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Some Customers Would Rather Leave Without Saying Goodbye

Author

Listed:
  • Eva Ascarza

    (Columbia Business School, New York, New York 10027)

  • Oded Netzer

    (Columbia Business School, New York, New York 10027)

  • Bruce G. S. Hardie

    (London Business School, London NW1 4SA, United Kingdom)

Abstract

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.

Suggested Citation

  • Eva Ascarza & Oded Netzer & Bruce G. S. Hardie, 2018. "Some Customers Would Rather Leave Without Saying Goodbye," Marketing Science, INFORMS, vol. 37(1), pages 54-77, January.
  • Handle: RePEc:inm:ormksc:v:37:y:2018:i:1:p:54-77
    DOI: 10.1287/mksc.2017.1057
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    References listed on IDEAS

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    1. Jonathan Z. Zhang & Oded Netzer & Asim Ansari, 2014. "Dynamic Targeted Pricing in B2B Relationships," Marketing Science, INFORMS, vol. 33(3), pages 317-337, May.
    2. Oded Netzer & James M. Lattin & V. Srinivasan, 2008. "A Hidden Markov Model of Customer Relationship Dynamics," Marketing Science, INFORMS, vol. 27(2), pages 185-204, 03-04.
    3. Michael Braun & David A. Schweidel, 2011. "Modeling Customer Lifetimes with Multiple Causes of Churn," Marketing Science, INFORMS, vol. 30(5), pages 881-902, September.
    4. W. Edwards Deming & Gerald J. Glasser, 1968. "A Markovian Analysis of the Life of Newspaper Subscriptions," Management Science, INFORMS, vol. 14(6), pages 283-293, February.
    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. Ricardo Montoya & Oded Netzer & Kamel Jedidi, 2010. "Dynamic Allocation of Pharmaceutical Detailing and Sampling for Long-Term Profitability," Marketing Science, INFORMS, vol. 29(5), pages 909-924, 09-10.
    7. Coussement, Kristof & De Bock, Koen W., 2013. "Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning," Journal of Business Research, Elsevier, vol. 66(9), pages 1629-1636.
    8. Drèze, Xavier & Bonfrer, André, 2008. "An empirical investigation of the impact of communication timing on customer equity," Journal of Interactive Marketing, Elsevier, vol. 22(1), pages 36-50.
    9. Sharad Borle & Siddharth S. Singh & Dipak C. Jain, 2008. "Customer Lifetime Value Measurement," Management Science, INFORMS, vol. 54(1), pages 100-112, January.
    10. Peter S. Fader & Bruce G. S. Hardie & Jen Shang, 2010. "Customer-Base Analysis in a Discrete-Time Noncontractual Setting," Marketing Science, INFORMS, vol. 29(6), pages 1086-1108, 11-12.
    11. David A. Schweidel & Young-Hoon Park & Zainab Jamal, 2014. "A Multiactivity Latent Attrition Model for Customer Base Analysis," Marketing Science, INFORMS, vol. 33(2), pages 273-286, March.
    12. David A. Schweidel & George Knox, 2013. "Incorporating Direct Marketing Activity into Latent Attrition Models," Marketing Science, INFORMS, vol. 32(3), pages 471-487, May.
    13. Eric M. Schwartz & Eric T. Bradlow & Peter S. Fader, 2014. "Model Selection Using Database Characteristics: Developing a Classification Tree for Longitudinal Incidence Data," Marketing Science, INFORMS, vol. 33(2), pages 188-205, March.
    14. 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.
    15. Eva Ascarza & Bruce G. S. Hardie, 2013. "A Joint Model of Usage and Churn in Contractual Settings," Marketing Science, INFORMS, vol. 32(4), pages 570-590, July.
    16. David C. Schmittlein & Donald G. Morrison & Richard Colombo, 1987. "Counting Your Customers: Who-Are They and What Will They Do Next?," Management Science, INFORMS, vol. 33(1), pages 1-24, January.
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    9. Woong Park & Hyunchul Ahn, 2022. "Not All Churn Customers Are the Same: Investigating the Effect of Customer Churn Heterogeneity on Customer Value in the Financial Sector," Sustainability, MDPI, vol. 14(19), pages 1-22, September.
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