Paper: arXiv 2304.03038

Authors: Greig Cowan, Salvatore Mercuri, Raad Khraishi

Abstract

Understanding customer lifetime value is key to nurturing long-term customer relationships, however, estimating it is far from straightforward. In the retail banking industry, commonly used approaches rely on simple heuristics and do not take advantage of the high predictive ability of modern machine learning techniques. We present a general framework for modelling customer lifetime value which may be applied to industries with long-lasting contractual and product-centric customer relationships, of which retail banking is an example. This framework is novel in facilitating CLV predictions over arbitrary time horizons and product-based propensity models. We also detail an implementation of this model which is currently in production at a large UK lender. In testing, we estimate an 43% improvement in out-of-time CLV prediction error relative to a popular baseline approach. Propensity models derived from our CLV model have been used to support customer contact marketing campaigns. In testing, we saw that the top 10% of customers ranked by their propensity to take up investment products were 3.2 times more likely to take up an investment product in the next year than a customer chosen at random.

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper presents a practical, production-tested customer lifetime value (CLV) framework using modern machine learning rather than heavy mathematical proofs. It demonstrates strong empirical rigor with a reported 43% improvement in out-of-time prediction accuracy and validated real-world marketing campaign results at a major bank.

Research Flowchart

  flowchart TD
    A[Goal: Improve Customer Lifetime Value Estimation in Retail Banking] --> B[Input: Banking Customer Contractual & Product Transaction Data]
    B --> C[Framework: General ML Framework with Propensity & Arbitrary Horizon Models]
    C --> D[Implementation: Production Deployment at Large UK Lender]
    D --> E[Outcome: 43% Reduction in Out-of-Time CLV Prediction Error]
    D --> F[Outcome: Top 10% Propensity Group 3.2x More Likely to Adopt Products]