Creating user stereotypes for persona development from qualitative data through semi-automatic subspace clustering

TL;DR

This paper introduces a semi-automatic clustering method to create user stereotypes for personas from qualitative data, reducing manual effort and increasing consistency in user modeling.

Contribution

The study presents a novel clustering algorithm for persona development that automates part of the process and compares it empirically to manual and existing semi-automated methods.

Findings

The proposed method yields consistent results across different datasets.

It identifies the largest variances in user data effectively.

It reduces manual labor in persona creation.

Abstract

Personas are models of users that incorporate motivations, wishes, and objectives; These models are employed in user-centred design to help design better user experiences and have recently been employed in adaptive systems to help tailor the personalized user experience. Designing with personas involves the production of descriptions of fictitious users, which are often based on data from real users. The majority of data-driven persona development performed today is based on qualitative data from a limited set of interviewees and transformed into personas using labour-intensive manual techniques. In this study, we propose a method that employs the modelling of user stereotypes to automate part of the persona creation process and addresses the drawbacks of the existing semi-automated methods for persona development. The description of the method is accompanied by an empirical comparison with a manual technique and a semi-automated alternative (multiple correspondence analysis). The results of the comparison show that manual techniques differ between human persona designers leading to different results. The proposed algorithm provides similar results based on parameter input, but was more rigorous and will find optimal clusters, while lowering the labour associated with finding the clusters in the dataset. The output of the method also represents the largest variances in the dataset identified by the multiple correspondence analysis.