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Publications
  • February 2018
  • Article
  • Journal of Marketing Research (JMR)

Retention Futility: Targeting High-Risk Customers Might Be Ineffective.

By: Eva Ascarza
  • Format:Print
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Abstract

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.

Keywords

Retention/churn; Proactive Churn Management; Field Experiments; Heterogeneous Treatment Effect; Machine Learning; Customer Relationship Management; Risk Management

Citation

Ascarza, Eva. "Retention Futility: Targeting High-Risk Customers Might Be Ineffective." Journal of Marketing Research (JMR) 55, no. 1 (February 2018): 80–98.
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About The Author

Eva Ascarza

Marketing
→More Publications

More from the Author

    • 2022
    • Faculty Research

    When Less Is More: Using Short-term Signals to Overcome Systematic Bias in Long-run Targeting

    By: Ta-Wei Huang and Eva Ascarza
    • August 2022
    • Faculty Research

    Retail Media Networks

    By: Eva Ascarza, Ayelet Israeli and Celine Chammas
    • March 2022 (Revised March 2022)
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    Managing Customers in the Digital Era

    By: Eva Ascarza
More from the Author
  • When Less Is More: Using Short-term Signals to Overcome Systematic Bias in Long-run Targeting By: Ta-Wei Huang and Eva Ascarza
  • Retail Media Networks By: Eva Ascarza, Ayelet Israeli and Celine Chammas
  • Managing Customers in the Digital Era By: Eva Ascarza
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