Customer lifetime value (CLV) is a forecasted expectation of the future value of a customer to the firm. There are two customer behavioral components of CLV that represent a particular modeling challenge: 1) how many transactions we expect from a customer in the future, and 2) how likely it is the customer remains active. Existing CLV models like the Pareto/NBD are valuable managerial tools because they are able to provide forward-looking estimates of transaction patterns and customer churn when the event of a customer leaving is unobservable, which is typical for most noncontractual goods and services.
The CLV model literature has for the most part maintained its original assumption that the number of customer transactions follows a stable transaction process. Yet there are many categories of noncontractual goods and services where the stable transaction rate assumption is violated, particularly seasonal purchase patterns. CLV model estimates are further biased when there is an excess of customers with no repeat transactions.
To address these modeling challenges, within this thesis I develop a generalized CLV modeling framework that combines three elements necessary to reduce bias in model estimates: 1) the incorporation of time-varying covariates to model data with transaction rates that change over time, 2) a zero-inflated model specification for customers with no repeat transactions, and 3) generalizes to different transaction process distributions to better fit diverse customer transaction patterns. This CLV modeling framework provides firms better estimates of the future activity of their customers, a critical CRM application.