Do Public Real Estate Returns Really Lead Private Returns?

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. Existing approaches show unsatisfactory performance 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. Future revenue is forecast for each customer using a predictive probability distribution based on customers exhibiting similar behavioural characteristics in previous periods. The model is developed and implemented for a UK retail bank; it is shown to perform well in comparison to other benchmark models.