A Survey of 15 Years of Data-Driven Persona Development
: Salminen Joni, Guan Kathleen, Jung Soon-Gyo, Jansen Bernard J.
Publisher: Taylor & Francis
: 2021
: International Journal of Human-Computer Interaction
: INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION
: INT J HUM-COMPUT INT
: 24
: 1044-7318
: 1532-7590
DOI: https://doi.org/10.1080/10447318.2021.1908670(external)
: https://www.tandfonline.com/doi/full/10.1080/10447318.2021.1908670(external)
: https://research.utu.fi/converis/portal/detail/Publication/56622282(external)
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.
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