Extracting Summary Piles from Sorting Task Data
Journal of Marketing Research, Forthcoming
68 Pages Posted: 17 Aug 2016
Date Written: 2016
In a sorting task, consumers receive a set of representational items (e.g., products or brands) and sort them into piles such that the items in each pile “go together”. The sorting task is flexible in accommodating different instructions, and has been used for decades in exploratory marketing research in brand positioning and categorization. Yet, no general analytic procedures currently exist to analyze sorting task data without performing arbitrary conversions that influence the results and insight obtained. This manuscript introduces a flexible framework for analyzing sorting task data, as well as a proposed optimization approach to identify summary piles, which provide an easy way to explore associations consumers make among a set of items. Using two Monte Carlo simulations and an empirical application of single-serving snacks from a local retailer, we demonstrate that the resulting procedure is scalable, can provide additional insights beyond those offered by existing procedures, and requires mere minutes of computational time.
Keywords: Sorting Task, Categorization, Positioning, Optimization
Suggested Citation: Suggested Citation