD46 in B2B 
data flows distinguishes between global value chain (GVC) data, engineering, IoT and financial/human 
resources data. The desk research showed that in the broader platform data discussions online data is 
also sometimes classified (implicitly or explicitly) by:  

Source (e.g., web, IoT, surveys, etc.);  

- 
-  Level  of  structure,  i.e.,  structured  (including  some  kind  of  identification  allowing  to 
organise the datasets accordingly (e.g. customers and links it to their behaviour), or not (e.g. 
likes, tweets, clicks and videos and so on);  
-  Level of specificity (platform, sector, general); 
-  Object (businesses, countries, individuals, etc.); 
-  Level of aggregation and granularity. 

This  variety  illustrates  to  some  extent  the  lack  of  common  vocabulary  in  operationalising  the  rather 
vague  concept  of  “data”  and  its  needs  in  the  platform-to-business  relationships.  Nonetheless,  some 
earlier studies, as well as insights collected in preparation of this analytical paper allow to crystallize 
several more relevant dimensions of what is broadly referred to as data relevant for platform business 
users. To begin with, a study by VVA distinguishes between six categories of data generated by online 
platform users, to facilitate an operational analysis of data sharing practices in the P2B relationships:47 

44 Crémer, J., de Montjoye, Y.-A., & Schweitzer, H. (2019). Competition policy for the digital era. European Commission. Retrieved from 
https://ec.europa.eu/competition/publications/reports/kd0419345enn.pdf 

45  Crémer, J., de Montjoye, Y.-A., & Schweitzer, H. (2019). Competition policy for the digital era. European Commission. Retrieved from 
https://ec.europa.eu/competition/publications/reports/kd0419345enn.pdf 

46 OECD. (2019). Data in the digital age. Retrieved from https://www.oecd.org/going-digital/data-in-the-digital-age.pdf  

47 Hausemer, P., Rabuel L., & Graux, H. (2017). Study on data in platform-to-business relations. VVA. Retrieved from 
https://op.europa.eu/en/publication-detail/-/publication/4af6cec1-48fb-11e8-be1d-01aa75ed71a1/language-en 

17 

      
 
 
                                                           
  Business  identification  details,  i.e.  information  on  the  business  itself  (company’s  address,  VAT 

number, country of operation).  

  User identification details, i.e. information on customers/potential customers’ identity and profile 
(name, age, gender), contact details (email and delivery/home address), geographical provenance 
(IP address). This includes data which allows to identify the customer across different platforms, 
e.g., marketplaces and social media.  

  Data  on  individual  transactions  between  businesses  and  customers,  i.e.  the  information 
generated  through  a  specific  transaction  on  the  platform  (good/service  provided,  price,  payment 
method,  communications  between  the  business  and  the  customer,  reviews  and  ratings  of  the 
transaction, items viewed before/after the transaction and provenance on the Internet). 

  Business  performance,  i.e.  information  on  all  transactions  taking  place  through  the  platform 
(number of products/service offered, prices and price changes, number of transactions through the 
platform, total value of sales, user traffic).  

  User  behaviour,  i.e.  data  on  customers’/potential  customers’  behaviour  on  the  platform  (clicks, 
browsing  history,  other  products  or  services  purchased  on  the  platform,  provenance  on  the 
Internet, conversion rate).  

  Analyses  of  market  trends/developments,  i.e.  aggregated  data  and  analysis  of  data  collected  by 

the platform and sometimes made (partly) available to business users ("data-as-a-service"). 

The  conducted  interviews  and  desk  research  largely  allowed  to  validate  this  typology  and 
operationalise the categories, clearly distinguishing one from another. However, the collected evidence 
showed  that  several  additional  dimensions  of  data  should  be  taken  into  account  concerning  business 
user data needs, data access and lack thereof.  

First,  especially  in  relation  to  the  four  latter  data  categories  (i.e.  data  on  individual  transactions, 
business  performance,  user  behaviour  and  market  trends)  and  their  usefulness  for  businesses,  the 
interviewed business users emphasised the distinction between: 

-  Data on their own listings, sales, customers, transactions and business performance. It is used by 
businesses to manage the accounting, stocks, sales and delivery, forecast future distribution needs 
and  so  on.  This  data,  as  we  demonstrate  in  the  following  chapter,  is  extensively  provided  by 
platforms to their business users. 

-  Data on the competitor or overall listings, customers, transactions and performance of businesses 
on  the  platform.  This  kind  of  data  (e.g.,  search  keywords,  search  volumes,  consumer  behaviour, 
etc.) is collected by the platforms, that therefore gain the full overview of the activities taking place 
on  it.  However,  as  our  platform  case  studies  show  (Chapter  4),  this  data  is  shared  with  the 
business users to  a  very limited extent.  Meanwhile, studying competition is one  of the  key  steps 
for businesses to succeed on platforms48.  

As  we  discuss  in  the  following  sections,  while  access  to  the  first  category  of  data  is  largely  not 
problematic, lack of access to the second category often becomes an issue for business users - especially 
those  operating  on  vertically  integrated  platforms.  Therefore,  most  discussions  about  the  lack  of 
platform data access revolve namely around this information about the market trends, competition and 
comparative  business  or  product  performance,  market  niche  identification  and  so  on  -  accessible  by 
platforms but not by other market players.  

48 Gerber, S. (2018). Want to sell on Amazon? 15 strategies for success. The Next Web (TNV). Retrieved from 
https://thenextweb.com/contributors/2018/01/19/want-sell-amazon-15-strategies-success/  

18 

      
 
 
                                                           
Second,  several  conceptual  levels  of  data  and  its  use  and  applications  exist,  related  to  the  data  value 
chain49 (see the figure below). Based on the data value chain framework, data follows a consequential 
approach, and at each step its different data features important. Looking into value as the key feature, 
most of it is derived from data once it is processed and used for business decisions (in the figure below, 
the  “+”  signs  graduate  the  importance  of  data  characteristics  at  each  step).  The  typology  of  six 
categories of data presented above already integrates this view, introducing a hierarchy of data to some 
extent  (from  identification  details  being  a  its  bottom,  and  market  analyses  at  the  top).  The  platforms 
create this value by collecting data on various actors in a centralised way, as well as aggregating data 
and drawing insights. Meanwhile, the business users have a restricted access to it, often limited to data 
on  their  own  business,  rather  than  broader  overview  of  the  marketplace  (we  elaborate  on  this  in 
Chapter 4).  

FIGURE 5. DATA VALUE CHAIN 

Source: Moro Visconti, et al (2017). Big Data-driven value chains and digital platforms: from Value Co-Creation to Monetization.  

From the business user perspective too, data is as useful, monetizable or needed, as it can be effectively 
used in making business decisions. Analytically, therefore, what is very broadly referred to as data, can 
be  divided  into  several  levels,  as  seen  by  data  scientists,  closely  related  to  data  aggregation  and 
processing, and data-based decision-making (see the figure below). First, it is raw data collected by the 
platforms,  business  users  or  third  parties.  It  is  used  as  an  input  for  specific  actions  or  in  analytics, 
outputs of which is the second level. The third level is insights (or the actionable guidelines for business 
decisions), developed based on the analytics, and directly applicable in business processes.  

FIGURE 6. LEVELS OF DATA FROM THE PLATFORM BUSINESS USER PERSPECTIVE 

Source: developed by the authors. 

Not all data collected by players in the digital platform economy is equally relevant for business users. 
Acquiring  and  using  each  level  of  relevant  data,  as  presented  above,  is  related  to  specific  conditions 
and prerequisites.  

49 Moro Visconti, R., Larocca, A., & Marconi, M. (2017). Big Data-Driven value chains and digital platforms: from Value Co-Creation to 
Monetization. Available at SSRN 2903799. 

19 

      
 
 
 
 
                                                           
Raw data 

As illustrated in the figure above, it is useful to distinguish between two kinds of raw data: one that is 
used for further business analytics (i.e. the big data collected by platforms and other players); and data 
which is needed to perform an action (from contacting clients to developing data products).  

To be used for further analytics and then business decisions, raw data used for further analysis needs 
to be collected continuously and have certain characteristics. The big data frameworks talk about four 
Vs of big data, determining its value: volume,  variety, veracity (i.e. trustworthiness)  and  velocity (i.e. 
frequency of incoming data)50. Conditions to implement these criteria should be ensured before starting 
the data collection. Although some improvements can in data quality can be made once it is collected, it 
mostly depends on the steps taken at the starting point. Online platforms, as explained, are usually the 
players with the greatest capacities to collect and store large volumes of real-time data on every activity 
that  is  taking  place  on  their  websites  or  apps.  Relatively  few  platform  business  users  from  selected 
sectors have the capacities to set up such data collection operations themselves.  

Furthermore, the content and character of raw data collection differs - and sometimes to a great extent - 
by sector in which business users operate. For example, app developers have the possibility to gather 
large  amounts  of  real-time  data  on  how  their  products  are  used  directly  though  their  applications, 
while many hospitality sector players still use paper-based forms and rely on customer willingness to 
collect data on them and their satisfaction.  

Generally, it seems that rather few business users are interested in getting such raw and unprocessed 
data, which could be used in further analysis, from platforms directly. The main reason is that they do 
not have enough capacity to derive value from unprocessed data themselves (as illustrated in Figure 5, 
raw  data  has  least  immediate  value).  We  elaborate  more  on  the  business  user  analytical  capacities  in 
Section 3.2 and Chapter 5, and on actors helping businesses to fill the capacities gap in Chapter 6.  

Then,  access  to  specific  kinds  of  unaggregated  data  in  some  cases  is  also  a  prerequisite  for  the 
implementation  business strategies  or  development  of  data-based  products.  For  example,  customer 
contacts  (which  could  be  classified  as  raw  data)  are  essential  for  business  users  to  carry  out  direct 
marketing campaigns. Another obvious example  for the application of raw data  is data products (e.g. 
applications  and  software,  business  analytics,  predictive  maintenance  of  a  machine)  and  AI  models 
used  in  business  processes.  For  instance,  a  study  by  Accenture  estimates  that  appropriate  use  of  AI 
could  achieve  additional  annual  growth  of  1.9%  in  retail  and  wholesale  sectors,  and  2.3%  in 
manufacturing  in  mature  economies51.  Nonetheless,  to  programme  and  train  AI,  as  well  as  to  use  it 
later, a lot of quality data is necessary as an input.   

In  summary,  the  business  user  needs  for  unaggregated  data  mostly  concern  specifically  the  data  as  a 
prerequisite to act (e.g., distribute a marketing campaign or develop a data product), and not raw data 
to use for analytics.    Most business users, especially in e-commerce and travel sectors, usually do not 
have  the  capacities  (nor  the  need)  to  work  with  unaggregated  and  unprocessed  data,  generated  by 
platforms.  

50 Mohanty, S. (2015). The four essential V’s for a big data analytics platform. Dataconomy. Retrieved from https://dataconomy.com/2015/06/the-
four-essentials-vs-for-a-big-data-analytics-platform/  

51 Institut für Innovation und Technik (2018). Potenziale der Künstlichen Intelligenz im produzierenden Gewerbe in Deutschland. Retrieved from 
https://www.bmwi.de/Redaktion/DE/Publikationen/Studien/potenziale-kuenstlichen-intelligenz-im-produzierenden-gewerbe-in-
deutschland.pdf?__blob=publicationFile&v=8; Accenture (2017). Industry Spotlights: How AI boosts industry profits and innovation. Retrieved 
from https://www.accenture.com/_acnmedia/accenture/next-gen-5/insight-ai-industry-growth/pdf/accenture-ai-industry-growth-industry-
report.pdf 

20 

      
 
 
                                                           
Analytics 

The essential value of data lies not in collection and ownership per se, but in the information which can 
be  used  to  improve  competitiveness  of  products/  services  or  to  develop  new  products  and  services. 
Therefore, the value of data for businesses increases with its level of processing. The main prerequisites 
for  generating  analytics  are  the  competence  and  capacity  (either  in-house  or  external)  to  analyse  raw 
data.  This  includes  appropriate  hardware  and  software,  and,  more  importantly,  employees  with  the 
necessary skills. Specific uses of  analytics then depend a lot on the  business model of platform users, 
type of a product, existence of possibilities to increase efficiency and so on.  

The  evidence  collected  in  preparation  of  this  paper  shows  that  business  users  usually  lack  analytical 
capacities to derive such value from data. Most platforms, in turn, often provide some analytics data. 
Nonetheless,  the  level  or  detail  and  granularity  of  this  data  is  often  very  low.  In  addition  to  this,  the 
analytics  provided  mostly  concern  the  performance  of  individual  businesses  (i.e.,  business  get  the 
analytics about themselves), while the business users also express the need of a broader overview of the 
processes, activities and competition taking place on the platforms.  

Insights 

The most usable  and monetizable  level of information for  all types of business  users are  insights and 
actionable guidelines, stemming from the results of analytics. While analytics can be understood as an 
input into the evidence-based decision-making process, specific  insights and  action guidelines are the 
ultimate  output  (e.g.,  optimise  product  titles,  based  on  the  search  behaviour  data;  develop  a  new 
product  based  on  predictions  what  the  consumer  will  buy;  add  tea  kettles  to  hotel  rooms  to  attract 
Asian  tourists,  based  on  customer  feedback  data;  change  the  monetisation  model of  an  app  based  on 
user  engagement  trends;  etc.).  Such  insights  can  concern  various  aspects  of  running  of  business.  The 
interviews and desk research most often mention the following examples: 

  How to improve business processes, 
  How to improve or build new business models,  
  How to build a better customer experience, 
  How to increase customer satisfaction and conversion rates, 
  Whom to target in marketing, ads and recommendations, 
  How to adjust pricing, 
  How to customise products or services 
  What new products or services to develop, 
  How to improve listings, and so on.  

This  level  of  data-based  insight  is  quite  often  provided  by  the  platforms,  especially  for  new  business 
users. Various platforms provide detailed guidelines, “tips”, seasonal advice and similar, based on the 
analytics  that  they  generate.  At  the  same  time,  many  businesses  report  arriving  to  such  insight 
themselves with the analytics provided by platforms or acquired from other sources.  

3.2.  Sector-specific data needs 

Businesses from different sectors face different challenges in their day to day operations, so their needs 
vary  a  lot  depending  on  their  activities  and  platforms  used.  For  example,  earlier  research  shows  that 
hospitality  platform  users  highly  value  customer  identification  data  (which  is  necessary  to  contact 
customers  for  promotional  activities),  whereas  user  behaviour  data  (which  is  necessary  to  target 

21 

      
 
 
communication  to  individual  customers  and  personalise)  is  of  particular  value  for  app  stores  and  e-
commerce platform users52. These insights were strongly corroborated in the interviews with businesses 
conducted in preparation of this analytical paper.  

Furthermore,  a  VVA  study53  also  shows  a  great  difference  in  the  data  savviness of  different  size  and 
type of businesses – from large hotel chains, to very small hotel owners, from app developers to small 
shops  selling  products  on  market  places,  or  from  large  corporations  monitoring  and  optimising  their 
presence  on  search  engines,  to  minor  website  owners.  Some  types  of  users,  such  as  app  developers, 
have  great  opportunities  to  collect  data  about  the  use  of  their  products  themselves  (sometimes  even 
balancing  on  the  narrow  line  of  serious  privacy  breaches).  In  contrast,  because  of  the  nature  of  their 
offerings, retailers and hotels do not have such possibilities to monitor all aspects of how their offerings 
are purchased and used.  

Related  to  this,  the  analytical  capabilities  vary  a  lot  by  sector  of  the  business  user  and  often  their 
company size. While  some  business users would be  very  happy with ‘raw’ data, others would prefer 
ready  and  actionable  insights.  In  fact,  some  earlier  analyses  as  well  as  interviews  with  business 
associations show that data analysis and required analytical capabilities are often the “pain points” that 
need  to  be  addressed.  For  example,  in  a  2015  survey,  almost  a  third  (32%)  of  travel  and  hospitality 
industry marketers rated data science as the skill they most lack, with programming coming in a close 
second  at  28%54.  Although  comparable  data  was  not  available  for  e-commerce  seller  and  app 
developers, it is obvious that the latter are considerably more data-savvy.  

Further  we  provide  an  overview  of  more  specific  data  needs  of  businesses  operating  in  different 
industries.  

3.2.1.  E-commerce sellers 

Interviews with e-commerce sellers and desk research data shows that the distinction between the data 
on  the  seller’s  own  activities  and  overall  activities  on  the  platform,  as  explained  above,  is  very 
relevant. Regarding the first category, the sellers expressed a need for the following data: 

  Customer  data,  including  phone  number  and  address  that  are  crucial  to  safely  deliver  the 

purchased goods.  

  Financial data to understand business performance and margins.  
  Listings, sales and product data to manage stocks. 
  Advertising data, attribution rates, return on investment, to make better informed and targeted 

advertising decisions.  

  Customer interaction with their listings (e.g., how do customers reach their listing pages, click-

through rates, bounce rates, conversion, reviewer characteristics, etc.).  

Most (but not all55) of these indicators are provided by the platforms for their business users, although 
not always at the level of actionable insight.   

52 Hausemer, P., Rabuel L., & Graux, H. (2017). Study on data in platform-to-business relations. VVA. Retrieved from 
https://op.europa.eu/en/publication-detail/-/publication/4af6cec1-48fb-11e8-be1d-01aa75ed71a1/language-en 

53 Hausemer, P., Rabuel L., & Graux, H. (2017). Study on data in platform-to-business relations. VVA. Retrieved from 
https://op.europa.eu/en/publication-detail/-/publication/4af6cec1-48fb-11e8-be1d-01aa75ed71a1/language-en 

54 Tornquist, S. (2017). Digital trends in the travel and hospitality sectors. Econsultancy. Retrieved from https://econsultancy.com/reports/digital-
trends-in-the-travel-and-hospitality-sector/  

55 E.g., the email address are provided very rarely, phone numbers are also excluded by some platforms (e.g., Etsy). 

22 

      
 
 
                                                           
What  sellers  commonly  identified  as  an  unmet  data  need  is  the  market  analytics  allowing  them  to 
better  understand  customer  behaviour,  market  trends  and  competition  (i.e.,  the  data  on  overall 
platform activities). According to an article by Akter and Fosso Wamba56, analytics based on such data 
can be applied in e-commerce for five main broad purposes: needs identification, market segmentation, 
decision  making  and  performance  improvement,  new  product/  market/  business  model  innovations 
and creating infrastructure and transparency. The interviewed marketplace sellers mostly emphasised 
performance improvement and product/  business model innovations  as the main purposes for  which 
they  would  like  to  access  more  data  generated  by  platforms.  However,  these  are  difficult  to  achieve 
with data on their own business only.  

More  specifically,  to  be  successful  in  online  marketplaces,  sellers  need  several  key  types  of  data  -  all 
concerning a broader perspective than their own business operations and transactions: 

  Data  to  understand  the  success  of  other  players  on  the  marketplace  so  that  they  could 
understand better the competition. For example, how many competing sellers there are for the 
specific range of product, what products they sell, how many and what reviews they get, what 
are  their  ranks,  and  so  on.  This  includes  competitor’s  sales  data.  If  they  had  it,  sellers  could 
better  understand  the  demand  and  its  seasonality,  make  more  precise  marketing  decisions, 
ensure  that  they  are  using  the  right  keywords,  choose  the  right  pricing,  optimize  their 
advertising  campaigns  and  track  own  performance  and  profits  in  a  more  informed  way. 
Competitor research could also help to uncover hidden products with large sales potential. For 
large companies, and especially luxury brands, sales data and details of counterfeit products is 
also important.  

  Customer  behaviour,  especially  search  keywords  and  search  volume  data,  to  optimize  their 
stores on platforms for  search, as well as identify new products to sell57. Understanding their 
reviews and shopping habits, responses to texts, images and videos is also crucial.  

  Price data of products  similar  to theirs, to develop (dynamic) pricing strategies. For  example, 
Amazon’s  algorithm  would  choose  which  seller  would  win  that  default  position  —  the  Buy 
Box.  While  the  exact  formulas  used  to  pick  the  winner  are  not  known,  Amazon’s  website 
advises sellers that they can increase their chances by having low prices, having items in stock, 
offering free shipping and getting excellent customer service ratings. To optimize their chances, 
many  sellers  start  using  algorithmic  software  to  constantly  change  prices  to  adapt  to 
competitors’ moves58. 

The  data  collected  for  this  paper  corroborated  the  findings  of  previous  research,  showing  that 
companies using online  marketplaces  are often unsatisfied with the  levels of  access to  such data they 
receive  from platforms.  For  example,  around two thirds of sellers who responded to  the  VVA survey 
disagreed  with  the  statement  that  the  information  they  receive  through  the  online  marketplace  about 
the  behaviour  and  preferences  of  customers  is  useful  for  the  development  or  improvement  of  their 
products or  services59. Our  business user  survey shows that 52% of enterprises whose key activity on 

56 Akter, S., & Wamba, S. F. (2016). Big data analytics in E-commerce: a systematic review and agenda for future research. Electronic Markets, 26(2), 
173-194. 

57 Gerber, S. (2018). Want to sell on Amazon? 15 strategies for success. The Next Web (TNV). Retrieved from 
https://thenextweb.com/contributors/2018/01/19/want-sell-amazon-15-strategies-success/  

58 Angwin, J., & Mattu, S. (2016). Amazon says it puts customers first. But its pricing algorithm doesn’t. ProPublica. Retrieved from 
https://www.propublica.org/article/amazon-says-it-puts-customers-first-but-its-pricing-algorithm-doesnt  

59 Hausemer, P., Rabuel L., & Graux, H. (2017). Study on data in platform-to-business relations. VVA. Retrieved from 
https://op.europa.eu/en/publication-detail/-/publication/4af6cec1-48fb-11e8-be1d-01aa75ed71a1/language-en 

23 

      
 
 
                                                           
platforms is selling goods60, cannot access some types of data held by the platform, which are essential 
to their business. The issue of access to the platform-generated data is especially relevant in vertically 
integrated  e-commerce  platforms.  Without  data  access,  third-part  sellers  cannot  viably  compete  with 
the marketplace controller which has unique insight into the market.  

Interestingly, several interviewed e-commerce sellers, when asked about what data is most important 
to successfully run a business, mentioned the data, metrics and indicators that are already provided by 
platforms. One of them explained that they are not aware of all the  metrics and indicators that can be 
useful  to  them,  if  the  platforms  or  other  providers  do  not  provide  them.  He  provided  an  example: 
during  a  phone  call  with  an  e-commerce  platform,  the  platform  representative  mentioned  that  he 
should  do  something  about  the  issue  that  most  of  his  customers  find  his  listings  on  the  platform 
(internally),  and  not  through  external  links  (e.g.,  advertising).  He  was  surprised  because  he  was  not 
only  not  aware  about  this  issue;  he  also  did  not  know  that  such  an  indicator  is  worth  tracking  for 
business decisions. The platform, meanwhile, tracks this information, but does not provide the sellers 
with access to it.  

3.2.2.  App developers 

App developers are a specific group of business users, as data is not only needed to understand their 
business performance - it is the key component of the products they offer. The specific datasets that are 
needed (e.g., geolocation, speech recognition, etc.) depend a lot on the type and specific characteristics 
of an app.  

App  developers  also  have  broader  possibilities  to  collect  data  themselves  using  their  own  software. 
Apps  can  interact  with  the  user’s  device  so  as  to  retrieve  and  share  such  information  as  photos, 
biometric  data,  financial  information,  contact  details,  login  credentials,  correspondence,  location,  IP 
number, etc. They can also activate some of the device’s functions, such as the camera and microphone, 
that  can  be  used  to  collect  additional  personal  data.  In  fact,  interviewed  developer  representatives 
confirm  that  this  is  the  most  important  data  for  their  activities.  On  the  negative  side,  however,  the 
extent of their data collection activities sometimes even clash with the data protection rules61.  

This business user  group can also be  characterised by greatest capacities to run analytics on  this data 
and  make  insights  on  how  to  further  develop  their  products,  monetisation  models  and  marketing 
strategies.  In  addition  to  this,  many  analytics  providers  offer  services  specifically  to  app  developers, 
promising to help them better understand their users, user habits and behaviour62.   

Nonetheless,  the  interviewed  app  developers  noted  that  there  are  important  limitations  in  what 
insights  they  can  reach  without  help  and  data  sharing  by  the  app  stores.  Several  app  developers 
explained that they need to understand how end-users engage with similar apps, and what makes the 
users  like  them.  For  example,  developers  of  video  games  need  to  understand  how  to  make  a  game 
neither too easy, not too hard for the players. Data generated through their app alone cannot provide a 
comprehensive  answer  to  this  question,  and  therefore  a  broader  overview  of  similar  apps  and  user 
behaviour in that segment is desired.  

60 That is, 86 out of 165 businesses that use platforms to sell goods only but do not engage in selling services, listing accommodation or distributing 
apps. 

61 Reardon, J., Feal, Á., Wijesekera, P., On, A. E. B., Vallina-Rodriguez, N., & Egelman, S. (2019). 50 Ways to Leak Your Data: An Exploration of Apps' 
Circumvention of the Android Permissions System. In 28th {USENIX} Security Symposium ({USENIX} Security 19), 603-620.  

62 Grguric, M. (2019). Top 12 mobile app analytics platforms in 2019 (pricing included). Medium. Retrieved from https://medium.com/swlh/top-11-
mobile-app-analytics-platforms-pricing-included-cdc553578fd; Dogtiev, A. (2019). Top app analytics tools (2019). Business of Apps. Retrieved from 
https://www.businessofapps.com/guide/app-analytics/ 

24 

      
 
 
                                                           
In  addition  to  this,  similarly  to  businesses  in  other  sectors,  app  developers  make  use  of  consumer 
behaviour  data,  business  analytics,  market  trends  analysis,  and  so  on.  This  concerns  analysis  of  their 
own business performance, as well as competition.   

3.2.3.  Accommodation and hospitality industry 

Based  on  the  interview  and  desk  research  findings,  the  business  users  of  hospitality  sector  have 
relatively  lower  data  needs,  as  compared  to  e-commerce  sellers  and  especially  app  developers.  What 
further  distinguishes  competition  in  the  accommodation  sector  (and  influences  the  data  needs)  from, 
e.g.,  e-commerce  or  software  sectors,  is  the  geographic  scope.  Hotels  compete  for  customers  within  a 
limited  geographic  area,  while  online  retailers,  and  especially  app  developers,  often  distribute  their 
products  globally.  The  data  that  hotels  need  on  their  competition  or  market  trends,  therefore,  also 
primarily concern defined locations in which the properties are located. However, considering the data 
needs, the distinction should be made between smaller properties and large hotel chains.  

The key information that large, multi-city hotel chains are interested in is the customer contact data. It 
is the  hotels’ chance to  build guest relationships with loyalty marketing. It is  also crucial in targeting 
the  right  profiles  with  promotions  and  rewarding  the  best  guests  with  campaigns  that  encourage 
loyalty  -  important  aspects  of  their  business  models.  Some  real-world  examples  show  that  loyalty 
programmes  are  a  very  important  tool  for  hotel  chains  to  secure  clientele  and  acquire  new  frequent 
customers63.  These  facilitate  for  easy  direct  booking  campaigns,  which  allow  hotels  to  bypass  online 
and  offline  travel  agencies.  The  largest  global  hotel  chains  (such  as  Marriott,  Choice,  Hilton,  IHG, 
Wyndham,  Hyatt)  estimate  that  direct-booking  campaigns  —  even  those  offering  discounted  rates  to 
consumers  —  are  generating  more  revenue  for  hotel  owners  than  bookings  via  the  OTAs64.  Smaller 
properties,  on  the  contrary,  do  not  have  a  goal  to  bypass  OTAs,  as  they  allow  the  properties  to  be 
visible  for  broad  audiences  of  travellers  from  around  the  world.  Therefore,  the  need  for  customer 
contact details is not as pronounced in this size segment.  

Besides  contact  data,  properties  of  various  sizes  work  with  several  areas  of  indicators.  As  a  starting 
point, knowing the competition is key to attract customers and revenue management. For example, to 
set competitive prices, hotel owners need to know how much other properties in that market charge. To 
define  this  market,  hotels  need  information  on  location,  average  daily  rates,  accommodation  types, 
quality/amenities offered, traveller types65. In addition to this, analysis of market trends (e.g., seasonal 
trends, trends related to external events, such as Brexit, etc.) is important to make informed and timely 
changes to the booking strategy. With resulting insights hotels and marketers stand a better chance of 
engaging with guests and, more importantly for large hotels, to drive engagement, guest loyalty, direct 
booking, and ancillary revenues.  

In addition to this, the  interviewed hotel owners mentioned that they need data to deal with fake or 
undesirable reservations. The platforms are reluctant to provide such data, because it usually concerns 
personal  details  of  customers,  such  as  the  IP  address,  email  validity,  or  reservation  success  on  the 
platform.  

63 Jelski, C. (2019). Hilton CEO says direct marketing paid off in 2018. Travel Weekly. Retrieved from https://www.travelweekly.com/Travel-
News/Hotel-News/Hilton-CEO-says-direct-marketing-paid-off-in-2018  

64 Ting, D. (2019). Who’s really winning the direct booking wars between hotels and online travel agencies? Retrieved from 
https://skift.com/2019/03/04/whos-really-winning-the-direct-booking-wars-between-hotels-and-online-travel-agencies/  

65 BookingSuite (2017). Rate setting: understand your comp set first. Retrieved from https://suite.booking.com/blog/understand-competition-to-
determine-hotel-prices/; Savike, M. (2018). 5 useful insights hotels can get from analysing guest data. hi Inc. Retrieved from 
https://news.hiinc.com/blogs/5-useful-insights-hotels-can-get-from-analyzing-guest-data    

25 

      
 
 
                                                           
Large hotel chains, as compared to small hotel owners, often have own capacities to source and work 
with  large  amounts  of  raw  data,  draw  insights,  and  base  large-scale  business  decisions  on  those 
insights66.  These 
inventory 
management. The analysed data usually come  from other sources than the  online  platforms that they 
use to list the properties. This is related to the fact that usually limited shares of their rooms are listed 
on online platforms.  

include  customer  preferences,  marketing  and  pricing  strategies, 

3.3.  General data needs of platform business users 

Several broader insights on business user data needs could be made based on the evidence collected in 
preparation of this analytical paper. Rather specific types or characteristics of platform data (please see 
the typologies presented in Section 3.1) are relevant for businesses across all reviewed sectors.  

First,  all  interviewees  mentioned  that  they  mostly  use  and  appreciate  the  data  on  the  individual 
business user’s performance, listings, advertising effectiveness and so on. This is essential in managing 
the  online  business.  Nonetheless,  data  on  user  identification,  transactions  and  performance  of  other 
businesses is also sought after by business users operating in various sectors and on various platforms.  

Second, based on the insights from interviews and desk research, the need for data on competition and 
market  trends  seems  to  mostly  concern  certain  types  of  businesses  across  sectors,  specifically  those 
looking for opportunities to scale up.  

Third, most business users indicate that they need processed data - analytics and insights, rather than 
raw  datasets.  Most  interviewees  acknowledged,  that  they  would  not  know  how  to  analyse  data  for 
business insights themselves. Meanwhile, almost a third enterprises that responded to the business user 
survey do not analyse any kind of raw data (see more details in Section 5.2). Nonetheless, many players 
in  the  data  market  can  offer  such  analytics  services  for  companies  operating  in  various  sectors  -  we 
elaborate on this issue in Chapter 6.  

In general, the discussion about data access, especially among retailers and hotels is still rather vague 
and  sometimes  contradictory.  Many  businesses  do  not  know  what  data  is  (or  can  be)  collected  by 
platforms,  and  how  could  they  use  this  data  to  get  competitive  advantage.  There  is  no  shared  vision 
among  business  users  in  different  sectors  on  how  the  data  could  be  shared  and  under  what 
circumstances.  This  could  be  attributed  to  both,  lack  of  data  savviness  of  businesses,  as  well  as  the 
general lack of knowledge on what data platforms collect and what specific metrics they monitor.  

The  evidence  on  platform  business  user  data  needs,  collected  in  preparation  of  this  paper,  also 
indicated some unrealistic expectations of business users. To begin with, there is lack of understanding 
among the business users what data can be legally shared. This especially concerns the notable tension 
between the business users’ desire to have access to more customer and competitor data on one hand, 
and  the  GDPR  and  anti-trust  law  on  the  other.  At  the  same  time,  open  access  to  some  data  could  be 
detrimental to the consumers (e.g., flooding with advertisement) or competition (e.g., competitor sales 
data with identification information) more generally. Further, not all data is feasible to be provided by 
the  platforms  due  to  other  barriers.  We  elaborate  on  the  platforms’  perspective  and  barriers  to  data 
sharing more in Chapter 4 and Section 4.5 specifically.  

66 For example, Hilton hotels have a detailed framework for analysing negative reviews and drawing managerial implications, see 
https://www.slideshare.net/georgeskiadopoulos5/analysis-of-negative-hotel-reviews-hilton-hotel  

26 

      
 
 
                                                           
In the following chapter, we analyse in more detail the specific data collection and sharing practices of 
the four selected online platforms: Amazon, eBay, Google Play and Booking.com, as well as provide a 
broader overview of data sharing practices in the platform economy.  

27 

      
 
 
4.

 Platform data and business user access 

Online platforms are in the best position to collect data and capture its value, given their capacities to 
invest in infrastructure, notable data advantage against other parties in the platform economy, and lack 
of  knowledge  among  internet  users  on  how  to  value  their  own  data.  The  users  (both  customers  and 
businesses)  provide  data  actively  and  passively.  For  example,  browsing  the  internet  they  generate  a 
wealth of search and user logs, customer transaction records, user-generated content, and rich network 
information67. In addition to this, major platforms, such as Amazon and Google have developed a range 
of  gadgets  (e.g.,  Amazon  Echo,  Google  Nest68)  and  operational  systems  linking  phones,  personal 
computers and smart home appliances (e.g., Alexa, Google Home, Apple HomeKit), allowing to collect 
the  data  on  customers  outside  the  ‘traditional’  web-browsing  environment.  Internet  of  Things,  5G 
networks,  and  the  emerging  online-to-offline  transition  are  further  accelerating  the  speed  of  data 
accumulation69.  

The basic model of how platforms collect and derive value from data is similar across all sectors of the 
platform  economy  (see  the  figure  below):  the  data  is  generated  by  the  activities  of  end-users 
(customers)  and  third-part  business  users  on  the  platform.  This  data  is  then  analysed  by  the  online 
platform  companies  to  develop  data-based  products  or  services,  or  used  in  making  their  business 
decisions.  

FIGURE 7. DATA SOURCES AND CREATION OF THE VALUE OF DATA 

Source: Li, W., et al (2018). Value of data: There’s no such thing as a free lunch in the digital economy. 

67 Chen, H., Chiang, R. H. L., Storey, & V. C. (2012). Business intelligence and analytics: from big data to big impact. MIS Quarterly, 36(4), 1165-1188. 
Retrieved from https://pdfs.semanticscholar.org/f5fe/b79e04b2e7b61d17a6df79a44faf358e60cd.pdf%3E. 

68 Google Store. Retrieved from https://store.google.com/ie/?hl=en-IE&regionRedirect=true  

69 Li, W. C., Nirei, M., & Yamana, K. (2019). Value of data: There’s no such thing as a free lunch in the digital economy. Discussion papers 19022, 
Research Institute of Economy, Trade and Industry (RIETI). Retrieved from 
https://www.bea.gov/system/files/papers/20190220ValueofDataLiNireiYamanaforBEAworkingpaper.pdf  

28 

      
 
 
 
                                                           
Nonetheless,  what  data  exactly  the  platforms  collect,  how  the  data  flow  within  online  platform 
networks,  how  they  are  monetised,  and  how  their  value  is  captured,  can  differ  significantly  by  the 
platform business model (e.g. degree of vertical integration in the data value chain)70. Differences can 
also be observed in the practices of sharing their data with business users, who are key in generating 
them.  

Further in this section we provide a detailed overview of the data that the selected platforms - Amazon, 
eBay, Google Play and Booking.com - collect, and what level of access to this data they provide to the 
business users. Then we present a broader overview of platform motives to provide or restrict access to 
their data.  

4.1.  Amazon Marketplace 

Media articles often present Amazon as the largest retail data repository in the world71. Amazon collects 
data  from  customers  as  they  navigate  the  site  or  use  other  Amazon’s  products  or  services.  As  the 
platform itself informs, they monitor the following: 

 

Actions such as searching for products or services, placing an order, submitting a review or 
compiling a Wishlist gives Amazon all details on browsing the site, from the time customers 
spend browsing each page to the language used in the user reviews they leave. 

 

  When customers are downloading, streaming, viewing, or using digital content on a device, 
or  through  an  app,  especially  on  a  GPS-enabled  smartphone  or  tablet,  Amazon  can  also 
gather  location  data  and  information  about  other  apps  and  services  they  use  on  the  phone. 
Using  Amazon’s  streaming  content  services,  such  as  Amazon  Prime  and  Audible,  provides 
them with more detailed information on where, when and how you watch and listen to TV, 
film and audio. 
By providing or  configuring settings, Account or  Profile information, customers reveal their 
name,  address  and  phone  number,  age,  personal  description  and  photograph,  signature, 
payment information, privacy preferences. 
Talking  to  Amazon’s  Alexa  virtual  assistant  allow  to  gather  customer  voice  recordings  that 
are stored by the platform. 
Communicating with Amazon by phone, e-mail, or otherwise provides data on the content of 
reviews, calls and e-mails72. 

 

 

Some  evidence  also  shows  that  the  platform  invests  in  expensive  experiments  for  data  collection.  For 
example, according to some reports, to gather information of consumer price sensitivity, Amazon runs 
experiments that involve funding discounts on third-party products, and exploring customer behaviour 
in  relation  to  decreased  prices73.  While  occasional  anecdotal  evidence  is  provided  by  various  whistle-
blowers  (often  the  platform’s  former  employees),  the  full  extent  of  the  collection  of  customer  data  by 
Amazon and the methodologies applied are not disclosed publicly.  

70 Li, W. C., Nirei, M., & Yamana, K. (2019). Value of data: There’s no such thing as a free lunch in the digital economy. Discussion papers 19022, 
Research Institute of Economy, Trade and Industry (RIETI). Retrieved from 
https://www.bea.gov/system/files/papers/20190220ValueofDataLiNireiYamanaforBEAworkingpaper.pdf  

71 Milnes, H. (2019). “A slippery slope”: Amazon wants to control third-party sellers’ product pricing. ModernRetail. Retrieved from 
https://www.modernretail.co/platforms/a-slippery-slope-amazon-wants-to-control-third-party-sellers-product-pricing/  

72 Amazon. (n.d.). What data does Amazon collet and use? Retrieved from 
https://www.amazon.co.uk/gp/help/customer/display.html?nodeId=G6RZ4RMNMLUQRLY2  

73 Financial times. (n.d.). Amazon’s ever-increasing power unnerves vendors. Retrieved from https://www.ft.com/content/c82ce968-bc8a-11e8-94b2-
17176fbf93f5  

29 

      
 
 
                                                           
The  platform  has  a  lot  of  information  on  the  businesses  registered  on  the  platform  as  sellers  as  well. 
Besides  all  the  activities  and  transactions  on  the  platform,  Amazon  collects  product  information, 
insurance  data,  trade  licenses  (for  service  providers),  contacts,  bank  and  tax  information,  as  well  as 
business  demographics,  such  as  sector,  business  type,  registration  address,  year  of  business  and 
number of employees.  Linked with the  customer data, this results in a wealth  of information on how 
the market works.  

The  possibilities  to  analyse  this  data  to  draw  commercially-viable  insights  are  basically  unlimited. 
Interviews  with  business  users  and  desk  research  indicate  that  the  platform  has  information  ranging 
from  the  click-through  rates  of  each  listing,  paths  through  which  customers  end-up  viewing  specific 
products, complementarity of products and price level adjustments74 (none of which is shared with the 
sellers), to very detailed information on individual customers and their shopping habits. The platform 
also makes use of external datasets, such as census data for gathering demographic details75, which can 
help to develop a very detailed profiling of each customer. Through advanced analytics, Amazon has a 
clear understanding of what people actually buy76, search for and do not buy, and what they may buy 
next - this gives it an extreme competitive advantage77.  

The  platform  shares  only  a  very  limited  amount  of  this  knowledge  with  its  third-party  sellers, 
especially those who do not use Amazon’s additional services, such as fulfilment and advertising. As 
the interviewed Amazon sellers reported, this becomes even more of an issue because of the platform’s 
vertical  integration:  Amazon  Retail  -  often  their  direct  competitor  on  Amazon  Marketplace  -  knows 
everything about its competition and its competitors. 

To  give  a  little  bit  more  context,  Amazon  Marketplace  P2B  relationships  can  be  described  by  the 
distinction between vendors and sellers. Vendors are retailers that sell wholesale to Amazon Retail (first 
party),  while  sellers  are  third  parties  operating  on  Amazon  Marketplace.  Vendor  relationship  with 
Amazon is somewhat similar to that between a supermarket and its suppliers. Generally, vendors are 
outside  our  definition  of  platform  business  users,  but  their  example  offers  interesting  insights  into 
Amazon’s data services for businesses.   

Businesses  that  become  direct  vendors  to  Amazon,  with  Amazon  Retail  Analytics  (ARA)  Basic,  get 
elementary  data,  such  as  sales  and  inventory  data,  weekly.  However,  this  package  does  not  include 
more in-depth data that vendors would like to have, such as conversion rates or customer details. The 
Amazon’s position is that they, not the vendor, ship the product and own the customer relationship, so 
they do not give anyone customer profiles, email addresses or allow contact78. Brands that sell through 
Vendor  Central  have  to  pay  for  a  subscription  to  Amazon  Retail  Analytics  (ARA)  Premium  to  access 
more data at a higher level of granularity. However, it is expensive, with vendors reportedly paying 1% 
of their wholesale cost of goods sold to Amazon or a minimum of USD 100,00079 (although some other 

74 Crémer, J., de Montjoye, Y.-A., & Schweitzer, H. (2019). Competition policy for the digital era. European Commission. Retrieved from 
https://ec.europa.eu/competition/publications/reports/kd0419345enn.pdf 

75 Marr, B. (2019). Amazon: using big data to understand customers. Bernard Marr & Co. Retrieved from 
https://www.bernardmarr.com/default.asp?contentID=712 

76 van Rijmenam, M. (2013). How Amazon is leveraging big data. Datafloq. Retrieved from https://datafloq.com/read/amazon-leveraging-big-
data/517 

77 Financial times. (n.d.). Amazon’s ever-increasing power unnerves vendors. Retrieved from https://www.ft.com/content/c82ce968-bc8a-11e8-94b2-
17176fbf93f5  

78 Pathak, S. (2017). Retailers chafe at Amazon’s tight hold on data. Digiday. Retrieved from https://digiday.com/marketing/retailers-chafe-amazons-
tight-hold-data/  

79 Creswell, J. (2018). How Amazon steers shoppers to its own products. The New York Times. Retrieved from 
https://www.nytimes.com/2018/06/23/business/amazon-the-brand-buster.html  

30 

      
 
 
                                                           
sources also indicate smaller prices, such as $30,000 per year80) to get access to a database that lets them 
see  some,  but  not  all,  of  the  data  Amazon  has  compiled81.  Also,  some  reports  from  former  Amazon 
employees  show  that  some  larger  vendors  may  be  provided  with  more  information  on  case-by-case 
basis, if they get a green light from form Amazon superiors82. Interestingly, an interviewee representing 
several large businesses active on Amazon mentioned that sometimes vendors on Amazon also become 
third-party sellers in order to get an access to more data.  

FIGURE 8. AMAZON RETAIL ANALYTICS: BASIC VS PREMIUM PACKAGE83 

For third-party sellers, especially those who own a brand, Amazon offers more data. In contrast to  the 
Amazon vendor model (or similarly to a brick-and-mortar supermarket), when a consumer purchases 
goods  online  through  the  Amazon  Marketplace,  not  only  Amazon  but  also  the  third-party  seller  can 
acquire some consumer data. Nonetheless, there is a difference in terms of the degree and detail of the 
data.  A  seller  can  get  the  data  displayed  in  the  transaction,  while  Amazon  obtains  consumer  data 
beyond the transaction data, including browsing history and clickstreams84. 

80 Bhattacharyya, S. (2019). Amazon rolls out customer demographic analytics for third-party sellers. Digiday. Retrieved from 
https://digiday.com/retail/amazon-rolls-customer-demographic-analytics-third-party-sellers/  

81 Creswell, J. (2018). How Amazon steers shoppers to its own products. The New York Times. Retrieved from 
https://www.nytimes.com/2018/06/23/business/amazon-the-brand-buster.html s 

82 Pathak, S. (2017). Retailers chafe at Amazon’s tight hold on data. Digiday. Retrieved from https://digiday.com/marketing/retailers-chafe-amazons-
tight-hold-data/  

83 Alloy Client Solutions. (2019). Amazon retail analytics basic vs premium: what’s the value? Medium. Retrieved from 
https://medium.com/alloytech/amazon-retail-analytics-basic-vs-premium-whats-the-value-
b9ab489cdbd8#:~:targetText=Both%20ARA%20Basic%20and%20ARA,along%20with%20units%20in%20transit. 

84 Li, W. C., Nirei, M., & Yamana, K. (2019). Value of data: There’s no such thing as a free lunch in the digital economy. Discussion papers 19022, 
Research Institute of Economy, Trade and Industry (RIETI). Retrieved from 
https://www.bea.gov/system/files/papers/20190220ValueofDataLiNireiYamanaforBEAworkingpaper.pdf  

31 

      
 
 
 
                                                           
Further, sellers on Amazon get access to some conversion metrics — essential for improving content on 
their product pages. They can retrieve (including though an API85) information on their own business 
performance, such as: 

  Various  reports  that  help  sellers  manage  their  “Sell  on  Amazon”  business,  including  Brand 
Dashboard, which provides suggestions what can be optimised, customer reviews (“Voice of a 
customer”); 

  Business reports, including listing traffic, conversion rates, information on the seller’s accounts, 

shipping, fulfilment, finances, orders, invoicing and products; 

  Amazon Selling Coach recommendations (representing the “insights” information category, as 

presented in Figure 6) for the following categories:  

o  Advertising:  Recommendations  for  advertising  your  products  and  for  using  the 

sponsored products program. 

o  Fulfilment:  Recommendations  for  the  top  products  to  fulfil  through  Fulfilment  by 

Amazon (FBA). 

o  Global  Selling:  Recommendations  for  expanding  your  products  to  more  regions  and 

o 

marketplaces. 
Inventory:  Recommendations  for  restocking  low  or  out-of-stock  items  in  your 
inventory. 

o  Pricing:  Recommendations  to  review  pricing  on  items  in  your  inventory  where  your 

offer is not the lowest price86. 

In addition to this, since 2019, the Brand Analytics data is provided at to sellers at an aggregated level 
(see the table below). This data provides a  valuable but rather broad overview of the relevant market 
segments and competition on the platform. s 

TABLE 1. AMAZON BRAND ANALYTICS, AS ACCESSED BY A SELLER 

Amazon Search Terms 

• 

• 
• 

For each search term: 
o 
o 
o  #1, #2 and #3 most clicked product (by Amazon Standard Identification Number - ASIN) for that search 

department (.de, .co.uk, .com, etc); 
search frequency rank; 

term, with its click share (%) and conversion rate (%). 

o  No actual search volume or volume of purchases in absolute terms. 
They can be filtered by reporting range (e.g., weekly, specific week). 
Could be downloaded in excel or CSV or explored in a web browser. 

85 Amazon MWS Sellers API section of the Amazon Marketplace Web Service (Amazon MWS). 

86 Amazon Services Europe. What you should know about the Amazon MWS Reports API section. Retrieved from 
http://docs.developer.amazonservices.com/en_UK/reports/Reports_Overview.html  

32 

      
 
 
 
                                                           
Market Basket Analytics 

• 

• 
• 
• 

For each of the seller’s products: 
o  #1, #2 and #3 most purchased products on Amazon, purchased together with that specific product of 
the seller, with its combination percentage (i.e., what percentage of customers, who bought a seller’s 
product, also bought other top 3 products). 

Can be filtered by category, subcategory, brand (among the seller’s brands), reporting range. 
Could be downloaded in excel or CSV or explored in a web browser. 
No actual volume of purchases. 

Item Comparison and Alternative Purchase Behaviour 

• 

• 
• 
• 

For each product most frequently viewed by customers together with (i.e., the same day as) the seller’s 
products, in addition to customer’s final choice after viewing seller’s product: 
o  #1, #2, #3, #4 and #5 compared products titles and ASIN (sold by competing sellers), with compared 
percentage (% of times this alternative product was viewed by customers who also viewed the seller’s 
product within the same day). 

Can be filtered by category, subcategory, brand (among the seller’s brands), reporting range. 
Could be downloaded in excel or CSV or explored in a web browser. 
No actual volumes of products sold 

Demographics  

• 

Breakdown of the seller’s Amazon customers (in aggregate) by age, household income, education, gender, 
and marital status, by a purchased product brand or category, but not by a specific product. 

In  addition  to  the  Brand  Analytics  Tool,  businesses  using  Amazon’s  paid  advertising  tool  can  get 
additional  data  and  analytics,  such  as  ACoS  (advertising  cost  of  sales),  showing  the  ratio  between 
advertising  costs  and  sales  revenues.  Moreover,  in  autumn  2019,  the  platform  released  a  beta  for 
advertising  attribution  for marketplace  sellers.  Amazon  Attribution  is  a  measurement  tool  that  tracks 
the  performance  of  Amazon  ads  on  channels  off  Amazon.  The  attribution  measures  page  views, 
purchases, and sales driven by external campaigns87. 

The table  below broadly summarises seller access to  the  categories of data and  analytics provided by 
Amazon,  using  the  typologies  presented  in  Chapter  3.  Generally,  according  to  most  interviewed 
Amazon  sellers  and  desk  research,  information  on  an  individual  seller’s  operations  and  performance 
that the platform provides is useful and sufficient for simple insights. Interviews with Amazon sellers 
and desk research also indicated that  generally the  seller community appreciates the  Brand Analytics 

87 Masiello, P. (2019). Using Amazon Ad attribution to track offsite campaigns. PracticalEcommerce. Retrieved from 
https://www.practicalecommerce.com/using-amazon-ad-attribution-to-track-offsite-campaigns  

33 

      
 
 
 
                                                           
Tool88, as it is free, allows, at least to some extent, to “keep an eye” on the competitors, helps to apply 
potential  keywords,  and  provides  better  knowledge  of  the  target  audience  and  products89.  As  we 
discuss in more detail in Chapter  6, this data  becomes especially valuable  when integrated with sales 
data - a feature provided by third-party analytics companies (e.g., Jungle Scout).  

TABLE 2. TYPES OF DATA AND ANALYTICS ACCESSIBLE BY AMAZON MARKETPLACE SELLERS 

AMAZON 

USER 
IDENTIFICATION 
DETAILS 

DATA ON 
TRANSACTIONS 
BETWEEN 
BUSINESSES 
AND 
CUSTOMERS 

BUSINESS 
PERFORMANCE 

USER 
BEHAVIOUR 

ANALYSES OF 
MARKET 
TRENDS/ 
DEVELOPMENTS 

Raw data 

Some 

Yes 

No 

Yes  No 

No 

No 

Own 

Competitor  Own  Competitor  Own 

Competitor 

NA 

NA 

Analytics 

Insights/ 
Actionable 
guidelines 

Some  No 

Yes  No 

Some  Some  

Some 

Yes 

Some 

NA 

Some 

Some 

Nonetheless,  some  interviewed  sellers  argued  that  the  extent,  comprehensiveness  and  granularity  of 
data  provided  is  still  very  limited.  Brand  Analytics,  according  to  them,  is  a  miniscule  part  of  the  full 
picture.  Most of the interviewed sellers indicated that they, as well as other sellers they know, widely 
use third-party data and analytics tools to compensate for the lack of  information on the  marketplace 
trends  and  activities.  Some  interviewees  also  argued  that  Brand  Analytics  is  by  far  not  enough, 
especially considering what access to data Amazon Retail has.  

In this context, Amazon third-part sellers are not on the level playing field with Amazon private label 
brands:  the  latter  have  “all  the  data  in  the  world  to  know  what  products  to  create  and  exactly  what 
keywords to target”90. Some sellers noted the platform has all the needed means and information (from 
the  manufacturer, logistics, to customer data) to remove  and replace any of its successful sellers with 
own private label products (though this does not mean that it would have a reason to do this)91. Recent 
media  articles  provide  a  wealth  of  stories  on  unfair  competition  between  Amazon  private  labels  and 
third-party Marketplace sellers92, using the aggregated seller data. Amazon’s practices of sharing seller 
data between Retail and Marketplace, as well as sophisticated use of metrics to boost its own sales was 
also  corroborated  by  the  platform’s  former  employees93.  In  November  2019,  Amazon  itself  confirmed 

88 Sellersessions. (n.d.). Brand analytics – the game changer for Amazon sellers. Retrieved from https://sellersessions.com/brand-analytics-the-game-
changer-for-amazon-sellers/;  Bhattacharyya, S. (2019). Amazon rolls out customer demographic analytics for third-party sellers. Digiday. Retrieved 
from https://digiday.com/retail/amazon-rolls-customer-demographic-analytics-third-party-sellers/    

89 SellerApp. (n.d.). Amazon Brand Analytics. Retrieved from https://www.sellerapp.com/blog/amazon-brand-analytics/  

90 Sellersessions. (n.d.). Amazon seller data. Podcast. Retrieved from https://sellersessions.com/amazon-seller-data/  

91 Sellersessions. (n.d.). Amazon seller data. Podcast. Retrieved from https://sellersessions.com/amazon-seller-data/  

92 The Washington Post. (n.d.). Amazon sellers say online retail giant is trying help itself not consumers. Retrieved from 
https://www.washingtonpost.com/technology/2019/10/01/amazon-sellers-say-online-retail-giant-is-trying-help-itself-not-consumers/?arc404=true;   
Creswell, J. (2018). How Amazon steers shoppers to its own products. The New York Times. Retrieved from 
https://www.nytimes.com/2018/06/23/business/amazon-the-brand-buster.html  

93 Hu, K. (2019). Revealed: how Amazon uses seller data to build a private label juggernaut. Yahoo! Finance. Retrieved from 
https://finance.yahoo.com/news/amazon-uses-thirdparty-sellers-data-to-build-private-labels 

34 

      
 
 
 
                                                           
that  it  uses  “aggregated  data”  from  sellers  in  its  third-party  marketplace  to  improve  its  overall 
business94.  

Indeed, as pointed out by the  EU and US policymakers95, Amazon’s data advantage over its business 
users constitutes a key problem with policy relevance. As a platform in a dominant position (in terms of 
its reach; around 200 million customers each month96), Amazon can use the data to identify successful 
products with a corresponding price, then offer them itself, at a better price or delivery conditions.  

Meanwhile,  the  platform  itself  argues  that  third-party  sellers  generate  an  important  and  increasing 
share of its revenues (third-party sales amounted $160 billion globally in 201897), so it has no incentive 
to harm the sellers - on the contrary, it invests in tools to help their businesses scale. Amazon’s private 
label  products,  according  to  an  interviewed  Amazon  representative,  generate  approximately  1%  of 
Amazon’s  total  sales  (this  information  is  difficult  to  verify,  as  it  is  not  provided  in  the  company’s 
Annual  Report;  some  reports  provide  the  same  figures  as  Amazon98,  others  estimate  that  the  private 
label business totalled in $7.5 billion in 201999 or around 5% as compared to the total turnover), and this 
is far less than other online retailers, many of whom have private label products that represent  over a 
quarter of their sales. Also, not all private brands are successful100. However, some sources estimate that 
Amazon’s  overall  private  brands  sales  grew  over  2%  from  2017  to  2018,  and  some  segments  grew 
significantly  more  (e.g.,  81%  growth  of  private  label  consumer  packaged  goods)101.  Meanwhile,  an 
American  investment  bank  projects  that  Amazon’s  private  label  business  could  see  revenues  of  $25 
billion by 2022102. The data that Amazon collects on all the transaction on the Marketplace contributes 
significantly to this.  

4.2.  eBay 

Compared to Amazon, eBay, active in the e-commerce segment of platform economy, has fewer users, 
as  well  as  a  simpler  business  model  (without  vertical  integration)  in  which  the  core  source  of  its 
revenues is the marketplace. As of 2018, eBay was significantly smaller than Amazon in terms of global 
third-party sales ($95 billion)103.  

The total scope of data collection, therefore, is significantly smaller than Amazon’s too, limited mostly 
to  the  activities  on  its  websites  and  applications.  Nonetheless,  similar  to  Amazon,  eBay  collects  a 
variety of customer and business data when they use the platform (search, transact, etc.), create an eBay 

94 Dastin. J., & Bose, N. (2019). Amazon uses aggregated seller data to help business, it tells lawmakers. Reuters. Retrieved from 
https://www.reuters.com/article/us-usa-antitrust-amazon-com/amazon-uses-aggregated-seller-data-to-help-business-it-tells-lawmakers-
idUSKBN1XT2N9  

95 Green, D. (2018). One of Amazon’s most powerful tools is putting a target on its back, and it could turn into a problem for the company. Business 
insider. Retrieved from https://www.businessinsider.com/amazon-scrutinized-third-party-seller-data-use-2018-12  

96 Dayton, E. (2020), „Amazon statistics you should know: opportunities to make the most of America‘s top online marketplace”. Available at 
https://www.bigcommerce.com/blog/amazon-statistics/#a-shopping-experience-beyond-compare  

97 Amazon. (2018). Annual report. Retrieved from https://ir.aboutamazon.com/static-files/0f9e36b1-7e1e-4b52-be17-145dc9d8b5ec  

98 Green, D. (2019). Amazon says its private labels are only 1% of its business, but new data shows some are seeing huge growth. Business insider. 
Retrieved from https://www.businessinsider.com/amazon-private-labels-some-grow-quickly-data-shows-2019-4  

99 Sun, L. (2019). Is Amazon’s private-label business a paper tiger? The Motley Fool. Retrieved from https://www.fool.com/investing/2019/03/22/is-
amazons-private-label-business-a-paper-tiger.aspx  

100 Hu, K. (2019). Revealed: how Amazon uses seller data to build a private label juggernaut. Yahoo! Finance. Retrieved from 
https://finance.yahoo.com/news/amazon-uses-thirdparty-sellers-data-to-build-private-labels  

101 Green, D. (2019). Amazon says its private labels are only 1% of its business, but new data shows some are seeing huge growth. Business insider. 
Retrieved from https://www.businessinsider.com/amazon-private-labels-some-grow-quickly-data-shows-2019-4  

102 Mavin, D. (2019). Yeld-hungry investors pile into Credit Suisse supply-chain funds. Barron’s. Retrieved from 
https://www.barrons.com/articles/yield-hungry-investors-pile-into-credit-suisse-supply-chain-funds-20191202?fnr=true  

103 Amazon. (2018). Annual report. Retrieved from https://ir.aboutamazon.com/static-files/0f9e36b1-7e1e-4b52-be17-145dc9d8b5ec  

35 

      
 
 
                                                           
account,  list  a  product,  provide  information  via  a  web  form,  add  or  update  information  in  the  eBay 
account, participate in online community discussions or otherwise interact with the platform. Some of 
this  data  is  actively  provided  by  a  user  when  using  the  platform  or  creating  an  account  (e.g. 
identification data, financial information, contacts); while some of it is collected automatically when a 
user uses the platform or creates an account (e.g. transactions, location data, computer connection data). 
The  platform  also  uses  cookies  to  track  customer  activities  (e.g.  page  visits,  user  segment,  device 
information). In addition to this, eBay also collects personal data from other sources, including: 

  public sources (e.g. demographic data);  
 
 
  data providers (e.g. demographic, interest-based and online advertising related data).104 

social media (e.g., data on viewing of videos); 
credit agencies or bureaus (e.g. credit reports/checks); 

In data analysis, eBay deals with structured, unstructured, and semi-structured data, where Hadoop, as 
a  big  data  platform,  provides  key  technology  features105.  The  company  is  also  said  to  be  actively 
contributing its knowledge back to the open source community106. 

Similar  to  Amazon,  eBay  provides  a  number  of  analytics  metrics  to  its  business  users  though  eBay 
Seller  Hub.  Similar  to Amazon,  the  platforms’  analytics  package  is  sometimes  criticised for  not  being 
flexible,  granular  or  extensive  enough107.  While  in  2018  the  range  of  seller  tools  provided  by  eBay  - 
including  data  and  analytics  -  was  slightly  broader  than  provided  by  Amazon,108  this  trend  changed 
with Amazon introducing its Brand Analytics in 2019.  

The  most  important  metrics  that  sellers  can  subscribe  to  on  eBay  (Listing  Analytics,  on  individual 
listings, as well as overall averages) for free109 include: 

Sales history, changes in sales, overall performance over time. 
Selling costs, including specific eBay fees, shipping labels, PayPal fees. 

 
 
  Competitor’s  pricing  (also  in  comparison  to  the  seller’s  pricing)  and  some  competitor 

performance data, in also comparison to seller’s performance. 

  Listing  data,  including  ranks,  click-through  rates,  page  views,  sales  conversion  rates, 
transactions, where they appear in search results, how many people see them in results110. 

eBay business users can access this information through their profile in the eBay Seller Hub or an API 
connection. In fact, eBay seller community claims that Listing Analytics is the most popular eBay Apps 
Centre application of all time. Sellers report it to be free, simple to use and generally useful.  

104 eBay. (2019). User Privacy Notice. Retrieved from https://www.ebay.com/help/policies/member-behaviour-policies/user-privacy-notice-privacy-
policy?id=4260  

105 Liang, A. (2019). How eBay governs its big data fabric. eBay. Retrieved from https://tech.ebayinc.com/engineering/how-ebay-governs-its-big-
data-fabric/  

106 Lopez, M. (2016). How eBay uses big data and machine learning to drive business value. Forbes. Retrieved from 
https://www.forbes.com/sites/maribellopez/2016/10/04/how-ebay-uses-big-data-and-machine-learning-to-drive-business-value/#61d46f031f35  

107 Miller, D. (2019). What you need to know about selling on eBay vs. Amazon. GoDaddy. Retrieved from 
https://www.godaddy.com/garage/selling-on-ebay-vs-amazon/  

108 Where to sell online. (2018). Amazon vs. eBay: Which marketplace is best for sellers? Retrieved from 
https://www.wheretosellonline.com/amazon-vs-ebay/  

109 Hsiao, A. (2019). How using eBay analytics can improve your sales. The balance small business. Retrieved from 
https://www.thebalancesmb.com/use-ebay-analytics-to-improve-your-sales-1140574  

110 Godin, M. (2018). How we used eBay analytics to increase sales. CrazyLister. Retrieved from https://crazylister.com/blog/ebay-analytics-increase-
sales/  

36 

      
 
 
                                                           
In  the  recent  years,  eBay  has  bought  and  incorporated  third-party  analytics  applications  such  as 
Terapeak111. When the acquisition was announced by the platform, it was reported that it underscored 
the company’s current outlook on building out better data tools for third-party sellers on its platform, 
to  help  it  compete  better  with  the  likes  of  Amazon  and  other  marketplaces  to  attract  their  business. 
Basically, the extended data tools were expected to allow the sellers improve and scale their businesses, 
as well as attract more inventory and sellers to the platform, and therefore increase the general volumes 
of sales made on eBay112.  

Terapeak currently powers the Listing Analytics tool, as described above. Moreover, for sellers with a 
Basic, Premium, Anchor or Enterprise Store, an additional product Terapeak Research is included in the 
subscription free of charge. All other sellers using eBay  (basically Starter accounts) have the option to 
pay for a yearly or monthly subscription113. We discuss this and similar third-party data and analytics 
products in more detail in Chapter 6. The table below broadly summarises seller access to the categories 
of data and analytics provided by eBay, using the typologies presented in Chapter 3. 

TABLE 3. TYPES OF DATA AND ANALYTICS ACCESSIBLE BY EBAY SELLERS 

EBAY 

USER 
IDENTIFICATION 
DETAILS 

DATA ON 
INDIVIDUAL 
TRANSACTIONS 
BETWEEN 
BUSINESSES AND 
CUSTOMERS 

BUSINESS 
PERFORMANCE 

USER 
BEHAVIOUR 

ANALYSES OF 
MARKET 
TRENDS/ 
DEVELOPMENTS 

Raw data 

Some 

NA 

NA 

Analytics 

Insights/ 
Actionable 
guidelines 

Own 

Competitor 

Own 

Competitor 

Own 

Competitor 

Yes 

No 

Some  No 

Some 

No 

No 

Yes 

Yes 

Yes 

No 

No 

NA 

NA 

Some  Some  

Some 

Some 

Some 

Some 

4.3.  Google Play app store 

Google generally collects user data in a variety of ways. The most obvious is the active data provision, 
with  the  user  directly  and  consciously  communicating  information  to  Google,  as  for  example  by 
signing-in  to  any  of  its  widely  used  applications  such  as  YouTube,  Gmail,  Search,  etc.,  or  developer 
listing  and  distributing  an  application.  Less  obvious  ways  for  Google  to  collect  data  are  “passive” 
means, whereby an application is used to gather information while it is running, possibly without the 
user’s knowledge. Google’s passive data gathering methods are integrated into platforms (e.g. Android 
and  Chrome),  applications  (e.g.  Search,  YouTube,  Maps),  publisher  tools  (e.g.  Google  Analytics, 
AdSense)  and  advertiser  tools  (e.g.  AdMob,  AdWords).  The  full  scope  of  data  collected  is  generally 
difficult  to  grasp  to  outsiders;  academic  studies  are  conducted  on  the  issue114,  and  various  whistle 

111 Terapeak. (2020). Terapeak Research. Retrieved from https://www.terapeak.com/products/professional/  

112 Lunden, I. (2017). eBay acquires Terapeak to provide more analytics to marketplace sellers. Techcrunch. Retrieved from 
https://techcrunch.com/2017/12/13/ebay-acquires-terapeak-to-provide-more-analytics-to-marketplace-sellers/; Team, T. (2016). Here’s why eBay is 
acquiring SalesPredict? Forbes. Retrieved from https://www.forbes.com/sites/greatspeculations/2016/07/14/heres-why-ebay-is-acquiring-
salespredict/#23dc4b7e4292 

113 eBay Customer Service. (2020). Terapeak Research. Retrieved from https://www.ebay.com/help/selling/selling-tools/terapeak-research?id=4853  

114 Schmidt, D. C. (2018). Google data collection. Vanderbilt University. Retrieved from 
https://www.ftc.gov/system/files/documents/public_comments/2018/08/ftc-2018-0074-d-0018-155525.pdf  

37 

      
 
 
 
                                                           
blowers occasionally bring to attention new ways and areas in which the platform collects user data115. 
Some  findings  indicate  that  Google  has  the  ability  to  connect  the  anonymous  data  collected  through 
passive means with the personal information of the user116. 

Given  that  Google  Play  app  store  is  tightly  connected  to  Google’s  ecosystem  through  Android  OS, 
which  is  the  key  enabler  of  data  collection  for  Google117,  it  is difficult  to  strictly  delineate  data  that  is 
collected  exclusively  on  Google  Play  from  data  generated  in  Google’s  other  activities.  Obviously,  it 
collects information on all the activities and transactions going on the app store, as well as transactions 
within  apps.  However,  an  interviewed  Google  Play’s  representative  argued  that  “it’s  a  common 
misconception  that  [the  Play  Store]  collects  everything”.  On  the  contrary,  their  objective  is  to  be  very 
strategic  about  data  collection,  “so  that  it  could  be  stored  safely  and  securely,  and  compliant  with 
GDPR”.   

As explained by Google Play’s representative, as an app store and payment processor, they track: 

signals of abuse and fraud, 

  user installs and uninstalls,  
  purchases and refunds, 
 
  user settings, 
  user generated content, including rankings and reviews, 
  detailed user activity on the app store, 
 

specific device information.  

The  app  store  claims  that  they  aim  to  provide  the  developers  with  the  optimal  amount  of  data  to 
successfully operate and grow their apps. The platform presents various statistics to its app developers 
for their individual apps (on the Play Console web version or app) as well as extensive material on how 
to use this data in decision-making. The main categories are detailed in the box below. Some of the data 
on  apps  of  Google  Play  is  also  accessible  publicly,  and  is  being  used  in  academic  research118.  Some 
developers report that this information enabled them to improve customer retention, engagement and 
monetisation119.  

TABLE 4. GOOGLE PLAY CONSOLE: KEY METRICS FOR APP DEVELOPERS 

Statistics  and  insights:  developer  can  find  and  review  their  app  statistics  on  their  computer  or  Android  apps.  There  are 

several  pages  within  the  Play  Console  where  they  can  review  their  app's  installs,  uninstalls,  ratings,  revenue,  and  crashes 
data.  Dashboard  page  provides  overview  of  key  metrics,  trends,  notifications,  and  insights;  Statistics  page  provides 

customizable, detailed reports with key metrics and dimensions. Some metrics are calculated based on data from users who 

115 Statt, N. (2019). Google has been tracking nearly everything you buy online – see for yourself with this tool. Google claims the little-known web 
tool keeps data private. The Verge. Retrieved from https://www.theverge.com/2019/5/17/18629789/google-purchase-history-gmail-email-receipts 

116 Schmidt, D. C. (2018). Google data collection. Vanderbilt University. Retrieved from 
https://www.ftc.gov/system/files/documents/public_comments/2018/08/ftc-2018-0074-d-0018-155525.pdf  

116 Statt, N. (2019). Google has been tracking nearly everything you buy online – see for yourself with this tool. Google claims the little-known web 
tool keeps data private. The Verge. Retrieved from https://www.theverge.com/2019/5/17/18629789/google-purchase-history-gmail-email-receipts 

117 Schmidt, D. C. (2018). Google data collection. Vanderbilt University. Retrieved from 
https://www.ftc.gov/system/files/documents/public_comments/2018/08/ftc-2018-0074-d-0018-155525.pdf  

117 Statt, N. (2019). Google has been tracking nearly everything you buy online – see for yourself with this tool. Google claims the little-known web 
tool keeps data private. The Verge. Retrieved from https://www.theverge.com/2019/5/17/18629789/google-purchase-history-gmail-email-receipts  

118 E.g. Mokarizadeh, S., Rahman, M. T., & Matskin, M. (2013). Mining and analysis of apps in Google Play. ICT School, Royal Institute of 
Technology (KTH). In Proceedings of the 9th International Conference on Web Information Systems and Technologies (BA-2013), 527-535. Retrieved 
from https://scitepress.org/papers/2013/45020/pdf/index.html 

119 Elliott, D. (2018). A guide to the Google Play Console. Medium. Retrieved from https://medium.com/googleplaydev/a-guide-to-the-google-play-
console-1bdc79ca956f  

38 

      
 
 
 
                                                           
have agreed to share their data with developers in aggregate, but adjusted to more closely reflect data from all of the users 

of that specific app developer. For example, a developer’s data can be compared to peer groups, over time or before-after 
the dates of some key events (e.g., subscription price change, roll-out of new releases).  The reports can be accessed and 

downloaded as CSV files from Google Could Storage. They are generated daily and accumulated in monthly CSV files. More 
specifically, the provided metrics cover: 

 
 
 
 
 

install-related statistics (e.g., users and devices (new, active, returning), install and uninstall events); 
ratings (e.g., average and cumulative average rating, rating volume, Google Play rating); 

revenue (e.g., total revenue, revenue per time period, buyers);  
crashes and application not responding errors (crashes and ANRs); 

specific metrics for Android Instant Apps (launches by device, launch events, conversion events).  

The Play Console Help portal provides support to developers, explaining, for example, how to analyse ratings and reviews, 

measure apps acquisition and retention, monitor app’s technical performance, compare ratings with custom peer groups, 
use  the  data  to  stop  a  staged  rollout,  run  A/B  tests  on  store  listing  and  apply  the  results  and  manage  app’s  orders/ 

subscription cancellations - among other things.  
In  addition  to  this,  developers  can  use  the  ‘Growth  rate  compared  to  peers’  chart  to  understand  how  their  metrics  are 

performing in relation to groups of peer apps from one period to the next. This data allows you to judge the performance 
against key areas of the ecosystem. 

Monetization: Google Play also provides a developer’s app’s revenue and buyer data. They can review app's financial data 
to see how their sales, managed products, subscriptions, and rewarded products perform over time. Revenue data is based 

on estimated sales (amounts paid by buyers, including tax). The data categories include: 

  Overview - a high-level breakdown of revenue sources, average revenue per paying user (ARPPU), and buyer 

information. 
Revenue - per product, individual product details, export revenue data. 

Buyers - total, new and returning, by device and country; using time filters.  
Conversions - rate, spending per buyer. 

 
 
 

The app store offers two types of transaction reports:  

 
 

Earnings Reports: Generated near the beginning of the month. 

Estimated  Sales  Reports:  Generated  daily  by  adding  all  transactions  that  were  CHARGED  or  REFUNDED 
recently to the current month's file. It can take several days for all new transactions to appear. 

An interviewed representative of a developer association summarised that the data that developers get 
from  platforms  do  not  necessarily  show  how  other  businesses  are  doing,  but  have  a  very  good 
understanding  how  the  developer  is  doing.  In  addition  to  this,  the  platform  invests  in  developing 
various  materials  aimed  at  providing  tips  and  guidelines  for  smaller  developers  to  improve  their 
products, adjust their monetisation models and grow.  

The  Google  Play  representative  explained  that  the  metrics  provided  are  continuously  reviewed,  with 
two goals in mind: usefulness to the developers and customer data protection. On  the one hand, they 
gather developer feedback to find out what metrics would be useful to introduce. On the other hand, 
they may limit the access to protect the end users. For example, since May 2018 some metrics have been 
calculated based on data from users who have agreed to share their data with developers in aggregate. 
The metrics are adjusted to more closely reflect data from all of  individual developer’s end-users, but 
Google does not display data that falls under certain minimum thresholds anymore.120  

120 Google. (n.d.). Changes to your Play Console data. Play Console Help. Retrieved from https://support.google.com/googleplay/android-
developer/answer/9023647  

39 

      
 
 
                                                           
Out of the data that it collects, Google Play does not provide developers with user device information, 
detailed activities on the Play store or metrics of fraud detection. Although Google Play is a vertically 
integrated  platform  (i.e. it distributes  Google  applications),  the  platform’s  representative  assured  that 
according to their strict internal policies, first-party app developers have the same access to Google Play 
data as the third-party app developers.  

Furthermore,  the  data  that  developers  need  the  most  concerns  the  user  engagement  with  their  own 
apps  -  something  that  Google  Play  says  it  does  not  collect,  and  it  is  up  to  developers  themselves. 
Therefore,  the  data  that  developers  have  (or  do  not  have)  access  to  depends  on  the  end-user 
permissions.  The  app  store  has  certain  requirements  for  app  privacy  policy121,  but  the  scope  of  data 
(including  personal  and  sensitive  data)  that  individual  apps  can  access  is  based  on  requests  made 
directly  to  the  users  and  the  permission  that  users  are  asked  or  choose  to  grant  on  Android  system. 
Android’s permissions system is based on the security principle of least privilege: an app should only 
have the minimum data needed to perform its task. Developers must declare the permissions that their 
apps  need  beforehand,  and  the  user  is  given  an  opportunity  to  review  them  and  decide  whether  to 
install the  app. Interestingly, however, a recent study by the International Computer Science Institute 
(ICSI, the US) claims that thousands of Android apps can access restricted data even when users deny 
them  permission122. Google  said  they  would  address the  issues in Android  10, which  was  released in 
September 2019123.  

The  broad  categories  of  data  and  analytics  accessible  for  app  developers  active  on  Google  Play  are 
summarised in the table below.  

TABLE 5. TYPES OF DATA AND ANALYTICS ACCESSIBLE BY GOOGLE PLAY APP DEVELOPERS ON THE 
PLATFORM 

GOOGLE 
PLAY 

USER 
IDENTIFICATION 
DETAILS 

DATA ON 
TRANSACTIONS 
BETWEEN 
BUSINESSES 
AND 
CUSTOMERS 

BUSINESS 
PERFORMANCE 

USER 
BEHAVIOUR 

ANALYSES OF 
MARKET 
TRENDS/ 
DEVELOPMENTS 

Raw data 

Depends on user 
permissions 

NA 

NA 

Analytics 

Insights/ 
Actionable 
guidelines 

Own 

Competitor  Own  Competitor  Own  Competitor 

Yes  No 

Yes  No 

No 

No 

Yes   No 

Yes 

Some 

Yes 

Some  

Yes 

Yes 

Yes 

NA 

Yes 

Yes 

121 Google. (n.d.). Upload an app. Play Console Help. Retrieved from https://support.google.com/googleplay/android-
developer/answer/113469?hl=en&visit_id=636733945304418846-2175441800&rd=1#privacy  

122 Reardon, J., Feal, A., Wijesekera, P., et al. (2019). 50 Ways to Leak Your Data: 
An Exploration of Apps’ Circumvention of the Android Permissions System. In 28th USENIX Security Symposium. Retrieved from 
https://www.ftc.gov/system/files/documents/public_events/1415032/privacycon2019_serge_egelman.pdf  

123 Ng., A. (2019). More than 1,000 Android apps harvest data even after you deny permissions. Cnet. Retrieved from 
https://www.cnet.com/news/more-than-1000-android-apps-harvest-your-data-even-after-you-deny-permissions/  

40 

      
 
 
 
                                                           
4.4.  Booking.com 

Booking.com,  a  hotel  booking  platform,  is  one  of  the  major  European-based  online  platforms  in  the 
world. Much like all the multi-sided platforms, Booking.com collects data on its users both actively and 
passively. More specifically: 

 

 

 

Some of the data, the customers (or e.g. travel agents on their behalf) provide directly to the 
platform. For example, to make a reservation, the minimum required data is name and email 
address.  Usually,  however,  the  platform  also  asks  for  home  address,  telephone  number, 
payment information, date of birth, the names of guests travelling together and preferences. 
Further, the platform requests to leave reviews for the booked properties after the stay, which 
many customers do.  
Booking.com also collects customer data from their mobile devices, if the permission is given 
(e.g. location data, contact details).  
Customer  participation  in  referral  programs  or  sweepstakes  provides  Booking.com  with 
personal data. 

 

  Most of the customer data is collected automatically as they visit and navigate Booking.com 
and  affiliated  websites  or  apps.  This  includes  IP  address,  the  date  and  time  they  accessed 
Booking.com  services,  the  hardware,  software,  internet  browser  and  computer’s  operating 
system, app version and language used, clicks and pages shown to the customer. From mobile 
devices,  the  platform  collects  device  identification  data,  device-specific  settings  and 
characteristics, app crashes and other system activity.  
In addition to this, Booking.com receives customer information from other sources, including 
“affiliate partners, subsidiaries of the Booking.com corporate family and other affiliates of the 
Booking  Holdings  Inc.  corporate  family  and  other  independent  third  parties”124.  All  the 
information that the platform acquires from these sources may be combined with information 
provided by customers themselves.  
Booking.com also monitors all communications between customers and service providers that 
are conducted through Booking.com communication means.  
If  Booking.com  account  is  linked  to  social  media  accounts,  Booking  receives  data  from  the 
social media platform, such as cookies and other data used for advertising125.  

 

 

Significant  amounts  of  data  are  also  collected  on  the  business  users,  including  detailed  and 
comprehensive  information  on  the  listed  properties  and  their  policies,  pricing,  transactions,  free  and 
booked rooms and so on. Generally, the platform’s data flows result into a lot of personal information 
of customers, as well as detailed information on around 28 million properties126 using the Booking.com 
platform.  This,  in  turn,  also  provides  the  platform  with  insights  on  destinations  and  countries  all 
around the world.  

The  platform  claims  to  provide  the  listed  properties  with  all  the  tools,  including  data,  to  help  their 
business succeed. Booking.com Connectivity APIs allow businesses to send and retrieve data for their 
properties listed on Booking.com. They can  manage  room availability, reservations, prices, and  many 
other  things  within  their  own  systems.  Moreover,  they  can  access  the  platform’s  data  on  room  rates, 
reservations, promotions, and guest reviews. This enables business users to build a "one-stop shop" for 

124 Booking.com. (2020). Privacy Statement. Retrieved from https://www.booking.com/content/privacy.en-gb.html#policy-booking-holdings  

125 Booking.com. (2020). Privacy Statement. Retrieved from https://www.booking.com/content/privacy.en-gb.html#policy-booking-holdings  

126 Booking.com. (2020). About Booking.com. Retrieved from https://www.booking.com/content/about.en-gb.html  

41 

      
 
 
                                                           
their  connected  properties,  allowing  property  owners  to  easily  manage  their  information  on  multiple 
websites127. 

Furthermore,  Booking.com  conducts  analytics  on  these  data  to  provide  third-party  sellers  data 
targeting  services,  such  as  pricing  strategy,  demand  forecast,  and  consulting  services.  Booking.com 
Partner Hub portal provides an analytics dashboard for the platform’s business users, including some 
aggregated data on their competition128. According to the platform, the aim is to help hotels boost your 
sales  and  find  ways  of  getting  more  bookings.  The  box  below  provides  a  detail  list  of  items  that 
Booking.com provides to its business users in the Analytics section. 

TABLE 6. ANALYTICS FOR BOOKING.COM BUSINESS USERS 

  Analytics dashboard, offering Business reports with real-time information, helping to monitor past and future 

sales, as well as compare the user’s hotel’s results against their competitors; 
Booker Insights report, that gives information on the hotel’s current bookers, helping to better understand who 
their customers are129; 
Information useful for reservations management, for example, how far in advance customers are booking their 

 

 

rooms, the number of room nights sold, sold out dates, as well as revenue broken down in a range of charts. 
  Guest review scores’ tool, allowing to analyse user review scores over time and to compare performance against 

properties in their competitive set130 (~10-15 properties). 
“Genius” programme reports (for participating properties), including statistics on Genius guests and gains 
(additional revenues) from the programme131. 
 Visibility Dashboard, providing data to track and improve property’s visibility in Booking.com search results, such 

 

 

as: 

o 
o 

o 
o 

o 
o 

search result views (the number of times potential guests saw the property in search results);  

click-through rate (CTR; the percentage of potential guests that clicked on the property in the search 
results, relative to the number of search results views);  

property page views (the number of times potential guests viewed the property page);  
bookings (the total number of bookings received, including cancellations) and the overall conversion 

rates.  
visibility performance in Booking.com search results the past 30, 90 or 365 days. 

comparative visibility performance against other properties in the market for the past 90 days (see the 
figure below. 

o  Conversion (how well the property converts guest searches into bookings). 
o  Key factors that influence performance, compared to other properties in the area, and pointing to 

127 Booking.com. (n.d.). About Booking.co Connectivity APIs. Retrieved from https://connect.booking.com/user_guide/site/en-US/#  

128 BookingSuite. (2017). Rate setting: understand your comp set first. Booking.com. Retrieved from https://suite.booking.com/blog/understand-
competition-to-determine-hotel-prices/  

129 Partner Hub. (2019). What is the Analytics dashboard? Booking.com. Retrieved from https://partner.booking.com/en-gb/help/growing-your-
business/what-analytics-dashboard  

130 Partner Hub. (2019). How can I analyse my guest review scores? Booking.com. Retrieved from https://partner.booking.com/en-gb/help/growing-
your-business/how-can-i-analyse-my-guest-review-scores  

131 Partner Hub. (2019). How should I read the Genius report? Booking.com. Retrieved from https://partner.booking.com/en-gb/help/growing-your-
business/how-should-i-read-genius-report  

42 

      
 
 
 
 
                                                           
possible actions action to increase exposure to more potential guests (see the figure below)132. 

In  addition  to  this,  the  BookingSuite  division  of  Booking.com  provides  its  partner  hotels  with 
additional  data  solutions,  including  RateIntelligence  -  a  free  tool  for  smaller  properties133,  providing 
market data (demand, room rates,  local events) for informed pricing decisions134. It was reported that 
Booking.com  data  analytics  service  on  pricing  strategy  on  average  increased  third-party  sellers’  sales 
revenue by 7%135. Business users of Booking.com also reported having access to broader travel trends, 
such as popular days for travel, what types of properties make more sales (e.g. 3-strar hotels vs 5-star 
hotels vs hostels). Upon request, the platform can provide additional insights to business user account 
managers (e.g., impact of Brexit on hotel bookings).  

Importantly, Booking.com, as many other OTAs does not provide the hotels with full customers contact 
data (i.e., they share phone number, but not email address). All the communication between properties 
and their customers goes through Booking.com communication tools136. With an OTA booking, hotels 
are not passed along any first-party data, leaving the  only chance to capture customer contact data at 
the  point  of  sale137.  It  is  difficult  for  hotels  to  capture  this  information  at  the  front  desk  since  it  is  a 
request  rather  than  a  requirement  (as  it  is  for  registering  and  making  reservations  on  Booking.com). 

132 Partner Hub. (2019). What is the Visibility Dashboard? Booking.com. Retrieved from https://partner.booking.com/en-gb/help/growing-your-
business/what-visibility-dashboard  

133 While larger properties with own IT departments can get this data through an API and analyse it themselves. 

134 BookingSuite. (2020). Our mission. Booking.com. Retrieved from https://suite.booking.com/about-us/  

135 Li, W. C., Nirei, M., & Yamana, K. (2019). Value of data: There’s no such thing as a free lunch in the digital economy. Discussion papers 19022, 
Research Institute of Economy, Trade and Industry (RIETI). Retrieved from 
https://www.bea.gov/system/files/papers/20190220ValueofDataLiNireiYamanaforBEAworkingpaper.pdf s 

136 Engels, J. (2017). Why hotels should invest in direct bookings over OTAs. Hotelchamp. Retrieved from https://www.hotelchamp.com/blog/direct-
bookings-over-otas/ 
137 An interesting exception is the TripAdvisor instant booking product, which does provide the properties with all customer contact data. However, 
the company considers it unsuccessful as they did not manage to attract significant numbers of hotels.  

43 

      
 
 
 
 
                                                           
The  customer  contact  data,  meanwhile,  is  hotels’  chance  to  build  guest  relationships  with  loyalty 
marketing and promotions138. 

Therefore,  while  the  interviewed  hotels  reported  that  they  appreciate  the  analytics  and  insights  that 
Booking.com  generates  for  them,  they  lack  customer  information.  This  concerns  not  only  contact 
details,  but  also  a  more  detailed  profiling  information.  In  this  regard,  Booking.com  is  contrasted  to 
Airbnb,  which  provides  listed  properties  with  more  detailed  customer  profiles  and  additional 
information so that, according to an interviewee, “hosts are aware in advance and can serve customer 
better”.  Nonetheless,  smaller  establishments  on  Booking.com,  as  reported  by  Booking.com  and  other 
interviewed platforms in tourism sector, are less likely to use these insights at all.  

The broad categories of data and analytics accessible for properties using Booking.com are summarised 
in the table below.  

TABLE 7. TYPES OF DATA AND ANALYTICS ACCESSIBLE BY PROPERTIES LISTED ON BOOKING.COM 

BOOKING.
COM 

USER 
IDENTIFICATIO
N DETAILS 

DATA ON 
TRANSACTIONS 
BETWEEN 
BUSINESSES AND 
CUSTOMERS 

BUSINESS 
PERFORMANCE 

USER BEHAVIOUR 

ANALYSES OF 
MARKET TRENDS/ 
DEVELOPMENTS 

Raw data 

Some (no 
emails!) 

Analytics 

Insights 

NA 

NA 

Own 

Competitor 

Own 

Competitor 

Own 

Competitor 

Yes 

No 

Yes 

No 

No 

No 

NA 

Some  No 

Some 

Yes 

Yes 

Some 

Some 

Some  

Some 

Yes 

Yes 

4.5.  Reasons for providing data or restricting the access 

As the four platform case studies show, the kinds of data provided and not provided by these players 
are  rather  similar.  First,  all  the  overviewed  platforms  have  made  notable  investments  in  analytics 
capacities to draw insights from this data and provide it to the business users.  Therefore, all business 
users get access to some data and analytics important to their commercial activities. However, they do 
not share the raw big data, as well as detailed data on customers and competitors. Although platforms 
reportedly  collect  and  analyse  loads  of  data  relevant  for  competition  on  their  marketplaces  (basically 
everything that takes place on their apps and websites every second), only a fraction of this usually is 
shared.  

Online  platforms  provide  several  main  justifications  for  not  granting  the  access  to  data  (or  a 
combination of them), that could be summarised into several main categories: 

  Protection of business model. For example, OTAs argue that if they provide customer contact 
data to establishments that are listed on these platforms, the establishments will be able bypass 
them,  destroying  the  source  of  revenue.  Similarly,  e-commerce  platforms  do  not  share 
customer  identification  details  to  avoid  businesses  targeting  the  customers  through  ads  and 
directing them to their personal websites, bypassing the platforms.  

138 Cendyn. (2017). The OTA shift. How to combat OTAs & drive direct hotel bookings. Retrieved from https://www.edhotels.com/wp-
content/uploads/2017/08/The-OTA-Shift-FINAL2017.pdf  

44 

      
 
 
 
                                                           
 

  Protection  of  trade  secrets.  As  an  interviewed  marketplace  representative  of  a  European 
platform  explained,  data  openness  is  risky.  Often,  they  detect  abusive  bots  originating  from 
China,  crawling  their  pages  or  trying  to  use  their  APIs.  At  the  same  time,  they  see  Chinese 
marketplaces as serious competitors, disregarding all the regulations they must comply with, 
and therefore gaining competitive advantages.  
It is useful to analytically distinguish between  data as basis of the business model and data as by-
product  of  activities  on  the  platform.  Although  the  latter  category  of  data  could  be  easier  for 
platforms  to  share,  they  point  out  other  challenges,  such  as  data  interoperability.  In  many 
cases,  the  obstacles  to  interoperability  are  technical.  Overcoming  this  to  develop  new 
is  costly  and  technically  complicated  according  to  a 
interoperability  functionalities, 
representative  of a developer’s organisation. However, often interoperability issues  arise  due 
to misaligned incentives as such interoperability might have broader benefits but to the cost of 
the  dominant  companies139.  Some  platform  companies,  nonetheless,  are  already  making 
substantial  efforts  in  this  regard,  such  as  the  Data  Transfer  Project  that  includes  Microsoft, 
Google, Facebook and Twitter140.  

  The provision of raw data generated by the platform is problematic, both because of its volume 
and usability. To be useful for business users, most types of data have to be analysed and be 
used  to  provide  insights.  Meanwhile,  business  analytics  and  insights,  which  could  really 
benefit the business users, require significant investments from the platforms. From platform 
business point of view, such investment could not pay off.  

  Lack  of  need  to  provide  data.  Related  to  the  previous  point,  platforms  often  argue  that  the 
majority of business users would not know what to do with data and how to make it useful. In 
fact,  many  interviewed  business  users  who  reported  feeling  disadvantaged  by  limited  data 
access,  could  not  provide  a  detailed  vision  of  how  they  would  analyse  the  raw  data  from 
platforms and draw insights. On the other hand, the interviewed large companies using online 
platforms claim that this justification is a “nonsensical excuse”.  

  Company status. Platform companies that are privately owned (e.g., Allegro) are not required 
by law to disclose detailed financial and operating information in most instances. They enjoy 
wide latitude in deciding what types of information to make available to the public and have 
the flexibility to release their data whenever they need, e.g. real-time. Meanwhile, US publicly 
traded companies (e.g. Amazon) are obliged to make market-sensitive information available to 
all parties at the  same  time141. This allows Allegro to share their  real-time  gross merchandise 
volume (GMV) data at any time, while Amazon - a publicly traded company - cannot do this 
because of the disclosure laws and requirements that apply to it.  

  Data  protection  legislation.  The  interviewed  platforms  seemed  to  be  rather  concerned  with 
meeting the requirements of GDPR, while some of the business users see this as a justification 
for  not  providing  customer  data  to  them.  In  fact,  from  the  legal  point  of  view,  this  concerns 
personal  data  only  to  the  extent  that  they  cannot  be  anonymized.  Moreover,  the  platforms 
could comply with the requirements of GDPR by giving their business users the opportunity to 
obtain their own consent from individual customers.  

139 Open Government License. (2019). Unlocking digital competition. Report of the Digital Competition Expert Panel. Retrieved from 
https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/785547/unlocking_digital_competition_furman_
review_web.pdf  

140 Data Transfer Project. (n.d.). Retrieved from https://datatransferproject.dev/  

141 Reference for Business. (n.d.). SEC disclosure laws and regulations. Retrieved from https://www.referenceforbusiness.com/small/Qu-Sm/Sec-
Disclosure-Laws-and-Regulations.html  

45 

      
 
 
                                                           
Besides personal data, platforms also must protect the platform business user data  in order to 
comply  with  competition  policy  rules.  For  example,  a  representative  of  an  e-commerce 
platform  explained  that  sharing  information  on  the  revenue  of  a  specific  seller  active  on  the 
platform  with  other  sellers  can  be  interpreted  as  providing  a  competitive  advice  under  the 
German anti-trust law.  

  Customer  satisfaction.  All  the  interviewed  platforms  emphasised  customer  satisfaction  as 
their key goal of their policies. E-commerce and accommodation platforms expressed a concern 
that if the sellers get customer email information, they will flood the customers with marketing 
messages.  Meanwhile  additional  requests  for  data  might  confuse  the  customers  (e.g.,  at  the 
moment  of  hotel  check-in  or  during  the  transaction),  and  this  is  how  platforms  justify  not 
providing  business  users  the  possibility  to  obtain  customer  consent  to  process  their  data 
themselves.  

From  the  economic  point  of  view,  the  incentives  for  companies  to  grant  access  to  their  data  mostly 
depends  on  the  role  of  data  in  their  business  model.  If  the  exploitation  of  a  particular  dataset  that  it 
collects is at the core of its business model, an online platform will be unlikely to share it142. Data and its 
analysis form the core of online platforms business models, which could explain the general reluctance 
of these companies to share their data143. As explained by an association representing several platform 
operators, data is a very sensitive question for them.  

Nonetheless,  in  some  cases  sharing  of  data  is  beneficial.  Data  access  for  business  users,  as  well  as 
expensive advanced analytics and tips, are granted to the extent that it could be generate more income 
for  the  platform.  First,  it  is  in  the  interest  of  platforms  to  allow  the  business  users  develop  better 
products and services, attract more customers, conduct more transactions and therefore generate more 
income  for  the  platform  through  commission  on  intermediation  services.  Second,  platform  business 
users also often want more data, so if a competing platform invests in analytics, others are motivated to 
do so too to maintain their business user base. Most interviewed platforms emphasised these as the key 
incentives to invest in analytics capacities for business-user oriented analytics and insights. One of the 
clearest limits to data provision is any kind of data that would allow businesses to reduce dependency 
on  the  intermediating  platform  by  shifting  the  traffic  to  personal  online  stores  or  bypassing  the 
platforms overall (i.e. customer contact/identification data). Meanwhile, vertically integrated platforms 
want  to  avoid  providing  data  that  would  allow  business  users  to  become  serious  competitors. 
Depending  on  other  variables,  such  as  investment  and  competition,  these  “limits”  of  data  provision 
could be expanded or lowered.  

It is important to note that the four  analysed cases do not fully reflect the  full variety of data sharing 
practices  in  the  platform  economy.  This  overview  did  not  allow  to  identify  notable  data  sharing 
differences by sector or between vertically integrated and non-integrated platforms144 - a larger sample 
comparative study is needed to determine this. For example, the vertically integrated German platform 
Real.de does not provide its sellers with any kind of comparative analytics or data on the overview of 
other user  activities on the  platform,  unless they’re using platform’s  advertising services. Meanwhile, 

142 Competition Policy International. (2018). Retrieved from https://www.competitionpolicyinternational.com/wp-content/uploads/2018/05/CPI-
Garces.pdf  

143 Richter, H., & Slowinski, P. R. (2019). The Data Sharing Economy: On the Emergence of New Intermediaries. IIC-International Review of 
Intellectual Property and Competition Law, 50(1), 4-29. 

144 To determine this, more in-depth analysis into the functionalities provided by the platforms to their business users is needed. The desk research 
generated some but not sufficient evidence. For example, eBay shares more data with third party data and analytics provider than Amazon; eBay 
and Etsy allow to connect the business account to Google Analytics, while Amazon does not. This information can be retrieved from 
https://community.ebay.com/t5/Archive-Selling/How-To-Use-Google-Analytics-To-See-your-Store-Traffic-Stats/td-p/24250195; 
https://help.etsy.com/hc/en-us/articles/360000337967-Setting-up-Google-Analytics?segment=selling ; and 
https://sellercentral.amazon.com/forums/t/can-i-use-my-google-analytics-account-to-track-stats-on-my-amazon-shop/212291 

46 

      
 
 
                                                           
the Polish marketplace Allegro engaged purely in 3p activities (i.e. non-integrated) provides all the data 
on transactions real-time through its API, including individual-level anonymised data on all the sellers. 
However, these differences may as well be determined by other factors than vertical integration, related 
to specific contexts, a platform’s mission and vision, as well as decisions made by platform companies.  

47 

      
 
 
5.

 Business user perspective on data access 

In this chapter, we briefly present the business user perspective on the data that they receive from the 
platforms, alternative data sources and strategies to acquire data. We build on data from desk research 
interviews and the business user survey to present a general overview of their views towards platform 
data  access.  Ultimately,  this  helps  to  better  understand  the  key  gaps  between  the  business  user  data 
needs presented in Chapter 3, and data and analytics provided by platforms, presented in Chapter 4. In 
the second part of the chapter, we discuss the options that business users have to fill these gaps.  

It must be noted that the business user survey, results of which are presented in this chapter, targeted 
the  business  users  of  all  internet  platforms  that  fit  the  study  definition,  not  only  e-commerce  sellers, 
accommodation  providers  and  app  developers.  More  specifically,  the  enterprises  that  participated  in 
the  survey  use  platforms  for  one  or  several  of  the  following  purposes:  selling  goods,  selling  services, 
distributing  apps  or  software,  listing  accommodation  and  advertising.  Therefore,  the  survey  results 
reflect a broader overview of data sharing in P2B relationships. 

5.1.  Business users’ perspective on platform data access 

A notable share of business users of online platforms report that they do not get satisfactory access to 
data  from  platforms  -  both  in  the  earlier  studies145,  as  well  as  the  survey  and  interview  programme 
conducted  for  this  paper.  For  instance,  Ecorys  study  showed  that  33%  of  the  ‘heavy’  users  of  online 
platforms responding to a survey146 stated that they had experienced data access related issues in their 
trading practices with online platforms. Meanwhile, our business user survey results show that around 
27% of surveyed enterprises disagreed, strongly disagreed or partly disagreed with the statement that 
their  business  can  easily  access  data  collected  by  the  main  platform,  which  is  important  to  their 
business (Figure 9).  

145 According to a study by Ecorys, 26% of surveyed business users said they had access to data on transactions and market analysis. Only 21% said 
they  could  access  user  behaviour  data.  Access  to  the  data  of  other  users,  e.g.  potential  customers,  is  granted  only  in  rare  cases.  Furthermore, 
according  to  a  Eurobarometer  study  (Eurobarometer  439),  42%  of  surveyed  SMEs  reported  not  getting  the  data  they  needed  from  online 
marketplaces. 
146 Bertschek, I., Kesler, R., & Laitenberger, U. (2017). Business-to-business trading practices in the online platform environment. Ecorys, Kantar TNS 
Nederland and WIFO; European Commission, DG GROW. Retrieved from https://publications.europa.eu/en/publication-detail/-
/publication/04c75b09-4b2b-11e7-aea8-01aa75ed71a1/language-en  

48 

      
 
 
                                                           
FIGURE 9. MY BUSINESS CAN EASILY ACCESS DATA COLLECTED BY THE PLATFORM THAT IS 
IMPORTANT TO MY BUSINESS 

2% 3% 

5% 

27% 

19% 

Strongly agree

Agree

Partly agree, partly disagree

Disagree

Strongly disagree

Don't know/ Not applicable

Source: PPMI; n=1,667 

44% 

Further, quite in line with the business user interview and platform analysis data presented in Chapter 
4, the  majority of business  users  report to  receive  data on their  performance and their  customers (see 
the  figure  below).  Meanwhile,  significantly  smaller  shares  of  businesses  report  also  having  access  to 
data on other businesses and other customers.  

FIGURE 10. DO YOU HAVE ACCESS TO THE FOLLOWING TYPES OF DATA HELD BY THE MAIN ONLINE 
PLATFORM? 

0,0%

Other customers' behaviour on the platform

Identification details of other users on the platform (i.e.
potential customers)

Business identification of other business users on the
platform
Data from individual transactions carried out between
businesses and customers on the platform

Your customers’ behaviour on the platform 

Analysis of market trends/ developments

Identification details of your customers on the platform

Your business performance

45,4% 

46,1% 

48,2% 

51,8% 

55,5% 

54,6% 

58,8% 

63,0% 

Yes

No

Don't know

100,0%

12,1% 

11,9% 

42,5% 

41,9% 

38,8% 

13,1% 

35,5% 

12,7% 

33,5% 

11,0% 

32,0% 

13,4% 

30,1% 

11,1% 

26,9% 

10,1% 

Source: PPMI; n=1,667. Please note that various sub-types of data were displayed for each of the types presented as question items. If at 
least one of these sub-types (or a similar one) was applicable to them, the respondent selected the whole type.  

Most  of  the  respondents  receive  the  platform  data  in  aggregated  from  or  summarised  and  visualised 
into  reports  -  without  any  data  at  individual  level  (see  the  figure  below).  Meanwhile,  over  a  third  of 
respondents  also  receive  raw,  individual  level  data.  Interview  results  allow  to  interpret  this  as 
primarily  the  data  related  to  individual  transactions  of  the  business  user,  which  is  provided  by  all 
platforms.  

49 

      
 
 
 
FIGURE 11. HOW MUCH PROCESSING DOES THE MAIN PLATFORM CARRY OUT ON DATA IT PROVIDES 
TO YOUR BUSINESS? 

0,0%

10,0%

20,0%

30,0%

40,0%

50,0%

The platform provides aggregated data (data sets or
reports without data at individual level)
The platform provides raw data (data sets containing
individual-level data)
The platform provides summarised and visualised data
(presented as a report)

42,4% 

36,2% 

35,6% 

Don’t know 

12,1% 

Other

0,7% 

Source: PPMI; n=1,500 

Generally,  platform  business  users  (participating  in  interviews  and  voicing  their  positions  on  the 
internet)  claim  that  they  would  like  to  have  more  of  both:  individual  level  data  about  their  own 
operations  or  own  customers/consumers,  as  well  as  aggregated  data  on  the  entire  market  including, 
information on  competing  business users' operations  and their  customers/consumers. Also, in a more 
detailed  survey  question,  around  a  third  of  the  respondents  indicated  that  they  cannot  access  more 
specific  kinds  of  data  held  by  the  main  online  platform  that  they  use,  which  are  essential  to  their 
business  (Figure  12).  This  concerns  indicators  under  most  data  types,  as  the  variation  across  the 
different data types is marginal.  

FIGURE 12. NEEDED BUT INACCESSIBLE DATA HELD BY THE ONLINE PLATFORM 

0%

100%

Identification details of your customers on the platform

Identification details of other users on the platform (i.e.…

Your customers’ behaviour on the platform 

Other customers' behaviour on the platform

Business identification of other business users on the…

Your business performance

Data from individual transactions carried out between…

Analysis of market trends/ developments

37% 

33% 

36% 

34% 

35% 

33% 

37% 

34% 

Source: PPMI; N=1,667 

Yes

No

Don't know

46% 

48% 

47% 

47% 

46% 

50% 

45% 

47% 

17% 

18% 

17% 

18% 

19% 

17% 

17% 

18% 

Among  those  survey  respondents,  who  indicated  that  they  cannot  easily  access  the  data  (Figure  9)147, 
the  most  frequently  perceived  difficulty  in  accessing  data  from  the  main  platform  is  legislative  or 
regulatory restrictions (Figure 13), followed by prohibitively high prices for accessing the data.   

147 Those who disagreed, party disagreed or strongly disagreed. 

50 

      
 
 
 
 
                                                           
FIGURE 13. DIFFICULTIES EXPERIENCED IN ACCESSING DATA FROM THE PLATFORM 

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

Legislative or regulatory restrictions
Prohibitively high prices for accessing the data

Unfair or abusive terms or conditions imposed by…

Denial of access to data
Unexpected termination of access
Other

10% 

4% 

43% 

29% 

27% 

25% 

21% 

Source: PPMI; n=438148.  

In  fact,  in  another  survey  question,  around  half  of  respondents  reported  that  their  main  platform 
charges  additional  fees  for  access  to  specific  kinds  of  data.  Around  60%  of  these  businesses  (N=877) 
reported paying up to €5,000 for data annually, while around 40% - more than €5,000149.  

Finally,  among  the  surveyed  businesses  who  have  experienced  problems,  disputes  or  disagreements 
with their main platform (N=769), 16.5% reported lack of access to data as a key reason.  This indicates 
that in some cases restricted platform data access can be an especially important issue for businesses.  

5.2.  Data alternatives for business users 

Platform  business  users  have  a  range  of  options  to  collect  data  and  generate  business  intelligence 
outside  the  online  platforms, as well as triangulate  data sources  to build more valuable  insights.  This 
depends on several aspects. 

First, that is analytical capacity. As explained in Chapter 3, different business users have very different 
levels of data savviness and ability to collect and draw actionable insights from raw data. For example, 
capacities of app developers can be hardly comparable to that of small hotel owners; larger businesses 
in  all  analysed  sectors  tend  to  invest  more  in  own  IT  departments  and  data  analytics  to  develop 
analytics  and  business  insights.  According  to  results  of  the  business  user  survey,  the  majority  of  the 
companies reported that they analyse some kinds of  raw data: over 42% of enterprises do it in-house, 
25% outsource  such  analysis  to  external  service  providers  (Figure  14).  Around  7%  of  companies  both 
analyse  raw  data  in-house  and  outsource.  Meanwhile,  almost  a  third  of  surveyed  enterprises  do  not 
analyse any kind of raw data.  

148 Those who responded in survey Q10 that they cannot easily access data (i.e. disagreed, strongly disagreed or party disagreed with the statement 
‘My business can easily access data collected by the platform that is important for my business’). See questionnaire in Annex 2.  

149 Please note that after a closer inspection of the survey results, the research team considers that some of the respondents had in mind not only the 
fees charged by platforms, but also the fees of third party providers, offering insights on the specific platforms.  

51 

      
 
 
 
                                                           
FIGURE 14. BUSINESS USER SURVEY: DOES YOUR BUSINESS ANALYSE RAW DATA PROVIDED BY 
THE PLATFORM, BY THIRD-PARTY SOURCES (DATA BROKERS) OR COLLECTED BY ITSELF? 

Yes, we have in-house employees analysing such…

45,4% 

0,0%

10,0%

20,0%

30,0%

40,0%

50,0%

Yes, we outsource analysis of such data to…

No, we do not analyse raw data

25,3% 

29,3% 

Don’t know 

7,0% 

Source: PPMI; n=1,575. 

The  second  key  factor  is  financial  capacity.  Emerging  models  of  synthetic  data  for  training  Artificial 
Intelligence  applications  and  cloud  solutions  offer  new,  innovative  approaches  to  data  challenges, 
avoiding  the  need  for  accessing  the  vast  volumes  of  data  collected  by  platforms,  and  sometimes 
competing  with  the  platforms’  own  applications150.  Certainly,  access  to  such  innovations  comes  at  a 
cost, so analytical capacities often depend directly on the availability of financial resources.  

The  third  key  precondition  for  accessing  the  needed  information  for  businesses  is  the  existence  of 
specific  data  outside  the  platform.  While  acknowledging  the  data  asymmetry  between  platforms  and 
their  business  users,  some  competition  researchers  argue  that  data  markets  are  active  and  thriving. 
Most of these data  are  actively traded and  accessible  to  anybody willing to pay  for  it151. Nonetheless, 
opponents of this view claim that specific data is not necessarily available on the market, and most of 
the data that platforms collect and not share is unique to specific activities going on those platforms and 
cannot be duplicated152. Such non-duplicable data include: 

 

 

 

 

Total  data  on  overall  commercial  transactions  on  the  platform:  the  total  database  of 
transactions  on  the  platform  cannot  be  collected  by  individual  sellers  or  other  players 
(platforms, data brokers) in the platform ecosystem. 
Data  on  real-time  of  activities  on  the  platform  (e.g.  X  currently  has  product  Y  added  to  the 
shopping cart), which allows to effectively address the customer. 
Transaction-related data about the customers, sales activities and listings of specific business 
user also cannot be duplicated by that user, as the data is recorded exclusively by the platform 
on which the transaction takes place.  
Analytics developed by a platform and based on the data that it has also cannot be duplicated 
at the same level of precision by other players.  

As  an  interviewed  Amazon  seller  explained,  they  need  data  on  the  specific  platforms  that  they  use. 
Increasingly, the market power is concentrated within one or two large platforms, so the sellers do not 
have much choice on which one to sell if they want a successful business (“Amazon has a 60% market 
share in  Germany, you cannot just go sell on eBay  and  be  OK; eBay is marginal in Europe”153). If the 
major platform refuses to share its data, this is something that its business users must deal with.  

150 Several start-ups creating, for their own use, or selling synthetically generated data for training AI models are emerging, some of which are 
successful European-based companies. See, for example https://www.wired.com/story/some-startups-use-fake-data-to-train-ai/  

151 Martens, B. (2016). An economic policy perspective on online platforms. Institute for prospective technological studies digital economy working 
paper 2016/05, JRC Technical Reports. Retrieved from https://ec.europa.eu/jrc/sites/jrcsh/files/JRC101501.pdf  

152 Graef, I. (2016). EU Competition Law, Data Protection and Online Platforms: Data as Essential Facility. Kluwer Law International BV.  

153 Interview with an association representing large businesses that use online marketplaces.  

52 

      
 
 
 
                                                           
Nonetheless,  business  users  have  two  broad  ways  to  get  access  to  additional  data  or  analytics:  (1)  to 
manually  collect  and  analyse  data  from  sources  available  to  them;  (2)  rely  on  third  party  providers. 
Although innovative approaches to acquire data are emerging (see the box below), most platform users 
apply one of these or a combination of them.  

TABLE 8. COOPERATIVE PLATFORM AS A WAY TO GENERATE DATA 

A report by the German Economic Institute154 suggests that the model of a cooperative could be particularly well-suited to 
organise sharing of data among e-commerce traders. Data knowledge-based services could be made available to members 

of cooperatives who, for example, build a digital platform that members can use to offer their goods and services. One such 
example is Fairmondo.de155, a platform which belongs to its business users and employees. Through a cooperative structure, 
the users can share the platform as a resource for mutual benefit without being dependent on large e-marketplaces.  

Authors of the report suggest that the data accumulated on a cooperative platform, and thus also the economic benefits 

derived from these data, would be available to all members who contribute to the data creation process on the platform, 
rather  than  to  the  central  platform  provider  only.  Meanwhile,  the  practical  usability  of  data,  which  is  often  difficult  for 

individual  providers  to  achieve  alone,  can  be  greatly  facilitated  by  a  common  body.  Cooperative  structure  can  create  the 
opportunity for business users to use not only their own data, but a much larger database, as well as to acquire and maintain 

expensive analytical capacities collectively. Such cooperation in data generation and analytics can be used to improve the 
competitiveness  of  SMEs  vis-à-vis  larger  competitors  with  more  market  power,  as  well  as  data  access  and  analysis 
capabilities. 

Based  on  the  results  of  the  business  user  survey  conducted  for  this  study,  four-fifths  of  respondents 
(81%)  indicated  that  their  business  collects  some  data  itself  (i.e.  collects  at  least  one  data  type).  The 
most  prevalent  data  type  collected  by  them  is  identification  details  of  own  customers  (57%  of 
enterprises  collect  this  data),  followed  by  business  performance  data  (55%)  and  analysis  of  markets 
trends/ developments (55%), see Figure 15. 

154 Demary, V., & Rusche, C. (2019). Data sharing im e-commerce. Rechtliche und ökonomische Grundlagen. The German Economic Institute. 
Retrieved from https://www.iwkoeln.de/fileadmin/user_upload/Studien/Gutachten/PDF/2019/Gutachten_Data_Sharing_Final.pdf  

155 Fairmondo.  

53 

      
 
 
 
                                                           
FIGURE 15. DATA COLLECTION BY BUSINESSES THEMSELVES, BY DATA TYPE 

0%

100%

Identification details of your customers on the…

57% 

33% 

Identification details of other users on the platform…

Your customers’ behaviour on the platform 

Other customers' behaviour on the platform

Business identification of other business users on…

Your business performance

Data from individual transactions carried out…

Analysis of market trends/ developments

42% 

49% 

39% 

40% 

55% 

48% 

51% 

Yes

No

Don't know

Source: PPMI; n=1667. 

47% 

40% 

49% 

48% 

34% 

40% 

36% 

9% 

11% 

11% 

12% 

13% 

11% 

13% 

12% 

However, very few of the interviewed business users said they regularly collect data themselves to use 
it in  their analyses and  business decisions. The notable  exception is app developers: as described in 
Section 3.2.2, they not only receive a significant amount of business-relevant data from the app stores, 
but  also  collect  a  lot  of  the  needed  information  through  their  own  applications.  Meanwhile,  in  e-
commerce,  autonomous  data  collection  by  businesses  mostly  concerns  business  data  which  is  not 
marketplace-specific,  such  as  their  business  financials,  advertising  and  product  testing  among  target 
groups. In addition to this, some of the marketplaces allow tools or integrations allowing to automate 
this  additional  data  collection  for  businesses.  For  instance,  eBay  and  Etsy  allow  integrations  with 
Google Analytics. In accommodation/hospitality segment, only large multi-city hotels seem  to engage 
in active collection and analysis of data besides financials, such as customer details, customer feedback 
and trends in tourism. These large players tend to employ sophisticated automated data collection and 
analysis  methodologies.  Nonetheless,  this  mostly  concerns  the  performance  of  their  own  company. 
Collection of competitor data is a challenge for individual business users, and they rely on specialised 
providers for this.  

Meanwhile, most of the interviewees - especially in the e-commerce sector - reported that they use the 
services  of  third-party  data  and  analytics  providers  (i.e.  data  brokers).  For  many  of  them  these  data 
were essential in developing new products, managing business and maintaining their business model 
more generally. Given that most competing business users are using the same data tools, others are also 
encouraged  to  use  them  too:  data  advantage  means  competitive  advantage  in  the  platform  economy. 
The  rather  wide  use  of  data  companies  is  corroborated  by  the  survey  results.  A  third  of  surveyed 
companies  (33%;  546)  reported  that  they  use  third-party  sources  (data  brokers)  to  access  data  (Figure 
16).  

54 

      
 
 
 
FIGURE 16. USE OF THIRD-PARTY SOURCES TO ACCESS ANY TYPES OF DATA 

33% 

14% 

53% 

Source: PPMI; n=1,666. 

Yes

No

Don't know

The following chapter, therefore, investigates these data companies, the services they provide and their 
value to platform business users in more detail.  

55 

      
 
 
 
6.

 Data companies 

The  importance  of  data  companies  is  increasing.  To  illustrate,  according  to  PwC  estimations,  data 
companies earned USD 21 billion in 2018156. Some authors even claim that the  assumption that online 
platform  giants  Google  or  Facebook  are  the  players  that  know  every  single  detail  on  individuals  is 
highly  misleading157.  Compared  to  the  platforms,  data  brokers  are  not  only  equipped  with  larger 
datasets from online and offline sources, but also possess advanced and diversified techniques to refine 
and  mine  the  data.  They  create  a  whole  ecosystem  of  connected  links  on  competitors,  customers, 
market trends, products, services, and so on158.  

These  advantages  attract  not  only  smaller  business  players,  but  also  big  online  platform  economy 
players like Google, eBay, Amazon, Microsoft, AliExpress to actively purchase data brokers’ services159.  
For example, the study on partnerships between Facebook and data brokers has discovered that a large 
percentage  of  Facebook  accounts  is  linked  to  the  data  broker  information160.  In  2017,  it  launched 
‘Partner  categories’  programme  with  the  goal  to  establish  a  stronger  relationship  between  Facebook 
and data brokers, such as Acxiom and Epsilon. Through the partnership, Facebook accessed the lists of 
people  compiled  by  the  data  brokers,  which  enabled  the  campaign  of  targeted  ads161.  Despite 
terminating  the  programme  in  early  2018  as  a  part  of  a  security  reform,  Facebook  is  one  of  the 
prominent  examples  in  the  data  exchange  between  data  brokers  and  platforms.  Microsoft,  eBay, 
Rakuten,  Google  are  also  known  to  use  market  intelligence  tools  such  as  SimilarWeb,  Builtwith  and 
SimilarTech to uncover the market dynamics and the competitive landscape. Generally, while platform 
data  comes  mostly  from  their  own  online  activities,  some  data  companies  allow  to  put  this  into 
perspective of other online players and offline domain162. 

As  it  was  presented  in  the  previous  chapters,  the  value  of  data  broker  services  can  be  especially 
significant  for  business  users  of  online  platforms.  In  fact,  the  evidence  collected in  the  preparation  of 
this  paper  shows  that  the  data  broker  industry  has  become  a  key  non-platform  source  for  platform 
business  users  to  access  customised  and  relevant  data163  that  their  platforms  often  do  not  provide 
(whether they have it or not). 

156 Gröne, F., Péladeau, P., & Samad, R. A. (2019). Tomorrow’s data heroes. Strategy+business. Retrieved from https://www.strategy-
business.com/article/Tomorrows-Data-Heroes?gko=5f270  

157 Mirani, L., & Nisen, M. (2014). The nine companies that know more about you than Google or Facebook. Quartz. Retrieved from 
https://qz.com/213900/the-nine-companies-that-know-more-about-you-than-google-or-facebook/  

158 Singer, N. (2012). Mapping, and sharing, the consumer genome. The New York Times. Retrieved from 
https://www.nytimes.com/2012/06/17/technology/acxiom-the-quiet-giant-of-consumer-database-marketing.html  

159 Riam, A., & Murgia, M. (2019). Data brokers: regulators try to rein in the ‘privacy deathstars’. Financial Times. Retrieved from 
https://www.ft.com/content/f1590694-fe68-11e8-aebf-99e208d3e521  

160 Venkatadari, G., Sapiezynski, P., et al. (2019). Auditisng offline data brokers via Facebook’s advertising platform. International World Wide Web 
Conference Committee. 

161 Horczek, S. (2018). You wanna see something wild? Check out all the advertisers targeting you on Facebook. Popular Science. Retrieved from 
https://www.popsci.com/advertisers-targeting-facebook-account-settings/#page-2 

162 Venkatadari, G. Sapiezynski, P. et al. (2019). Auditisng offline data brokers via Facebook’s advertising platform. International World Wide Web 
Conference Committee.  

163 Ganuza, J. J. & Llobet, G. (2018). Personlaized prices in the digital economy. In “Economic Analysis of the Digital Revolution”, ed. Ganuza, J. J. & 
Llobet, G. Spain: Funcas. 

56 

      
 
 
                                                           
Such  data,  analytics  and  insight  providers  are  an  important  part  of  large  ecosystems  (or  very  broad 
networks164)  of companies evolving around the largest online platforms and providing various services 
for  their  business  users  -  from  consultancy,  accounting  and  copyrighting  services,  to  conferences  and 
seminars,  as  well  as  specific  data,  analytics  and  insights  aimed  at  helping  business  users  to  increase 
their market shares and profits. For example, some sources estimate hundreds of “trusted” companies 
offering  solutions  for  selling  on  Amazon165,  with  a  notable  category  of  them  focused  on  data  and 
analytics166.  The  actual  numbers,  including  smaller  and  more  specialised  providers,  as  well  as 
companies  not  endorsed  by  the  platforms,  could  be  significantly  higher.  Similar  clusters  of  service 
companies can be observed around all major multi-sided online platforms, including those examined in 
Chapter 4.  

Given the large number of relevant providers in such ecosystems (like Unicorn Smasher, ZIK Analytics, 
Marmalead,  Amalyze,  Seller  Board,  Seller  Labs,  Helium10,  AMZScout,  etc.,  in  the  e-commerce  and 
Appfollow, Appsflyer, Apptica, Flurry, App Census, etc., in the apps and software sector), we focussed 
our analysis to the key players serving the three types of business users analysed in the previous parts 
of this paper.  

To identify and select data brokers for this analysis, we first searched for the most common and talked 
about  data  companies  on  business  user  online  forums,  such  as  Webretailer,  Airhostforum,  Reddit, 
Amazon  Seller, Apple  Developer  Forum,  etc. After  this initial  search,  we  identified  the  most  relevant 
data  brokers  by  focusing  on  their  customers,  usage,  and  outreach.  Ultimately,  we  identified  and 
analysed  15  data  companies,  serving  the  specific  data  needs  of  the  three  types  of  platform  business 
users investigated this paper, and two data brokers covering multiple sectors of the platform economy 
and specific platforms analysed in this paper: 

-  App Annie, App figures, Apptopia, Mobile Action, Sensor Tower, serving the business users of 

application stores;  

-  Algopix, Jungle Scout, Sellics, Teikametrics, Terapeak for the ecommerce,  
-  AirDNA,  Beyond  Pricing,  Uplisting,  Wheelhouse,  Skift  for  the  accommodation/hospitality 

sector business users, 
Similar Web and Zirra for multiple sectors. 

- 

Although targeting businesses from different sectors and users of different platforms, all the  business 
and  monetisation  models  of  selected  companies  depend  on  the  access,  quality  and  volume  of  data167. 
They  extract  data  from  both  businesses  and  consumers  from  various  online  (and  sometimes  offline) 
sources,  and  then  aggregate  for  further  analysis  and  development  of  insight  products168,  tailored  to 
platform business user needs. Their basic value proposition is comprehensive market intelligence based 
on the data in their possession and driven by  advanced data analytics solutions169. These activities, in 
turn, require an exceptional knowledge of data sources, collection methods and levels of innovation in 
analysis. The bigger, better and more diverse the accessible data is, in more services they can apply the 

164 Amazon Seller Central. (2020). Find help from external providers with the Amazon Service Provider Network. Amazon. Retrieved from 
https://sellercentral.amazon.com/gp/help/external/G201687890?language=en_US 

165 Thomson, J. (2016). The Amazon sellers’ solution provider directory. BigCommerce. Retrieved from 
https://www.bigcommerce.com/blog/amazon-solution-provider-directory/ 

166 Thomson, J., & Hansen, J. (2016). The Amazon sellers’ solution provider directory. BigCommerce & Prosper Show. Retrieved from 
http://grow.bigcommerce.com/rs/695-JJT-333/images/amazon-directory.pdf?aliId=7202022&_ga=2.5182195.1992825670.1575559093-
987636416.1575559093  

167 Yiquan G., Madio, L., & Reggiani, C. (2019). Data brokers co-opetition. CESifo Working Paper No. 7523, Category 14: Economics of Digitization. 

168 Clearcode. (n.d.). What is a data broker and how does it work? Retrieved from https://clearcode.cc/blog/what-is-data-broker/ 

169 Pahwa, A. (2019). The data monetization. Big data business models. Feedough. Retrieved from https://www.feedough.com/the-data-
monetization-big-data-business-models/ 

57 

      
 
 
                                                           
data  and  increase  their  value  to  consumers.  However,  in  all  these  processes  they  usually  strongly 
depend on the very platforms whose business users they serve, and the data that these platforms make 
available in one way or another.  

The table overleaf overviews the main characteristics, such as revenue, ownership status, headquarters 
(HQ), the presence of branches in Europe, the presence of branches in other regions, overall number of 
users  (clients),  the  main  names  of  business  users  and  platforms  that  are  clients,    of  data  brokers 
analysed in this paper. Despite the fact that the majority of data companies (in our sample and overall) 
are located in the US, internet and branches in other regions enable a worldwide outreach, and they are 
widely used by European businesses. The report published by Transparency Market Research forecasts 
that North America will be the  most attractive  market for data brokers until 2026170. Europe and Asia 
Pacific, in the  meantime,  will remain as other key markets for  the  data  brokers171. The  main clients of 
investigated data brokers are business users of online platforms. However, during in-depth interviews 
some data brokers were not willing to share more specific information on their clients, especially names 
platforms. 
of 

170 Transparency Market Research. (2017). Data Broker Market: Global Industry Analysis, Size, Share, Growth, Trends and Forecast 2017 – 2026. 
Retrieved from https://www.transparencymarketresearch.com/data-brokers-market.html 

171 Transparency Market Research. (2017). Data Broker Market: Global Industry Analysis, Size, Share, Growth, Trends and Forecast 2017 – 2026. 
Retrieved from https://www.transparencymarketresearch.com/data-brokers-market.html 

58 

      
 
 
                                                           
TABLE 9. DATA BROKERS’ OWNERSHIP STATUS AND LOCATIONS172 

REVENUE 

OWNERSHIP*   HQ 

BRANCHES: EU  

APP DEVELOPERS 
App Annie 

$100M 

App Annie 

USA 

Germany, United 
Kingdom, France, 
Netherlands 

App figures  

$3.8M 

App figures 

USA 

No 

Apptopia 

$4.2M 

Apptopia 

USA 

No 

Mobile Action 

$ 7.5M 

Mobile Action 

USA 

No 

BRANCHES: 
OTHER 
REGIONS 

Canada, 
Japan, 
Singapore, 
China, Korea  
No 

No 

No 

Sensor Tower 

$ 15M 

Sensor Tower 

USA 

United Kingdom 

China, Korea 

OVERALL NR OF 
USERS 

NOTABLE USERS 

SPECIFIC PLATFORMS/ 
SECTORS COVERED 

Over one million 
worldwide 

More than 
150,000 
companies 
NA 

Over 160,000 
customers in over 
65 countries 
NA 

Visa, EPIC games, SoftBank, 
Tencent , Google, Linkedin, 
Pinterest, Alibaba, Amazon App 
store 
Trello, Capcom, Snapchat, 
OneSignal, Microsoft, Airbnb 

Spotify, Mattel, Animoca, 
Universal, RedBull, Uber, Google, 
Facebook, Google, Twitter, Uber, 
Pinterest 
Adobe, Letgo, Playtika, Topps, 
eBay 

Apple App Store, Google 
Play, Amazon Appstore, 
Windows Store 

Apple App Store, Google 
Play, Amazon Appstore   

Apple App Store, Google 
Play 

Apple App Store, Google 
Play 

Zynga, NetEase Games, Funplus, 
Adobe, Reddit 

Apple App Store, Google 
Play 

E-COMMERCE SELLERS 
$2.7M 
Algopix 

Algopix 

Jungle Scout 

$24.4M 

Jungle Scout 

Sellics 

$9.6M 

Sellics 

Terapeak 

$12M 

eBay 

Israel/ 
USA 
USA 

German
y 
USA 

Teikametrics 

$18.5M 

Teikametrics 

USA 

No 

No 

NA 

No 

No 

Israel 

Over 65,000 
worldwide 

Canada, China  Over 200,000 

Spreetail, Sweetwater, Pulse 
commerce, Acenda, Blinq 
Entrepreneurs 

USA 

Canada 

worldwide 
Thousands of 
brands and sellers 
Millions of sellers 

No 

NA 

Spectrum, ERIMA, ABG, Roland, 
Bosch, Mammut, BRITA 
Hoopswagg, Bikewagon, 
WatchWarehouse 
Razer, The Vitamin Shoppe, 

Amazon, eBay  

Amazon 

Amazon 

eBay 

Amazon 

172 Information from. 

59 

      
 
 
 
 
                                                           
REVENUE 

OWNERSHIP*   HQ 

BRANCHES: EU  

ACCOMMODATION SERVICE PROVIDERS 
$6M 
AirDNA 

AirDNA 

USA 

Spain 

Beyond Pricing 

$3.2M 

Beyond Pricing 

USA 

No 

BRANCHES: 
OTHER 
REGIONS 

No 

No 

Uplisting 

<$1M 

Uplisting 

USA 

United Kingdom 

No 

Wheelhouse 

<$1M 

Wheelhouse 

USA 

Skift 

$38.3M 

Skift 

USA 

No 

No 

No 

No 

OTHER 
SimilarWeb 

$30M 

SimilarWeb 

Israel 

United Kingdom  

USA 

Over 10,000 
active subscribers 
in over 80 
countries 
Users in more 
than 7,000 cities 
Over 14,000 
successful 
properties 
Thousands of 
hosts and owners 
NA 

From over 90 
countries 
worldwide 

Zirra 

$6.5M 

Zirra 

Israel 

No 

No 

NA 

Source: PitchBook, Crunchbase, Zoominfo, and data broker websites. 

OVERALL NR OF 
USERS 

NOTABLE USERS 

SPECIFIC PLATFORMS/ 
SECTORS COVERED 

Swanson, Two Rivers Coffee 

Hostmaker, Pass the Keys, 
Guestable, BookingPal 

Casiola, Vacations Perfected, 
Sunset Properties 
Airmanaged 

AirbnbSecrets, Blanket Homes 

Airbnb, Vrbo, 
Booking.com, 
HomeAway 

Airbnb, Vrbo 

Airbb, Booking.com, 
Trivago, Vrbo, 
HomeAway, Expedia 
Airbnb, TripAdvisor 

Best Western, Hilton, Airbus, 
Hyatt, Air Canada, Google, 
Booking.com, Airbnb, Tripadvisor 

Accommodation/ 
hospitality/ tourism 

Booking.com, Ryanair, Deloitte, 
P&G, Adidas, Samsung, Pepsico, 
eBay, Google, Rakuten, Alibaba, 
Ryanair, Skyscanner, 
Booking.com 
Deutsche Telekom Capital 
Partners, Silverlake, Microsoft, 
Aol 

Retail, travel, consumer 
goods, publishing, ad 
tech, consumer finance 

Financial, investments, 
trading 

60 

      
 
 
 
In the following sections, we use the selected sample of data brokers to explain the data processes within 
those companies, while also answering key research questions: first, what data do data brokers provide to 
business  users  on  the  three  online  platform  categories  and,  second,  to  what  extent  data  markets 
compensate for the data shortage, arising because of platform’s refusal to share this data. In the analysis, 
we also focus on the data brokers’ core features, such as the character of data they work with, sources and 
collection methods, management, and privacy and GDPR.  

6.1.  Types of data supplied by data brokers 

Generally,  as  mentioned,  some  data  brokers  (including  those  in  the  analysed  sample)  prioritise  particular 
industries and industry-specific data, while others grasp a wider field of information, based on which they 
develop services and products. On the one hand, the more varied and broader their data types are, the more 
opportunities  to  diversify  their  clients’  portfolio.  On  the  other  hand,  collecting  and  building  on  industry-
specific data types, allows to produce targeted in-depth insights, as well as builds credibility in a particular 
industry and value in the fast-growing and constantly changing data market.  

Even  though  different  industries  require  specific  data  collection  and  analysis,  the  data  collected  and 
provided by these players can be classified using the categories presented in Chapter 3: 

 

 

 

 

 

Customer  demographics,  e.g.  age,  gender,  location,  education,  marital  status,  contact  details.  It 
basically builds profiles of business users’ customers. 
Business  demographics,  e.g.  size,  business  model,  products,  location,  contact  details.  Through 
this data type businesses can evaluate their own profile against competitors. 
Customer  behaviour,  e.g.,  clicks,  browsing  history,  products  and  services  purchased,  allowing 
further insight into the current and future demand of specific products, trends, business niches. 
Business performance, e.g. ranks, daily visitors, search traffic, sales, revenue, web page activity, 
price changes, and similar aspects contributing to performance data. As mentioned earlier, data 
on competitors provides most value to individual businesses.  
Transaction  information,  e.g.,  transactions  between  businesses  and  consumers,  payment  details 
and methods; volume and value of the transactions.  

  Market  trends  and  analytics  basically  build  on  all  the  data  types  listed  above  to  provide 
comprehensive  overviews, leading to actionable  insights and informed business decisions. Data 
brokers  deliver  detailed  insights  that  may  not  accessible  through  online  platforms  or  even 
business users’ own data analysis. 

They  combine  anonymised  and  non-anonymised  data  that  is  bundled  to  deliver  the  product  –  market 
intelligence services. As illustrated in Table 10. Data types collected and supplied by data brokers), some 
differences  in  the  data  types  provided  to  platform  business  users  across  the  three  sectors  exist.  E-
commerce sellers access very similar data types across all companies, and very few data brokers provide 
information  on  customer  demographics  and  behaviour,  with  the  overall  focus  on  own  and  competitor 
product metrics. App developers receive very similar information across all data companies, focusing on 
all  the  investigated  data  types.  Accommodation  service  providers  mainly  access  own  performance 
metrics,  where  the  main focus is pricing optimisation. Nonetheless, AirDNA  also offers information on 
customer  behaviour  and  demographics,  as  well  as  competitor  performance  data,  while  Skift  offers 
research on the aggregate customer demographics, behaviour and market trends. Similar Web and Zirra, 
targeting  broader  user  audiences,  collect  and  provide  information  on  performance  metrics  and  market 

61 

      
 
 
trends  on  a  wide  range  of  companies.  Similar  Web  also  offers  information  on  customer  demographics 
and behaviour, while Zirra offers information on business demographics and transaction.  

All of the analysed data companies largely focus on three types of data - business demographics, business 
performance  and  market  trends.  This  leads  another  important  point  about  data  collected  by  these 
companies  -  their  data  allows  the  businesses  buying  them  to  learn  about  their  competition  and  get  a 
broader  market  overview.  As  presented  in  Chapter  4,  this  is  usually  the  kind  of  data,  analytics  and 
insights that many platform business users feel that they are missing.  

Speaking  about  the  level  of  data,  as  discussed  in  Chapter  3,  the  overviewed  data  brokers  develop  and 
supply analytics and insights products - that is, processed data to be used in business decisions, rather 
than  raw  data.  Moreover,  some  of  the  data  brokers  offer  advertising  solutions  by  creating  automations 
and promotions (e.g. Jungle Scout, App figures) - this would fall under the different category of our data 
classification, given its actionable character. 

All  the  reviewed  data  companies  offer  this  information  in  an  interactive  dashboard  format,  which 
includes graphs, pop-up boxes, tables, etc. A number of data brokers, such as AirDNA, Apptopia, Skift, 
Zirra,  also  produce  reports  filled  with  relevant  information  on  competitor,  performance,  market, 
consumer trends. Some of them also offer browser integrations and API access. 

Considering  all  the  data,  services  and  products  that  the  analysed  data  companies  provide,  they  can  be 
located  at  the  left-hand  end  of  the  data  value  chain  (Figure  5).  Whether  they  provide  analytics  and 
insights for individual sellers or broader overviews of market and competition, they mostly use the data 
that platforms share/allow to collect for anyone (but not everyone can use it for actionable insights). Their 
added value, in turn, is derived from their innovative tools and approaches to draw insights of this data. 
Sometimes  these  methodologies  are  so  sophisticated  that  allow  them  to  quite  precisely  estimate  or 
extrapolate values of metrics that the platforms do not share. 

62 

      
 
 
 
TABLE 10. DATA TYPES COLLECTED AND SUPPLIED BY DATA BROKERS 

CUSTOMER 
DEMOGRAPHICS 

BUSINESS 
DEMOGRAPHICS 

CUSTOMER 
BEHAVIOUR 

OWN BUSINESS 
PERFORMANCE 

COMPETITOR BUSINESS 
PERFORMANCE 

TRANSACTION 
INFORMATION 

MARKET TRENDS 

E-COMMERCE SELLERS 

Algopix 

NA 

Company address, 
VAT number, country 
of operation 

NA 

Jungle Scout 

Consumer 
demographics 

Company address, 
VAT number, country 
of operation 

Browsing history, 
search volume 

Pricing, expenses, demand, 
profit, revenue, shipping fees, 
marketplace fees, product 
identifiers, demand  

Company rank, sales, revenue, 
product identifiers, inventory, 
costs, operating expenses 

Sales rank, reviews, revenue, 
costs, FBA fees, Amazon fees, 
shipping fees, rating, stock 

Pricing, product identifiers 

Reviews, ratings, 
sales volume 

Competitor, business, 
performance  

Product ranks, reviews, pricing, 
product identifiers 

Sales volume, 
reviews, ratings 

Competitor, keyword, 
sales 

Pricing, daily sales, keyword 
rankings 

Reviews, ratings, 
sales volume 

Competitor, keyword, 
price  

Top sellers, performance levels 

Reviews, ratings 

Inventory, competitor 

Company address, 
VAT number, country 
of operation 

NA 

Company address, 
VAT number, country 
of operation 

Browser type, pages 
visited,  time spent 

Inventory, pricing, product 
identifiers, revenue, clicks 

Company address, 
VAT number, country 
of operation 

NA 

Sales metrics, inventory, pricing, 
top sales, regions, product 
identifiers 

Top sellers, volumes sold, activity, 
pricing, (old) listing details 

Reviews, ratings 

Sales, pricing, keyword 

Company address, 
VAT number, country 
of operation 

Company address, 
VAT number, country 
of operation 

Company address, 
VAT number, country 
of operation 

Cross-app/ cross-
product usage, 
spending behaviour, 
traffic sources 

In-app usage 
behaviour 

Downloads, revenue, countries, 
devices 

Revenue, usage metrics, 
downloads, sales, in-app-
purchases, ad revenue 

Downloads, revenue, usage, 
engagement, devices, app store, 
market share, ad campaigns, 
monetisation 

Top apps, ranks 

Reviews, ratings 

Mobile, competitor, app 
market, rank, growth 

Reviews, user 
feedback 

Competitor, rank, 
market, keyword, sales 

Cross-app/ cross-
product usage 

Products offered, pricing, number 
of transactions, total value of 
sales, user traffic, downloads 

Market share, downloads, 
revenue, engagement, retention 
levels 

Reviews, amount of 
purchases, ratings 

Market, competitor, 
audience, usage 

Sellics 

NA 

Teikametrics 

Location 

Terapeak 

Location 

APP DEVELOPERS 

App Annie 

Gender, age 
(aggregate form) 

App figures 

Apptopia 

Consumer 
demographics, 
contacts 

Age, gender 
(aggregate form) 

Mobile Action 

Consumer 

Company address, 

NA 

Active users on apps, revenue 

Top apps, revenue, downloads, 

Reviews, ratings 

Market, competitor, 

63 

      
 
 
 
demographics 

Sensor Tower 

Age, gender 
(aggregate form) 

VAT number, country 
of operation 

Company address, 
VAT number, country 
of operation 

ACCOMMODATION SERVICE PROVIDERS 

AirDNA 

Location, gender, 
age (aggregate 
form) 

Company address, 
VAT number, country 
of operation 

Beyond 
Pricing 

NA 

Uplisting 

NA 

Wheelhouse 

NA 

Company address, 
VAT number, country 
of operation 

Company address, 
VAT number, country 
of operation 

Company address, 
VAT number, country 
of operation 

NA 

NA 

NA 

NA 

estimates, download, revenue, 
daily and monthly active users 

ad campaign 

keyword 

Usage behaviour and 
time spent on apps 

Sales metrics, revenue, 
downloads, active users, app 
identifiers, pricing 

Downloads, revenue, apps 
distribution, active users, ranking, 
app titles, icons, screenshots, 
keywords, descriptions 

Reviews, ratings 

Competitor, keyword, 
performance, traffic 

Pricing, average daily rate, 
occupancy rate, revenue, ratings, 
demand/ growth 

Top properties, daily rate, 
occupancy, pricing, revenue, 
amenities, ratings 

Currencies, reviews, 
booking time, 
amount paid, ratings 

Market, sub-market, 
competitor, 
performance  

Pricing, bookings, demand, 
occupancy 

NA 

Pricing, average daily rate, nights 
sold, average length of stay 

NA 

Pricing, average occupancy, 
average night rate 

NA 

NA 

Reviews, ratings 

Localised market, 
neighbourhood, 
seasonality 

Reviews, ratings 

Performance 

Reviews, ratings 

Performance 

NA 

Market 

Skift  

Customer 
demographics 

NA 

Customer habits and 
behaviour 

NA 

OTHER 

Similar Web 

Gender (aggregate 
form) 

NA 

Audience interests, 
traffic sources, clicks, 
browsing history, 
other products or 
services purchased 
on the platform 

Website, industry and app ranks, 
total visits, traffic per country 

Popular pages, ad campaigns 

NA 

Consumer, business, 
performance, keyword 

Zirra 

NA 

Company address, 
VAT number, country 
of operation 

NA 

Web traffic, employee growth 
and venture, business models, 
key risks, achievements, 
employee count 

Overall financial information, 
reviews 

Company, stock 

NA 

Source: Data broker websites. 

64 

      
 
 
Data  brokers  serving  app  developers,  ecommerce  sellers  and  accommodation  service  providers  focus  on 
their markets by providing services that reflect their specific customers’ needs  - depending on a business 
segment  or  a  specific  platform  that  they  operate  in.  We  further  discuss  these  in  more  detail  by  business 
segment.  

6.1.1.  E-commerce  

The desk research and interview data indicate that platforms in the e-commerce segment are the focus of 
large  networks  of  ancillary  service  and  Software-as-a-Service  (SaaS)  companies,  engaging  in  everything 
from  accounting,  copywriting  and  advertising,  to  development  of  sophisticated  data-powered  tools  for 
sellers. The platform’s representative estimated that around 1,700 data and software companies serve the 
sellers of Amazon alone, offering various optimisation products based on listing, business user, keyword 
and sales analyses. 

Data  brokers  specialising  in  e-commerce,  mostly  collect  and  analyse  data  on  business  demographics, 
market, consumer, product trends, and similar data, on third-party sellers (i.e. business users) on specific 
platforms. Fewer data brokers provide transaction information and customer demographics, and basically 
none offer detailed customer data.  

Some SaaS companies, such as Teikametrics for Amazon business users, provide sellers with data-powered 
tools to build successful brands by discovering new keyword opportunities, automating keyword bids or 
eliminating advertising which does not generate the satisfactory levels of conversion173. The analytics and 
insights  that  they  offer  mostly  focus on  individual  seller’s  business  and  advertising  performance,  based 
on the data the seller provides (which, in turn, is often generated and provided by platforms). 

Nonetheless,  as  outlined  in  Chapter  3,  e-commerce  sellers  are  especially  interested  to  know  about  the 
business performance of their competitors, as well as overall market trends. A significant number of data 
providers offer these services for the businesses active on each major platform. For instance,  Jungle Scout 
and  similar  providers,  such  as  Helium10,  AMZScout  and  Unicorn  Smasher,  as  several  interviewed 
Amazon sellers explained, supply them with  comprehensive  market insights and competitor overviews 
that the platform has itself, but does not share. They offer such analytics products as data on best-selling 
listings, monthly revenues of other sellers, search volumes and search trends.  Although these data brokers 
provide  estimations  and  extrapolations  based  on  what  data  they  could  gather  rather  than  exact 
information,  the  sellers  claim  this  data  is  very  valuable  given  the  Amazon’s  reluctance  to  provide  such 
insights for the sellers. In fact, some of the seller-oriented materials online warn them that if they do not 
want  their  competitors  to  see  their  revenue,  they  should  stay  away  from  Amazon  -  even  though  the 
platform does not share this data itself. The mentioned providers allow to  acquire substantial data on the 
competitors,  and  the  estimations  are  said  to  be  “spookily  accurate”174.  We  provide  more  details  on  how 
data brokers collect such data in the following section.  

Also,  Amazon,  eBay  and  Etsy  sellers  note  that  compared  to  the  analytics  and  insights  provided  by 
platforms, third-party providers are faster and more comprehensive, especially useful if the sellers need to 
quickly understand how their business are doing. On the other hand, their data is not always precise, and 

173 Salesfunnelhq. (n.d.) Teikametrics Review – Taking a look at teikametrics for selling on Amazon. Retrieved from 
https://salesfunnelhq.com/teikametrics-review/  

174 Gerber, S. (2018). Want to sell on Amazon? 15 strategies for success. The Next Web (TNV). Retrieved from 
https://thenextweb.com/contributors/2018/01/19/want-sell-amazon-15-strategies-success/  

65 

 
 
                                                           
in some cases can give only a very rough idea on how things actually are. Moreover, they may not always 
know how marketplace algorithms change to be able to swiftly adjust.  

The  use  of  such  solutions  is  very  widespread  in  the  e-commerce  segment.  As  several  interviewees 
reported, as far as they knew, most sellers (especially the larger ones) use such services. As an interviewee 
explained, if your competitors use it, you must use it too to be successful; data disadvantage can be very 
costly in this business.  

6.1.2.  Software development 

The  analysed  data  companies  specialising  in  serving  software  developers  data  needs,  mostly  focus  on 
mobile economy metrics. They collect and sell data, analytics and insights on business demographics, such 
as  businesses’  and  app  products’  identifiers,  and  performance  data,  such  as  apps  downloaded,  global 
rankings  and  revenue.  These  are  then  used  to  produce  historical  market,  competitor  or  performance 
trends.  Some  of  the  reviewed  companies  also  collect  consumer  demographics  and  behaviour.  In  an 
aggregated form, these result into products such as app analytics, app optimisation, market research, app 
information, providing of a good overview of the specific app markets and competition.  

Such  analytics  and  insights  are  important  given  that,  just  as  in  the  other  reviewed  segments  of  platform 
economy,  business  users  receive  very  little  information  on  their  competition  from  the  platforms.  Data 
providers  for  app  developers,  such  as  AppAnnie  and  SensorTower,  fill  this  gap  by  presenting 
comprehensive app market data, including performance of specific apps and markets, as their main selling 
point.  Interviewed app  developers mentioned that they use the  sources together  with the  app  store data 
extensively. 

In  addition  to  this,  just  as  in  case  of  other  segments  of  business  users,  many  app  developers  use  the 
services  of  third-party  insight  providers  to  better  understand  their  own  customers  and  performance  of 
monetisation  strategies.  For  instance,  companies  such  as  Amplitude175  provide  solutions  based  on  the 
developers  data,  aimed  at  profiling  users  to  increase  their  engagement  and  optimise  conversion.  App 
Figures,  which  also  provides  insights  into  market  and  competitors,  offers  data  products  to  track  the 
performance of individual apps of the app developer in one place.  

Generally,  app  developers  tend  to  use  a  number  of  data  and  analytics  services  for  developing  their 
products and running the business. The interviewees mentioned around  two to four different third-party 
data/analytics/insights providers whose solutions they use.  

Another segment of data providers, focused on app developer needs, specialises in data feeding into the 
apps  themselves.  According  to  an  interviewed  developer’s  association,  whenever  there  is  a  demand  for 
specific data, an ecosystem develops around it. Data companies active in these markets built sophisticated 
tools that allow their customers to access the data in a controlled way. For example, numerous companies 
exist  in  the  geolocation  data  segment,  engaged  in  collecting  and  selling  this  data.  Many  app  developers 
subscribe to their geotechnical datasets. Players like Amazon and Google also act as data providers and are 
launching a speech recognition or translation engines as a service for app developers. Generally, this data 
industry  is  maturing,  providing  app  developers  with  convenient  access  to  multiple  sources  of  data  and 
allowing to avoid digital hoarding.  

175 Amplitude. (2020). Improve user engagement. Retrieved from https://amplitude.com/enhance-user-engagement  

66 

 
 
                                                           
6.1.3.  Accommodation and hospitality 

Data  providers  specialising  in  tourism,  accommodation  and  hospitality  mostly  focus  on  rental  listings’ 
data on various OTAs. They provide information on business demographics, market trends, and business 
performance  behind  the  analysed  listings.  Accommodation  providers  use  these  data  services  to  access 
insights on pricing strategies,  market performance, potential market locations, etc. For  example, AirDNA 
uses listing data to offer metrics for optimisation of the rental business, aggregating data on every Airbnb 
rental worldwide, as well as listings of other OTAs. Its wide coverage helps understand own market and 
other market trends from different angles176.  

Importantly,  companies  such  as  AirDNA  provide  estimated  and  extrapolated  insights  into  the  data  that 
the  OTAs  do  not  share.  To  illustrate,  in  late  2015  Airbnb  stopped  providing  the  overall  real-time 
reservation data. AirDNA, in turn, uses an algorithm based on 16 indicators picked up in historical data to 
determine  the  reservation  status  for  each  listing.  They  argue  that  their  algorithm  has  an  error  margin  of 
only  5%  and  therefore  provides  very  precise  information,  which  can  be  further  used  by  accommodation 
providers to estimate the future demand and set appropriate prices.  

Other providers, such as Phocuswright177 and Skift178 gather data to provide analytics and insights into the 
overall travel industry (online and offline, instead of specific platforms) and sell datasets and reports. As 
the interviews and websites of the mentioned providers indicate, their products are used by platforms and 
their business users alike. The information that they provide is important to run hotel business, but does 
not provide specific information on business on various online platforms.  

Nonetheless, according to the interviews and desk research into the specificities of hotel business, the use 
of  data  tools  in  the  accommodation  sector  is  much  less  widespread,  as  compared  to  other  platform 
business  segments.  This  is  related,  at  least  in  part,  to  the  fact  that  vast  majority  of  European  properties 
listed on OTAs are small businesses, whose market and competition is very strictly geographically limited.  

6.1.4.  Non sector-specific data providers 

Some platform business users across all platforms and sectors also use third-party data providers to track 
the more general and non-platform-specific developments in online markets. Some of them allow to better 
understand  the  individual  business  user’s  performance.  For  instance,  Google  Analytics  is  used  widely, 
especially  by  the  business  users  of  platforms  that  allow  Google  Analytics  integrations  (e.g.  Etsy  and 
others).  It  provides  various  metrics  on  the  business  user  listing  pages,  as  well  as  allows  to  check  the 
performance  of  their  marketing  and  content.  What  is  more,  according  to  an  accommodation  sector 
interviewee, the added value of Google Analytics is information on customer age, clicks on a website, how 
long customers spend on a website, why and when they leave, and so on - something that most platforms 
do not provide.  

Other providers that do not focus on specific platforms also allow to take a look into the performance of 
other businesses. For example, ComScore was mentioned by an interviewed business user as an important 
source of marketing data. Among other insights, it provides metrics on traffic of specific websites, allowing 

176 OptimizeMyAirbnb.com. (2018). AirDNA MarketMinder Review + Analysis. Medium. Retrieved from 
https://medium.com/@AnalyzeMyAirbnb.com/airdna-marketminder-review-analysis-58c8627cf143 

177 https://www.phocuswright.com/   

178 https://skift.com/   

67 

 
 
                                                           
to  understand  specific  markets  (e.g.,  travel)  and  competitors  better.  SimilarWeb  offers  its  customers 
information  on  their  clients'  and  competitors'  website  traffic  volumes,  referral  sources,  and  website 
"stickiness" (e.g., time on site, page views, bounce rate). Zirra specialises in ratings and analytics of private 
companies. These providers, however, are more interesting for large businesses in competitive markets.  

6.2.  Data sources and collection methods 

Given that the access to data points unavailable elsewhere and generation of unique insights is the basis of 
data  brokers’  value  proposition  for  their  customers,  they  have  become  very  sophisticated  in  data 
acquisition  and  analysis  methods.  Data  brokers  use  a  wide  array  of  similar  online  and  offline  sources 
(including  commercial  and  independent  entities)  to  acquire  the  data179  as  well  as  a  number  of  highly 
advanced technical methods. While some of this information is easily accessible with the right tools, some 
requires  significant  financial  resources.  We  further  describe  the  main  sources  as  well  as  data  collection 
methods  that  the  data  companies  exploit  in  more  detail.  For  clarity,  the  box  below  also  describes  key 
definitions of technical data collection method terms used further in this paper. 

BOX 1. KEY DEFINITIONS OF TECHNICAL DATA COLLECTION METHODS 

Artificial Intelligence 

The  ability  of  a  digital  computer  or  computer-controlled  robot  to  perform  tasks  commonly 

associated with intelligent beings. 

Cookies 

An electronic monitoring or a file or part of a file saved to a web user’s hard disk by a website. 

Digital fingerprinting 

A fingerprinting algorithm is one that reduces a larger data set to a very small data set, sometimes 
called a bit string, to promote efficient identification and search protocols. 

Proprietary importing  

These are algorithms that allow to remove and consolidate duplicates. 

Web beacons 

A tiny graphic image placed on a website or in an email that is used to monitor the behaviour of the 

user visiting the Web site or sending the email. It is often used in combination with cookies. 

Web crawlers 

An Internet bot which helps in Web indexing. They crawl one page at a time through a website until 

all pages have been indexed. Web crawlers help in collecting information about a website and the 
links related to them, and also help in validating the HTML code and hyperlinks. 

Web indexing 

The processing of the information gathered by the bots from their crawling activities. 

Web scraping 

A  form  of  data  mining,  which  is  done  through  a  software  that  simulates  human  web  surfing  to 

collect specific bits of information from different websites. 

Web tracking 

The act of archiving existing websites and tracking changes to the website over time. 

Source: Encyclopædia Britannica & Techopedia.  

Online  sources,  unsurprisingly,  are  key  to  the  data  companies  serving  platforms  business  users.  These 
cover automated collection of  all the  publicly  available  data on the  internet,  including social media, web 
browsing, blogs, media reports, websites, application stores, and so on. This type of information is mostly 
gathered from websites on submitted job  applications, cookies, ‘contact us’ forms,  and is a  very popular 
data source180. Every data broker in our sample uses internet data as the main source of information.  

179 Grauer, Y. (2018). What are ‘data brokers’, and why are they scooping up information about you? Vice. Retrieved from 
https://www.vice.com/en_us/article/bjpx3w/what-are-data-brokers-and-how-to-stop-my-private-data-collection 

180 Grauer, Y. (2018). What are ‘data brokers’, and why are they scooping up information about you? Vice. Retrieved from 
https://www.vice.com/en_us/article/bjpx3w/what-are-data-brokers-and-how-to-stop-my-private-data-collection 

68 

 
 
 
 
                                                           
Generally, the online data collection methods that data brokers employ depend on the specific information 
source. Many data brokers use data science- and technology-powered tools such as AI, machine learning, 
proprietary importing infrastructure, web crawlers, website tracking, internet indexing. For example, data 
companies  track  cookies,  which  in  addition  to  easing  the  user  experience  online,  such  as  saved  login 
details  or  search  history,  is  a  great  tool  to  collect  information  on  individuals’  online  behaviour 181.  Other 
means  may  involve  digital  fingerprints  that  trace  information  from  web  browsers,  computers,  or 
smartphones.  

Furthermore,  for  data  providers  focusing  on  specific  platforms,  these  platforms  are  the  main  source  of 
data  used  in  further  analytics.  Below  we  provide  several  examples  of  the  most  widely  used  automated 
online data collection methods to collect platform-specific information.  

  Web  scraping.  A  large  number  of  third-party  data  companies  engage  in  scraping  of  platform 
websites.  Amazon  representatives  revealed  in  an  interview  that  around  35%  of  the  traffic  to 
Amazon’s  stores  comes  from  web  scraping  tools.  The  online  retailers  and  data  companies 
contribute  significantly  to  this.  As  a  blogger  and  speaker  on  Amazon  selling  explained,  the 
platform’s approach to the data companies is “to let them ride” by leaving “the backdoor open”, 
although  this  is  not  completely  compliant  with  the  platform’s  terms  and  conditions.  Given  the 
precision of insight they have, it is likely that the top data providers to Amazon sellers (including 
Jungle Scout) should have a more direct contact with the platform itself. Many other platforms do 
not  permit  scraping  for  commercial  purposes,  but  that  does  not  prevent  data  companies  from 
collecting data this way.  

  Crowdsourcing  business  user  account  data.  Some  third-party  data  providers  use  the  platform-
generated data of their customers (i.e. platform business users). More specifically, as described in 
Chapter 4, each seller on e-commerce marketplaces gets access to their  own  business insights, as 
well as some insights into their competitors and the broader market overviews. Some third-party 
data providers ask online sellers to share their marketplace information, and then link this data of 
thousands of users to draw market insights. For example, Jungle Scout collects data from a large 
number  of  actual  sellers  (over  225,000)  who  have  opted  in  to  share  their  sales  information  with 
them182.  When  merged  with  the  data  gathered  by  scraping  the  platform’s  front-end  (e.g.,  Best 
Seller  rank  on  Amazon),  this  can  become  basis  of  quite  precise  estimations  and  extrapolations. 
Similarly,  if  AirDNA  users  wish  to  receive  performance  analytics,  they  will  be  asked  to  upload 
their  Airbnb  host  IDs.  After  doing  this,  they  can  see  their  performance  trends,  comparative  and 
financial  analysis  on  all  vacation  rental  listings.  Other  data  brokers,  such  as  Beyond  Pricing, 
Apptopia (with 300,000 developers opted-in), Wheelhouse, AppAnnie (with a million developers 
opted-in) and others use such technique as an important data source.  

  Processing  and  analysing  data  provided  by  platforms.  Some  platforms  tend  to  provide  a  lot  of 
raw data. As many business users simply do not have these large data science and IT departments 
that could work with it, third-party data providers step in to fill this gap. For example, Terapeak 
has had this role for eBay sellers (both before and after its acquisition by eBay). However,  relying 
on  these  sources  can  be  risky  for  the  data  companies.  First,  their  business  model  fails  if  the 

181 Zawadziński, M. (2015). What is cookie syncing and how does it work? Clearcode. Retrieved from https://clearcode.cc/blog/cookie-syncing/ 

182 Rohler, M. (2019). How are Estimated Sales and Revenue Calculated? What are AccuSales? Jungle Scout Help Center. Retrieved from 
https://support.junglescout.com/hc/en-us/articles/360008616814-How-are-Estimated-Sales-and-Revenue-Calculated-What-are-AccuSales-
#:~:targetText=We%20gather%20it%20from%20a,their%20sales%20info%20with%20us.&targetText=This%20means%20that%20as%20we,analyze%20and
%20test%20it%20daily.  

69 

 
 
                                                           
platform  starts  providing  the  same  data  or  analytics  itself.  For  instance,  Allegro,  a  Polish  e-
commerce platform freely shares most of the data on the activities on their platform via an API. It 
has been long used by an insight provider focusing on this data. However, recently the platform 
itself made significant investments into analytics, to develop new data products (Allegro Statistics) 
that are now provided to its sellers, endangering the business of third-party analytics providers. A 
different example is Amazon, which until recently provided exact and broad match search volume 
and  product  relevance  data  via  one  of  its  APIs.  It  was  feeding  several  third-party  software 
providers such  as Viral Launch and  Helium10 until late  2018, when the  platform removed these 
metrics  from  the  API.  Similarly  to  the  AirDNA  approach  presented  above,  these  third-party 
providers ultimately adapted to these changes by leveraging precise historical data from the API 
together with sophisticated means of forecasting and estimating183.  However, their product is not 
of the same 100% level of accuracy anymore.  

Overall, however, the relationships between specific platforms and data brokers that collect data on them 
vary from case to case. Some of the interviewed platforms did not comment on their relationships or the 
validity of data that the third-party data companies provide. Several interviewed data companies, in turn, 
claimed that they have good relationships with the platforms they focus on, while others did not comment 
on  this.  Reviewed  online  sources  also  show  that  platforms  invest  in  fighting  illegal  data  collection.  For 
example, Amazon banned several entities that were acquiring the user data from the platform in violation 
of its policies184. Nonetheless, the interviewed representative of Amazon presented the current landscape of 
third-party providers, as helping to meet the sellers’ data needs.  

Further,  non-automated  (or  semi-automated)  data  collection  methods  are  also  applied  by  data  brokers, 
especially those that do not focus on specific platforms but rather develop broader market and consumer 
insights. Some of the main ones are the following: 

  Data  acquisition  from  commercial  and  marketing  entities/  third  parties.  Private  contractual 
agreements  between  data  brokers  and  private  entities  are  quite  common  when  acquiring 
commercial  data,  which  includes  but  is  not  limited  to  consumer  demographics,  consumer 
behaviour,  transaction  information,  etc185.  The  contractual  agreements  also  include  a  range  of 
provisions  and  warranties  that  secure  a  legal  collection  and  distribution  of  their  data186.  Some 
specific examples of such entities selling data to data brokers include: 
  Platform  operators,  including  those  in  active  in  advertising  (e.g.  Facebook,  Google, 

Twitter, etc.) and the platforms of interest themselves.  

  Retailers and other commercial companies that sell information about their customers, e.g. 
purchasing habits, registration information, transactions, etc. Specific names of companies 
are  not  disclosed  since  this  information  is  private  and  not  shared  by  data  companies 

183 Gauss, C. (2018). Amazon search volume & relevance change: the future of search volume. Viral Launch. Retrieved from https://blog.viral-
launch.com/news-updates/amazon-news-updates/amazon-search-volume-relevance-change/?fbclid=IwAR2Ou8uP3UyKwERBuvH3bLcF6k-
e7o1bdpn3OBawta6GP11pXJoOk45i_Jk 

184 Matsakis, L. (2019). Amazon cracks down on third=-party apps over privacy violations. Wired. Retrieved from https://www.wired.com/story/amazon-
marketplace-apps-privacy/ 

185 Australian Competition & Consumer Commission. (2018). Digital Platforms enquiry. Retrieved from 
https://www.accc.gov.au/system/files/ACCC%20Digital%20Platforms%20Inquiry%20-%20Preliminary%20Report.pdf; Federal Trade Commission. 
(2014). Data brokers. A call for transparency and accountability. Retrieved from https://www.ftc.gov/system/files/documents/reports/data-brokers-call-
transparency-accountability-report-federal-trade-commission-may-2014/140527databrokerreport.pdf 

186 Rieke, A., Yu, H., Robinson, D. & von Hoboken, J. (2016). Data brokers in an open society. Open Society Foundations.  

70 

 
 
                                                           
because  of  claimed  competition  issues187.  However,  we  discovered  that  several  data 
brokers  analysed  in  this  paper  do  use  third  parties  to  receive  information  about  their 
business users188. 

  Financial  institutions,  such  as  banks,  credit  unions,  brokerage  services,  insurers, 

employers. 

  Other data brokers specialising on specific sectors/platforms.     

  More  “traditional”  data  collection  methods,  such  as  manual  sourcing,  desk  research,  direct 
phone  interviewing,  customer  panels, and  use  of  official  statistics.  For  instance,  AppAnnie, 
among other sources, use data of consumer panels. ComScore maintains a group of users who 
have  monitoring  software  installed on their computers. In exchange  for  joining the  ComScore 
research  panels,  users  are  presented  with  various  incentives,  such  as  free  computer  security 
software, Internet data storage, virus scanning and chances to win cash or prizes. Meanwhile, 
Skift, in addition to using automated data collection, also employs the old-fashioned methods 
of manual research, expert interviews and publicly available information.  

The  overviewed  sample  of  data  brokers  use  a  combination  of  data  collection  methods  (see  Table  11). 
This way they can expand their outreach and competitiveness. Generally, the main source is business 
user data, where business users integrate their accounts  with the data brokers’ platform. Data brokers 
mostly employ data science and technology-driven tools to gather data from online sources.  

TABLE 11. DATA COLLECTION SOURCES AND METHODS OF ANALYSED DATA BROKERS 

I

S
E
T
R
A
P
D
R
H
T

I

E
C
N
E
G
I
L
L
E
T
N

I

I

L
A
C
I
F
I
T
R
A

I

G
N
N
R
A
E
L

I

E
N
H
C
A
M

E-COMMERCE SELLERS 
Algopix 
Jungle Scout 
Sellics 
Teikametrics 
Terapeak 
APP DEVELOPERS 
App Annie 
App figures 
Apptopia 
Mobile Action 
Sensor Tower 
ACCOMMODATION SERVICE PROVIDERS 
AirDNA 
Beyond Pricing 
Uplisting 
Wheelhouse 
Skift  
OTHER 
Similar Web 
Zirra 

x 

x 
x 

x 
x 
x 

x 

x 

x 

x 

x 
x 

x 
x 

x 
x 

I

G
N
P
A
R
C
S
B
E
W

x 
x 

x 

x 
x 
x 
x 
x 

x 
x 

M
O
R
F

A
T
A
D

x 

x 

x 
x 
x 

x 

x 
x 

x 

A
T
A
D
R
E
S
U

S
S
E
N
S
U
B

I

M
R
O
F
T
A
L
P

A
T
A
D

x 
x 
x 
x 
x 

x 
x 
x 
x 
x 

x 
x 
x 
x 

x 

x 
x 
x 
x 
x 

x 
x 

x 

x 

x 

x 
x 

187 Beckett, L. (2014). Everything we know about what data brokers know about you. ProPublica. Retrieved from 
https://www.propublica.org/article/everything-we-know-about-what-data-brokers-know-about-you 

188 App Annie. (2018). App Annie Privacy Policy. Retrieved from https://www.appannie.com/en/legal/privacy/ 

H
C
R
A
E
S
E
R

L
A
U
N
A
M

x 

x 

x 

x 
x 

x 

71 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                           
Source: Data broker websites 

6.3.  Data management and protection 

Data  brokers’  objective  is  to  add  value  to  customers,  by  providing  needed  information  at  high  levels  of 
quality, validity, volume and  granularity189. We further describe how data brokers manage  and structure 
their data on internet users.  

A  general  practice  in  data  management  is  to  create  a  unique  ID  for  every  entity  (individual,  company, 
product  etc.),  which  serves  as  the  main  identifier  in  the  data  brokers’  system190.  Some  of  the  main  data 
elements that create  a unique  ID are personal data, such as name, address, phone  number, email, device 
ID191. Those data elements  can be of  finite  or free  form, which  influences data structuring. The free-form 
data  elements  (e.g.  name  and  address)  are  harder  to  assign  as  an  ID  and  can  lead  to  matching  errors 
(because  of  more  symbols  used  and  hence  more  possible  variation  in  how  they  are  entered  in  different 
datasets), whereas finite elements (e.g. email and phone number) create straightforward matching192.  

Once  the  unique  IDs  are  created,  they  are  assigned  values  and  characteristics.  One  of  the  most  common 
ways to build datasets is  ID synchronisation: matching IDs assigned to internet users by one entity (data 
broker, platform, website, etc.),  to IDs assigned to other entity. Website  visits, cookies, media, apps are a 
part of the ID syncing. For example, data broker has assigned a visitor ID 1234. Another platform knows 
this visitor by ID 4321. The data broker maps these IDs together during the synchronization process. The 
results add new data points to what they know about the internet users (be it app end-user or e-commerce 
buyers, etc.). 

Nonetheless, the ID syncing is only one side of the coin. The accumulation of information creates the need 
for big data analytics with correct linkages of multiple data points. Data mining helps to classify data into 
different categories, and therefore create a structured dataset suitable to data buyers’ needs193. It also lets 
data  brokers  profile  internet  users.  To  ensure  correct  linking  between  the  data  and  unique  IDs,  data 
brokers may also perform data enrichment,  appending and enhancement194. Data brokers enrich datasets 
by appending information  to the  IDs from the  data  buyer’s list. Data enhancement  is a  tool  that ensures 
every  data  point  is  relevant  with  a  maximised  value195.  This  structures  the  data  and  allows  for  detailed 
insights on companies,  individuals and  households, such  as demographic, household, financial, lifestyle, 
behaviour  and  any  other  information  that  might  be  of  interest  to  the  data  buyer.  This  ultimately  results 
incomprehensive datasets, enabling the data scientists to make seamless and traceable inquiries, and quick 
extraction of specific data points.  

189 Di Porto, F. & Ghidini, G. (2018). Big data between privacy and competition: dominance by exploitation? Which remedies? ASCOLA.  

190 Glasgow, J. B. (2018). Data brokers: should they be reviled or revered? In “The Pervasiveness and Value of Tracking Technologies”, ed. Selinger, E., 
Rochester Institute of Technology, New York. 

191 Glasgow, J. B. (2018). Data brokers: should they be reviled or revered? In “The Pervasiveness and Value of Tracking Technologies”, ed. Selinger, E., 
Rochester Institute of Technology, New York. 

192 Glasgow, J. B. (2018). Data brokers: should they be reviled or revered? In “The Pervasiveness and Value of Tracking Technologies”, ed. Selinger, E., 
Rochester Institute of Technology, New York. 

193 Llorca-Abad, G. & Cano Orón. (2016). How social networks and data brokers trade with private data. Open Knowledge, No. 14. 

194 Glasgow, J. B. (2018). Data brokers: should they be reviled or revered? In “The Pervasiveness and Value of Tracking Technologies”, ed. Selinger, E., 
Rochester Institute of Technology, New York. 

195 Experian. (2019). Data enrichement. Retrieved from https://www.experian.co.uk/business/data-management/data-purchasing/data-enrichment/  

72 

 
 
                                                           
During  our  interviews,  none  of  the  interviewed  data  brokers  provided  a  thorough  answer  on  data 
management techniques. Nonetheless, the  interviewees revealed that data  brokers regularly update  their 
models to ensure accuracy and appropriateness of their data management. For instance, Apptopia shared 
that  they  update  their  models  at  least  once  a  quarter.  Also,  all  data  brokers  claim  to  anonymise  and 
aggregate the final data product that they offer to clients.  

At a first glance, therefore, data brokers seem compliant with data protection and privacy laws, especially 
GDPR  in  the  EU.  However,  the  huge  amounts  of  data  that  data  brokers  collect,  store,  possibly  re-
personalise  and  disseminate  do  pose  privacy  concerns.  With  today’s  technological  capabilities  and  vast 
data  sharing  networks,  data  brokers  have  the  tools  to  continuously  enrich,  trade  and  share  personal 
data196. Most of the time individuals or companies are unaware of what information data brokers collect on 
them  or  even  that  they  collect  information  at  all197.  Due  to  this  information  asymmetry,  the  data  broker 
industry  is  quite  often  characterised  as  opaque,  non-transparent,  arbitrary,  biased,  unfair  and 
unaccountable198.  Their  goal  is  to  accumulate  as  much  data  as  possible,  especially  personal  data  of 
individuals and businesses. This raises many concerns in media, policy and research communities.  

Since 2018, the GDPR provides the general data protection framework. It requires companies that collect 
data to present instructions on cookies, opt-in/opt-outs and ask for explicit consent from users199. Privacy 
policies, terms and conditions must provide information on data gathering and processing. Under this new 
regulation,  data  companies  had  to  undergo  massive  changes  to  ensure  the  compliance  of  their  data 
gathering  and  processing  practices.  Some  of  them  had  to  restructure  and  drop  some  of  their  more 
controversial services. For example, Acxiom sold off a part of its business that offered identity  resolution 
services (LiveRamp200), while some other companies withdrew their data products from the market201.  

The compliance of data companies has not always been ensured successfully. For example, a few months 
after  the  GDPR  came  into  force,  Privacy  International  filed  a  complaint  against  seven  data  brokers: 
Acxiom,  Oracle,  Criteo,  Quantcast,  Tapad,  Equifax,  and  Experian202.  The  main  argument  was  their 
incompatibility  with  the  GDPR  and  failure  to  comply  with  data  protection  principles  (such  as  acquiring 
consent,  providing  detailed  and  transparent  information  for  the  data  subject  access  requests),  exploiting 
data in unknown ways.  

Nonetheless,  personal  data  allowing  to  match  and  link  the  data  from  online  and  offline  realms,  remains 
paramount  for  data  brokers203.  All  data  brokers  analysed  in  this  paper  collect  personally  identifiable 
information  (PII)  and  non-personally  identifiable  information.  PII  refers  to  any  data  that  can  be  used  to 
identify a specific individual or business204. The data companies claim that they anonymise and aggregate 
information  to  such  a  level  that  does  not  allow  personal  identification.  For  this,  some  data  brokers  use 

196 Christl, W. (2017). Corporate surveillance in everyday life. Cracked Labs, Institute for Critical Digital Culture. Retrieved from 
https://crackedlabs.org/en/corporate-surveillance   

197 Christl, W. (2017). How companies use personal data against people. Working paper by Cracked Labs, Institute for Critical Digital Culture. 

198 Christl, W. (2017). How companies use personal data against people. Working paper by Cracked Labs, Institute for Critical Digital Culture.  

199 Redmore, S. (2019). Privacy, a year later: How the GDPR has affected AI-powered marketing. Marketing Land. Retrieved from 
https://marketingland.com/privacy-a-year-later-how-the-gdpr-has-affected-ai-powered-marketing-260868  

200 https://liveramp.com/  

201 Riam, A., & Murgia, M. (2019). Data brokers: regulators try to rein in the ‘privacy deathstars’. Financial Times. Retrieved from 
https://www.ft.com/content/f1590694-fe68-11e8-aebf-99e208d3e521  

202 Privacy International. (2018). Why we’ve filed complaints against companies that most people have never heard of – and what needs to happen next. 
Retrieved from https://privacyinternational.org/advocacy/2434/why-weve-filed-complaints-against-companies-most-people-have-never-heard-and-what    

203 Oh, H., Park. S., et al. (2019). Personal data trading scheme for data brokers in IoT Marketplaces. IEEE Access. 

204 Grimes, A. R. (2019). What is personally identifiable information (PII)? How to protect it under GDPR. CSO. Retrieved from 
https://www.csoonline.com/article/3215864/how-to-protect-personally-identifiable-information-pii-under-gdpr.html 

73 

 
 
                                                           
advanced encryption tools to encrypt the sensitive and private information. However,  according to some 
accounts, it is questionable whether the security measures that data broker have in place can guarantee a 
secure data storage and transmission205.  

Currently,  many  data  brokers  collecting  data  on  EU  users  apply  complex  analysis  of  trends  rather  than 
focus  on  individual  level  data206.  Nonetheless,  in  some  cases  the  most  basic  metrics  can  be  a  credible 
resource  of  building  individual  profile  or  re-personalisation.  In  fact,  the  information  that  data  brokers 
collect  in  internet  users  may  still  be  sufficient  to  prevent  proper  protection  of  their  data  (see  the  box 
below).  Moreover,  a  number  of  news  articles  exist  on  new  leaks  about  data  broker’s  (illegally)  stored 
data207.  

The protection and privacy of personally identifiable information of individuals and businesses has become a contentious topic. 

Nonetheless, data brokers continue to thrive on data by collecting and processing a lot of it, because internet users continue to 
agree to terms and conditions and grant access to data collection and use for sometimes unknown purposes.  

Data  brokers’  privacy  statements  are  often  long  and  confusing,  and  have  complicated  wording.  Policies  also  tend  to  change 
quite often, leaving no time to keep up with updates and detect every new detail added to the privacy statement. One can also 

claim that most of the alleged openness and transparency is rather complicated. To be sufficiently aware of what is done with 
their data, individuals have to invest significant time, energy, and sometimes  money to find how to file the data subject access 

request  (DASR)  or  understanding  on  how  exactly  the  data  is  being  collected.  But  when  asking  for  the  latter  information,  a 
number  of  companies  request  to  register  in  order  to  apply  for  DASRs.  We  also  tried  to  file  DASR  on  MasterCard,  Equifax, 

Epsilon, Quantcast websites. Proceeding with the request meant giving full name, email, contact number, birth date, address 
details; some company websites required to enable cookies.  

The increasing demand for personal data has created the ‘privacy paradox’. The term has become increasingly relevant in the 
21st  century  and  refers  to  internet  users  that  are  concerned  about  their  data  security  and  eroding  privacy,  and  yet,  they  are 
willing to give up personal data in exchange of accessing services (sometimes those can be  “free” in exchange for data). Data 
brokers use this to get data from internet users, who give up their personal data without realising it. Long privacy statements, 

terms  and  conditions  with  ‘take  it  or  leave  it’  position,  result  in  consequences  on  privacy  and  control  over  personal  data 
processing.  

BOX 2. ISSUES WITH DATA PROTECTION AND PRIVACY 

Analysis of the selected data brokers shows that in some cases the level of their compliance with GDPR is 
not clear. Some of them do not provide information on GDPR compliance. We gathered information from 
privacy  policy  statements  of  selected  data  brokers.  The  table  below  presents  the  findings,  including 
privacy policy updates and information about the GDPR compliance measures.  

205 For isntance, Ruhaak, A. (2019), „Data Brokers Are Cruising for a Bruising“ Wired. Available at:  https://www.wired.com/story/opinion-data-brokers-
are-cruising-for-a-bruising/ 

206 Zezulková, M. (2018). What are data brokers and why should you care? Vetri. Retrieved from https://vetri.global/what-are-data-brokers-and-why-
should-you-care/   

207 Newman, L. H. (2019). 1.2 billion records found exposed online in a single server. Wired. Retrieved from https://www.wired.com/story/billion-
records-exposed-online/  

74 

 
 
 
                                                           
 
 
TABLE 12. ANALYSED DATA COMPANIES' COMPLIANCE WITH GDPR 

PRIVACY POLICY: 
LAST MODIFIED 

STATED GDPR 
COMPLIANCE208 

INFORMATION ON GDPR IN THE PRIVACY 
POLICY 

E-COMMERCE SELLERS 

Algopix 

6 June 2019 

Yes 

“…Data subjects with respect to whose data 
GDPR applies, have rights under GDPR and local 
laws, including, in different circumstances, rights 
to data portability, rights to access data, rectify 
data, object to processing, and erase data...”  

Jungle Scout 

3 February 2016 

Sellics 

Teikametrics 

Terapeak 

APP DEVELOPERS 

App Annie 

App figures 

Apptopia 

Mobile Action 

Sensor Tower 

Unknown 

6 February 2014 

Unknown 

Unknown 

Unknown 

27 November 2017 

Unknown 

23 May 2018 

24 May 2018 

Unknown 

Unknown 

15 November 2018 

Unknown 

24 May 2018 

15 June 2018 

Unknown 

Unknown 

ACCOMMODATION SERVICE PROVIDERS 

AirDNA 

Unknown 

Yes 

Beyond Pricing 

Uplisting 

Wheelhouse 

Skift  

25 May 2018 

2 March 2018 

7 June 2018 

Unknown 

Unknown 

Unknown 

26 September 2018 

Yes 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

“AirDNA has undergone a full audit under the 
new GDPR regulations, which came into force 
on May 25, 2018.” 

“…If you wish to be deleted entirely from the 
AirDNA customer database under the GDPR 
Article 17 “Right to Erasure” (May 2018), email 
us…” 

NA 

NA 

NA 

“…On this page, we’ll explain our approach to 
achieving GDPR-compliance, both for ourselves 
and for you...” 

“…We’re committed to our users enjoying Skift 
to help them understand and prepare for the 
General Data Protection Regulation (“GDPR”). 
The GDPR is the most comprehensive EU data 
privacy law in decades…” 

OTHER 

Similar Web 

Zirra 

Source: Data broker websites. 

25 May 2018 

2019 

Unknown 

Unknown 

NA 

NA 

208 For some data analytics companies that have updated their privacy policy around May 2018, but mention no specifics on the GDPR, one may assume 
that they took into account the GDPR. Alternatively, if data brokers last modified their privacy policy a few months prior to or after the GDPR 
enforcement, but mention no specifics in their privacy policy, it is either that those companies are compliant or they have other ways to deal with the 
law. Even though these are just assumptions, they are a good indicator of the lack of transparency and dubious approaches to data privacy within the 
EU, and invite to question further. 

75 

 
 
 
                                                           
7.

 Conclusions and discussion 

This paper presents a detailed analysis of the platform business user data needs, their access to data and 
analytics generated by the platforms that they use, as well as the alternative means of gathering the needed 
information. Building on the diverse evidence from research literature, interviews, online sources and an 
online survey, the analysis provided insights into several topics related to data in the P2B relationships. 

To  begin  with,  so  far,  the  research  and  policy  discussion  of  business  access  to  platform  data  have  been 
rather unspecific. Several earlier studies have indicated the dissatisfaction of business  users with the level 
of  platform  data  access.  However,  there  was  little  information  on  what  data  exactly  is  needed,  for  what 
purposes,  and  what  are  its  potential  costs.  Chapter  3  of  this  paper  took  a  deeper  look  into  this  topic  by 
classifying the types and levels of data demanded by the platform business users from three segments of 
platform  economy:  e-commerce,  hospitality  and  app  distribution.  Three  general  dimensions  of  data 
relevant to platform business users were distinguished:  

 
the type of data by object (customers, businesses, user behaviour, markets, transactions, etc.);  
  whether the data is about an individual business which receives it, or other businesses  on the 

platform (competition)/ whole marketplace.  

  by  the  level  of  data  processing  and  its  value,  from  raw  datasets  to  insights  guiding  business 

decision-making.  

When  asked  about  their  data  needs,  businesses  using  online  platforms  do  not  significantly  differentiate 
between the usefulness and relevance of data on customers, other businesses, user behaviour, markets and 
transactions, and report their interest in all these types of data. Similarly, most of them use (or would like 
to use) both, data on their individual performance, as well as data on their competitors and overall market 
trends. Meanwhile, what concerns the level of data processing, most firms do not have the capacity to deal 
with  raw  datasets  but  need  data  which  is  sufficiently  processed  and  can  provide  actionable  insights  to 
keep pace with rapid market developments and improve their business performance.  

Second,  in  Chapter  4,  the  paper  investigated  the  data  access  provided  by  the  four  major  platforms  - 
Amazon, eBay, Google Play and Booking.com to their business users. The analyses showed that  the kinds 
of  data  provided  and  not  shared  by  these  players  are  rather  similar.  To  start  with,  all  the  analysed 
platforms  have  made  notable  investments  in  analytics  capacities  to  draw  insights  from  this  data  and 
provide  them  to  the  business  users.  Sharing  of  such  data  is  beneficial  for  the  platforms.  Data  access  for 
business  users,  as  well  as  expensive  advanced  analytics,  are  granted  to  the  extent  that  it  could  generate 
more income for the platform as the business users make more transactions. Nonetheless, the platforms do 
neither  share the  raw  big  data on day-to-day activities,  nor detailed data on customers  and competitors. 
Generally,  although  they  collect  and  analyse  loads  of  data,  only  a  fraction  of  this  is  provided  to  other 
players. 

A  notable  share  of  business  users  report  that  they  do  not  get  sufficient  access  to  data  from  platforms.  
Platforms,  in  turn,  provide  numerous  justifications  for  not  granting  the  access  to  data,  including  the 

76 

 
 
protection  of  their  business  model,  protection  of  their  trade  secrets,  issues  of  interoperability,  need  for 
significant investments to make the data useful, lack of necessity to provide certain data, legal barriers and 
customer interests.  

The  refusal  of  platforms  to  share  data  can  be  especially  problematic  when  a  platform  is  vertically 
integrated  and  has  significant  market  power.  Unique  and  comprehensive  datasets  on  all  the  firms  and 
their  customers  operating  in  the  marketplace  can  give  a  huge  business  advantage  to  the  platform 
operators.  If  the  platform  is  also  a  significant  player  in  the  market,  its  business  users  do  not  have  much 
choice of switching marketplaces without serious losses.  

Third,  the  analysis  found  that  platform  business  users  can  get  the  data  and  insights  they  need  from 
alternative  sources,  by  either  collecting  and  analysing  it  themselves  or  using  services  of  third-party 
providers and data brokers.  

In  fact,  analysis  of  the  latter  showed  that  all  the  major  platforms  have  become  the  centre  of  large 
ecosystems  of  companies,  providing  various  services  to  the  business  users  operating  on  these  platforms. 
Companies specialising in data and insights are significant players in these ecosystems, providing services 
to large numbers of business users. These data brokers fill some of the important data gaps, especially  as 
concerns data on competition and actionable business insights.  

However, the data needs of platform business users are often very specific, and differ  depending on their 
analytical  capacities,  and  especially  on  the  platforms  or  marketplaces  they  use.  Most  of  the  information 
they need concerns specifically the platforms that they use. Data brokers do collect a lot of data. However, 
whether  this  data  presents  a  full-fledged  substitute  for  the  data  collected  by  specific  marketplaces,  app 
stores or OTAs and not shared with the business users, cannot be answered straightforwardly. 

Importantly, most of the data these companies work with comes from the platforms themselves - either by 
scraping platform front-end or acquiring access to individual analytics from registered platform business 
users  (who  provide  this  access  to  the  third  parties  to  be  able  to  use  their  data  services).  It  seems  that 
platforms virtually close the door (by not providing some data and insights) but leave a window open for 
data collection, and third-party providers exploit this. The data brokers step in with superior technical and 
analytical  capacities,  innovative  tools  and  approaches  to  collect  and  develop  the  insights  that  businesses 
need. Whether the data companies provide insights for individual traders or broader overviews of market 
and competition, they mostly use the data that platforms do share or allow to collect for  everyone. The 
added value of these companies is that business users themselves do not need to invest into any analytics 
or  IT,  but  rather  buy  products  tailored  to  their  needs.  Sometimes  the  methodologies  applied  are  so 
sophisticated that allow them to quite precisely estimate or extrapolate the  metrics that the platforms do 
not share. However, at the same time, the data companies remain highly dependent on the platforms that 
they analyse and on any changes in their data sharing policies. 

The question whether the platforms should be obliged to share more data is complicated. While improving 
the  access  to  data  to  many  economic  players,  this  may  have  negative  effects  on  all  sides  in  platform 
economy.  To  begin  with,  providing  everyone  with  the  same  access  to  data  may  be  detrimental  to  the 
business  users  themselves,  whose  financial,  client  and  performance  information  would  be  disclosed  to 
competitors. Secondly, the impact of data sharing should also be assessed from the consumer perspective. 
Platforms  invest  a  lot  in  customer  satisfaction,  and  data  protection  is  part  of  the  consumer  experience. 
Consumers prefer to deal with centralised services rather than with multiple actors asking for consent, and 

77 

 
 
they are not willing to be flooded with marketing messages. Further, mandatory data sharing may have a 
negative  impact  on  platforms’  business  and  profits209.  In  such  a  case,  platforms  may  even  engage  in 
strategic behaviour, for instance, by adding noise or large amounts of non-material and raw information of 
little value in the public disclosure.  

While  this  analytical  paper  presents  an  overview  on  the  state  of  the  art  of  the  data  access  for  platform 
business users, it also allows to raise new questions and hypotheses for further research. To begin with, the 
analysis  revealed  concrete  issues  in  specific  segments  of  the  platform  economy.  Firstly,  the  most 
problematic  cases  of  not  sharing  data  concern  vertically  integrated  platforms  with  significant  market 
power,  who  can  use  data  to  their  advantage.  Secondly,  this  paper  looked  into  three  key  sectors  of  the 
platform economy: e-commerce, app development and accommodation/ hospitality. In order to get further 
insights on data access, further investigation into other sectors not covered in this paper is needed. Thirdly, 
further  research  would  be  useful  into  the  legal  and  technical  aspects  of  the  platform  data  and  sharing, 
including interoperability.  

209 Easley, D., Huang, S., Yang, L., & Zhong, Z. (2018). The Economics of Data. Available at SSRN 3252870. 

78 

 
 
 
 
 
                                                           
List of interviewees  

# 

ORGANISATION/ COMPANY 

BUSINESS TYPE 

INTERVIEW DATE 

ONLINE PLATFORMS  
  Amazon marketplace  
  Allegro 
  eBay 
  eMAG 
  Etsy 
  Real.de 
  Zalando marketplace 
  Booking.com 
  Expedia Group 

1. 
2. 
3. 
4. 
5. 
6. 
7. 
8. 
9. 
10.    TripAdvisor 
11. 
12.    Google (Play store, Android, Fuchsia) 

  Apple (App store, iOS, sdk) 

13. 

  Facebook 
BUSINESS USERS 

14.    Business user 1 
15.    Business user 2 
16.    Business user 3 
17. 
  Business user 4 
18.    Business user 5 
19.    Business user 6 
20.    Business user 7  
21.    Business user 8 
22.    Business user 9 
23.    Business user 10 
24.    Business user 11 

25.    Business user 12 

26.    Business user 13 

27.    Business user 14 
28.    Business user 15 
29.    Business user 16 
30.    Business user 17 

31. 
  Business user 18 
32.    Business user 19 

E-commerce 

E-commerce 

E-commerce 
E-commerce 
E-commerce 
E-commerce 
E-commerce 
Accommodation/ hospitality 

Accommodation/ hospitality 

Accommodation/ hospitality 

Apps and software 

Apps and software 

Social media 

E-commerce: Amazon 

E-commerce: Amazon 

E-commerce: Amazon 

E-commerce: Amazon 

E-commerce: Amazon, Etsy 

E-commerce: Etsy 

E-commerce: Etsy 

E-commerce: eBay, Etsy 

E-commerce: Undisclosed 

E-commerce: Undisclosed 

03/12/2019 

06/12/2019 

17/10/2019 

31/10/2019 

22/11/2019 

16/11/2019 

20/11/2019 

08/11/2019 

19/11/2019 

13/11/2019 

30/10/2019 

31/10/2019; 
05/12/2019 

27/11/2019 

07/11/2019 

18/10/2019 

20/11/2019 

26/11/2019 

11/11/2019 

25/11/2019 

04/12/2019 

02/12/2019 

21/11/2019 

18/12/2019 

Accommodation/ Hospitality: Booking.com, 

13/11/2019 

Airbnb 

Accommodation/ Hospitality: HomeAway, 

27/11/2019 

Airbnb 

Accommodation/ Hospitality: Booking.com, 

18/11/2019 

Airbnb 

Accommodation/ Hospitality: Booking.com 

Accommodation/ Hospitality: Booking.com 

Accommodation/ Hospitality: Booking.com 

Accommodation/ Hospitality: Booking.com, 
Expedia 

Apps and software: Google Play, App Store 

Apps and software: Google 

09/12/2019 

06/12/2019 

04/12/2019 

09/12/2019 

10/12/2019 

17/10/2019 

79 

 
 
 
 
33.    Business user 20 
34.    Business user 21 

35.    Business user 22 
36.    Business user 23 
37. 
  Business user 24 
38.    Business user 25 
39.    Business user 26 
40.    Business user 27 
41.    Business user 28 
42.    Business user 29 
43.    Business user 30 
44.    Business user 31 
45.    Business user 32 

46.    Business user 33 
DATA BROKERS 

47.    Algopix 
48.    Jungle Scout 
49.    AirDNA 
50.    Apptopia 
51.    SimilarWeb 
52.    Zirra 

ASSOCIATIONS 

Apps and software Undisclosed 

Apps and software: Google Play, App Store, 
Facebook 

20/11/2019 

15/10/2019 

Apps and software: App Store, Google Play 

04/12/2019 

Apps and software: Google Play 

Apps and software: App Store 

Apps and software: App Store 

Apps and software: Android (Google) 

Apps and software: App Store 

Apps and software: Android (Google) 

Apps and software: Android (Google) 

Apps and software: App Store 

Apps and software: App Store, Google Play 

Apps and software: Google, App Store, 
Microsoft Store, Facebook 

14/11/2019 

25/11/2019 

11/10/2019 

22/10/2019 

10/10/2019 

07/10/2019 

25/09/2019 

11/10/2019 

11/09/2019 

17/09/2019 

Apps and software: App Store 

18/09/2019 

E-commerce 

E-commerce 

Accommodation/ hospitality 

Apps and software 

Other 

Other 

53.    Computer & Communications Industry 

General: online platforms 

Association (CCIA) 

General: online platforms 

General: online platforms 

54.    EDiMA 
55.    Digital Europe 
56.    The Marketplace Coalition  
57.    Centre for data innovation 
58.    Allied for Startups 
59.    European Digital SME Alliance 
60.    Developers Alliance  
Apps and software: business users 
61.    European Games Developer Federation (EGDF)    Apps and software: business users 
62.    Interactive Software Federation of Europe 
Apps and software: business users 

E-commerce: online platforms 

General: online platforms 

General: online platforms 

General: business users 

(ISFE) 

63.    European Brands Association (AIM) 
64.    EU Travel Tech (ETTSA) 
65.    Independent Retail Europe  
66.    The European Broadcasting Union (EBU)  

E-commerce: business users 

Accommodation/ hospitality: business users 

E-commerce: business users 

Apps and software: business users 

23/10/2019 

08/11/2019 

07/11/2019 

24/10/2019 

05/11/2019 

28/10/2019 

29/11/2019 

26/09/2019 

10/10/2019 

17/10/2019 

10/10/2019 

09/10/2019 

12/11/2019 

26/09/2019 

23/10/2019 

10/10/2019 

27/09/2019 

14/11/2019 

25/09/2019 

23/10/2019 

80 

 
 
 
 
 
 
 
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Company websites (platforms, data brokers, others) 

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https://suite.booking.com/blog/understand-competition-to-determine-hotel-prices/  

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policies/user-privacy-notice-privacy-policy?id=4260 

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gb/help/growing-your-business/what-analytics-dashboard  

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#:~:targetText=We%20gather%20it%20from%20a,their%20sales%20info%20with%20us.&targetText=This%20means%20
that%20as%20we,analyze%20and%20test%20it%20daily.  

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Similar Web. (2020). Retrieved from https://www.similarweb.com/ 

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GETTING IN TOUCH WITH THE EU 

In person 

All over the European Union there are hundreds of Europe Direct information centres. You can find the 
address of the centre nearest you at: https://europa.eu/european-union/contact_en 

On the phone or by email 

Europe Direct is a service that answers your questions about the European Union. You can contact this 
service: 

– by freephone: 00 800 6 7 8 9 10 11 (certain operators may charge for these calls), 

– at the following standard number: +32 22999696 or  

– by email via: https://europa.eu/european-union/contact_en 

FINDING INFORMATION ABOUT THE EU 

Online 

Information about the European Union in all the official languages of the EU is available on the Europa 
website at: https://europa.eu/european-union/index_en 

EU publications 

You can download or order free and priced EU publications at: 
https://publications.europa.eu/en/publications. Multiple copies of free publications may be obtained by 
contacting Europe Direct or your local information centre (see https://europa.eu/european-
union/contact_en). 

EU law and related documents 

For access to legal information from the EU, including all EU law since 1952 in all the official language 
versions, go to EUR-Lex at: http://eur-lex.europa.eu 

Open data from the EU 

The EU Open Data Portal (http://data.europa.eu/euodp/en) provides access to datasets from the EU. Data 
can be downloaded and reused for free, for both commercial and non-commercial purposes. 

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91