The Perils of Proactive Churn Prevention Using Plan Recommendations: Evidence from a Field Experiment

Facing the issue of increasing customer churn, many service firms have begun recommending pricing plans to their customers. One reason behind this type of retention campaign is that customers who subscribe to a plan suitable for them should be less likely to churn because they derive greater benefits from the service. In this article, the authors examine the effectiveness of such retention campaigns using a large-scale field experiment in which some customers are offered plan recommendations and some are not. They find that being proactive and encouraging customers to switch to cost-minimizing plans can, surprisingly, increase rather than decrease customer churn: whereas only 6% of customers in the control condition churned during the three months following the intervention, 10% did so in the treatment group. The authors propose two explanations for how the campaign increased churn, namely, (1) by lowering customers’ inertia to switch plans and (2) by increasing the salience of past-usage patterns among potential churners. The data provide support for both explanations. By leveraging the richness of their field experiment, the authors assess the impact of targeted encouragement campaigns on customer behavior and firm revenues and derive recommendations for service firms.

[1]  Naufel J. Vilcassim,et al.  When Talk is “Free”: The Effect of Tariff Structure on Usage under Two- and Three-Part Tariffs , 2012 .

[2]  D. Rubin,et al.  Assessing the effect of an influenza vaccine in an encouragement design. , 2000, Biostatistics.

[3]  C. Fornell,et al.  Why Do Customer Relationship Management Applications Affect Customer Satisfaction? , 2005 .

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

[5]  B. Handel Adverse Selection and Inertia in Health Insurance Markets: When Nudging Hurts. , 2013, The American economic review.

[6]  G. Imbens,et al.  Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score , 2002 .

[7]  Vardit Landsman,et al.  Dם Customers Learn from Experience? Evidence from Retail Banking , 2012, Manag. Sci..

[8]  R. Thaler,et al.  Libertarian Paternalism is Not an Oxymoron , 2003 .

[9]  D. Rubin,et al.  The central role of the propensity score in observational studies for causal effects , 1983 .

[10]  Peter C Austin,et al.  A critical appraisal of propensity‐score matching in the medical literature between 1996 and 2003 , 2008, Statistics in medicine.

[11]  Rajeev Dehejia,et al.  Propensity Score-Matching Methods for Nonexperimental Causal Studies , 2002, Review of Economics and Statistics.

[12]  Jennifer Hill,et al.  A Broader Template for Analyzing Broken Randomized Experiments , 1998 .

[13]  Sendhil Mullainathan,et al.  Comparison Friction: Experimental Evidence from Medicare Drug Plans , 2011, The quarterly journal of economics.

[14]  R. Goettler,et al.  Tariff Choice with Consumer Learning and Switching Costs , 2010 .

[15]  Gerald Häubl,et al.  "Double Agents": Assessing the Role of Electronic Product Recommendation Systems , 2005 .

[16]  Chris Arney Nudge: Improving Decisions about Health, Wealth, and Happiness , 2015 .

[17]  Ran R. Hassin,et al.  Can Consumers Make Affordable Care Affordable? The Value of Choice Architecture , 2013, PloS one.

[18]  Eric T. Anderson,et al.  Wal-Mart's Impact on Supplier Profits , 2012 .

[19]  Raghuram Iyengar,et al.  A Model of Consumer Learning for Service Quality and Usage , 2007 .

[20]  P. Danaher Optimal Pricing of New Subscription Services: Analysis of a Market Experiment , 2002 .

[21]  Young-Hyuck Joo,et al.  Encouraging customers to pay less for mobile telecommunication services , 2002 .

[22]  J. Avorn,et al.  Variable selection for propensity score models. , 2006, American journal of epidemiology.

[23]  Ulrike Malmendier,et al.  Paying Not to Go to the Gym , 2006 .

[24]  P. Verhoef,et al.  Understanding Customer Switching Behavior in a Liberalizing Service Market , 2007 .

[25]  B. Libai,et al.  Social Effects on Customer Retention , 2011 .

[26]  Peter C Austin,et al.  A comparison of propensity score methods: a case‐study estimating the effectiveness of post‐AMI statin use , 2006, Statistics in medicine.

[27]  C. Wheeler,et al.  Natural History of Progression of HPV Infection to Cervical Lesion or Clearance: Analysis of the Control Arm of the Large, Randomised PATRICIA Study , 2013, PloS one.

[28]  I. Simonson,et al.  Experimental Evidence on the Negative Effect of Product Features and Sales Promotions on Brand Choice , 1994 .

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

[30]  Alex Berson,et al.  Building Data Mining Applications for CRM , 1999 .

[31]  Peter J. Danaher,et al.  The Impact of Tariff Structure on Customer Retention, Usage, and Profitability of Access Services , 2011, Mark. Sci..

[32]  Matthew E. Kahn,et al.  Energy Conservation "Nudges" and Environmentalist Ideology: Evidence from a Randomized Residential Electricity Field Experiment , 2010 .

[33]  Bart J. Bronnenberg,et al.  Do Digital Video Recorders Influence Sales? , 2010 .

[34]  G. Imbens,et al.  Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score , 2000 .

[35]  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 .

[36]  D. Rubin,et al.  Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score , 1985 .

[37]  Robert J. Meyer,et al.  Consumer Dynamic Usage Allocation and Learning Under Multipart Tariffs , 2015, Mark. Sci..

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

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

[40]  Ken Kwong-Kay Wong,et al.  Should wireless carriers protect residential voice subscribers from high overage and underage charges? Insights from the Canadian telecommunications market , 2010 .

[41]  P. Leeflang,et al.  Impact of Online Channel Use on Customer Revenues and Costs to Serve: Considering Product Portfolios and Self-Selection , 2011 .

[42]  Pradeep K. Chintagunta,et al.  The role of self selection, usage uncertainty and learning in the demand for local telephone service , 2007 .

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

[44]  Ignacio Palacios-Huerta,et al.  Consumer Inertia, Choice Dependence, and Learning from Experience in a Repeated Decision Problem , 2014, Review of Economics and Statistics.

[45]  Donald Rubin,et al.  Estimating Causal Effects from Large Data Sets Using Propensity Scores , 1997, Annals of Internal Medicine.

[46]  Daniel G. Goldstein,et al.  Beyond nudges: Tools of a choice architecture , 2012 .

[47]  R. D'Agostino Adjustment Methods: Propensity Score Methods for Bias Reduction in the Comparison of a Treatment to a Non‐Randomized Control Group , 2005 .

[48]  Anand V. Bodapati Recommendation Systems with Purchase Data , 2008 .

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

[50]  N. Azad,et al.  The customer relationship management process: its measurement and impact on performance , 2015 .

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

[52]  Raghuram Iyengar,et al.  Consumer Dynamic Usage Allocation and Learning under Multi-Part Tariffs , 2014 .

[53]  Ken Kwong-Kay Wong,et al.  Getting what you paid for: Fighting wireless customer churn with rate plan optimization , 2011 .

[54]  R. Thaler,et al.  Nudge: Improving Decisions About Health, Wealth, and Happiness , 2008 .

[55]  B. Skiera,et al.  Paying Too Much and Being Happy about It: Existence, Causes, and Consequences of Tariff-Choice Biases , 2006 .

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

[57]  R. Thaler,et al.  Save More Tomorrow™: Using Behavioral Economics to Increase Employee Saving , 2004, Journal of Political Economy.

[58]  Larry Yu Taking cues from the public sector , 2006 .

[59]  Stephen S. Tax,et al.  Growing Existing Customers’ Revenue Streams through Customer Referral Programs , 2013 .

[60]  D. Lehmann,et al.  Reactance to Recommendations: When Unsolicited Advice Yields Contrary Responses , 2004 .