A Hidden Markov Model of Customer Relationship Dynamics: DSA ADS Course - 2023

DSA ADS Course - 2023

This DSA ADS course is part of a series of courses that demonstrate how to use applied data science with high performance compute and high quality data to optimize decision making in real world scenarios.

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.