A New Approach to Proportional Hazards Modeling for Estimating Customer Lifetime Value

TL;DR

This paper introduces a flexible proportional hazards model that leverages churn prediction to estimate customer lifetime value more accurately in contractual business settings.

Contribution

It presents a novel modeling approach that integrates churn models with proportional hazards to improve CLV estimation.

Findings

Enhanced CLV estimation accuracy in contractual contexts

Effective integration of churn models into lifetime value calculations

Potential for better business decision-making based on improved CLV estimates

Abstract

Estimating customer lifetime value (CLV or LTV) is extremely important for making better business decisions. The proposed flexible proportional hazards model allows an estimation of lifetime value in contractual settings. This approach takes advantage of a churn model, which is assumed to be available.