A Survey of 15 Years of Data-Driven Persona Development

• Published in: International journal of human-computer interaction 2021-11, Vol.37 (18), p.1685-1708
• Main Authors: Salminen, Joni, Guan, Kathleen, Jung, Soon-Gyo, Jansen, Bernard J.
• Format: Article
• Language: English
• Subjects: Algorithms; Data science; Digital computers; Digitization; Evolutionary algorithms; Human-computer interaction; Personal development; Standardization
• ISSN: 1044-7318; 1532-7590; 1044-7318
• DOI: 10.1080/10447318.2021.1908670

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 data-driven persona research articles from 2005-2020. The results indicate three periods: (1) Quantification (2005-2008), 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.