An approach for clustering consumers by their top-box and top-choice responses

John Castura/ July 21, 2023/ Peer-reviewed Paper/ 0 comments

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

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Clustering consumers based on their hedonic responses

John Castura/ November 9, 2022/ Oral Presentation/ 0 comments

Consumers are diverse in their product perceptions. But within the consumer population there are often consumer segments whose product perceptions are relatively homogeneous. To discover these consumer segments, consumers’ product-related responses are submitted to a cluster analysis. The particular cluster analysis is chosen by the researcher. But the choice of clustering algorithm can have profound consequences on the clustering solution

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Clustering consumers based on product discrimination in check-all-that-apply (CATA) data

John Castura/ March 4, 2022/ Peer-reviewed Paper/ 0 comments

Consumers can be clustered based on their product-related check-all-that-apply (CATA) responses. We identify two paradoxes that can occur if these clusters are derived from conventional similarity coefficients. The first paradox is that clustering similar consumers can nullify within-cluster sensory differentiation of products. The second paradox is that consumers who check many attributes yet disagree can be clustered together, whereas consumers

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Analysis of sensory check-all-that-apply (CATA) data which includes the evaluation of a single ideal product

John Castura/ August 6, 2019/ Poster/ 0 comments

When evaluating samples in sensory tests, consumers are sometimes asked not only about real samples but also about imagined ideal products. Check-all-that-apply (CATA) questions are one way to understand consumers’ perceptions of products and their ideal product. We propose the following statistical analyses of consumer CATA data: (i) confidence intervals for head-to-head comparisons based on CATA data; (ii) panel (dis)agreement

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The Role of Imputation in Clustering BIB Data

Sara King/ July 28, 2014/ Tutorial/ 0 comments

Clustering consumer data reveals important information to help refine products for specific market segments. There are compelling reasons to use incomplete block designs to collect consumer data; however, this presents the challenge of dealing with missing data. The purpose of this workshop is to investigate the effect of different imputation techniques on the results of cluster analysis of balanced-incomplete-block (BIB)

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You know what you like, but what about everyone else? A case study on incomplete block segmentation of white-bread consumers

Sara King/ June 20, 2012/ Oral Presentation/ 0 comments

“One man’s meat is another man’s poison.” There will always be a wide range of consumer liking response across any product category. Cluster analysis can provide consumer segments based upon common liking that reflect underlying sensory preferences. To determine valid population segments requires a large sample of consumers. As the number of products tested by each consumer increases, experimental bias

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