A Survey of 15 Years of Data-Driven Persona Development

Abstract

Data-driven persona development unifies methodologies for creating robust personas from the behaviors and demographics of user segments. Data-driven personas have gained popularity in human-computer interaction due to digital trends such as personified big data, online analytics, and the evolution of data science algorithms. Even with its increasing popularity, there is a lack of a systematic understanding of the research on the topic. To address this gap, we review 77 datadriven persona research articles from 2005-2020. The results indicate three periods: (1) Quantification (2005)(2006)(2007)(2008), which consists of the first experiments with data-driven methods, (2) Diversification (2009-2014), which involves more pluralistic use of data and algorithms, and ( 3) Digitalization (2015-present), marked by the abundance of online user data and the rapid development of data science algorithms and software. Despite consistent work on data-driven personas, there remain many research gaps concerning (a) shared resources, (b) evaluation methods, (c) standardization, (d) consideration for inclusivity, and (e) risk of losing in-depth user insights. We encourage organizations to realistically assess their data-driven persona development readiness to gain value from data-driven personas.

Personas for Artificial Intelligence (AI) an Open Source Toolbox

“…The classical approach for data collection for personas' development is using qualitative methods such as ethnographic interviews, open-ended survey questions, or contextual inquiries and field studies [24], [59], [53]. In the last 15 years, a collection of large amounts of quantitative data (for example from web analytics, social media, online customer data, and online surveys) has become popular and utilising these data together with techniques such as machine learning has led to so-called digital data-driven persona development mainly used in marketing and customer research [74]. So-called hybrid personas are created by utilising quantitative data from online analytics together with qualitative insights [70].…”

Developing virtual fingerprint: identifying trustworthy interaction behaviors of web users

“…Machine learning techniques focus on solving the problem of modeling and testing human behavior (Martín et al, 2021). Based on the analysis of website log data, Gao et al (2017) More detailed is the behavioral analysis and modeling using machine learning techniques driven by interactor preferences (Salminen et al, 2021), with the aim of targeted optimization of websites and recommender systems (Cami et al, 2018), which has been widely adopted in industry and research communities. Notably, these studies are more about the behavior of groups of interactors rather than individuals, and the unique behavior patterns of each interactor are buried in the model training .…”

Data-Driven Image Interaction-Based Software Infrastructure for Graphic Design Research and Implementation

“…e artistic effects presented by applying the surface according to different needs are rich and diverse and bring different feelings to the audience [24]. For example, if the faces are arranged parallel or perpendicular to each other, it will give the audience a straight, dull, and stable feeling; while when the faces are set in a free curve by inputting variables, they can become beautiful, smooth, and dynamic, giving the audience a visual feeling of natural form, free expression, and rich changes.…”