Persona Design Method Based on Data Augmentation by Social Simulation

In the field of marketing, 'persona' is widely used as a method to support customer-oriented product and service design. However, in many cases, the persona design process is confined to the classification of customers based on limited attribute information projected from the past to the present using cross-sectional data. In this study, we propose a method, based on attribute information extended by social simulation, to improve the persona design process. The framework consists of the following procedures: 1) treat changes in the external environment explicitly and handle target customers' possible attribute changes (i.e., 'virtual life'); 2) classify customers based on a large quantity of attribute information supported and extended by social simulation. As a demonstration of the proposed method, this paper presents a specific case study that focuses on the customer asset situations. We conduct a simulation of customer asset situations at a future point in time, taking into account asset succession and price fluctuations of risky assets, based on individual questionnaire data concerning asset formation and withdrawal in retirement. We design personas using the conventional method and the proposed method, and we compare the two. The results show that the proposed method facilitates a) estimation of future changes in customer states, b) incorporation of possible future events in each persona, and c) consideration of future customer state changes in the state of classification of personas.