Personas are a well-known technique in human-computer interaction. However, there is a lack of rigorous empirical research evaluating personas relative to other methods.
In this 34-participant experiment, my colleagues and I compare a persona system and an analytics system, both using identical user data, for efficiency and effectiveness for a user identification task.
Results show that personas afford a faster task completion than the analytics system, as well as outperforming analytics with significantly higher user identification accuracy.
Persona is a common human-computer interaction technique for increasing stakeholders’ understanding of audiences, customers, or users.
Applied in many domains, such as e-commerce, health, marketing, software development, and system design, personas have remained relatively unchanged for several decades.
However, with the increasing popularity of digital user data and data science algorithms, there are new opportunities to progressively shift personas from general representations of user segments to precise interactive tools for decision-making.
In this vision, the persona profile functions as an interface to a fully functional analytics system.
With this research, we conceptually investigate how data-driven personas can be leveraged as analytics tools for understanding users.
We present a conceptual framework consisting of (a) persona benefits, (b) analytics benefits, and (c) decision-making outcomes.
We apply this framework for an analysis of digital marketing use cases to demonstrate how data-driven personas can be leveraged in practical situations.
We then present a functional overview of an actual data-driven persona system that relies on the concept of data aggregation in which the fundamental question is defining the unit of analysis for decision making.
The system provides several functionalities for stakeholders within organizations to address this question.
Personas provide alternative to numbers. Therefore, you can use personas to present your online analytics data as people instead of nameless, faceless target groups. This can help decision makers to “get into the shoes” of customers, offering a more immersive understanding of the customers than the “cold”, raw numbers. This is best paraphrased as “personas give faces to data.”
