Model Based Segmentation with Continuous Variable Selection

We develop a model based segmentation approach that accommodates and exploits heterogeneous data. A finite mixture regression model is extended with variable selection abilities through likelihood penalization. This approach merges simultaneous estimation of a finite mixture model based on the EM algorithm with continuous variable selection into a single feasible procedure. The result is a flexible and powerful modeling algorithm that is able to deal with todays complexity and high-dimensionality of datasets. The model combines the value of mixture modeling and continuous feature selection resulting in a synergy of their advantages. The flexibility allows for finding groups of related observations while selecting the optimal subset of variables within these groups independently. First, the model is applied on a heterogeneous population of individuals. We succeed in identifying four segments containing customers with varying desirable characteristics and behavior making them valuable for the company. Two segments with less desirable properties are revealed. The results provide a foundation for a more efficient targeted marketing approach in comparison to treating the population as a whole. Second, we use a simulation to study performance and to display the advantages of this approach. The results indicate that extending a finite mixture model with variable selection abilities yields a powerful tool. Good performance is observed in terms of selecting the correct subset of variables to include while accurately estimating the effects of these variables. The model excels in high-dimensional settings where a relatively large amount of variables are of interest.