In this research, we examine uses in automatically generating personas from online data using qualitative methods.
The increased access to data and computational techniques enable innovations in the space of automated customer analytics, for example, automatic persona generation.
Automatic persona generation is the process of creating data-driven representations from user or customer statistics. Even though automatic persona generation is technically possible and provides advantages compared to manual persona creation regarding the speed and freshness of the personas, it is not clear (a) what information to include in the persona profiles and (b) how to display that information.
To query these aspects relating to the information design of personas, we conducted a user study with 38 participants. In the findings, we report several challenges relating to the design of automatically generated persona profiles, including usability issues, perceptual issues, and issues relating to information content.
Our research has implications for the information design of data-driven personas.
Salminen, J., Sengun, S., Jung, S.G., and Jansen, B. J. (2019) Design Issues in Automatically Generated Persona Profiles: A Qualitative Analysis from 38 Think-Aloud Transcripts. The ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR2019). Glasgow, UK. 10-14 March.