Structural topic modelling segmentation: a segmentation method combining latent content and customer context

Jorge E. Fresneda, Thomas A. Burnham, Chelsey H. Hill

Research output: Contribution to journalArticlepeer-review

Abstract

This research introduces a method for segmenting customers using Structural Topic Modelling (STM), a text analysis tool capable of capturing topical content and topical prevalence differences across customers while incorporating metadata. This approach is particularly suitable for contexts in which textual data is either a critical component or is the only data available for segmentation. The ability to incorporate metadata by using STM provides better clustering solutions and supports richer segment profiles than can be produced with typical topic modelling approaches. We empirically illustrate the application of this method in two contexts: 1) a context in which related metadata is readily available; and 2) a context in which metadata is virtually non-existent. The second context exemplifies how ad-hoc generated metadata can increase the utility of the method for identifying distinct segments.

Original languageEnglish (US)
Pages (from-to)792-812
Number of pages21
JournalJournal of Marketing Management
Volume37
Issue number7-8
DOIs
StatePublished - 2021

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Marketing

Keywords

  • Market segmentation
  • cluster analysis
  • social media
  • strategic marketing
  • structural topic modelling

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