Modelling customer lifetime-value in the retail banking industry

TL;DR

This paper introduces a flexible machine learning framework for predicting customer lifetime value in retail banking, enabling more accurate long-term and product-specific predictions, and demonstrating significant improvements over traditional methods.

Contribution

The paper presents a novel, adaptable CLV modeling framework that incorporates arbitrary time horizons and product-based propensity predictions, currently implemented in a real banking environment.

Findings

43% improvement in CLV prediction accuracy over baseline

Top 10% customers by propensity are 3.2 times more likely to adopt new products

Framework applicable to industries with long-term customer relationships

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.