Research: Predicting Customer Lifetime Value and Optimizing Churn Interventions in Subs...

Research question: How can predictive modeling of customer lifetime value be leveraged to design effective churn interventions in subscription direct-to-consumer businesses?

Background & Motivation: Subscription-based direct-to-consumer (D2C) businesses have surged in popularity, but high customer churn rates threaten profitability. Accurate customer lifetime value (CLV) estimation and proactive churn intervention are critical for sustaining growth and maximizing marketing ROI.

Research Gap & Question: While CLV modeling and churn prediction are individually studied, there is limited empirical work integrating these approaches to guide targeted, cost-effective churn interventions in real-world D2C contexts.

Approach & Expected Contribution: This project will systematically review current literature, develop predictive models (e.g., survival analysis, machine learning), and evaluate how CLV insights can inform personalized retention strategies using transactional and behavioral datasets from a subscription D2C company. The research will assess intervention effectiveness through experimental or quasi-experimental methods.

Why it Matters: By bridging predictive analytics with actionable marketing strategies, this research can help subscription firms allocate resources efficiently, reduce churn, and improve long-term profitability, offering both academic and practical contributions to marketing science.