Forecasting Financial Products Acquisition via Dynamic Segmentation: An Application to the Italian Market

Abstract

The topic of market segmentation is still one of the most pervasive in marketing. Among clustering techniques, finite mixture models have gained recognition as a method of segmentation with several advantages over traditional methods; one variant of finite mixture models – the latent class (LC) model – is probably the most popular. The LC approach is innovative and flexible, and can provide suitable solutions to several problems regarding the definition and development of marketing strategies, because it takes into account specific features of the collected data, such as their scale of measure (often ordinal or categorical, rather than continuous), their hierarchical structure and their longitudinal component. Dynamic segmentation is of key importance in many markets where it is unrealistic to assume stationary segments due to the dynamics in consumers' needs and product choices. In this paper, a mixture latent class Markov model is proposed to dynamically segment Italian households with reference to financial products ownership. The mixture approach is compared with the standard one in terms of its ability to forecast customers' behaviour in the reference market.