Comparing Churn Prediction Techniques and Assessing Their Performance

Customer retention has become a focal priority. However, the process of implementing an effective retention campaign is complex and dependent on firms’ ability to accurately identify both at-risk customers and those worth retaining. Drawing on empirical and simulated data from two online retailers, we evaluate the performance of several parametric and nonparametric churn prediction techniques, in order to identify the optimal modeling approach, dependent on context. Results show that under most circumstances (i.e., varying sample sizes, purchase frequencies, and churn ratios), the boosting technique, a nonparametric method, delivers superior predictability. Furthermore, in cases/contexts where churn is more rare, logistic regression prevails. Finally, where the size of the customer base is very small, parametric probability models outperform other techniques.

[1]  Forrest V. Morgeson,et al.  Service Failure Severity, Customer Satisfaction, and Market Share , 2014 .

[2]  David J. Curry,et al.  Prediction in Marketing Using the Support Vector Machine , 2005 .

[3]  Tammo H. A. Bijmolt,et al.  Analytics for Customer Engagement , 2010 .

[4]  Katherine N. Lemon,et al.  Return on Marketing: Using Customer Equity to Focus Marketing Strategy , 2004 .

[5]  Carl F. Mela,et al.  Choice Models and Customer Relationship Management , 2005 .

[6]  Udo Wagner,et al.  Customer Base Analysis: The Case for a Central Variant of the Betageometric/NBD Model , 2007 .

[7]  Florian von Wangenheim,et al.  Instant Customer Base Analysis: Managerial Heuristics Often “Get it Right”: , 2008 .

[8]  A. Ehrenberg.  The Pattern of Consumer Purchases , 1959 .

[9]  Kristof Coussement,et al.  Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning , 2013 .

[10]  L. Meyer-Waarden,et al.  The influence of loyalty programme membership on customer purchase behaviour , 2008 .

[11]  Kristof Coussement,et al.  Improving customer attrition prediction by integrating emotions from client/company interaction emails and evaluating multiple classifiers , 2009, Expert Syst. Appl..

[12]  V. Kumar,et al.  Regaining “Lost” Customers: The Predictive Power of First-Lifetime Behavior, the Reason for Defection, and the Nature of the Win-Back Offer , 2015 .

[13]  Julia Eichmann.  Customer Relationship Management Concept Strategy And Tools , 2016 .

[14]  Tammo H. A. Bijmolt,et al.  Staying Power of Churn Prediction Models , 2010 .

[15]  Couchen Wu,et al.  Counting your customers: Compounding customer's in-store decisions, interpurchase time and repurchasing behavior , 2000, Eur. J. Oper. Res..

[16]  Rajkumar Venkatesan,et al.  A Customer Lifetime Value Framework for Customer Selection and Resource Allocation Strategy , 2004 .

[17]  Wagner A. Kamakura,et al.  Defection Detection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models , 2006 .

[18]  W. Reinartz,et al.  On the Profitability of Long-Life Customers in a Noncontractual Setting: An Empirical Investigation and Implications for Marketing , 2000 .

[19]  Dirk Van den Poel,et al.  CRM at a pay-TV company: Using analytical models to reduce customer attrition by targeted marketing for subscription services , 2007, Expert Syst. Appl..

[20]  Gila E. Fruchter,et al.  Dynamic Targeted Promotions , 2004 .

[21]  Christophe Croux,et al.  Bagging and Boosting Classification Trees to Predict Churn , 2006 .

[22]  Anders Gustafsson,et al.  Understanding Frequent Switching Patterns , 2007 .

[23]  Peter S. Fader,et al.  Forecasting Repeat Sales at CDNOW: A Case Study , 2001, Interfaces.

[24]  Christian Homburg,et al.  Personal characteristics as moderators of the relationship between customer satisfaction and loyalty—an empirical analysis , 2001 .

[25]  M. Tahar Kechadi,et al.  Customer churn prediction in telecommunications , 2012, Expert Syst. Appl..

[26]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[27]  Bruce G. S. Hardie,et al.  Probability Models for Customer-Base Analysis , 2009 .

[28]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[29]  Jun Guo,et al.  An extended support vector machine forecasting framework for customer churn in e-commerce , 2011, Expert Syst. Appl..

[30]  Jian Pei,et al.  Data Mining: Concepts and Techniques, 3rd edition , 2006 .

[31]  Dominique M. Hanssens,et al.  Modeling Customer Lifetime Value , 2006 .

[32]  Dirk Van den Poel,et al.  Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting , 2005, Eur. J. Oper. Res..

[33]  David C. Schmittlein,et al.  Counting Your Customers: Who-Are They and What Will They Do Next? , 1987 .

[34]  Ethem Alpaydın,et al.  Combined 5 x 2 cv F Test for Comparing Supervised Classification Learning Algorithms , 1999, Neural Comput..

[35]  A. Athanassopoulos.  Customer Satisfaction Cues To Support Market Segmentation and Explain Switching Behavior , 2000 .

[36]  Dirk Van den Poel,et al.  Handling class imbalance in customer churn prediction , 2009, Expert Syst. Appl..

[37]  Koen W. De Bock,et al.  An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction , 2011, Expert Syst. Appl..

[38]  W. Reinartz,et al.  The Impact of Customer Relationship Characteristics on Profitable Lifetime Duration , 2003 .

[39]  Peter S. Fader,et al.  Counting Your Customers the Easy Way: An Alternative to the Pareto/NBD Model , 2005 .

[40]  Susan M. Keaveney,et al.  Customer Switching Behavior in Service Industries: An Exploratory Study , 1995 .

[41]  Anke Thede,et al.  Applicability of Customer Churn Forecasts in a Non-Contractual Setting , 2005 .

[42]  F. Bass,et al.  Introduction to the Special Issue: Empirical Generalizations in Marketing , 1995 .

[43]  João Falcão e Cunha,et al.  Customer attrition in retailing: An application of Multivariate Adaptive Regression Splines , 2013, Expert Syst. Appl..

[44]  M. Sirgy,et al.  A path analytic model of store loyalty involving self-concept, store image, geographic loyalty, and socioeconomic status , 1985 .