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Predicting Customer Retention and Profitability by Using Random Forests and Regression Forests Techniques

  • B. LARIVIÈRE
  • D. VAN DEN POEL

    ()

In an era of strong customer relationship management (CRM) emphasis, firms strive to build valuable relationships with their existing customer base. In this study we attempt to better understand three important measures of customer outcome: next buy, partial defection and customers’ profitability evolution. By means of random forests techniques we investigate a broad set of explanatory variables, including past customer behavior, observed customer heterogeneity and some typical variables related to intermediaries. We analyze a real-life sample of 100,000 customers taken from the data warehouse of a large European financial services company. Two types of random forests techniques are employed to analyze the data: random forests are used for binary classification, whereas regression forests are applied for the models with linear dependent variables. Our research findings demonstrate that both random forests techniques provide better fit for the estimation and validation sample compared to ordinary linear regression and logistic regression models. Furthermore, we find evidence that the same set of variables have a different impact on buying versus defection versus profitability behavior. Our findings suggest that past customer behavior is more important to generate repeat purchasing and favorable profitability evolutions, while the intermediary’s role has a greater impact on the customers’ defection proneness. Finally, our results demonstrate the benefits of analyzing different customer outcome variables simultaneously, since an extended investigation of the next buy - partial defection - customer profitability triad indicates that one cannot fully understand a particular outcome without understanding the other related behavioral outcome variables.

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Paper provided by Ghent University, Faculty of Economics and Business Administration in its series Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium with number 04/282.

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Length: 35 pages
Date of creation: Dec 2004
Date of revision:
Handle: RePEc:rug:rugwps:04/282
Contact details of provider: Postal: Hoveniersberg 4, B-9000 Gent
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Web page: http://www.ugent.be/eb

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  1. Hemant Ishwaran & Eugene H. Blackstone & Claire E. Pothier & Michael S. Lauer, 2004. "Relative Risk Forests for Exercise Heart Rate Recovery as a Predictor of Mortality," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 591-600, January.
  2. Van den Poel, Dirk & Lariviere, Bart, 2004. "Customer attrition analysis for financial services using proportional hazard models," European Journal of Operational Research, Elsevier, vol. 157(1), pages 196-217, August.
  3. B. Larivière & D. Van Den Poel, 2004. "Investigating the role of product features in preventing customer churn, by using survival analysis and choice modeling: The case of financial services," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/223, Ghent University, Faculty of Economics and Business Administration.
  4. W. Buckinx & D. Van Den Poel, 2003. "Customer Base Analysis: Partial Defection of Behaviorally-Loyal Clients in a Non-Contractual FMCG Retail Setting," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/178, Ghent University, Faculty of Economics and Business Administration.
  5. B. Baesens & G. Verstraeten & D. Van Den Poel & M. Egmont-Petersen & P. Van Kenhove & J. Vanthienen, 2002. "Bayesian Network Classifiers for Identifying the Slope of the Customer - Lifecycle of Long-Life Customers," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 02/154, Ghent University, Faculty of Economics and Business Administration.
  6. Dudoit S. & Fridlyand J. & Speed T. P, 2002. "Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 77-87, March.
  7. Baesens, Bart & Viaene, Stijn & Van den Poel, Dirk & Vanthienen, Jan & Dedene, Guido, 2002. "Bayesian neural network learning for repeat purchase modelling in direct marketing," European Journal of Operational Research, Elsevier, vol. 138(1), pages 191-211, April.
  8. Athanassopoulos, Antreas D., 2000. "Customer Satisfaction Cues To Support Market Segmentation and Explain Switching Behavior," Journal of Business Research, Elsevier, vol. 47(3), pages 191-207, March.
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