A Hidden Markov Model of Customer Relationship Dynamics

This research models the dynamics of customer relationships using typical transaction data. It permits the evaluation of the effectiveness of customer-brand encounters on the dynamics of customer relationships and the subsequent buying behavior. Our approach to modeling relationship dynamics is structurally different from existing approaches. In the proposed model, customer-brand encounters may have an enduring impact by shifting the customer to a different (unobservable) relationship state. We constructed and estimated a hidden Markov model (HMM) to model the transitions among latent relationship states and effects on buying behavior. This model enables to dynamically segment the firms customer base, and to examine methods by which the firm can alter the long-term buying behavior. We use a hierarchical Bayes approach to capture the unobserved heterogeneity across customers. We calibrate the model in the context of alumni relations using a longitudinal gift-giving dataset. Using the proposed model, we are able to probabilistically classify the alumni base into three relationship states, and estimate the marginal impact of alumni-university interactions on moving the alumni between these states. The application of the model for marketing decisions is illustrated using a what-if analysis of a reunion marketing campaign. Additionally, we demonstrate improved prediction ability on a validation sample.