# Data-Driven Personalized E-Government Services: Literature Review and Case Study

Mariia Maksimova, Mihkel Solvak, Robert Krimmer

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Mariia Maksimova, Mihkel Solvak, Robert Krimmer. Data-Driven Personalized E-Government Services: Literature Review and Case Study. 13th International Conference on Electronic Participation (ePart), Sep 2021, Granada, Spain. pp.151-165, 10.1007/978-3-030-82824-0_12. hal-04014062

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# Data-driven Personalized E-government Services: Literature Review and Case Study*

Mariia Maksimova¹,²[0000-0001-8971-8462], Mihkel
Solvak¹,³[0000-0003-0179-4036], and Robert Krimmer¹,⁴[0000-0002-0873-539X]

¹ Johan Skytte Institute of Political Studies, University of Tartu, Ülikooli 18, 51003, Tartu, Estonia

² mariia.maksimova@ut.ee

³ mihkel.solvak@ut.ee

⁴ robert.krimmer@ut.ee

**Abstract.** Better targeted and more personalized service offering to cit-
izens has the potential to make state-citizen interactions more seamless,
reduce inefficiencies in service provision, and lower barriers to service
access for the less informed and disadvantaged social groups. What con-
stitutes personalization and how the service offering can be customized
to meet individual user demand is, however, much less clear and under-
developed partially due to the technical and legal dependencies involved.
The paper gives an overview of how personalization and customization of
digital service offering have been discussed in the literature and system-
atizes the main strand emerging from this. It follows up with a case study
of the Estonian X-road log data as one potential way to detect latent user
demand emerging from an experienced life-event that could form a basis
for letting users define their service needs as holistically as possible. The
results show the existence of distinct service usage clusters, with specific
user profiles behind them, a clear indication of latent demand that leads
to a simultaneous consumption of otherwise independent digital services.

**Keywords:** life event e-service · personalized digital service · e-service logs

# 1 Introduction

Improving the quality and accessibility of public services is a priority for govern-
ments regardless of their digital development level. The introduction of electronic
government portals has become a widespread means of doing this. However, the
interaction between the citizen and the state in such a digital gateway remains
one-sided. The provision of services still has a one-fits-all approach and the state,
as a service provider, does not analyze the individual needs of citizens. Personal-
ization of service offering where user demand and supply matched by advanced

<footnote>* The work for this paper has received funding from European Union's Horizon 2020 research and innovation programme under grant agreement No.s 857622 and 959072.</footnote>
data analytics and AI approaches have the potential to make state-citizen inter-
actions seamless on an entirely new level [29]. At least, this is the hoped potential
of applications utilizing recent advantages in machine learning and AI. In most
cases, the reality is still a situation where the citizen needs to actively seek out
the service from the relevant authority instead of being offered proactively based
on individual needs. Often, a service needs to be pulled by the citizen instead of
being pushed by the state, and seamless interactions still a faraway dream.

The technological advances in machine learning and AI are, however, can
improve service delivery only when they are embedded in a service delivery design
that allows for personalization to happen in the first place, i.e., capture data that
is indicative of demand. It can be used to offer a tailor-made service response
that overcomes legal and organizational boundaries set by a state organization
that is hierarchical and horizontally organized in domains that don't cooperate
nor share data. Such a situation is still very much a rarity, but this is starting
to change slowly. The ongoing pandemic might paradoxically be a force that
expedites movement towards more personalized service delivery in Europe.

The European Recovery and Resilience Facility (RFF), established in 2021 to overcome the damage brought about by the coronavirus pandemic, prescribes that the each recovery and resilience plan by member states has to spend a minimal of 20% to "foster digital transformation"<fnref n="5" />, these are substantial funds that some digitally more advanced countries are planning to use to do exactly that, develop new service delivery ways that are much closer to citizen demand understood holistically, the Estonian government foresees that up to a total of 5-6% of the full RRF fund for Estonia to be spent on life-event and pro-active service delivery development.

Focusing on citizen needs is increasingly supported both in the research lit-
erature and in government development strategies. It is believed to be an impor-
tant element in the process of public administration transition towards a new
interaction model between the state and society. An individual approach to each
citizen, as a consumer, will allow analyzing the needs, interests, and rights of a
citizen to receive a particular service and create a more convenient and up-to-
date list of services. Thus, the state policy is formed from a more active position
as a supplier of services, moving towards the model of service state. The case of
Estonia supports this statement by introducing a way to see if and how latent
user demand could be detected based on the X-road log data. It opens up an
opportunity for more personalization and letting users define their service needs
as holistically as possible.

## 2 Methodology

For this paper, two methodological approaches were selected. First, to explore
the relevant approaches in personalized, data-driven, and life-event strategies

<footnote n="5">⁵ Recovery and Resilience Facility https://ec.europa.eu/info/business-economy-euro/recovery-coronavirus/recovery-and-resilience-facility_en</footnote>
for electronic state portal design, a systematic literature review (SLR) was per-
formed, which consists of consecutive steps, according to which all publications
containing the results of the studies on the phenomenon in interest are collected,
followed by an assessment of their quality and the synthesis of the results. The
steps are as follows: (1) planning and formulating the research question; (2) lo-
calization and searching the literature; (3) data gathering and quality evaluation;
(4) data extraction and data analysis; and (5) interpretation and presenting re-
sults [5,25]. This research method helps to analyze existing studies and creates a
foundation to conclude what is already known and what is unknown [24]. Using
it in this study helps to identify current trends in the research literature and
highlight their shortcomings.

The selected sources for the search were Scopus, Web of Science, and Google Scholar databases. The relevant articles were searched using the combination of chosen terms as most commonly used in articles on the topic of more data-driven citizen-oriented design of electronic service provision: “data driven”, “life-event”, “proactive”, “personalized”, “public administration”, “e-government”, “electronic service”, “e-service”, and “portals” as titles and keywords. There were no limitations concerning the year of the publication. In total, 864 articles were identified; however, after duplicates were deleted and the second round of screening based on the title, 103 publications remained. The third round included the paper’s abstract analysis, after which 33 papers were selected. Further examination of each article took place upon retrieving the full text, after which an additional ten articles were excluded, as they did not fit the inclusion criteria: 1) the subject of study – approaches for more personalized services provision via electronic state portals; 2) the language of the paper – English; 3) type of the paper – journals papers, international conferences and book chapters; 4) the paper is not a summarizing paper of the previous research by the same author. The final sample of articles consists of 23 studies. The studies’ date of publication ranged from 2002 to 2020.

Second, the paper conducts a case study based on log file data from the
Estonian x-road data exchange layer. The hierarchical cluster analysis using
Ward's linkage for individual unique service usage was carried out on a random
sample of 100 000 individuals. Estonia was chosen as a critical case [27], as it
is one of the few countries (if not the only one) where such a large number of
base registries is connected in one eco-system, whose logfiles and thus exchanges
between databases can be analyzed. Limitations include, but are not limited to,
the lack of comparative data and a possible bias due to researchers' location in
Estonia. Due to the uniqueness of the case, these shortcomings need to be in
future research.

3 Literature review

## 3.1 Conceptual foundations

Before turning to the literature analysis, it is necessary to dwell in more detail
on the conceptual foundations of this study. There are quite a few theoretical
approaches to personalization. In a broad sense, personalization is understood
as the process of selecting suitable electronic services for a citizen on any cri-
teria related to the citizen at a given time. It can affect different sets of the
government web portal functionality e.g., prefiltered search results, tailored rec-
ommendations, banners to match a person's interests, or links to other related
to the topic of interest sites [11]. It is one of the most promising solutions that
can be implemented in state electronic portals due to its' ability to establish
a dialogue between citizens and the state. Sometimes personalization and cus-
tomization are used interchangeably [6] or customization considered to be a part
of personalized electronic service [7].

One of the most common personalization methods is a recommendation sys-
tem whose task is to provide recommendations by automatic filtering of unnec-
essary services. In most solutions, ontology-based semantic reasoning is used for
the data organization and management of information. Accurate data descrip-
tion, qualitative classification, knowledge management, and defined relationships
between data entities create a system where information is ontologically grouped,
making it much easier to process, sort, and present it [3,21,26,16,6,12]. Personal
recommendations are divided into ‘auto’ and ‘on-demand’. On-demand recom-
mendations are performed at the user’s request, such as searching for a specific
service on a website. Auto recommendations are carried out by processing per-
sonal information and preferences linked to the user’s profile and presenting
information or changing site interface based on it [3,28].

The user profile on the state portal can be used for personalization in several
ways. From all the variety, the main ones can be distinguished: content-based,
CF-based, knowledge-based, and a hybrid approach [3,6]. A content-based is
using historical information about the user's interaction with the site. The profile
is compared with the content of the services in which the user was previously
interested. Within this framework, various weighted schemes compile indices
based on different indicators that help rank services for a specific user. A CF-
based compares an array of users to find similar profiles and recommends services
based on the preferences of the user's group. In this way, the user submits their
rating or uses the service. After that, the system algorithm compares users and
groups them based on the similarity of patterns. Recommendations are provided
based on which services were liked or used by the group to which the user belongs.
A knowledge-based approach is using a knowledge base with predefined possible
interests as a reference model for content filtering. It is based on a system that
contains a knowledge base with a set of specific solutions and algorithms, on the
basis of which recommendations are given. A hybrid is a mixture of the listed
above approaches in different configurations.

Another way to provide citizens with more personalized services and simplify
access to them is the life-event approach. The life-event based state portals use
a model that groups services by the most critical events in a person's life. The
assumption behind this is that most users don't know what kind of service they
need but assume the end result [23]. This grouping often implies a hierarchical
system of services [6] connected by the fact that they are used to solve any ques-
tion that arises at a particular period of life. There are different ways to define
the life-event, however, it is possible to trace their similarity to the life stages
of a person. This division of a person's life into stages helps separate services
using the same logic and sequence of events. Using life events to categorize ser-
vices allows the user to navigate the electronic portal better, as this division is
intuitive.

## 3.2 Literature analysis

Studying a personalized approach to serving citizens in the public sector goes in different directions, both as a purely technical solution and as something that requires more conceptualization and understanding of this process. The analysis of selected studies allows us to identify four main directions in the study of the personalization of state portals: 1) developing of models and frameworks for electronic portals personalization; 2) implementation of different techniques for better personalization of government electronic portals; 3) modelling of web portal/model框架 from end-user perspective; 4) discussion about existing cases of government electronic portals personalization.

As part of the first research direction, the study of personalization in state
electronic portals is carried out based on prototypes or reference models. This
category includes attempts by several researchers to conceptualize the life event
and propose how it can be integrated into government systems. The main focus
is on the categorization of life-even services and producing working prototypes
of active web portals [23,22,19,16] and the construction of multi-level systems
that take the selected categories as a basis [13]. Researchers use different clas-
sification criteria but similarly note the diversity of personalization models and
the prospects for implementing them in government electronic systems. Another
characteristic of this category is that the concepts of “life-event” and “user-centric
personalization” presented in these works do not have explanatory power but are
more fragmented and descriptive.

The second group of researchers considers personalization in the context of
its implementation in electronic portals. It suggests the possibility of a better
application of it by complementing various technologies and data analysis meth-
ods. One of the proposed solutions is ontology-based semantic reasoning for the
data organization and management of information. Accurate data description,
qualitative classification, knowledge management, and defined relationships be-
tween data entities create a system where information is grouped ontologically,
making it much easier to process, sort, and present it [3,26,6,12]. Another ap-
proach is focusing on the different data mining techniques like web mining [11],
association rules mining [20], mining with the use of time-decayed Bayesian algo-
rithm [10] and algorithm for computing an item-based CF similarity [26] used to
improve recommendation accuracy and coverage. This also includes works that
pay more attention to the use of various user data obtained from profiles on the
service [4,28,2] or from social media [1] to understand the needs of users better.

The third direction of research on personalization focuses on modeling the
architecture of proactive electronic portals, taking into account the process par-
ticipants' point of view on what obtaining an electronic service should look like. For a better understanding of the processes, it is proposed to get insights from civil servants and experts [15] as well as end-user [14,8,21] to create a methodology for proactive service design. These studies are also based on the analysis of existing services, taking them into account to improve the proposed methodology. The fourth group also focuses on the analysis of implemented personalization cases. Thus, D. Linders et al. (2018) look at Taiwan's approach to proactive actions based on sending notifications to citizens based on data on the due dates for fines, fees, and taxes [9]. In turn, V. Homburg & A. Dijkshoorn (2013) consider the factors influencing the adoption and diffusion of personalized e-government services in Dutch municipalities and develop a set of recommendations for future adoption [7].

The proposed solutions are creating top-down approaches since pre-designed structural elements are used. This kind of design leads to limited personalization, as matches can mostly occur across large categories. In case if some service is not included in the logic of a predefined system, then it is likely that the search for this service will be difficult. In addition, existing research is primarily theoretical and does not use real data, which can significantly increase its applicability of proposed solutions. Thus, despite a significant amount of academic research, there is still no single approach to the personalization of government electronic portals, making it necessary to search for new frameworks and models that integrate real empirical data.

## 3.3 Dynamic bundling of services to meet demand

The discussion above shows that all possible approaches taken to align user needs better with the service offering and provide a seamless service consumption experience depend on distilling user needs in the first place and not simply concerning one service, but the total universe of possible services from the user perspective. In essence - the citizens need the service bundle that addresses the particular needs arising out of the experienced events. This leads to holistic service demand where the adequate supply depends on multiple organizational, legal, and technical dependencies. Overcoming these involves substantial legislative, administrative and organizational challenges. One needs to bear in mind that state portals tend to be service gateways and actual service owners are usually separate institutions. Though forming one single digital gateway, they do not include the whole digital service universe and hence also don't allow to take the personalization very far since they have limited user information, service selection, and in most cases, no access to secondary data sources to be able to complement the limited information to truly personalize user experience after authentication.

It is safe to say that proof-of-concept (POC) types of studies and trials are needed before any administrative or organizational changes to address user demand holistically are possible. But these studies are faced with difficulties, as such POCs depend on the availability of service consumption data on individuals that still spans domains, institutions, and time, i.e., combined datasets that
capture the full service demand from the client point of view regardless of the
service owner. As a rule such combined datasets do not exist or cannot even be
created due to data privacy reasons. Next we explore one possible workaround
for solving this problem by using service call logs from a data exchange layer to
determine the latent service demand structures.

4 User defined service demand

4.1 Client data source

Short of a single super database on everything citizens do, some state information
system organizational methods might allow for a potential workaround for this
problem, albeit limited in nature. The possible solution stems from the need to
exchange data and the subsequent interoperability inherent in providing (most)
digital services. In the Estonian case, such interoperability is ensured through
a centrally managed distributed data exchange layer called X-road, which al-
lows public and private institutions to exchange data for offering various public
and private digital services. As of 2021, roughly 2900 different digital services
are offered over the X-road (https://www.x-tee.ee/factsheets/EE/#eng), which
generate approximately 100 million service request queries a month. The cumu-
lative service call queries stand at 7.6 billion since the inception of X-road in
2002. Though a majority of these involve machine-machine interactions, close to
3%, i.e., 3 million are human-initiated service call queries which are logged cen-
trally by X-road Center in the central log for compliance monitoring (see Figure
1). The actual data exchange happens between the parties, and no one outside
of this has access to the data, but the identities of the parties, which mostly
make up base registries and service portals, are stored centrally. Therefore, the
central log stores limited information on service consumption, such as who issued
when a service request to what service producer and when was that answered for
traffic monitoring and compliance purposes. The byproduct of this central log is
a single, albeit limited dataset that includes all service call requests by citizens.
This entails a substantial share of total citizen interactions with the state.

Estonia is offering most state services also or only digitally, but they are still
owned by separate institutions resulting in a fractured service delivery landscape
from the client point of view. Citizens still need to: a) have a clear understanding
of which services are designed for their specific needs; b) whether they are eligible
for these; c) seek the service out and; d) actively pull it by requesting the service
through various service portals or the central digital gateway www.eesti.ee. This
state of affairs is due to different state service providers not offering services
in a conjoined way even though they might be sharing data with other service
providers in the back-end over X-road. Such service offering design does not
reflect how the citizens' needs arise.

In reality, a life event, for example having a child, leads to a bundle of service
needs – child health care needs, parental benefits registration, tax exemptions
on certain benefits, and other in kind and monetary benefits requests. The state
<figure><img src="image_4.png" /><figcaption>Fig. 1: Schematic picture of X-road (Reprinted from: [18])</figcaption></figure>

observes these as single service consumption instances consumed by the indi-
vidual – the latent demand for which is determined by some life event of the
citizen. While we can reasonably deduct what basic needs arise from specific
life events, everything outside of this theoretical cluster is by definition excluded
even though additional services might be consumed in close temporal space or a
particular order and be related through the latent unobserved demand triggered
by the life-event. This leads to the unnecessary need to pull different services
from different institutions while they could be proactively offered based on the
currently unobserved latent event leading to service demand.

Rather than redesign the administrative structure of state service provision,
we propose to study and pilot the applicability of service call query clustering to
identify if certain service needs cluster together temporally or follow a specific
temporal sequence. We also look for lead events that can seem to be triggering
more service consumption events. By identifying actual service consumption bund-
dles from the service consumption logs, we pinpoint single domains and domain
crossing areas in which service consolidation could bring the most significant
value for both the client and the service provider. Value for the citizen would
be a reduced need to pull services, reducing informational inequalities on knowl-
edge of and eligibility for services, and more seamless interactions with various
service-providing state organizations. Value for the service provider would be
to reduce customer support need and channel more clients directly to service
provision gateways/ portals.

## 4.2 Service consumption patterns

**Logged information** Table 1 shows how a service call query looks like by example of the query 5875968103, the data is available as open data at https://logs.x-tee.ee/EE/gui/, this particular one is a service request made on the 16th January 2021 by the East Tallinn Central Hospital (client) for a digital
health record made to the Health and Welfare Information Systems Center (service provider logged as a member in the log) which maintains this particular service under the auspice of the Ministry for Social Affairs. When an individual makes the service request, the client ID is anonymized in the open data, but a pseudonymized version of the logs has been made available to us for academic research. The individual only shows in the logs if he or she has accessed the service personally through authenticating before requesting the service, if a medical health record of a patient was accessed by a doctor in a hospital or private practice through a mini-information system portal (MISP) then this is recorded a service request by the owner of this particular MISP. As told above, the actual data exchange happens between the two parties to the exchange, and only the metadata of the request is logged by the X-road Center.

The fields of interest for us are marked in the table and are client ID, timestamp, and the service type based on which we can connect individual logs and see which services are consumed either temporally close together or in specific temporal sequences empirically without any theoretical priors to what should go together theoretically.

<table><caption>**Table 1:** Example of a X-road log from central log (not all fields shown)</caption><tr><td>"id":</td><td>5875968103,</td></tr><tr><td>"clientMemberClass":</td><td>"COM",</td></tr><tr><td>"clientMemberCode":</td><td>10822068,</td></tr><tr><td>"clientSubsystemCode":</td><td>"ehealth",
"clientXRoadInstance": "EE",
"messageProtocolVersion": 4,
"requestAttachmentCount": 0,</td></tr><tr><td>"requestInDate":</td><td>16.02.2021,</td></tr><tr><td>"requestInTs":</td><td>1613509200000,</td></tr><tr><td>"requestMimeSize":</td><td>"None",
"responseMimeSize": "None",
"responseSoapSize": 23578,</td></tr><tr><td>"securityServerType":</td><td>"Client",
"serviceCode": "hl7",
"serviceMemberClass": "GOV",
"serviceMemberCode": 70009770,</td></tr><tr><td>"serviceSubsystemCode":</td><td>"digilugu",
"serviceXRoadInstance": "EE",
"succeeded": "TRUE",
"totalDuration": 1122</td></tr></table>
**Service usage groups** A prior look at the service usage depth and duration showed two wider user groups emerging [17]. People who use a large number of services on a large number of days over the duration of a year and user with a small amount of service consumed over a few days a year. Solvak et al.[17] speculate that the former group is mainly civil servants for whom some of the work is organized around digital services. However, the latter is most likely regular citizens who need to pull such services to interact with the state and solve everyday issues related to the service.

We examine the potential latent demand structure based on log data from 2003 to 2015, i.e., the first 13 years since the establishment of the X-road system. Figure 2 shows the average annual service call queries (on a logarithmic scale) and the average number of unique services consumed. The query heatmap (Fig 2a) indicates that the most active user groups are 20-29 and 30-39 year olds<fnref n="6" />.

The average number of unique services by age groups (Fig 2b) shows a roughly similar picture indicating that not only is this group using services more intensely as indicated by the query number, but they also have a wider selection of services which they use. Therefore, there is variance in service usage depth and number. Moreover, it is non-randomly distributed across age groups, which already indicates that demand differs substantially and could be driven by life events.

<figure><img src="image_5.png" /><figcaption>**Fig. 2:** Annual averages of service call queries and unique services used by age group</figcaption></figure>

**Clusters of service usage time-series** We further selected a random sample of 100 000 individuals out of the total dataset and performed a hierarchical cluster analysis using Ward's linkage for individual unique service usage time-

<footnote n="6">⁶ Minors show in the graph when parents are using some services - mainly health record-related - as legal custodians in their name.</footnote>
series' by the year the individuals first appeared in the logs. Figure 3 shows the four biggest and most distinct clusters that emerged out of this exercise for each year of first appearance in the logs. We computed four independent clusters for the usage time series that started in the given year.

<figure><img src="image_6.png" /><figcaption>Fig. 3: Clusters in individual service usage time-series, calculated according to the time series that starts in the given year.</figcaption></figure>

Particularly interesting are the third and fourth clusters, where the service usage rate is initially the same for both, but it starts to grow continually in the fourth, whereas it starts to fall or stagnates in the third cluster. Interestingly this pattern repeats itself across the years in varying degrees. While the time series clustering does not tell us anything about the real content of the latent service demand, it nevertheless indicates clear, structured differences in usage intensity and service numbers. Given the relatively large number of unique services consumed per year in the fourth cluster (between 20 to 50 depending on the starting year) there seems to be room for consolidating service offering if these numbers are driven by some shared life-event driven latent demand.

This potential is further strengthened when one peeks into what kind of individuals cluster in the given groups. This is done in Figure 4, which shows substantial differences in the age and gender structure. The third and fourth clusters have an age distribution peak at 20-30 years and have a significantly higher share of women. While the first cluster is biggest in terms of individuals, it has a relatively flat age distribution, combining this with the low service
usage number in the cluster, we can conclude that this includes widely different
individual needs across age groups with most likely very heterogeneous service
demand menu.

The preliminary empirical investigation therefore points already towards
promising results as we have demonstrated that usage patterns take different
paths over time, with some of them - characterized by heavy demand for a wide
variety of services - being taken by a quite narrow age group with an above av-
erage share of female users. The fact that the highest intense service user groups
include disproportionally more women in active childbearing age (20-30 year old)
points clearly towards some latent demand structure, possibly life-event driven.

<figure><img src="image_7.png" /><figcaption>**Fig. 4:** Age and gender distributions in four clusters of individuals who appeared in logs in 2007 for first time</figcaption></figure>

# 5 Discussion and conclusion

The literature review showed that the personalization of digital service offerings
is not uniformly understood nor defined. There is a lack of understanding of how
user demand can be correctly captured and integrated into the service delivery
channel design and structure. Possible workarounds through getting additional
data from outside the knowledge domain of the service owners - such as using
social media data - are not viable strategies and place a heavy additional data
collection and managing burden, let alone possibly infringing on user privacy. A
content-based approach proposes to examine the user's prior interaction with a
given site, focusing only on a small selection of services and will not be viable
in a situation where the user demands are dynamic and time-dependent.

The alternative has been a knowledge-based approach where personalization
patterns are fixed based on a theoretical understanding of what users want and
what needs seem to go together. The live-event based approach is one of such
approaches. However, here further limitations enter, as a life-event is by defini-
tion a major structuring force in the individual's life which does trigger a large
cluster of potential needs hard to comprehend from the service owners point of
view or even impossible to detect due to the legal and organizational barriers
stopping effective data merging that might show co-consumption of widely dif-
ferent services by the same client. State portals tend not to include such wide
selection, and hence the data captured through usage sessions on the portal is,
as a rule, not sufficient to cover all the needs that arise, as these cross many
domains and are very likely leading to a sophisticated drawn out need for state
support.

We propose a fully bottom-up life event definition that is purely empirical
and hence also user-defined - something that combines all aspects of the per-
sonalization strategies outline in the literature - user data, data from widely
different domains, major structuring events, and a dynamic sequence of combi-
nations of service being consumed. Taking a client-centric view of a life event -
something that is big and triggers a myriad of service needs only the individual
can comprehend - comes with the major challenge of properly identifying the
latent needs arising from it. The latent needs are by definition not observable,
but the latent demand leads to observable service consumption clusters if we
find ways of joining data from different service owners as it is easier to move
data than it is to move institutions or people. We use limited technical service
call logs of a data exchange layer for a distributed state information system to
switch the view from 2000 separate service owners to one individual that has
a need that can be addressed with a specific collection of some of these 2000
services.

The results show that usage is clustered, and some clusters exemplify pat-
terns that indicate a life-vent driven demand - high service usage intensity with
multiple unique services being consumed by a narrow age group. Though poten-
tially heterogeneous in content, the service consumption bundles are not overly
large when one counts the consumption events and the usage intensity. A further
examination of the actual service content of such clusters plus additional clus-
tering definitions on updated data is ongoing. This will show what type of latent
demand - defined by the combination of specific services in a service shopping
basket - describes what groups.

We consider this latent demand detection one fundamental building block of
fully personalized service menus that are dynamic over time and adjusted under-
lining population dynamics change. Though the full integration of the services
in the identified bundles is still a major obstacle, we can demonstrate that the
demand can be empirically detected and the content exposed, which paves the
way for possible integration solutions.
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