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Journal of Classification
Erschienen in: Journal of Classification 2/2022

08.09.2021

The Spatial Representation of Consumer Dispersion Patterns via a New Multi-level Latent Class Methodology

verfasst von: Sunghoon Kim, Ashley Stadler Blank, Wayne S. DeSarbo, Jeroen K. Vermunt

Erschienen in: Journal of Classification | Ausgabe 2/2022

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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.
Vorheriger Artikel Editorial: Journal of Classification Vol. 39-2
Nächster Artikel Erratum to: The Spatial Representation of Consumer Dispersion Patterns via a New Multi-level Latent Class Methodology

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Fußnoten
1
We focus on latent class models and do not consider clustering techniques. Comparing latent class models to non-latent class clustering techniques presents a methodological challenge for the following reasons: 1) we are not aware of any clustering techniques designed to accommodate bi-level data; 2) we are not aware of any past research to inform which clustering techniques would provide similar versus different results for bi-level data; 3) past research documents the fact that different clustering techniques can provide different results, which makes it difficult to select an appropriate clustering technique; and 4) while we can specify distributional forms for latent class models, clustering techniques do not allow us to do so—so we lack comparable model selection criteria, which makes it difficult to select the optimal number of clusters or directly compare results to a latent class model.
 
2
We can obtain the log-likelihood of the team-level BIC for all competing models by summing the log-likelihood value of each team segment that includes fan group vectors.
 
3
The multi-level latent class model in Vermunt (2003, 2008) does not force hard partitioning of fans nested within teams when segmenting teams. However, given that fans of a team are usually assigned to the same team segment in most applications, adding the hard-partitioning constraint to the model in Vermunt (2003, 2008) makes sense.
 
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Metadaten
Titel
The Spatial Representation of Consumer Dispersion Patterns via a New Multi-level Latent Class Methodology
verfasst von
Sunghoon Kim
Ashley Stadler Blank
Wayne S. DeSarbo
Jeroen K. Vermunt
Publikationsdatum
08.09.2021
Verlag
Springer US
Erschienen in
Journal of Classification / Ausgabe 2/2022
Print ISSN: 0176-4268
Elektronische ISSN: 1432-1343
DOI
https://doi.org/10.1007/s00357-021-09398-1

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