Retention Futility: Targeting High-Risk Customers Might be Ineffective
Open accessAbstract
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.
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- Authors
- Ascarza, E.
- Affiliations
- Columbia University (US)
- Corresponding author
- Ascarza, Eva
- ORCID
- Ascarza, Eva
- Venue
- Journal of Marketing Research
- Publisher
- SAGE Publications
- Published
- 2018-02
- Volume
- 55
- Issue
- 1
- Pages
- 80-98
- ISSN
- 0022-2437 (ISSN-L)
- 1547-7193
- OpenAlex
- W3122149192
- Cited by
- 295
- References
- 56
- Citations by year
- 2026: 48 · 2025: 38 · 2024: 41 · 2023: 34 · 2022: 37 · 2021: 36 · 2020: 33 · 2019: 18 · 2018: 5 · 2017: 4 · 2015: 1
- FWCI
- 29.69
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Journal of Marketing Research (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Customer churn and segmentation
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Customer Service Quality and Loyalty
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
- Keywords
- customer churn, customer retention, A/B testing, field experiments
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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