Inferring Market Structure with Aggregate Data: A Latent Segment Logit Approach

Abstract

In this paper, the authors introduce a “latent segment logit” (LSL) model that allows the identification of latent market segments when only macro-level time-series data (e.g., market share or sales, not individual choices) are available. The proposed model provides a paramorphic representation of market structure, based on the notion that “structure” implies heterogeneity in preferences and/or response to marketing mix elements. It assumes that independence of irrelevant alternatives (IIA) holds within latent segments (i.e., segments are homogeneous) but allows for heterogeneity across segments. Estimates for segment characteristics (including size, brand preferences, and sensitivity to marketing mix variables) are obtained by applying the model to aggregated longitudinal panel data. Validation tests are conducted on both the aggregated and disaggregated panel data. Aggregate validation demonstrates that the model is superior to standard market share models in terms of calibration and predictive fit. Disaggregated validation demonstrates that the latent segments recovered by the model account for much of the variation across household purchase histories, even though these data were not utilized in the estimation.