Extracting Summary Piles from Sorting Task Data

Abstract

In a sorting task, consumers receive a set of representational items (e.g., products, 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. However, no general analytic procedures yet exist for analyzing sorting task data without performing arbitrary transformations to the data that influence the results and insights obtained. This manuscript introduces a flexible framework for analyzing sorting task data, as well as a new 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, the authors demonstrate that the resulting procedure is scalable, can provide additional insights beyond those offered by existing procedures, and requires mere minutes of computational time.

Topic modeling in marketing: recent advances and research opportunities

“…While the former three represent about 89% of publications, the latter four comprise of 7 papers. In general, 10 of 61 papers were published in (mostly quantitative) marketing journals, of which 5 can be assigned to sales / retailing (Blanchard et al 2017;Hruschka 2014;Jacobs et al 2016;Schröder 2017;Trusov et al 2016), 3 to online textual consumer reviews and services research (Büschken and Allenby 2016;Calheiros et al 2017;Tirullinai and Tellis 2014), 1 to social media (Song et al 2017), and 1 to research in marketing literature (Amado et al 2017). Furthermore, we transferred the sum (numbers), and the relative importance (color) of the methodological strategies exerted by scholars into a matrix (Table 6) to better detect patterns and predict trends in research.…”

Locational Choices: Modeling Consumer Preferences for Proximity to Others in Reserved Seating Venues

“…Even without any such modification and for modest problem sizes, solving the problem in Equation 15 to a global optimum is difficult and we proposed a greedy heuristic to illustrate the problem. Further research could leverage the literature on metaheuristics to generate algorithmic approaches that in turn might produce sufficiently good solutions in real time (e.g., Variable Neighborhood Search; see Blanchard, Aloise, and DeSarbo 2017; Mladenović and Hansen 1997).…”

Incorporating Consumer Product Categorizations into Shelf Layout Design

“…Approaches seeking the shelf layout that best matches an average of internal categorizations have two drawbacks. First, they fail to capture the heterogeneity in the underlying individual internal categorizations (Blanchard et al 2017). 5 Second, their goal is matching layout with internal categorization rather than stimulating ultimate behavior.…”