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Evaluating the Added Value of Pictorial Data for Customer Churn Prediction

Listed author(s):
  • M. BALLINGS

    ()

  • D. VAN DEN POEL

    ()

  • E. VERHAGEN

The purpose of this paper is to evaluate whether pictorial data can improve customer churn prediction and, if so, which pictures are most important. We use Random Forest and five times twofold cross-validation to analyze how much pictorial stimulus-choice data increase the AUC and top decile lift of a churn model over and above administrative, operational, complaints and traditional survey data. We found that pictorial-stimulus choice data significantly increase models with administrative and operational data. The most important pictures are facial expressions, colors, and motivational scenes. Pictorial variables can reach importance of up to 21% of the importance of the best predictor included in the predictor set. Given this importance, managers could mine pictorial data from social media sites (e.g., Pinterest.com) in order to augment their internal customer database. To the best of our knowledge this study is the first that evaluates the added value of pictorial stimulus-choice data in predictive models. This is important because social media platforms are increasingly sharing their data and because of the recent rise of social media based on pictures. Pictorial data may soon become a viable option for data data-augmentation.

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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 13/869.

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Length: 38 pages
Date of creation: Dec 2013
Handle: RePEc:rug:rugwps:13/869
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  1. Buckinx, Wouter & Van den Poel, Dirk, 2005. "Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting," European Journal of Operational Research, Elsevier, vol. 164(1), pages 252-268, July.
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  4. Coussement, Kristof & Benoit, Dries Frederik & Van den Poel, Dirk, 2009. "Improved Marketing Decision Making in a Customer Churn Prediction Context Using Generalized Additive Models," Working Papers 2009/18, Hogeschool-Universiteit Brussel, Faculteit Economie en Management.
  5. K. Coussement & D. Van Den Poel, 2006. "Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparing Two Parameter-Selection Techniques," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 06/412, Ghent University, Faculty of Economics and Business Administration.
  6. Guo, Lin & Xiao, Jing Jian & Tang, Chuanyi, 2009. "Understanding the psychological process underlying customer satisfaction and retention in a relational service," Journal of Business Research, Elsevier, vol. 62(11), pages 1152-1159, November.
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  9. J. D’Haen & D. Van Den Poel & D. Thorleuchter, 2012. "Predicting Customer Profitability During Acquisition: Finding the Optimal Combination of Data Source and Data Mining Technique," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/818, Ghent University, Faculty of Economics and Business Administration.
  10. Thomas J. Steenburgh & Andrew Ainslie & Peder Hans Engebretson, 2003. "Massively Categorical Variables: Revealing the Information in Zip Codes," Marketing Science, INFORMS, vol. 22(1), pages 40-57, August.
  11. D. F. Benoit & D. Van Den Poel, 2012. "Improving Customer Retention In Financial Services Using Kinship Network Information," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/786, Ghent University, Faculty of Economics and Business Administration.
  12. P. Baecke & D. Van Den Poel, 2010. "Improving purchasing behavior predictions by data augmentation with situational variables," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 10/658, Ghent University, Faculty of Economics and Business Administration.
  13. D. Thorleuchter & D. Van Den Poel & A. Prinzie, 2011. "Analyzing existing customers’ websites to improve the customer acquisition process as well as the profitability prediction in B-to-B marketing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 11/733, Ghent University, Faculty of Economics and Business Administration.
  14. Gounaris, Spiros P., 2005. "Trust and commitment influences on customer retention: insights from business-to-business services," Journal of Business Research, Elsevier, vol. 58(2), pages 126-140, February.
  15. K. Coussement & D. Van den Poel, 2009. "Improving customer attrition prediction by integrating emotions from client/company interaction emails and evaluating multiple classifiers," Post-Print halshs-00581595, HAL.
  16. 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.
  17. M. Ballings & D. Van Den Poel, 2012. "The Relevant Length of Customer Event History for Churn Prediction: How long is long enough?," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/804, Ghent University, Faculty of Economics and Business Administration.
  18. Lemmens, A. & Croux, C., 2006. "Bagging and boosting classification trees to predict churn," Other publications TiSEM d5cb664d-5859-44db-a621-e, Tilburg University, School of Economics and Management.
  19. 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.
  20. K. Coussement & D. Van Den Poel, 2008. "Integrating the Voice of Customers through Call Center Emails into a Decision Support System for Churn Prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/502, Ghent University, Faculty of Economics and Business Administration.
  21. Coussement, Kristof & Buckinx, Wouter, 2011. "A probability-mapping algorithm for calibrating the posterior probabilities: A direct marketing application," European Journal of Operational Research, Elsevier, vol. 214(3), pages 732-738, November.
  22. K. Coussement & D. Van Den Poel, 2008. "Improving Customer Attrition Prediction by Integrating Emotions from Client/Company Interaction Emails and Evaluating Multiple Classifiers," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/527, Ghent University, Faculty of Economics and Business Administration.
  23. 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.
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  25. K. Coussement & D. Van den Poel, 2008. "Integrating the voice of customers through call center emails into a decision support system for churn prediction," Post-Print hal-00788086, HAL.
  26. K. Coussement & D.F. Benoît & D. Van den Poel, 2010. "Improved marketing decision making in a customer churn prediction context using generalized additive models," Post-Print halshs-00581701, HAL.
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