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Cited by 295Customer churn and segmentationCustomer Service Quality and LoyaltyConsumer Market Behavior and Pricing

The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.

Retention Futility: Targeting High-Risk Customers Might be Ineffective

Open access
Ascarza, Eva·2018·Journal of Marketing Research
DOI 10.1509/jmr.16.0163
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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.

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Research works printed but not linked: 4

  1. 10.1509/jmkr.45.1.077 · DOI
  2. 10.1509/jmkg.71.2.094 · DOI
  3. FellerAvi, and HolmesChris C. (2009), “Beyond Toplines: Heterogeneous Treatment Effects in Randomized Experiments,” unpublished manuscript, Oxford University.
  4. RadcliffeNicholas J., and SurryPatrick D. (1999), “Differential Response Analysis: Modeling True Response by Isolating the Effect of a Single Action,” in Proceedings of Credit Scoring and Credit Control VI. Edinburgh, Scotland, http://www.maths.ed.ac.uk/∼mthdat25/uplift/cscc99-1.

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Metadata

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
DOI
10.1509/jmr.16.0163
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
FWCI
29.69
Type
journal article
Language
en
Open access
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
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 › Marketing › 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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