{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,7]],"date-time":"2026-11-07T03:52:19Z","timestamp":1794023539833,"version":"4.8.0"},"reference-count":58,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2018,2,1]],"date-time":"2018-02-01T00:00:00Z","timestamp":1517443200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Marketing Research"],"published-print":{"date-parts":[[2018,2]]},"abstract":"<jats:p> 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. <\/jats:p>","DOI":"10.1509\/jmr.16.0163","type":"journal-article","created":{"date-parts":[[2017,8,24]],"date-time":"2017-08-24T15:28:59Z","timestamp":1503588539000},"page":"80-98","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":278,"title":["Retention Futility: Targeting High-Risk Customers Might be Ineffective"],"prefix":"10.1177","volume":"55","author":[{"given":"Eva","family":"Ascarza","sequence":"first","affiliation":[{"name":"Columbia Business School, Columbia University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2018,2,1]]},"reference":[{"key":"bibr1-jmr.16.0163","unstructured":"Accenture Analytics (2014), \u201cNordic Telco: Analytics Help Reduce Churn and Improve Marketing Campaigns,\u201d (accessed July 13, 2017), https:\/\/www.accenture.com\/us-en\/success-nordic-telco-analytics-marketing-campaigns."},{"key":"bibr2-jmr.16.0163","unstructured":"Analytics Magazine (2016), \u201cIBM Business Analytics Helps Improve Customer Retention,\u201d (accessed March 25, 2016), http:\/\/analytics-magazine.org\/ibm-business-analytics-helps-improve-customer-retention\/."},{"key":"bibr3-jmr.16.0163","doi-asserted-by":"publisher","DOI":"10.1509\/jmkr.40.2.131.19224"},{"key":"bibr4-jmr.16.0163","doi-asserted-by":"publisher","DOI":"10.1509\/jmr.15.0442"},{"key":"bibr5-jmr.16.0163","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-56941-3_10"},{"key":"bibr6-jmr.16.0163","doi-asserted-by":"publisher","DOI":"10.1287\/mksc.2013.0786"},{"key":"bibr7-jmr.16.0163","doi-asserted-by":"publisher","DOI":"10.1509\/jmr.13.0483"},{"key":"bibr8-jmr.16.0163","doi-asserted-by":"crossref","unstructured":"AscarzaEva, NeslinScott A., NetzerOded, AndersonZachery, FaderPeter S., GuptaSunil,  (2017), \u201cIn Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions,\u201d Customer Needs and Solutions, available at https:\/\/doi.org\/10.1007\/s40547-017-0080-0.","DOI":"10.1007\/s40547-017-0080-0"},{"key":"bibr9-jmr.16.0163","doi-asserted-by":"publisher","DOI":"10.1126\/science.aal4321"},{"key":"bibr10-jmr.16.0163","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1510489113"},{"key":"bibr11-jmr.16.0163","doi-asserted-by":"publisher","DOI":"10.1002\/dir.4000020305"},{"key":"bibr12-jmr.16.0163","doi-asserted-by":"publisher","DOI":"10.1057\/dbm.2010.21"},{"key":"bibr13-jmr.16.0163","volume-title":"Building Data Mining Applications for CRM.","author":"Berson Alex","year":"2000"},{"key":"bibr14-jmr.16.0163","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-72579-6"},{"key":"bibr15-jmr.16.0163","doi-asserted-by":"publisher","DOI":"10.1509\/jmkr.45.1.077"},{"key":"bibr16-jmr.16.0163","doi-asserted-by":"publisher","DOI":"10.1287\/mksc.17.1.45"},{"key":"bibr17-jmr.16.0163","doi-asserted-by":"publisher","DOI":"10.1177\/002224379903600203"},{"key":"bibr18-jmr.16.0163","doi-asserted-by":"publisher","DOI":"10.1509\/jmr.13.0377"},{"key":"bibr19-jmr.16.0163","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"bibr20-jmr.16.0163","doi-asserted-by":"publisher","DOI":"10.1509\/jmkg.71.2.094"},{"key":"bibr21-jmr.16.0163","unstructured":"FaderPeter S., and HardieBruce G.S. 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