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:
- Quantification (2005–2008), which consists of the first experiments with data-driven methods,
- Diversification (2009–2014), which involves more pluralistic use of data and algorithms, and
- 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
- shared resources
- evaluation methods
- consideration for inclusivity
- 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.
Salminen, J., Guan, K., Jung, S. G., and Jansen, B. J. (2021) A Survey of 15 Years of Data-Driven Persona Development, International Journal of Human-Computer Interaction.