Modelling customer lifetime-value in the retail banking industry
This work addresses the challenge of accurately predicting customer lifetime value for retail banks, enabling better customer relationship management and targeted marketing, though it is incremental as it builds on existing machine learning techniques.
The authors tackled the problem of estimating customer lifetime value in retail banking by developing a general framework that supports predictions over arbitrary time horizons and product-based propensity models, resulting in a 43% improvement in prediction error compared to a baseline and a 3.2 times higher likelihood of investment product uptake for top-ranked customers.
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.