Forecasting customer behaviour in a multi‐service financial organisation: A profitability perspective

This paper proposes a novel approach to the estimation of Customer Lifetime Value (CLV). CLV measures give an indication of the profit‐generating potential of customers, and provide a key business tool for the customer management process. The performances of existing approaches are unsatisfactory in multi‐service financial environments because of the high degree of heterogeneity in customer behaviour. We propose an adaptive segmentation approach which involves the identification of ‘neighbourhoods’ using a similarity measure defined over a predictive variable space. The set of predictive variables is determined during a cross‐validation procedure through the optimisation of rank correlations between the observed and predicted revenues. The future revenue is forecast for each customer using a predictive probability distribution based on customers exhibiting behavioural characteristics similar to previous periods. The model is developed and implemented for a UK retail bank, and is shown to perform well in comparison to other benchmark models.