Ideas for Persona Research Using Quantitative User Analytics

What Are Personas and Why Should We Care?

Personas, commonly applied in human-computer interaction (HCI) (Cooper, 1999), design (Aoyama, 2007), and business domains such as marketing and sales (Salminen et al., 2018), are fictional depictions of end-users, patients, customers, or other groups of interest (Cooper, 1999). Personas are cited to have many benefits, at least the following persona benefits mentioned in the literature:

  • BENEFIT 1: Personas convey end-users’ needs and requirements (Aoyama, 2007).
  • BENEFIT 2: Personas alleviate decision-makers’ self-referential bias (Anvari et al., 2019).
  • BENEFIT 3: Personas enable thinking of end-users even when none are physically present (Pruitt & Grudin, 2003).
  • BENEFIT 4: Personas give a human face to analytics data (Jansen et al., 2020; S.-G. Jung et al., 2020),
  • BENEFIT 5: Personas humanize nameless and faceless customer segments (Chapman et al., 2008).
  • BENEFIT 6: Personas give inspiration for design and other creative tasks (Nielsen, 2019).
  • BENEFIT 7: Personas help compare end-users (S. Jung et al., 2019), facilitating the discovery of key differences among the user base and the prioritization of end-user needs for system development (Qian et al., 2021).

Persona profiles show relevant information about end-users or customers (Nielsen et al., 2015). From a marketing perspective, personas aim to increase organizational performance via a heightened level of market orientation (Han et al., 1998), described also as user-centric design (Salminen, Şengün, et al., 2021).

For managers and other stakeholders dealing with customer-centric decision making, personas are easily digestible snapshots of end-users, audiences, or customers that can be used for decision making about product development, targeting, and needs prioritization.

Essential Concepts Relating to Quantitative Personas

This post discusses how personas can be effectively combined with the concept of analytics, i.e., the use of end-user data for drawing insights into human factors (Moran, 1981). We start by defining the key concepts, and we then explain our approach to infusing personas with user analytics, which we denote as Persona Analytics (PA). First, we are listing some crucial concepts.

  • Persona Analytics. In prior research (S. Jung et al., 2021a, 2021b), PA is defined as the systematic measurement of behaviors and interactions of persona users engaged with interactive persona systems; it refers to how researchers investigate the behaviors of persona users. In turn, examining persona users’ engagement with personas can generate vital insights for persona science and the design of personas and persona systems that better serve stakeholders’ information needs about end-users or customers. We refer to users when we mean stakeholders that use personas for decision making (e.g., designers, software developers, marketers). For other terminology, we refer to end-users when we mean people who personas portray.
  • Data-Driven Personas. Personas are increasingly being enriched with quantitative data (Salminen, Guan, Jung, et al., 2020), and their creation is partially or completely carried out by algorithmic processes, which is referred to as data-driven persona development (McGinn & Kotamraju, 2008). When using quantitative data for creating personas, the personas approach other analytics systems. Although quantitative personas were first created within software requirements engineering (Aoyama, 2005, 2007), the concept of data-driven personas was introduced by McGinn and Kotamraju (McGinn & Kotamraju, 2008) and later deployed by others (Kolbeinsson et al., 2021; Korsgaard et al., 2020; Miaskiewicz & Luxmoore, 2017; Spiliotopoulos et al., 2020; Watanabe et al., 2017). While data orientation has remained a consistent theme in the literature [16,17,32,9,10,50,51], three trends contribute to the rise of algorithmically generated personas (Jansen et al., 2020; Salminen, Guan, Jung, et al., 2020): (1) availability of user and customer data from online analytics and social media platforms; (2) democratization of data science tools and algorithms that enable automated persona generation; and (3) web technologies that remove the limitations of static personas via interactive user interfaces. These trends denote a shift from “flat file” personas into dynamic “full-stack personas” that update automatically and are traceable to individual user-level data (Jansen et al., 2020).
  • Interactive Persona Systems. Interactive persona systems (An, Kwak, Salminen, et al., 2018; Mijač et al., 2018; Salminen, Guan, Jung, et al., 2020) are interactive user interfaces (UI) that display persona profiles. This UI can, but not necessarily always, be accessed via web browsers (S. Jung et al., 2017, 2018; S.-G. Jung et al., 2018, 2020). The benefits of web technologies are their broad applicability and accessibility. Personas served via the web can be accessed using any device that supports web browsing. Supporting technologies, such as user account management, can be integrated with relative ease using standard libraries and best practices. Interactivity refers to users performing various actions on the personas, such as analyzing information on gender distributions, refreshing the persona quotes, filtering the quotes by sentiment and topic (Salminen, Jung, & Jansen, 2020), predicting a persona’s interest for a given topic (An, Kwak, Jung, et al., 2018; An, Kwak, Salminen, et al., 2018), and engaging in dialogue (J. Li et al., 2017; Liao & He, 2020).

Call for Persona Science

Persona research needs a strong empirical orientation to produce knowledge that is believable and can truly expand the boundaries of personas practice and theory and add to a coherent understanding of the persona user.

Advocates of the scientific method in persona research (Anvari et al., 2015, 2017; Chapman et al., 2008; Chapman & Milham, 2006; Grudin, 2006) have continuously mentioned the lack of empirical experiments and quantitative measurements as a bottleneck for progress in terms of theory and practice. To this end, persona science deals with real user behavior and formulating theories that are relevant to the design of personas.

The focus in these efforts lies in the study of the persona users, which is achieved by measuring user behavior of persona users. Therefore, persona science relies on the use of empirical scientific methods, such as experiments, to produce robust and generalizable information about persona creation, evaluation, use, and impact.

Carrying out persona science implies not only collecting data and conducting research on personas but also devising theories that explain the data and guide further data collection.  In practice, PA can assist in designing layouts, features, and information content in persona profiles. To achieve these benefits, it is necessary to incorporate analytics into personas, so that the interaction between the users and can be recorded.

Examples Variables to Study

Persona science necessitates progress on all fronts, eyeing on long-term theory formulation but also investing in short-term returns through the use of empirical methods. Persona science can contribute to a much-needed transition beyond the general claims that “personas work” or “personas do not work”, or the repetition of their “benefits” and “problems,” into systematically examining the conditions where the effects emerge.

Many human factors have not been investigated in persona studies. Based on case studies, variables of special interest include at least the following:

  • User’s level of experience (Salminen, Jung, Santos, et al., 2020),
  • Task type that personas assist in carrying out (Anvari et al., 2015),
  • Job role of the person using the personas (Nielsen et al., 2017), and
  • Culture of the organization employing the personas (Nielsen, 2010).

Investigation of combination of these human factors would be based on specific research objectives. First, the effect of users’ experience with personas on behaviors; this tends to be reported in persona studies but not included as a variable. How does novice persona users’ use of personas differ from more experienced users? Can the behaviors of more experienced users be used for guiding the novice users to learn to use personas more efficiently?

Moreover, task type of persona deployment is often reported but rarely varied or controlled – most typically, only one task type is deployed and in only one empirical setting, without repetition to achieve robustness. As a result, we do not know what kind of personas are ideal for the various task types and if users approach the personas differently based on the task type.

Similarly, comparison among different job roles and organizational units are rarely conducted, even though it is common sense that a person’s job position would greatly affect how they use personas to support their work. Studies tend to mention “designers” but looking deeper into these users’ job positions, it is revealed that they work in multiple departments, have multiple different perspectives to the end-user, and require much different information for their decision making.

Overall, systematic analysis of these variables in experimental studies can produce long-lasting, consistent, and robust knowledge on personas and their users, extending the scientific boundaries and impact of persona research.

Finally, there is evidence on cultural effects related to personas use (Salminen, Jung, Santos, et al., 2021), but there is no adequate understanding of how the cultural match between the shown personas and the users mediate the interaction and whether personas themselves can help bridge cultural gaps for design.

Contributions to Interactive Persona Systems and Human-Computer Interaction

Scientific developments in data-driven personas and interactive persona systems have been described as transformational (Mijač et al., 2018), multiple opportunities for using personas independently or as parts of intelligent systems can be envisioned. We highlight five such opportunities for system development around persona analytics.

  1. Interaction techniques and multimedia (e.g., persona chat/dialogue systems (Chu et al., 2018), video, AI agents (Salminen, Rao, Jung, et al., 2020)…) could be incorporated into persona systems to serve various end-user needs (Salminen, Jansen, An, et al., 2019).
  2. New techniques for comparing personas by design goal metrics, such as diversity (Salminen et al., 2018) and inclusivity (Goodman-Deane et al., 2018), could be added.
  3. Integrations into an external systems to enable persona-based recommendations (T. Li et al., 2019), content management, and customer relationship management, as well as facilitating online advertising (Salminen, Jung, & Jansen, 2019) via application programming interfaces (APIs) (S. Jung et al., 2018).
  4. Providing explainability, transparency, and context, which are important when applying algorithms for persona creation (Salminen, Jung, & Jansen, 2020; Salminen, Santos, Jung, et al., 2019).
  5. Using interactive systems can be used to drill down to the persona information and make quantitative predictions (An, Kwak, Salminen, et al., 2018).


While technology introduces novel opportunities for user-to-persona interaction, at the same time, these trends create an opportunity for better understanding of how persona users, such as designers, software developers, and marketers interact with personas. This better understanding of persona user behavior can lead to substantial advances in persona science (i.e., the academic study of personas and their usage), but it requires effective implementation of measurement.

The lack of empirical persona user research has been noted in academic literature (Marsden & Haag, 2016; Salminen, Guan, Jung, et al., 2020; Salminen, Jung, Chhirang, et al., 2021). The unifying factor behind these possibilities is the need for understanding the persona user behavior towards rigorous empirical discoveries, which requires measurement of engagement and interactions with personas. This capability is provided by PA.


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