A Hidden Markov Model of Customer Relationship Dynamics: DSA ADS Course - 2023
DSA ADS Course - 2023
Discuss enabling marketers to dynamically segment their customer base and to examine methods by which the firm can alter long-term buying behavior.
Discuss dynamics of customer relationships using typical transaction data.
The proposed method/model permits not only capturing the dynamics of customer relationships, but also incorporating the effect of the sequence of customer-firm encounters on the dynamics of customer relationships and the subsequent buying behavior.
Discuss architecting, constructing and estimating a nonhomogeneous hidden Markov model to model the transitions among latent relationship states and effects on buying behavior. In the proposed model, the transitions between the states are a function of time-varying covariates such as customer-firm encounters that could have an enduring impact by shifting the customer to a different (unobservable) relationship state.
Discuss using a hierarchical Bayes approach to capture the unobserved heterogeneity across customers. Explain calibrating model in context of alumni relations using a longitudinal gift-giving data set.
Discuss probabilistically classifying the alumni base into three relationship states and estimate the effect of alumni-university interactions, such as reunions, on the movement of alumni between these states. Demonstrate improved prediction ability on a hold-out sample.