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

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  • Audzeyeva, Alena
  • Summers, Barbara
  • Schenk-Hoppé, Klaus Reiner

Abstract

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.

Suggested Citation

  • Audzeyeva, Alena & Summers, Barbara & Schenk-Hoppé, Klaus Reiner, 2012. "Forecasting customer behaviour in a multi-service financial organisation: A profitability perspective," International Journal of Forecasting, Elsevier, vol. 28(2), pages 507-518.
    • Handle: RePEc:eee:intfor:v:28:y:2012:i:2:p:507-518
    DOI: 10.1016/j.ijforecast.2011.05.005
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    References listed on IDEAS

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    Cited by:

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    2. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W. & Lessmann, Stefan, 2020. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1563-1578.
    3. Sanchez-Barrios, Luis Javier & Andreeva, Galina & Ansell, Jake, 2016. "“Time-to-profit scorecards for revolving credit”," European Journal of Operational Research, Elsevier, vol. 249(2), pages 397-406.

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