Creating and validating predictive personas for target marketing

Abstract

A persona is a widely used tool in the design field to help understand users’ needs, experiences, behaviors, and goals. Quantitatively creating personas is gaining importance for its greater representability, objectivity, and fast creative speed. However, quantitative personas built using traditional statistical methods may not reflect the marketing goals of decision-makers, and they mainly focus on categorizing existing users rather than predicting target customers. To address the research gap, this study proposes a guideline to create a predictive persona by using data-mining algorithms resulting in personas that can be validated. The proposed method aims to improve the accuracy and credibility of personas to effectively predict new customers for target marketing. We demonstrate a step-by-step guideline for creating the proposed predictive persona using real user data, validating its credibility, and comparing its predictive power against traditional quantitative personas. We thoroughly discuss the strengths and weaknesses of qualitative personas, traditional quantitative personas, and predictive personas while portraying suitable scenarios in which each type of persona is appropriate.