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Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression


  • McCarty, John A.
  • Hastak, Manoj


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  • McCarty, John A. & Hastak, Manoj, 2007. "Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression," Journal of Business Research, Elsevier, vol. 60(6), pages 656-662, June.
  • Handle: RePEc:eee:jbrese:v:60:y:2007:i:6:p:656-662

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    References listed on IDEAS

    1. Zahay, Debra & Peltier, James & Schultz, Don E. & Griffin, Abbie, 2004. "The Role of Transactional versus Relational Data in IMC Programs: Bringing Customer Data Together," Journal of Advertising Research, Cambridge University Press, vol. 44(01), pages 3-18, March.
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    Cited by:

    1. Gitae Kim & Bongsug Chae & David Olson, 2013. "A support vector machine (SVM) approach to imbalanced datasets of customer responses: comparison with other customer response models," Service Business, Springer;Pan-Pacific Business Association, vol. 7(1), pages 167-182, March.
    2. Horvat Ivan & Pejić Bach Mirjana & Merkač Skok Marjana, 2014. "Decision Tree Approach to Discovering Fraud in Leasing Agreements," Business Systems Research, De Gruyter Open, vol. 5(2), pages 61-71, September.
    3. Coussement, Kristof & Van den Bossche, Filip A.M. & De Bock, Koen W., 2014. "Data accuracy's impact on segmentation performance: Benchmarking RFM analysis, logistic regression, and decision trees," Journal of Business Research, Elsevier, vol. 67(1), pages 2751-2758.
    4. repec:spr:empeco:v:52:y:2017:i:4:d:10.1007_s00181-016-1107-3 is not listed on IDEAS
    5. Hache, Emmanuel & Leboullenger, Déborah & Mignon, Valérie, 2017. "Beyond average energy consumption in the French residential housing market: A household classification approach," Energy Policy, Elsevier, vol. 107(C), pages 82-95.
    6. repec:eee:touman:v:33:y:2012:i:6:p:1408-1416 is not listed on IDEAS
    7. Marín Díazaraque, Juan Miguel & Albarrán Lozano, Irene & Alonso, Pablo J., 2011. "Why using a general model in Solvency II is not a good idea : an explanation from a Bayesian point of view," DES - Working Papers. Statistics and Econometrics. WS ws113729, Universidad Carlos III de Madrid. Departamento de Estadística.
    8. M. Ballings & D. Van Den Poel, 2012. "The Relevant Length of Customer Event History for Churn Prediction: How long is long enough?," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/804, Ghent University, Faculty of Economics and Business Administration.
    9. 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.
    10. repec:eee:touman:v:46:y:2015:i:c:p:359-366 is not listed on IDEAS
    11. Pagn, Jos A. & Pratt, William R. & Sun, Jun, 2009. "Which physicians have access to electronic prescribing and which ones end up using it?," Health Policy, Elsevier, vol. 89(3), pages 288-294, March.
    12. repec:spr:scient:v:112:y:2017:i:2:d:10.1007_s11192-017-2399-6 is not listed on IDEAS

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