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
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.
An, J., Kwak, H. and Jansen, B. J. (2016) Validating Social Media Data for Automatic Persona Generation. The Second International Workshop on Online Social Networks Technologies (OSNT-2016), 13th ACS/IEEE International Conference on Computer Systems and Applications AICCSA 2016. Agadir, Morocco. 29 November – 2 December.