A latent variable model for market segmentation
Francesca Greselin, Salvatore Ingrassia · BOA (University of Milano-Bicocca) · 2013
We model a set of quantitative variables provided by a telecom company, and related to the amount of traffic of customers. Motivated by the high correlation observed among the variables, we employ mixtures of factor analyzers, which -at the same time- perform dimension reduction. The main purpose is to assess whether, within the same traffic plan, customers have a unique behavior in terms of traffic extent, or if different latent structures can be discovered. Any significative difference could be an important information for marketing, for instance to analyze patterns of pre-churn customers, or to identify specific targets. We implement a data-driven constrained approach for model estimation, to reduce spurious local maximizers and avoid singularities in the EM algorithm. First results highlight a non-unique latent structure for customers within the same traffic plan.