Market segmentation via mixtures of constrained factor analyzers

In this paper we introduce a procedure for the parameter estimation of mixtures of factor analyzers, which maximizes the likelihood function in a constrained parameter space, to overcome the well known issue of singularities and to reduce spurious maxima of the likelihood function. A Monte Carlo study of the performance of the algorithm is provided. Finally the proposed approach is employed to provide a market segmentation, to model a set of quantitative variables provided by a telecom company, and related to the amount of services used by customers.