Partitioning methods outperform hierarchical methods for clustering consumers in preference mapping

Wajrock, S.; Antille, Nicolas; Rytz, Andréas; Pineau, Nicolas; Hager, Christopher

doi:10.1016/j.foodqual.2008.06.002

Cited by 43 publications

(23 citation statements)

References 28 publications

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“…The k-means clustering, as the fuzzy clustering (c-means), are partitioning methods that may be considered as non-probabilistic versions of the model-based methods. In the preference mapping context, Wajrock, Antille, Rytz, Pineau, and Hager (2008) claimed that the partitioning methods outperform the hierarchical methods. However, all these procedures are based on the Euclidean distance as dissimilarity index.…”

Section: The Clustering Of Variables Around Latent Variables (Clv) Mementioning

confidence: 99%

Segmentation of consumers in preference studies while setting aside atypical or irrelevant consumers

Vigneau

Qannari

Navez

et al. 2016

Food Quality and Preference

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Section: The Clustering Of Variables Around Latent Variables (Clv) Mementioning

confidence: 99%

Segmentation of consumers in preference studies while setting aside atypical or irrelevant consumers

Vigneau

Qannari

Navez

et al. 2016

Food Quality and Preference

View full text Add to dashboard Cite

“…In many consumer studies, clustering consumers based on liking/acceptability often yields nondistinct clusters where the cluster members overlap when they are plotted in a biplot (Young et al 2004;Carbonell et al 2008;Wajrock et al 2008). For easier understanding and future use (e.g., ingredient optimization), the results of a clustering analysis often are presented as group means for each of the clusters Sinesio et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Statistical Package Clustering May Not Be Best for Grouping Consumers to Understand Their Most Liked Products

Yenket

Chambers

Johnson

2011

Journal of Sensory Studies

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Ensuring that new products satisfy specific groups of consumers can impact successful product development. In sensory studies, cluster analysis has been used to segment consumers. Researchers often analyze mean values of products for consumer segments, presuming that the segmented consumers like or dislike similar products. This study investigates how well most/least liked products match for individual members of clusters using various cluster methods in two sensory studies. Four statistical package clustering (SPC) methods were used with hedonic data and data transformed to ranks. Next, the products most frequently rated/ranked highest in each cluster were examined. Four manual clustering groups were extracted and compared with the results of the SPC methods. Standard SPC was not found to separate consumers appropriately to understand their ranking/rating of most/least liked products. For these data, additional manual clustering was necessary to produce consumer cluster segments where the consumers within each group had the same highest/lowest scoring products. PRACTICAL APPLICATIONS Statistical package clustering (SPC) is a common method for determining consumer clusters, but it may not be the best method for separating consumers or for understanding their most or least liked products. The findings from this research show that the assumption that cluster analysis will produce clusters containing consumers who have the same most or least liked products is false. This is important because it shows that clustering consumers using typical SPC may not produce the homogeneous segments that researchers would like to obtain. This can impact further analyses of data for product optimization and preference mapping. SPC with further manual clustering is recommended for more homogeneous segments. In addition, new SPC methods should be developed that generate cluster segments that are more homogeneous.

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“…Segmentation continues to be a timely research topic in marketing, operations research (e.g., Buratto et al, 2006;Zhang and Bell, 2007;Bertsimas and Mersereau, 2007;Boztug and Reutterer, 2008;Mizuno et al, 2008), and related disciplines, such as tourism Dolnicar and Grün, 2008;Füller and Matzler, 2008;Tkaczynski et al, 2009) and food preferences (Westad et al, 2004;Rybowska and Babicz-Zielinska, 2007;Hottenstein et al, 2008;Wajrock et al, 2008;Berenguer et al, 2009). Within the context of tourism, for example, Dolnicar and Grün (2008, p. 63) odemographic profiles, who are profitable, who could easily be reached with marketing communication messages, who match the strengths of the tourism destination or business, and whose needs are not catered for by major competitors."…”

Section: Introductionmentioning

confidence: 99%

Amalgamation of partitions from multiple segmentation bases: A comparison of non-model-based and model-based methods

Andrews

Brusco

Currim

2010

European Journal of Operational Research

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Partitioning methods outperform hierarchical methods for clustering consumers in preference mapping

Cited by 43 publications

References 28 publications

Segmentation of consumers in preference studies while setting aside atypical or irrelevant consumers

Segmentation of consumers in preference studies while setting aside atypical or irrelevant consumers

Statistical Package Clustering May Not Be Best for Grouping Consumers to Understand Their Most Liked Products

Amalgamation of partitions from multiple segmentation bases: A comparison of non-model-based and model-based methods

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