Companies in a variety of sectors are increasingly managing customer churn proactively, generally by detecting customers at the highest risk of churning and targeting retention efforts towards them. While there is a vast literature on developing churn prediction models that identify customers at the highest risk of churning, no research has investigated whether it is indeed optimal to target those individuals. Combining two field experiments with machine learning techniques, the author demonstrates that customers identified as having the highest risk of churning are not necessarily the best targets for proactive churn programs. This finding is not only contrary to common wisdom but also suggests that retention programs are sometimes futile not because firms offer the wrong incentives but because they do not apply the right targeting rules. Accordingly, firms should focus their modeling efforts on identifying the observed heterogeneity in response to the intervention and to target customers on the basis of their sensitivity to the intervention, regardless of their risk of churning. This approach is empirically demonstrated to be significantly more effective than the standard practice of targeting customers with the highest risk of churning. More broadly, the author encourages firms and researchers using randomized trials (or A/B tests) to look beyond the average effect of interventions and leverage the observed heterogeneity in customers’ response to select customer targets.
We investigate the increasingly common business setting in which companies face the possibility of both observed and unobserved customer attrition (i.e., “overt” and “silent” churn) in the same pool of customers. This is the case for many online-based services where customers have the choice to stop interacting with the firm either by formally terminating the relationship (e.g., canceling their account) or by simply ignoring all communications coming from the firm. The standard contractual versus noncontractual categorization of customer–firm relationships does not apply in such hybrid settings, which means the standard models for analyzing customer attrition do not apply. We propose a hidden Markov model (HMM)-based framework to capture silent and overt churn. We apply our modeling framework to two different contexts—a daily deal website and a performing arts organization. In contrast to previous studies that have not separated the two types of churn, we find that overt churners in these hybrid settings tend to interact more, rather than less, with the firm prior to churning; that is, in settings where both types of churn are present, a high level of activity—such as customers actively opening emails received from the firm—is not necessarily a good indicator of future engagement; rather it is associated with higher risk of overt churn. We also identify a large number of “silent churners” in both empirical applications—customers who disengage with the company very early on, rarely exhibit any type of activity, and almost never churn overtly. Furthermore, we show how the two types of churners respond very differently to the firm’s communications, implying that a common retention strategy for proactive churn management is not appropriate in these hybrid settings.
Eva Ascarza is the Jakurski Family Associate Professor of Business Administration in the Marketing Unit, teaching the Marketing course in the MBA required curriculum.
As a marketing modeler, Professor Ascarza uses tools from statistics, economics, and machine learning to answer relevant marketing questions. Her main research areas are customer analytics and customer relationship management, with special attention to the problem of customer retention. She uses field experimentation (e.g., A/B testing) as well as econometric modeling and machine learning tools not only to understand and predict patterns of behavior, but also to optimize the impact of firms’ interventions. Her research has appeared in leading marketing journals including Marketing Science and Journal of Marketing Research. She received the 2014 Frank Bass award, awarded to the best marketing paper derived from a Ph.D. thesis published in an INFORMS-sponsored journal. Her research has been recognized as a Paul E. Green Award finalist in 2016 and 2017, awarded to the best article in the Journal of Marketing Research that demonstrates the greatest potential to contribute significantly to the practice of marketing research. She was named a Marketing Science Institute Young Scholar in 2017 and serves on the editorial review board of several top marketing journals including Marketing Science, Journal of Marketing Research, Journal of Marketing, and Quantitative Marketing and Economics.
Professor Ascarza earned a Ph.D. in marketing from London Business School, a B.S. in mathematics at the Universidad de Zaragoza (Spain), and a M.S. in economics and finance from Universidad de Navarra (Spain). Prior to joining HBS, she was an associate professor in the marketing department at Columbia Business School.
- Featured Work
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Companies in a variety of sectors are increasingly managing customer churn proactively, generally by detecting customers at the highest risk of churning and targeting retention efforts towards them. While there is a vast literature on developing churn prediction models that identify customers at the highest risk of churning, no research has investigated whether it is indeed optimal to target those individuals. Combining two field experiments with machine learning techniques, the author demonstrates that customers identified as having the highest risk of churning are not necessarily the best targets for proactive churn programs. This finding is not only contrary to common wisdom but also suggests that retention programs are sometimes futile not because firms offer the wrong incentives but because they do not apply the right targeting rules. Accordingly, firms should focus their modeling efforts on identifying the observed heterogeneity in response to the intervention and to target customers on the basis of their sensitivity to the intervention, regardless of their risk of churning. This approach is empirically demonstrated to be significantly more effective than the standard practice of targeting customers with the highest risk of churning. More broadly, the author encourages firms and researchers using randomized trials (or A/B tests) to look beyond the average effect of interventions and leverage the observed heterogeneity in customers’ response to select customer targets.
We investigate the increasingly common business setting in which companies face the possibility of both observed and unobserved customer attrition (i.e., “overt” and “silent” churn) in the same pool of customers. This is the case for many online-based services where customers have the choice to stop interacting with the firm either by formally terminating the relationship (e.g., canceling their account) or by simply ignoring all communications coming from the firm. The standard contractual versus noncontractual categorization of customer–firm relationships does not apply in such hybrid settings, which means the standard models for analyzing customer attrition do not apply. We propose a hidden Markov model (HMM)-based framework to capture silent and overt churn. We apply our modeling framework to two different contexts—a daily deal website and a performing arts organization. In contrast to previous studies that have not separated the two types of churn, we find that overt churners in these hybrid settings tend to interact more, rather than less, with the firm prior to churning; that is, in settings where both types of churn are present, a high level of activity—such as customers actively opening emails received from the firm—is not necessarily a good indicator of future engagement; rather it is associated with higher risk of overt churn. We also identify a large number of “silent churners” in both empirical applications—customers who disengage with the company very early on, rarely exhibit any type of activity, and almost never churn overtly. Furthermore, we show how the two types of churners respond very differently to the firm’s communications, implying that a common retention strategy for proactive churn management is not appropriate in these hybrid settings.
- Journal Articles
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- Ascarza, Eva, Scott A. Neslin, Oded Netzer, Zachery Anderson, Peter S. Fader, Sunil Gupta, Bruce Hardie, Aurelie Lemmens, Barak Libai, David T. Neal, Foster Provost, and Rom Schrift. "In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions." Special Issue on 2016 Choice Symposium. Customer Needs and Solutions 5, nos. 1-2 (March 2018): 65–81. View Details
- Ascarza, Eva. "Retention Futility: Targeting High-Risk Customers Might Be Ineffective." Journal of Marketing Research (JMR) 55, no. 1 (February 2018): 80–98. View Details
- Ascarza, Eva, Oded Netzer, and Bruce G.S. Hardie. "Some Customers Would Rather Leave Without Saying Goodbye." Marketing Science 37, no. 1 (January–February 2018): 54–77. View Details
- Ascarza, Eva, Peter Ebbes, Oded Netzer, and Matthew Danielson. "Beyond the Target Customer: Social Effects in CRM Campaigns." Journal of Marketing Research (JMR) 54, no. 3 (June 2017): 347–363. View Details
- Ascarza, Eva, Raghuram Iyengar, and Martin Schleicher. "The Perils of Proactive Churn Prevention Using Plan Recommendations: Evidence from a Field Experiment." Journal of Marketing Research (JMR) 53, no. 1 (February 2016): 46–60. View Details
- Ascarza, Eva, and Bruce G.S. Hardie. "A Joint Model of Usage and Churn in Contractual Settings." Marketing Science 32, no. 4 (July–August 2013): 570–590. View Details
- Ascarza, Eva, Anja Lambrecht, and Naufel Vilcassim. When Talk Is "Free": The Effect of Tariff Structure on Usage Under Two- and Three-Part Tariffs. Journal of Marketing Research (JMR) 49, no. 6 (December 2012): 882–900. View Details
- Book Chapters
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- Cases and Teaching Materials
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- Ascarza, Eva, and Ayelet Israeli. "Amazon Shopper Panel: Paying Customers for Their Data." Harvard Business School Case 521-058, January 2021. View Details
- Ascarza, Eva, Tomomichi Amano, and Sunil Gupta. "Othellonia: Growing a Mobile Game." Harvard Business School PowerPoint Supplement 520-056, December 2019. View Details
- Ascarza, Eva, Tomomichi Amano, and Sunil Gupta. "Othellonia: Growing a Mobile Game." Harvard Business School Spreadsheet Supplement 520-710, December 2019. (Revised July 2020.) View Details
- Ascarza, Eva, and Ayelet Israeli. "Spreadsheet Supplement to Artea Teaching Note." Harvard Business School Spreadsheet Supplement 521-705, September 2020. (Revised December 2020.) View Details
- Barasz, Kate, and Eva Ascarza. "Time Out: The Evolution from Media to Markets." Harvard Business School Case 520-128, June 2020. (Revised October 2020.) View Details
- Ascarza, Eva, and Ayelet Israeli. "Artea (A), (B), (C), and (D): Designing Targeting Strategies." Harvard Business School Teaching Note 521-041, September 2020. (Revised December 2020.) View Details
- Ascarza, Eva, and Ayelet Israeli. "Spreadsheet Supplement to Artea Teaching Note." Harvard Business School Spreadsheet Supplement 521-705, September 2020. (Revised December 2020.) View Details
- Ascarza, Eva, and Ayelet Israeli. "Artea (A), (B), (C), and (D): Designing Targeting Strategies." Harvard Business School Teaching Note 521-041, September 2020. (Revised December 2020.) View Details
- Ascarza, Eva, and Ayelet Israeli. "Spreadsheet Supplement to Artea (B) and (C)." Harvard Business School Spreadsheet Supplement 521-704, September 2020. View Details
- Ascarza, Eva, and Ayelet Israeli. "Artea (D): Discrimination through Algorithmic Bias in Targeting." Harvard Business School Exercise 521-043, September 2020. View Details
- Ascarza, Eva, and Ayelet Israeli. "Artea (C): Potential Discrimination through Algorithmic Targeting." Harvard Business School Exercise 521-037, September 2020. View Details
- Ascarza, Eva, and Ayelet Israeli. "Artea (B): Including Customer-level Demographic Data." Harvard Business School Exercise 521-022, September 2020. View Details
- Ascarza, Eva, and Ayelet Israeli. Spreadsheet Supplement to "Artea: Designing Targeting Strategies". Harvard Business School Spreadsheet Supplement 521-703, September 2020. View Details
- Israeli, Ayelet, and Eva Ascarza. "Algorithmic Bias in Marketing." Harvard Business School Teaching Note 521-035, September 2020. View Details
- Israeli, Ayelet, and Eva Ascarza. "Algorithmic Bias in Marketing." Harvard Business School Technical Note 521-020, September 2020. View Details
- Ascarza, Eva, and Ayelet Israeli. "Artea: Designing Targeting Strategies." Harvard Business School Exercise 521-021, September 2020. (Revised December 2020.) View Details
- Ascarza, Eva, and Ayelet Israeli. "Artea: Designing Targeting Strategies." Harvard Business School Exercise 521-021, September 2020. (Revised December 2020.) View Details
- Ascarza, Eva, and Keith Wilcox. "Kate Spade New York: Will Expansion Deepen or Dilute the Brand? Teaching Note." 2015. View Details
- Ascarza, Eva, Tomomichi Amano, and Sunil Gupta. "Othellonia: Growing a Mobile Game." Harvard Business School Teaching Note 520-041, November 2019. (Revised June 2020.) View Details
- Ascarza, Eva, Tomomichi Amano, and Sunil Gupta. "Othellonia: Growing a Mobile Game." Harvard Business School Case 520-016, September 2019. (Revised June 2020.) View Details
- Ascarza, Eva, and Keith Wilcox. "EPILOGUE: Kate Spade New York: Will Expansion Deepen or Dilute the Brand?" Columbia CaseWorks Series. 2015. View Details
- Wilcox, Keith, and Eva Ascarza. "Kate Spade New York: Will Expansion Deepen or Dilute the Brand?" Columbia CaseWorks Series. 2015. View Details
- Working papers
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- Ascarza, Eva, Oded Netzer, and Julian Runge. "The Twofold Effect of Customer Retention in Freemium Settings." Harvard Business School Working Paper, No. 21-062, November 2020. View Details
- Padilla, Nicolas, Eva Ascarza, and Oded Netzer. "The Customer Journey as a Source of Information." Working Paper, June 2019. View Details
- Padilla, Nicolas, and Eva Ascarza. "Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach." Harvard Business School Working Paper, No. 19-091, February 2019. (Revised May 2020.) View Details
- Research Summary
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Professor Ascarza’s research primarily focuses on providing researchers and marketers a better understanding of how to manage customer retention so as to reduce churn and increase firm’s profitability. She addresses these issues by building empirical models of customer relationship management with a focus on understanding and managing customer retention (i.e., reducing customer churn). While previous literature on customer relationship management (CRM) has predominantly used secondary data, she investigates most of these research questions from the lenses of causal inference (e.g., running field experiments). Some of her findings are counter-intuitive at first glance, but compelling once she pins down the underlying mechanisms. For example, some of her recent work challenges the very common practice of focusing on ‘risk of churning’ as the most important metric for proactive churn management. Combining two field experiments in different industries, professor Ascarza shows that, when the goal is to select customers for proactive/preventive retention efforts, identifying customers who have a high risk of churning might be missing the point. In turn, she empirically demonstrates that customers with the highest risk of churning and those who should be targeted are not necessarily the same. In another field study, Professor Ascarza investigates the role of social influence in retention campaigns. Specifically, she examines the role of the (telecommunications) network in influencing usage and retention decisions among customers who did not receive a marketing campaign, but who were connected to those who were targeted in the campaign. She finds a social multiplier of 1.28. That is, the effect of the campaign on first-degree connections of targeted customers is 28% of the effect of the campaign on the targeted customers.
- Awards & Honors
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Winner of the 2018 Paul E. Green Award from the Journal of Marketing Research for "Retention Futility: Targeting High-Risk Customers Might Be Ineffective" (February 2018).
Winner of the 2019 Erin Anderson Award for Emerging Female Marketing Scholar and Mentor from the American Marketing Association.
Finalist for the 2017 Paul E. Green Award from the Journal of Marketing Research for “Beyond the Target Customer: Social Effects in CRM campaigns” (June 2017) with Peter Ebbes, Oded Netzer and Matthew Danielson.
Finalist for the 2016 Paul E. Green Award from the Journal of Marketing Research for “The Perils of Proactive Churn Prevention using Plan Recommendations: Evidence from a Field Experiment” (February 2016) with Raghuram Iyengar and Martin Schleicher.
Selected as a Marketing Science Institute Young Scholar in 2017.
Selected as an AMA-Sheth Foundation Doctoral Consortium Faculty Fellow by the American Marketing Association in 2015 and 2018.
Winner of the 2014 Frank M. Bass Dissertation Paper Award for “A Joint Model of Usage and Churn in Contractual Settings.”
Selected as an INFORMS Doctoral Consortium Fellow at the University of British Columbia in 2008.
Selected as an AMA-Sheth Foundation Doctoral Consortium Fellow by the American Marketing Association in 2007.
Selected as a Marketing Science Institute Scholar in 2020.
- Additional Information
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- Areas of Interest
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