Top-Down vs Bottom-Up Approaches to User Segmentation: The Best of Both Worlds

Abstract

Within the fast-paced world of Lean and Agile software development, researchers are always on the lookout for methods that allow for rapid data gathering and analysis, while still yielding robust design recommendations. This paper considers the use cases for “top-down” hypothesis testing and “bottom-up” statistical cluster analysis, within survey research on user behaviors and needs. Comparing the application of each method on the same data set shows that statistical cluster analysis can create rich data-driven personas that inform user needs and preferences and provide design teams with insightful recommendations in a short amount of time. This method also increases the potential for gaining unexpected information from quantitative data—an achievement typically viewed as within the purview of qualitative research alone. Using both approaches to the same dataset allowed us to both answer specific questions for the design team, and learn new insights from the bottom up.