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

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Author Info

  • 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.

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Bibliographic Info

Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 28 (2012)
Issue (Month): 2 ()
Pages: 507-518

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Handle: RePEc:eee:intfor:v:28:y:2012:i:2:p:507-518

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Web page: http://www.elsevier.com/locate/ijforecast

Related research

Keywords: Profitability forecasting; Adaptive segmentation; Bootstrap; Customer lifetime value; Financial services;

References

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  1. Thomas, Lyn C., 2000. "A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers," International Journal of Forecasting, Elsevier, vol. 16(2), pages 149-172.
  2. Amit Goval & Ivo Welch, 2004. "A Comprehensive Look at the Empirical Performance of Equity Premium Prediction," NBER Working Papers 10483, National Bureau of Economic Research, Inc.
  3. Michael Greenacre, 1988. "Clustering the rows and columns of a contingency table," Journal of Classification, Springer, vol. 5(1), pages 39-51, March.
  4. Finlay, Steven, 2010. "Credit scoring for profitability objectives," European Journal of Operational Research, Elsevier, vol. 202(2), pages 528-537, April.
  5. Bas Donkers & Peter Verhoef & Martijn Jong, 2007. "Modeling CLV: A test of competing models in the insurance industry," Quantitative Marketing and Economics, Springer, vol. 5(2), pages 163-190, June.
  6. Peter S. Fader & Bruce G. S. Hardie & Ka Lok Lee, 2005. "“Counting Your Customers” the Easy Way: An Alternative to the Pareto/NBD Model," Marketing Science, INFORMS, vol. 24(2), pages 275-284, August.
  7. Donald G. Morrison & Richard D. H. Chen & Sandra L. Karpis & Kathryn E. A. Britney, 1982. "Modelling Retail Customer Behavior at Merrill Lynch," Marketing Science, INFORMS, vol. 1(2), pages 123-141.
  8. David C. Schmittlein & Donald G. Morrison & Richard Colombo, 1987. "Counting Your Customers: Who-Are They and What Will They Do Next?," Management Science, INFORMS, vol. 33(1), pages 1-24, January.
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