A Template for Data-Driven Personas: Analyzing 31 Quantitatively Oriented Persona Profiles

Following the proliferation ofpersonified big data and data science algorithms, data-driven user personas (DDPs) are becoming more common in persona design.However, the DDP templates are seemingly diverse and frag-mented, prompting a need for a synthesis of the information included in these personas. Analyzing 31 templates for DDPs, we find that DDPs vary greatly by their information richness, asthe most informative layout hasmore than 300% more information categories than the least informative layout. We also find that graphical complexity and information richness do not necessarily correlate. Fur-thermore, the chosen persona development methodmay carry over to the infor-mation presentation, with quantitative data typically presented as scores, metrics, or tables and qualitative data as text-rich narratives. We did not find one “general template” for DDPs and defining thisis difficultdue to the variety of the outputs of different methods as well as differentinformation needs of the persona users.