The Future of Data-driven Personas: A Marriage of Online Analytics
Numbers and Human Attributes
Joni Salminen
, Soon-gyo Jung
and Bernard J. Jansen
Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar
Turku School of Economics at the University of Turku, Turku, Finland
Data-driven Personas, Automatic Persona Generation, Online Analytics, Customer Segmentation, Marketing,
Big Data, Automation.
The massive volume of online analytics data about customers has led to novel opportunities for user segmen-
tation. However, getting real value from data remains challenging for many organizations. One of the recent
innovations in online analytics is data-driven persona generation that can be used to create high-quality hu-
man representations from online analytics data. This manuscript (a) summarizes the potential of data-driven
persona generation for online analytics, (b) characterizes nine open research questions for data-driven persona
generation, and (c) outlines a research agenda for making persona analytics more useful for decision makers.
Despite the increasing availability of online analytics
data (also referred to as “Big Data”), decision mak-
ers are trying to turn customer data into practical in-
sights (Salminen et al., 2017a). For this reason, var-
ious approaches for automatic analytics and insight
generation have been proposed (Salminen and Jansen,
2018; Wang et al., 2018).
One approach for better understanding customers
is the persona technique, popularized by Cooper
(2004). A persona is defined as a fictitious person
representing an underlying customer or user group,
often the core customers of an organization, although
they can also be the potential or desired users of a
system (Cooper, 2004) (see Figure 1 for an example).
Personas are deployed for various purposes, e.g., soft-
ware development, design, marketing, and health in-
formatics (Goodwin and Cooper, 2009). They facil-
itate the communication of data within an organiza-
tion, so that decisions can be made keeping the cus-
tomers in mind (Long, 2009).
From the analytics perspective, personas segment
similar customers under one archetype, aiding deci-
sion makers to understand customer needs and wants.
While it is not practical to cognitively process thou-
sands of individuals for customer decisions, a few
core customer segments is feasible for humans.
As online analytics data has become more preva-
lent and accessible, researchers have proposed novel
Figure 1: Example of a persona profile
. A typical persona
profile has a name, picture, and text description of the per-
methods for data-driven persona generation that uses
digital, rather than analog, data for persona cre-
ation (Zhang et al., 2016).
Data-driven persona generation addresses two ma-
jor challenges in persona creation: (a) the complexity
and cumbersomeness of using large amounts of cus-
tomer data for creating personas, and (b) the slow
and expensive process of creating personas manually.
Data-driven personas transform online analytics data
into representations that decision makers can easily
process (An et al., 2018c; Salminen et al., 2018a).
Further, data-driven personas are created rapidly and
updated easily, while preserving the privacy of indi-
viduals (An et al., 2018b).
Salminen, J., Jung, S. and Jansen, B.
The Future of Data-driven Personas: A Marriage of Online Analytics Numbers and Human Attributes.
DOI: 10.5220/0007744706080615
In Proceedings of the 21st International Conference on Enterprise Information Systems (ICEIS 2019), pages 608-615
ISBN: 978-989-758-372-8
2019 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
However, to generate useful and accurate data-
driven personas without any manual interventions,
there are several challenges to address. These chal-
lenges relate to sub-fields of computer science, such
as Image Generation, Natural Language Processing,
Topic Modeling, Algorithms, and Human-Computer
Interaction, as well as various “softer” topics such as
persona perceptions (Salminen et al., 2018c), persona
biases (Hill et al., 2017; Salminen et al., 2019b), and
value in use (Salminen et al., 2018b).
In this manuscript, we explore a contemporary
collection of research challenges related to data-
driven persona creation, particularly from the per-
spective of automatic persona generation. We aim
to inspire research within persona studies and related
subfields of computer science.
2.1 Limitations of Manual Persona
Personas are typically created with qualitative ap-
proaches. Brickey et al. (2012) found that 81% of per-
sona creation efforts reported in academic literature
have applied qualitative techniques, such as ethno-
graphic fieldwork and interviews. However, manual
persona generation has been thoroughly criticized in
the literature, the main criticism being:
Non-Representative Data: Manually created per-
sonas typically rely on data that does not represent the
whole customer base (Chapman and Milham, 2006).
Lack of Scaling: Because manual analysis relies
on human labor, it scales poorly with the big datasets
used in online analytics (An et al., 2018b).
High Cost: Manual persona generation is costly; it
typically takes several months and costs tens of thou-
sands of dollars. The high cost factor keeps personas
from the reach of small to medium-size businesses
and start-ups.
Expiration: Personas tend to expire when changes
in customer behavior take place. This is typical for
many fast-moving online businesses, including online
purchase behavior (Salminen et al., 2017b), search
behavior (Jansen et al., 2011), and online content con-
sumption (Abbar et al., 2015).
2.2 Advances in Data-driven Persona
To solve the challenges of manual persona generation,
researchers have suggested quantitative persona gen-
eration. The main techniques are as follows.
Quantitative Analysis of Survey Data: Several
prior attempts for data-driven persona creation rely on
survey-based data collection (Chapman et al., 2015;
Dupree et al., 2016; Vahlo et al., 2017). This survey
data is then most typically analyzed via cluster or fac-
tor analysis. However, survey-based data collection
can be costly and fallible compared to using behav-
ioral data due to many possible respondent and re-
searcher biases associated to survey data collection in
general (Podsakoff et al., 2003).
System Log Data: In addition to survey data,
personas can be created from system logs, and or-
ganizational records describing the users or cus-
tomers (Brickey et al., 2012). For example, Molenaar
(2017) analyzed 400,000 clickstreams from a period
of three months, grouping them into common work-
flows and classifying users into these workflows. Us-
ing a similar approach, Zhang et al. (2016) applied hi-
erarchical clustering to generate ve data-driven per-
sonas from clickstream data.
Procedural Personas: In video game context, re-
searchers have created procedural personas that cap-
ture the sequential game-playing choices. The ap-
plied techniques include, e.g., evolutionary algo-
rithms and neural networks (Holmgard et al., 2018).
The procedural personas are given names based on
their behaviors of playing the game (e.g., “Mon-
ster Killers”). Rather than being “rounded per-
sonas” (Nielsen, 2004) with name and demographic
information, these personas can be seen as virtual
agents that model the possible game-playing behav-
iors (Vahlo et al., 2017).
Latent Semantic Analysis (LSA): LSA has been
applied to create personas by differentiating users
based on their use of language (Miaskiewicz et al.,
2009). The weakness of this approach is the depen-
dency on the text corpus which is not always available
in online analytics data. In addition, not using behav-
ioral data (e.g., product engagement) can be consid-
ered as a weakness.
Discrete Choice Analysis: In the discrete choice
methodology for persona creation (Chapman et al.,
2015), customers explicitly state their preferences and
a conjoint analysis algorithm is then used to match
respondent to their best-fit persona. The method was
developed to answer the criticism of personas as lack-
ing quantitative information (Chapman et al., 2008),
as it enables, through forced assignment, to determine
The Future of Data-driven Personas: A Marriage of Online Analytics Numbers and Human Attributes
the proportional representativeness of personas within
the overall user base. The method also makes it pos-
sible to compare the algorithmic persona assignments
to randomly generated persona assignments. How-
ever, the major limitation is that stated preference data
can be expensive to collect and can also be more unre-
liable than observed behavioral data. This is also the
limitation of creating personas with principal compo-
nent analysis (Sinha, 2003) that uses preference data
from a limited number of customers.
Automatic Persona Generation (APG): APG is de-
fined both as a methodology and a system for au-
tomatic creation of personas from online analytics
data (An et al., 2018b). Automatically generated per-
sonas are (1) representative, as APG processes the en-
tire online analytics dataset, (2) behaviorally accurate,
inferring patterns from customers’ engagement with
products (e.g., digital content, e-commerce products,
flight destinations...), (3) rapidly generated due to fast
processing time, and (4) constantly up-to-date due to
period refreshing of the data and the associated re-
generation of the personas (An et al., 2018b,c).
The following section explains the APG method-
ology. We focus on this approach as it represents the
latest techniques for data-driven persona generation
and specifically utilizes online analytics data.
3.1 Data Collection from Online
Analytics Platforms
Online analytics platforms (e.g., Google Analytics,
YouTube Analytics, Facebook Insights) typically en-
able collection of user data automatically via applica-
tion programming interfaces (APIs)
. Typically, this
data is aggregated into segments to protect the pri-
vacy of individual users. An example of aggregated
user segment is [Male, 44-55, Qatar]. The segments
given by the online analytics platforms typically con-
tain information of the gender, age, and country of the
users. The platforms typically collect this informa-
tion from the users upon registration. Various inter-
action metrics can be retrieved for each group (e.g.,
clicks, views). For example, [Female, 24-35, USA]
1, 590 views for Video A.
Using the APIs of online analytics platform, APG
collects the aggregated data for products and engage-
ment metrics. For example, from YouTube Analytics,
Note that accessing the analytics data requires autho-
rization from the owner of the analytics property.
Figure 2: Output of APG. Denoted areas are: [A] Name and
demographic information, [B] Picture, [C] Text description,
[D] Life situation information, [E] Topics of interest, [F]
Social media comments, [G] Most interested content, and
[F] Audience size.
APG collects videos and their view counts, whereas,
from Google Analytics, APG collects pages and num-
ber of sessions.
3.2 Data Processing and Persona Profile
After collecting the data from an online analytics plat-
form, APG transforms it into an interaction matrix
that captures the interaction between customers and
online products (An et al., 2018c,b).
V denotes the g × c matrix of g customer groups
and c online products. The element V, V
i j
, can be any
metric that reflects the engagement of the customer
group G
for product C
. For example, in YouTube
Analytics, V
i j
is a view count for a particular video,
from customer group G
. The customer groups
contain gender, age, and country (e.g., Female, 54-
65, South Korea). Using V as the basis, non-negative
matrix factorization is applied to detect p latent pat-
terns (An et al., 2018c).
These patterns form the core of the personas, as
they represent the customer groups’ product prefer-
ences. APG then chooses a representative demo-
graphic group for each latent pattern and enriches
this demographic group with additional information
to produce a complete persona profile (see Figure 2).
ICEIS 2019 - 21st International Conference on Enterprise Information Systems
The vision of APG can be summarized as achieving
completely automatic generation of high-quality per-
sonas that addresses the limitations of manual per-
sona creation through the employment of online an-
alytics data. “Completely automatic” refers to elimi-
nation of manual steps. “High quality” refers to serv-
ing the persona user’s decision needs while accurately
representing the underlying data about the customers.
In the subsections, we present Proposed Research
Questions (PRQs) toward the vision.
4.1 Automatic Generation of Persona
In the current version of APG, a photo for each per-
sona is purchased through online stock photo banks.
However, it is difficult and costly to find an appro-
priate photo for all demographic groups, especially
worldwide. The potential solution could be to gener-
ate persona pictures automatically.
The nascent developments in Deep Learning,
particularly in Generative Adversarial Networks
(GAN) Goodfellow et al. (2014), have been applied
in generating and modifying human faces. This line
of work could be used for generating photos that vary
by persona’s age, gender, and ethnicity – possibly us-
ing these attributes as conditions in the Conditional
GAN architecture Isola et al. (2017). PRQ1: How to
automatically generate persona profile pictures?
4.2 Defining the Optimal Persona
Persona profiles typically contain name, age, and gen-
der, as well as other demographic information, such as
marital status, education level, job, and so on. How-
ever, there have been few studies into what informa-
tion should be included in a persona. This lack of
prior work speaks to a need for user studies, includ-
ing interviews and ethnographic investigations of ac-
tual users of personas in the workplace.
Related to this issue of defining the optimal in-
formation content of a persona profile, another ma-
jor limitation of data-driven persona methodologies
is that none of them currently infer in-depth insights
about the users such as needs and wants that are es-
sential for the thorough understanding of the users or
customers that the persona portrays (Cooper, 2004).
We summarize these issues in two research ques-
tions: PRQ2: What information should automat-
ically generated personas contain? PRQ3: How
could that information be automatically inferred
from online analytics data?
Two approaches could potentially address PRQ2:
(1) defining shared information needs for a given in-
dustry, and constructing industry-specific templates
(e.g., e-commerce, online media and news, e-health,
etc.); or (2) providing a self-selection options for
users to build personas by choosing from all avail-
able information. In the former case, the selection
of persona attributes should depend on persona users’
information needs that can be obtained via user stud-
ies (Salminen et al., 2018d). Overall, determining the
persona users’ information needs relies on implicit or
explicit user feedback.
A potential solution for PRQ3 is the use of com-
putational methods for inferring customer attributes
from social media.There is a substantial amount of
research using social media platforms, such as Twit-
ter, to infer user attributes (Volkova et al., 2015).
These studies have inferred, for example, social me-
dia users’ psychological traits, socioeconomic status,
relationship status, political orientation, and brand
likings, by using profile information, comments, and
connections of the user. The applied techniques are
diverse, including natural language processing, graph
analysis, and various machine learning classifiers. If
any of the above attributes are considered critical
by persona users in a specific application domain,
methods of inferring those attributes and associating
them with the automatically generated personas (most
likely using probabilistic matching) are called for.
Moreover, these additional customer attributes
could be available on-demand, so that the per-
sona users could construct their own personas from
ground-up by choosing the information elements that
matter in their respective industries or use cases.
4.3 Unsupervised Learning of Persona’s
In addition to demographic information, online ana-
lytics data contains information on customer prefer-
ences that can be inferred from the customers’ en-
gagement with various online products. However, due
to vast number of individual products, they need to
be categorized in order to provide a meaningful sum-
mary of the customer preferences. Thus, data-driven
personas should incorporate unsupervised topic mod-
els that can accurately classify the online products
based on their features, such as textual descriptions.
This prompts the research question: PRQ4: How
to generate a universal topic taxonomy for online
content? Here, unsupervised learning methods, such
as Latent Dirichlet Allocation (LDA) Topic Mod-
The Future of Data-driven Personas: A Marriage of Online Analytics Numbers and Human Attributes
Table 1: Research and development goals for automatic persona generation.
Persona section
Proposed solution Ideal outcome Applicable domains
Persona Picture Automatically producing persona
face pictures for the matching de-
mographic variables (age, gender,
Eliminates the need for manually
acquisition of persona pictures.
Computer Vision,
Generative Adversarial
Topics of interest Automatically creating a taxonomy
that is scalable across multiple top-
ical domains.
Eliminates the need for creat-
ing an organization- or industry-
specific taxonomy each time a
new one is added.
Topic Modeling: LDA,
LSA; Entity Resolu-
tion, Data Mapping
Persona quotes Providing comments that relevant
for persona user’s use case and do
not distract the persona user from
the important attributes of the per-
Increases empathy and customer
insights gained by the persona
user; eliminates distraction
caused by non-useful comments.
Social Computing; Text
Classification, Natural
Language Processing
Persona at-
Determining the persona attributes
that correspond to the persona
users’ needs in a given decision-
making situation and devising
methods to infer those attributes
from unstructured data such as
social media comments
Satisfying the persona users’ in-
formation needs, thereby en-
abling possible better decision-
making via the use of personas.
Human-Computer In-
teraction, Information
Design, Usability
Overall persona
Validating accuracy, consistency,
and usefulness of personas for indi-
viduals and organizations.
Ensuring that personas are reli-
able and valid, so that they can be
trusted in real decision-making
Case Studies, User
Studies, HCI
elling (Hong et al., 2018), could be helpful. In ad-
dition, Google’s Universal Sentence Encoder uses a
hybrid approach that outputs similarity with a known
taxonomy for any text content (Cer et al., 2018).
Another challenge related to inferring additional
customer attributes is their association with the per-
sona profiles generated using different source data.
For example, Platform A has information on a per-
sona’s topics of interest, and Platform B has informa-
tion on the persona’s movie preferences. Then, there
is a need for mapping these seemingly disconnected
pieces of information in order to include both of them
in the persona profile. To create high-quality personas
with attributes such as the persona’s goals, needs,
and wants, several data sources need to be combined.
Therefore, PRQ5: How can we map the personas
to online users across different platforms? Ap-
proaches studied in the domain of entity resolution
could be of help here.
4.4 Choosing High-quality Quotes for
the Persona
Descriptive quotes are typically shown in the per-
sona profile to provide a better understanding of the
customers (Cooper, 2004). However, it has been
found that the quotes can also distract the persona
users toward information that is not relevant for their
task. For example, Salminen et al. (2018d) found
that the ethnicity of the persona affected the persona
users’ interpretation of the persona. Moreover, when
showing unfiltered social media comments as persona
quotes, the impression of the persona can quickly turn
toxic (Salminen et al., 2018d). To counter this is-
sue, Salminen et al. (2019a) have proposed three crite-
ria for the automatic selection social media comments
as persona quotes:
1. Representativeness: the selected comments match
the behavioral patterns, topics of interest, and de-
mographics of the corresponding persona
2. Relevance: the selected comments are helpful for
the persona user in his or her purpose for using the
3. Non-toxicity: the selected comments are not of-
fensive to the degree where they would distract
the persona user from the other information in the
persona profile.
The associated solutions require filtering out the
most toxic comments by automatic classification (or
use of dictionary-based methods), but also mapping
the comments with the matching personas. In a re-
cent workshop on automatic persona generation, it
was suggested that the mapping could be done based
on demographic analysis of the social media users’
profile information (when publicly available) or by in-
ferring their gender, age and interest from the style of
ICEIS 2019 - 21st International Conference on Enterprise Information Systems
their writing (An et al., 2018a). Thus, the research
questions are: PRQ6: How can the the attributes
of the commenting customers be inferred only us-
ing the text of the social media comments (when
no public profile information is available) to select
the comments that meet a persona’s attributes?
PRQ7: How to select the most relevant comments
to the end user?
Moreover, in filtering out toxic comments, we
should be cautious of manipulating the actual data and
thereby biasing the information shown in the persona
profile. Therefore, if the data in fact contains a high
number of toxic comments, to maintain the truthful-
ness of the persona, data-driven personas should dis-
play those comments, even if some end users might
find them offensive. Thus, the challenge of toxicity
in personas involves a certain trade-off between truth-
fulness and user experience.
4.5 Avoiding Biasing the End Users of
One challenge of the personas is the fact that for the
selected attributes, only one dominant value can be
chosen. For example, the persona can have only one
age, even though the customers that the particular
persona represents form a distribution of ages. This
concern is highlighted in data-driven persona creation
methodologies that are based on behavioral or pref-
erence patterns, because many demographic groups
can share behavioral patterns or preferences. Choos-
ing one dominant value for an attribute, say, gender or
ethnicity, can easily result in biased interpretations by
persona users (Salminen et al., 2018d). Thus, PRQ8:
How can personas be debiased so that oversimpli-
cation of the customer base is avoided?
Two solutions can be thought of: (1) removing
ambiguous informational to debias the persona for
end users, and (2) purposefully introducing diversity
to display the variation in the underlying user base.
For example, it is possible to introduce an additional
layer of information in the persona profiles (Salmi-
nen et al., 2019a). Such an approach could be used to
mirror each active information element in a “deeper
layer” that holds breakdown information. By show-
ing deeper information, it may be possible to curb the
tendency of personas to evoke stereotypical thinking.
The drawback of this approach is that it may re-
duce the empathy-benefits of persona (immersion, un-
derstanding) (Cooper, 2004), so that instead of be-
ing a believable person, the persona becomes a frag-
mented group of different people. To maintain the
credibility of the persona, a coherence of the whole is
needed. These perceptual questions are conceptually
linked to evaluation of the persona profiles, an area
that is critical for adoption and real use of personas
in organizations. Toward that end, our final research
question is PRQ9: How to evaluate the usefulness
and value generated by data-driven personas?
Finally, it is not immediately evident how to mea-
sure the quality of data-driven personas. For exam-
ple, how can their accuracy (in terms of correspon-
dence with the data) be verified? Is accuracy even
correlated with the perceived usefulness of the per-
sonas? In disentangling these questions, researchers
have mostly focused on the technical aspects of per-
sona quality (Chapman and Milham, 2006; Chapman
et al., 2008). Yet, there is a nascent stream of studies
focusing on persona perceptions (Marsden and Haag,
2016; Hill et al., 2017; Salminen et al., 2018d).
For example, Salminen et al. (2018c) developed a
Persona Perception Scale that lists several perceptual
constructs associated with the use of personas. From
this scale, at least the following ones could be per-
ceived important for evaluating persona quality: cred-
ibility, consistency, completeness, and clarity. In or-
der for personas to be useful, the persona users need
to perceive them as credible (i.e., trustworthy, reli-
able). Moreover, the information in the data-driven
persona profiles needs to be consistent (e.g., topics of
interest need to match the quotes), or else there is a
risk of confusion among the persona users. In turn,
if the personas are not complete (i.e., contain all the
necessary information that the persona user needs for
accomplishing their task), they can hardly be consid-
ered useful. Finally, information should be presented
clearly; for example, unclear titles or description for
the persona content sections are likely to cause con-
fusion among end users (Salminen et al., 2018d).
Data-driven personas of the future should be low-cost,
accurate, and accessible by small and large organiza-
tions with varying budgets and needs. However, many
challenges await before reaching this vision.
To investigate these challenges, we formulated
nine research questions that deal with various aspects
of automatic persona generation. Addressing these
questions, we believe, would result in major progress
toward creating high-quality personas from customer
The Future of Data-driven Personas: A Marriage of Online Analytics Numbers and Human Attributes
analytics data. This goal is impactful for real organi-
zations deploying personas for use cases such as prod-
uct development, design, and marketing.
This manuscript represents a call for action to re-
searchers interested in “humanizing” online analyt-
ics, encouraging contributions in methodological and
practical development of data-driven personas. We
expect that addressing the research questions pro-
posed here requires several years of active research,
with the potential of several new avenues of inquiry
in multiple domains. Data-driven persona creation is
an on-going research field with potential for both fo-
cused disciplinary and cross-disciplinary research in
Algorithms, HCI, Online Analytics, and so on.
Personas are also opening new research av-
enues for experiments in Computational Social Sci-
ence, particularly revealing end users’ subjective
perceptions and biases about the audience or user
groups (Hill et al., 2017; Salminen et al., 2018d). By
classifying personas according to their attributes (e.g.,
age, gender, ethnicity), it is possible conduct user
studies that examine how end users perceive and re-
spond to different personas. Another line of research
is to investigate the possibility of algorithmic bias in
the automatically generated personas.
While automatically generated personas may not
replace numbers in online analytics, they do pro-
vide intuitive descriptions of the customer base us-
ing quantitative data. In the APG system (Jung et al.,
2017), numbers remain available as raw data that can
be downloaded by the end users and as data break-
downs. Thus, data-driven personas can support de-
cision making by providing humanlike renderings of
numerical customer data, while providing an access
to the underlying raw data.
The advancements in machine learning and Web
technologies, combined with online analytics data,
show great promise for data-driven persona genera-
tion. With these novel methods, it becomes possi-
ble to bring personas in the reach of more decision
makers in more organizations, enhancing customer-
oriented decision making and democratizing personas
for all organizations, including corporations, small
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The Future of Data-driven Personas: A Marriage of Online Analytics Numbers and Human Attributes