Extracting Summary Piles from Sorting Task Data

Journal of Marketing Research, Forthcoming

Georgetown McDonough School of Business Research Paper No. 2824518

68 Pages Posted: 17 Aug 2016

See all articles by Simon J. Blanchard

Simon J. Blanchard

Georgetown University - Robert Emmett McDonough School of Business

Daniel Aloise

Universidade Federal do Rio Grande do Norte (UFRN)

Wayne S. DeSarbo

Pennsylvania State University

Date Written: 2016

Abstract

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

Blanchard, Simon J. and Aloise, Daniel and DeSarbo, Wayne S., Extracting Summary Piles from Sorting Task Data (2016). Journal of Marketing Research, Forthcoming, Georgetown McDonough School of Business Research Paper No. 2824518, Available at SSRN: https://ssrn.com/abstract=2824518

Simon J. Blanchard

Georgetown University - Robert Emmett McDonough School of Business ( email )

3700 O Street, NW
Washington, DC 20057
United States

Daniel Aloise

Universidade Federal do Rio Grande do Norte (UFRN) ( email )

PO Box 1524
Natal-RN, 59078970
Brazil

Wayne S. DeSarbo (Contact Author)

Pennsylvania State University ( email )

University Park
State College, PA 16802
United States

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