Customer Journey Analytics: A Model for Creating Diagnostic
Insights with Process Mining
Daan Weijs and Emiel Caron
Tilburg University, Department of Management, Warandelaan 2, PO Box 90153, Tilburg, The Netherlands
Keywords: Customer Journey, Touchpoints, Customer Journey Analytics, Data Models, Process Mining.
Abstract: The customer journey is becoming more complex due to digitization of business processes, broadening the
gap between the proposed journey and the journey that is actually experienced by customers. Customer
Journey Analytics (CJA) aims to detect and analyse pain points in the journey in order to improve the customer
experience. This study proposes an extended version of the Customer Journey Mapping (CJM) model, to
measure the impact of different types of touchpoints along the customer journey on customer experience, and
to apply process mining to gain more insight in the gap between proposed and actual journeys. Moreover, this
model is used to develop dedicated CJA based on process mining techniques. A case study on e-commerce
applies the CJM-model in practice and shows how the combination of process mining techniques can answer
the analysis questions that arise in customer journey management.
1 INTRODUCTION
The digitization of businesses increases the
complexity of the customer journey. Customers now
interact with a firm trough many touchpoints and they
expect a seamless experience throughout their
journey with the firm. Companies try to manage the
customer journey to create an optimal experience, but
in practice there are many deviations between the
proposed journey and the journey that is actually
experienced by customers. The large amounts of data
that are being generated at touchpoints open up
opportunities to find out how to provide more value
to customers. This study aims to find out how to
analyse and gain better insights into the impact of
different types of touchpoints along the customer
journey on customer experience, by producing
Customer Journey Analytics (CJA) derived from
process mining.
The customer journey encompasses all inter-
actions a customer has with a firm during the end-to-
end purchase process (Lemon and Verhoef, 2016).
Customer experience is built over an extended period
of time and includes many touchpoints between a
customer and an organization (Zomerdijk and Voss,
2011). A touchpoint can be defined as “an instance of
communication between a customer and a service
provider” (Halvorsrud et al., 2016). All touchpoints
together constitute the customer journey (Zomerdijk
and Voss, 2011) and companies carefully design their
journeys to create an optimal experience. The journey
has a static state which reflects the hypothetical
journey of the service delivery process, i.e., the
expected journey, and a dynamic state that represents
the actual execution of the process (Halvorsrud et al.,
2016), i.e., the actual journey. The shift from offline
to online commerce increases the complexity of
marketing communications, broadening the gap
between the static and the dynamic state. This results
in new types of management questions about the
dynamic state of the journey, that support marketing
decision-making. These questions revolve around
determining the effectiveness and the impact of
different marketing communications on business
outcomes, such as revenue or customer experience.
The data that is generated at online touchpoints
open up opportunities for the visual representation of
the customer journey, i.e., maps, and applying
analytics (Holmlund et al., 2020; Lemon and
Verhoef, 2016), which helps to answer the questions
that arise in customer journey management. Figure 1
presents an overview of the elements of an online
customer journey. A firm creates different types of
advertising touchpoints to attract visitors to its
website. A website typically has a funnel structure
that supports visitors with relevant information at
every stage of the customer journey, moving from
418
Weijs, D. and Caron, E.
Customer Journey Analytics: A Model for Creating Diagnostic Insights with Process Mining.
DOI: 10.5220/0011263900003266
In Proceedings of the 17th International Conference on Software Technologies (ICSOFT 2022), pages 418-424
ISBN: 978-989-758-588-3; ISSN: 2184-2833
Copyright
c
 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

Figure 1: Elements of an Online Customer Journey, adapted from Anderl et al., 2016; de Haan et al., 2016; Lemon and
Verhoef, 2016.
Figure 2: Components of the online Customer Journey Mapping (CJM) model, adapted from Bernard and Andritsos (2017).
general interest to more specific consideration of the
actual purchase or download. The post-purchase
phase includes reviews, customer lifetime value and
repurchase rates. Every action that a visitor performs
on the website can be logged. In addition, a website
registers how the user landed on the website. Data
generated at the advertising and website touchpoints
is a rich source for analytical purposes. The goal of
CJA is to enable marketers to detect and analyse pain
points, allowing them to intervene and develop
possible actions to improve customer experience.
The objective of this research is threefold: (1) to
extend the Customer Journey Mapping (CJM) model
of Bernard and Andritsos (2017), to measure the
impact of different types of touchpoints along the
customer journey on customer experience, (2) to
develop dedicated CJA based on specific process
mining techniques, and (3) demonstrate the results of
(1) and (2) on a real case study in e-commerce.
This paper is structured as follows. In Section 2
we propose the extended CJM-model and connect
CJA with process mining techniques. After that, we
apply the model in a case study to produce CJA for
an e-commerce company. Finally, conclusions are
drawn in Section 4.
Customer Journey Analytics: A Model for Creating Diagnostic Insights with Process Mining
419

2 PROCESS MINING FOR CJA
2.1 Extended CJM-model
Process mining aims to discover, monitor and
improve business processes (Aalst, 2012). It can be
used in many different contexts and settings where
processes are executed. One of the main contributions
of process mining is that it uses real event data that is
generated during processes, closing the gap between
assumed process models and the actual execution of
the process. Bernard and Andritsos (2017) proposed
a CJM-model to store CJMs as XML structures and
they illustrated how process mining can be used to
analyse CJMs. While the original model provides a
structured starting point for mapping, the advertising
elements and the website funnel structure (see Figure
1) have to be included as model components to create
a more comprehensive overview of an online
customer journey. In addition, a further mapping
between process mining techniques and dedicated
managerial CJA questions is required to discover the
applicability of the techniques within this domain.
Figure 2 presents the complete CJM-model for
online customer journeys in terms of an UML
(Unified Modeling Language) class diagram. The
CJM-model consists of multiple customer journeys.
Each Journey is performed by a Customer, and a
customer can perform one or many journeys. A
customer journey might include six dimensions,
captured by the aggregations and components of the
Journey class. A Journey is started from a physical
Location (1), performed on a specific Device (2), is
started from a specific channel, i.e., Campaign, as
defined by Anderl et al. (2016) (3), has a Landing
page (4), and a Date (time) dimension (5), that
captures when the journey started. In addition, it
consists of multiple Touchpoints (6) that each have a
timestamp and a descriptive name, a Page dimension
where the touchpoint is encountered, a Stage (i.e.,
pre-purchase, purchase and post-purchase phases), as
defined by Lemon and Verhoef (2016), and an
Experience that can be measured with an emotion,
scale or quote (Bernard and Andritsos, 2017). Here
the model of Bernard and Adritsos (2017) is extended
by adding the Journey – Touchpoint hierarchy
(dimension 6) and dimensions 1 – 4.
The hierarchy of the online CJM-model is
reflected in the import data to make meaningful
analysis with process mining software. The
requirements of an event log - a case id, timestamp
and event - are met in the Touchpoint class. However,
a single flattened event log, i.e., fact table, including
all touchpoints is not enough to capture the structure
of the customer journey. The dimensions that
characterize the journey are important and need to be
included in the data model. This allows for slicing and
dicing the data from multiple perspectives and
enables marketers to detect and analyse pain points,
for example, by comparing journeys from different
campaigns or journeys that started from desktop
versus mobile.
2.2 Mapping Process Mining with CJA
The CJM-model in Figure 2 shows that the customer
journey and process mining can directly be related in
terms of the required data structure. Moreover, we
map classes of process mining techniques to specific
CJA analysis questions. The techniques applied in
process mining should be able to provide meaningful
insights that managers can use to manage the
customer journey. The goal of CJA is to enable
marketers to detect and analyse pain points and
opportunities in the customer journey in order to
develop possible optimization efforts. CJA should
focus on the gap between the proposed journey and
the actual journey as experienced by the customer.
Process mining includes three main classes of
analysis: discovery, conformance checking and
enhancement (Aalst, 2012). Process discovery uses
event log data to create a process model of the actual
execution of the process. Conformance Checking
compares the proposed model with the actual model
to check the process conformance. Enhancement aims
to change or extend the process model, based on the
insights from the other techniques. In addition to the
types of analysis, process mining covers different
perspectives (Aalst, 2016). The control-flow
perspective focuses on the ordering of activities, i.e.,
the control flow. The organizational perspective
focuses on the resources, i.e., actors, people, roles,
departments, that are involved in the process and how
their tasks are related. The case perspective includes
the properties of the cases. It focuses on the individual
characteristics of cases beyond the activities or
resources. Finally, the time perspective focuses on the
timing and frequency of events.
Process mining techniques have shown to be
effective in analysing gaps between proposed and
actual processes by discovering the actual process
model based on event logs (Aalst, 2016). The
objective of process mining is to discover, control and
improve actual processes. These objectives fit the
types of business questions that arise in customer
journey management, where process discovery can be
used to discover the actual customer journeys,
conformance checking to analyse the gap between the
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proposed and the actual journey, and enhancement
could focus on new events and attributes to create
richer analysis. In Table 1, CJA is aligned with
process mining analysis. The table presents an
overview of which mining techniques and
perspectives are used to answer the different types of
analysis questions that arise in customer journey
Table 1: Mapping process mining with CJA.
PM Analysis Perspective CJA questions
Discovery
Control-flow What is the most
common path on the
website?
Case What are the
average events per
case for different
journeys?
Time How quick do
visitors drop out in
the path to
purchase? (Lemon
and Verhoef, 2016)
Conformance
Control-flow Where do visitors
deviate from the
customer journey
map?
Control-flow Where in the
purchase process do
visitors drop out?
(Lemon and
Verhoef, 2016)
Case Was my marketing
campaign
successful?
Time Which types of blog
content are
engaging?
Enhancement
Control-flow Which new events
can be included to
create richer
analysis?
Case Can we cluster
visitor behaviour to
identify customer
segments? (Lemon
and Verhoef, 2016)
Time How does journey
duration deviate for
different product
segments?
1
https://analytics.google.com
management. The alignment is based on careful
review of the literature on process mining and CJA
(Aalst, 2012; Aalst, 2016; Lemon and Verhoef, 2016)
and validated by case studies (see e.g., Section 3). In
this respect, relevant mining algorithms for CJA are:
Directly-Follows Graphs, Heuristics miner, and
Fuzzy miner, that take into account the frequency of
events, and where the output of the algorithm is
transformed into dedicated CJA.
2.3 Customer Journey Data
The website is the central pillar in online customer
journeys (see Figure 1). Google Analytics
1
(GA) is a
popular technology for tracking and analytics of
websites. GA uses first-party cookies to track
individual users as they navigate through a website.
The latest version, GA4, tracks user behaviour in an
event structure. Every action that a user performs on
a website – e.g., clicking on a link, reading a blog
page, or playing a video – is called a ‘hit’ and triggers
an event, generating huge amounts of data. All user
behaviour on a website is hierarchically ordered in
three classes: A user has one or more website sessions
that each contain one or more events. The class
structure of GA4 corresponds with the extended
CJM-model as explained in Section 2.1. A user
corresponds to the customer, a session corresponds to
a journey including its dimensions and an event
corresponds to a touchpoint. The data, generated by
GA4 can be mapped directly to the CJM-model of
Figure 2. The end result of this mapping is an actual
customer journey that can be visualized with process
mining techniques.
3 CASE STUDY IN
E-COMMERCE
The case study is conducted for the ABC company
2
,
an e-commerce company that sells products via their
own website and via third-party e-tailers. On average
their website receives more than 150 thousand
visitors per month. Their goal is to guide visitors
through the journey to the product page where they
can buy the product or click through to a third-party
e-tailer (i.e., lead generation). The ABC-company
distinguishes touch-points in five categories related to
their website funnel: homepage, blog-post, category-
page, product-page, and checkout. An underlying
data model and (import) data set have been created
2
The actual name of the company is not disclosed.
https://www.celonis.com
Customer Journey Analytics: A Model for Creating Diagnostic Insights with Process Mining
421

Figure 3: Discovered actual customer journeys for the ABC-company.
based on the CJM-model (see Figure 2) and applied
in the case study. The data is loaded into Celonis
process mining software and subsequently a part of
the CJA questions from Table 1 are used to test and
validate its usability with the software.
Figure 3 shows the discovered process model with
‘fuzzy mining’ and provides insights in the flow of
visitors along the touchpoints in the customer
journey. Most of the journeys start on a blog-post and
the least start on the homepage. More than 50% of the
journeys end directly after viewing a blog-post.
Process discovery with a control-flow perspective
shows the most common path on the website. The
time perspective can be used to find out how quick
visitors drop out in the process. Process discovery
techniques help analyse the gap between the static
and the dynamic state of the customer journey.
Conformance checking and process analytics
techniques measure the performance of different
customer journeys. Table 2 shows the journey
conformance and purchase probability of four
expected journeys, based on the website funnel
depicted in Figure 1. Company ABC proposed four
types of expected journeys, that can start at either one
of the pages. Journey conformance uses the control-
flow perspective and measures the percentage of
journeys that conform to the proposed flow. It
answers the questions where visitors deviate from the
proposed journey and where in the purchase process
visitors drop out, by showing the actual journeys that
visitors have. The purchase probability measures the
probability that a visitor makes a purchase, given that
he followed the proposed journey, using conditional
probability P(A|B). These insights help a company to
measure the impact of different types of touchpoints
along the customer journey towards customer
experience. Table 2 shows the biggest gap between
the expected and the actual journey for the Blog-
Category-Product journey. Only 10.31% conforms to
the proposed journey, meaning that about 90%
follows a different path. This indicates some
bottlenecks in the journey and since the purchase
probability is relatively high, it is valuable to find
areas of improvement. By diving deeper into these
journeys and slicing the data on the 5 perspectives of
a journey, the root causes behind the pain points can
be identified. Two insights were a shifted ratio of
mobile/desktop traffic for these journeys (i.e., the
Device dimension) and a paid search campaign that
led visitors to a wrong blog category (i.e., Campaign
dimension).
Table 2: Conformance Checking and Process Analytics.
Expected
Journey
Journey
conformance
Purchase
probability
Homepage 
Category 
Product
46.97% 2.34%
Blog 
Category 
Product
10.31% 2.44%
Category 
Product
46.28% 2.99%
Product - 1.39%
The product page contains five types of
touchpoints that visitors can encounter. In addition to
the product information, these pages include elements
such as a video player or a button where visitors can
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download a file. The interactions on these elements
are measured by custom events. Process analytics
provides insights in what behaviour leads to a
purchase. The conditional probability P(A|B) of each
of these touchpoints has been calculated towards a
purchase or a lead generation. In this case, A is the
purchase and B is the journey that the visitor
followed. The impact on purchase, measures the
probability that the visitor makes a purchase, given
that (s)he followed the proposed journey. Table 3
shows the results. For example, for the review
impression the probability of making a purchase is
2.86% given the fact that the visitor has checked the
reviews of the product. The same calculation is done
for lead generation, where the probability of clicking
through to a third-party e-tailer is 13.59%. Both
measures indicate the effect of a touchpoint. Table 3
shows the values for all journeys, but these values can
be filtered on different product segments, campaigns
or mobile versus desktop journeys to gain more
insight into purchase behaviour under different
circumstances.
Table 3: Process Analytics on the product page.
Touchpoint Purchase
probability
Lead
generation
probability
View image
gallery
2.39% 7.05%
Select product
options
3.81% 11.80%
Check the
reviews
2.86% 13.59%
Play the
product video
3.52% 10.41%
Download a
file
1.49% 5.16%
Based on the findings of the process mining
analysis, some suggestions for improvement can be
made, for example, changing the blog categories for
paid search campaigns and optimizing the website for
mobile devices. The extended CJM-model, with the
Journey – Touchpoint hierarchy and additional
dimensions, allows for these analyses. The insights
provided result from slicing the data on location,
device, campaign and landing page characteristics.
Due to space limitations, only brief examples are
presented here. The case study shows that the
combination of process mining techniques can
answer the questions that arise in customer journey
management. Moreover, another case study has been
conducted in a business-to-business environment,
showing similar results. We are now working on
extending the model’s analytical capabilities to
ensure its generalizability to other web applications.
4 CONCLUSIONS
This paper contributes to the CJA and process mining
research by: (1) extending the CJM-model to measure
the impact of different types of touchpoints on
customer experience, by adding the Journey –
Touchpoint hierarchy with several analytical
dimensions that allow for a richer analysis of the
customer journey, (2) developing dedicated CJA
based on process mining, and (3) demonstrating the
results in an e-commerce case study. It further closes
the gap between CJA and the application of process
mining techniques. The case study applies the
extended CJM-model in practice and shows how the
different process mining techniques can be used to
create insights in the customer journey. The ability to
slice the data from multiple perspectives allows for
new insights that were not possible with the model of
Bernard and Andritsos (2017). In addition, it shows
the application of the theoretical model in practice,
closing the gap between academia and managers.
Further research could focus on applying the model
in a more extensive case study to validate if the
improvement efforts that result from the process
mining analysis indeed help a firm to improve
customer experience. In addition, a more complete
evaluation in comparison with existing CJA
techniques is envisaged.
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