  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.
