An approach for clustering consumers by their top-box and top-choice responses
Cluster analysis is often used to group consumers based on their hedonic responses to products. We give a motivating example in which conventional cluster analyses converge on a solution where consumers do not agree on which products they like. We show why this occurs. We state a goal: to group together consumers who have a shared opinion of which products are delightful and which products are not delightful, apart from consumers who have a different opinion. To meet this goal, we code consumers’ hedonic responses in ways inspired by top-k box analysis, then cluster consumers using b-cluster analysis. For comparison, we cluster consumers using two conventional methods. We interpret each cluster by focusing on which product(s) the cluster accepts and whether a large proportion of cluster members are aligned in accepting these products. Solutions from b-cluster analysis based on top-k box-inspired codings met our goal better than conventional approaches, indicating that these methods deserve further study.
Castura, J.C., Meyners, M., Pohjanheimo, T., Varela, P., & Næs, T. (2023). An approach for clustering consumers by their top-box and top-choice responses. Journal of Sensory Studies, e12860.