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Your search keyword '"Wayne S. DeSarbo"' showing total 3 results
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3 results on '"Wayne S. DeSarbo"'

3. The spatial representation of consumer dispersion patterns via a new multi-level latent class methodology

Author

Jeroen K. Vermunt, Wayne S. DeSarbo, Sunghoon Kim, Ashley Stadler Blank, and Department of Methodology and Statistics

Subjects
Computer science, IMPACT, Multi-level clustering, LEVEL, MODELS, Library and Information Sciences, computer.software_genre, Promotional mix, CLASSIFICATION, Geographic segmentation, Mathematics (miscellaneous), Benchmark (surveying), Latent class analysis, Statistical dispersion, FANDOM, Cluster analysis, Consumer behaviour, TEAM, IDENTIFICATION, Aggregate (data warehouse), Consumer dispersion, Latent class model, Survey data collection, Spatial heterogeneity, Psychology (miscellaneous), Data mining, Statistics, Probability and Uncertainty, computer
Abstract

Consumer dispersion analysis divides aggregate markets into smaller geographic units that marketers can target with their promotional mix. However, dispersion patterns are not always contiguous. Using survey data from National Football League (NFL) fans, we introduce a new hierarchical expectation-maximization (EM) bi-level clustering model that iteratively classifies both teams and fans (nested within teams) based on the spatial heterogeneity of fans in terms of both distance and direction. The proposed multi-level latent class model with a variable number of classes at the lower level outperforms benchmark models in a Monte Carlo simulation study and points to three non-contiguous team segments with a varying number of fan group vectors in the NFL application. We present these results in two-dimensional consumer dispersion maps and report corresponding differences in consumer behavior.

Published
2022

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