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

Sunghoon Kim, Ashley Stadler Blank, Wayne S. DeSarbo*, Jeroen K. Vermunt

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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.

Original languageEnglish
Number of pages22
JournalJournal of Classification
DOIs
Publication statusE-pub ahead of print - 2021

Keywords

  • CLASSIFICATION
  • Consumer dispersion
  • FANDOM
  • Geographic segmentation
  • IDENTIFICATION
  • IMPACT
  • LEVEL
  • Latent class analysis
  • MODELS
  • Multi-level clustering
  • Spatial heterogeneity
  • TEAM

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