Managing Churn to Maximize Profits

Customer defection threatens many industries, prompting companies to deploy targeted, proactive customer retention programs and offers. A conventional approach has been to target customers either based on their predicted churn probability, or their responsiveness to a retention offer. However, both approaches ignore that some customers contribute more to the profitability of retention campaigns than others. This study addresses this problem by defining a profit-based loss function to predict, for each customer, the financial impact of a retention intervention. This profit-based loss function aligns the objective of the estimation algorithm with the managerial goal of maximizing the campaign profit. It ensures (1) that customers are ranked based on the incremental impact of the intervention on churn and post-campaign cash flows, after accounting for the cost of the intervention and (2) that the model minimizes the cost of prediction errors by penalizing customers based on their expected profit lift. Finally, it provides a method to optimize the size of the retention campaign. Two field experiments affirm that our approach leads to significantly more profitable campaigns than competing models.

[1]  C. Bhattacharya.  When customers are members: Customer retention in paid membership contexts , 1998 .

[2]  Dirk Van den Poel,et al.  Predicting customer retention and profitability by using random forests and regression forests techniques , 2005, Expert Syst. Appl..

[3]  Ruth N. Bolton,et al.  A Dynamic Model of the Duration of the Customer's Relationship with a Continuous Service Provider: The Role of Satisfaction , 1994 .

[4]  Katherine N. Lemon,et al.  Dynamic Customer Relationship Management: Incorporating Future Considerations into the Service Retention Decision , 2002 .

[5]  Pedro Ferreira,et al.  The Effect of Subscription Video-on-Demand on Piracy: Evidence from a Household-Level Randomized Experiment , 2017, Manag. Sci..

[6]  Edward I. George,et al.  Estimation under Profit-Driven Loss Functions , 1992 .

[7]  Peter S. Fader,et al.  Understanding Service Retention within and across Cohorts using Limited Information , 2008 .

[8]  Roger J. Calantone,et al.  Artificial Neural Network Decision Support Systems for New Product Development Project Selection , 2000 .

[9]  Clive W. J. Granger.  Prediction with a generalized cost of error function , 2001 .

[10]  Siddharth S. Singh,et al.  A Generalized Framework for Estimating Customer Lifetime Value When Customer Lifetimes are Not Observed , 2007 .

[11]  Steven R. Lerman,et al.  The Estimation of Choice Probabilities from Choice Based Samples , 1977 .

[12]  Bruce G. S. Hardie,et al.  A Joint Model of Usage and Churn in Contractual Settings , 2013, Mark. Sci..

[13]  Xin Yan,et al.  Facilitating score and causal inference trees for large observational studies , 2012, J. Mach. Learn. Res..

[14]  Gary King,et al.  Logistic Regression in Rare Events Data , 2001, Political Analysis.

[15]  Greg M. Allenby,et al.  Modeling Interdependent Consumer Preferences , 2003 .

[16]  Eva Ascarza.  Retention Futility: Targeting High-Risk Customers Might be Ineffective , 2018 .

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

[18]  Roland T. Rust,et al.  Will the frog change into a prince? Predicting future customer profitability , 2011 .

[19]  Gary King,et al.  Explaining Rare Events in International Relations , 2001, International Organization.

[20]  Bart Baesens,et al.  Modeling churn using customer lifetime value , 2009, Eur. J. Oper. Res..

[21]  Michael Braun,et al.  Modeling Customer Lifetimes with Multiple Causes of Churn , 2011, Mark. Sci..

[22]  D. Rubin,et al.  Reducing Bias in Observational Studies Using Subclassification on the Propensity Score , 1984 .

[23]  Patrick L. Brockett,et al.  A Comparative Analysis of Neural Networks and Statistical Methods for Predicting Consumer Choice , 1997 .

[24]  Hannes Datta,et al.  The Challenge of Retaining Customers Acquired with Free Trials , 2014 .

[25]  B. Minasny.  The Elements of Statistical Learning, Second Edition, Trevor Hastie, Robert Tishirani, Jerome Friedman. (2009), Springer Series in Statistics, ISBN 0172-7397, 745 pp , 2009 .

[26]  Michael Lewis,et al.  Research Note: A Dynamic Programming Approach to Customer Relationship Pricing , 2005, Manag. Sci..

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

[28]  Weighting and calibration in sample survey estimation , 1997 .

[29]  J. Friedman.  Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .

[30]  Mélanie Frappier,et al.  The Book of Why: The New Science of Cause and Effect , 2018, Science.

[31]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[32]  Z. John Zhang,et al.  Competitive One-to-One Promotions , 2002, Manag. Sci..

[33]  Chris Tofallis,et al.  A better measure of relative prediction accuracy for model selection and model estimation , 2014, J. Oper. Res. Soc..

[34]  Farrokh Alemi,et al.  Improved Statistical Methods are Needed to Advance Personalized Medicine. , 2009, The open translational medicine journal.

[35]  Tom Fawcett,et al.  Data science for business , 2013 .

[36]  Klaus Nordhausen,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition by Trevor Hastie, Robert Tibshirani, Jerome Friedman , 2009 .

[37]  Peter S. Fader,et al.  A Bivariate Timing Model of Customer Acquisition and Retention , 2008, Mark. Sci..

[38]  George Knox,et al.  Customer Complaints and Recovery Effectiveness: A Customer Base Approach , 2014 .

[39]  Rajkumar Venkatesan,et al.  Optimal Customer Relationship Management Using Bayesian Decision Theory: An Application for Customer Selection , 2007 .

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

[41]  Kenneth R. Baker,et al.  An Optimal Contact Model for Maximizing Online Panel Response Rates , 2009, Manag. Sci..

[42]  John R. Hauser,et al.  Research Note---On Managerially Efficient Experimental Designs , 2007 .

[43]  Peter E. Rossi,et al.  Bayesian Statistics and Marketing , 2005 .

[44]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[45]  J. Friedman.  Stochastic gradient boosting , 2002 .

[46]  Peter S. Fader,et al.  Customer-Base Analysis in a Discrete-Time Noncontractual Setting , 2009, Mark. Sci..

[47]  Jaishankar Ganesh,et al.  Understanding the Customer Base of Service Providers: An Examination of the Differences between Switchers and Stayers , 2000, Journal of Marketing.

[48]  Jeffrey M. Wooldridge,et al.  What Are We Weighting For? , 2013, The Journal of Human Resources.

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

[50]  Peter E. Rossi,et al.  Estimating Price Elasticities with Theory-Based Priors , 1999 .

[51]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[52]  Jan Roelf Bult,et al.  Semiparametric versus Parametric Classification Models: An Application to Direct Marketing , 1993 .

[53]  Peter S. Fader,et al.  Model Selection Using Database Characteristics: Developing a Classification Tree for Longitudinal Incidence Data , 2013, Mark. Sci..

[54]  Peter S. Fader,et al.  Portfolio Dynamics for Customers of a Multiservice Provider , 2011, Manag. Sci..

[55]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[56]  Peter J. Lenk,et al.  Market Share Constraints and the Loss Function in Choice-Based Conjoint Analysis , 2008, Mark. Sci..

[57]  Michael Lewis.  Incorporating Strategic Consumer Behavior into Customer Valuation , 2005 .

[58]  Philip Hans Franses,et al.  Selective Sampling for Binary Choice Models , 2003 .

[59]  Robert C. Blattberg,et al.  Database Marketing: Analyzing and Managing Customers , 2008 .

[60]  Van den PoelDirk,et al.  Predicting customer retention and profitability by using random forests and regression forests techniques , 2005 .

[61]  Wei Chu,et al.  An unbiased offline evaluation of contextual bandit algorithms with generalized linear models , 2011 .

[62]  Eva Ascarza,et al.  The Perils of Proactive Churn Prevention Using Plan Recommendations: Evidence from a Field Experiment , 2015 .

[63]  Peter C. Verhoef,et al.  Modeling CLV: A test of competing models in the insurance industry , 2007 .

[64]  P. Verhoef.  Understanding the Effect of Customer Relationship Management Efforts on Customer Retention and Customer Share Development , 2003 .

[65]  Yuri Shvarev.  Observation and Experiment , 2018, Anesthesia & Analgesia.

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

[67]  Clive W. J. Granger,et al.  On the limitations of comparing mean square forecast errors: Comment , 1993 .

[68]  P. K. Kannan,et al.  Implications of loyalty program membership and service experiences for customer retention and value , 2000 .

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

[70]  Romana Khan,et al.  Dynamic Customer Management and the Value of One-to-One Marketing , 2009, Mark. Sci..

[71]  C. Croux,et al.  Unveiling the Relationship between the Transaction Timing, Spending and Dropout Behavior of Customers , 2014 .

[72]  LEO GUELMAN,et al.  Uplift Random Forests , 2015, Cybern. Syst..

[73]  W. Reinartz,et al.  Balancing Acquisition and Retention Resources to Maximize Customer Profitability , 2005 .

[74]  Leo Guelman,et al.  Random Forests for Uplift Modeling: An Insurance Customer Retention Case , 2012, MS.

[75]  Pedro Ferreira,et al.  Target the Ego or Target the Group: Evidence from a Randomized Experiment in Proactive Churn Management , 2018, Mark. Sci..

[76]  Peter C. Verhoef,et al.  The commercial use of segmentation and predictive modeling techniques for database marketing in the Netherlands , 2003, Decis. Support Syst..

[77]  Rick L. Andrews,et al.  An Empirical Comparison of Logit Choice Models with Discrete versus Continuous Representations of Heterogeneity , 2002 .

[78]  Sanjog Misra,et al.  Heterogeneous Treatment Effects and Optimal Targeting Policy Evaluation , 2018 .

[79]  Bart Baesens,et al.  New insights into churn prediction in the telecommunication sector: A profit driven data mining approach , 2012, Eur. J. Oper. Res..

[80]  Sunil Gupta,et al.  Valuing customers , 2007 .

[81]  V. Kumar,et al.  Practice Prize Report - The Power of CLV: Managing Customer Lifetime Value at IBM , 2008, Mark. Sci..

[82]  Foster J. Provost,et al.  Decision-Centric Active Learning of Binary-Outcome Models , 2007, Inf. Syst. Res..

[83]  D. Rubin.  Estimating causal effects of treatments in randomized and nonrandomized studies. , 1974 .

[84]  R. Winer.  A Framework for Customer Relationship Management , 2001 .

[85]  Peter S. Fader,et al.  Customer-Base Valuation in a Contractual Setting: The Perils of Ignoring Heterogeneity , 2010, Mark. Sci..

[86]  Bruce G. S. Hardie,et al.  In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions , 2017 .

[87]  D. Collings,et al.  Valuing customers , 2005 .

[88]  Gary King,et al.  Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference , 2007, Political Analysis.

[89]  Susan Athey,et al.  Recursive partitioning for heterogeneous causal effects , 2015, Proceedings of the National Academy of Sciences.

[90]  Peter Christoffersen,et al.  Série Scientifique Scientific Series the Importance of the Loss Function in Option Valuation the Importance of the Loss Function in Option Valuation , 2022 .

[91]  Jeffrey J. Harden,et al.  Monte Carlo Simulation and Resampling Methods for Social Science , 2013 .

[92]  Dick R. Wittink,et al.  Estimating and validating asymmetric heterogeneous loss functions applied to health care fund raising , 1996 .

[93]  Peter S. Fader,et al.  RFM and CLV: Using Iso-Value Curves for Customer Base Analysis , 2005 .

[94]  Peter E. Rossi,et al.  The Value of Purchase History Data in Target Marketing , 1996 .

[95]  David A. Schweidel,et al.  Incorporating Direct Marketing Activity into Latent Attrition Models , 2013, Mark. Sci..