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

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.

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