Modeling complex longitudinal consumer behavior with dynamic Bayesian networks: an acquisition pattern analysis application

Longitudinal consumer behavior has been modeled by sequence analysis. A popular application involves Acquisition Pattern Analysis exploiting typical acquisition patterns to predict a customer's next purchase. Typically, the acquisition process is represented by an extensional, unidimensional sequence taking values from a symbolic alphabet. Given complex product structures, the extensional state representation rapidly evokes the state-space explosion problem. Consequently, most authors simplify the decision problem to the prediction of acquisitions for selected products or within product categories. This paper advocates the use of intensional state definitions representing the state by a set of variables thereby exploiting structure and allowing to model complex, possibly coupled sequential phenomena. The advantages of this intensional state space representation are demonstrated on a financial-services cross-sell application. A Dynamic Bayesian Network (DBN) models longitudinal customer behavior as represented by acquisition, product ownership and covariate variables. The DBN provides insight in the longitudinal interaction between a household's portfolio maintenance behavior and acquisition behavior. Moreover, it exhibits adequate predictive performance to support the financial-services provider's cross-sell strategy comparable to decision trees but superior to MulltiLayer Perceptron neural networks