ID3P: Iterative Data-Driven Development of Persona based on Quantitative Evaluation and Revision

Personas are fictional characters used to understand users' requirements. Many researchers have proposed persona development methods from quantitative data (data-driven personas development). However, it is not assumed that personas in these works are used continuously and these personas cannot reflect on unpredictable changes in users. It is difficult to plan reliable strategies in a web service because users' preference dynamically changes. To develop more suitable personas for decision-making in a web service, this paper proposes Iterative Data-Driven Development of Personas (ID3P). In particular, to detect an unpredictable change in users' characteristics, our proposal includes an iterative process where the personas are quantitatively evaluated and revised in each iteration. Moreover, it provides a quantitative evaluation of business strategies based on GQM+Strategies and personas. To verify our proposal, we applied it to Yahoo!JAPAN's web service called Netallica.