Prediction and profitability in market segmentation typing tools
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DOI: 10.1057/s41270-021-00145-4
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- Pires, Guilherme D. & Stanton, John & Stanton, Patricia, 2011. "Revisiting the substantiality criterion: From ethnic marketing to market segmentation," Journal of Business Research, Elsevier, vol. 64(9), pages 988-996, September.
- Mora Cortez, Roberto & Højbjerg Clarke, Ann & Freytag, Per Vagn, 2021. "B2B market segmentation: A systematic review and research agenda," Journal of Business Research, Elsevier, vol. 126(C), pages 415-428.
- Georgios Marinakos & Sophia Daskalaki, 2017. "Imbalanced customer classification for bank direct marketing," Journal of Marketing Analytics, Palgrave Macmillan, vol. 5(1), pages 14-30, March.
- 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.
- K. Coussement & F.A.M. van den Bossche & K.W. de Bock, 2012. "Data Accuracy's Impact on Segmentation Performance: Benchmarking RFM Analysis, Logistic Regression, and Decision Trees," Post-Print hal-00788060, HAL.
- Gilseung Ahn & You-Jin Park & Sun Hur, 2021. "A Membership Probability–Based Undersampling Algorithm for Imbalanced Data," Journal of Classification, Springer;The Classification Society, vol. 38(1), pages 2-15, April.
- 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.
- Patricia M. West & Patrick L. Brockett & Linda L. Golden, 1997. "A Comparative Analysis of Neural Networks and Statistical Methods for Predicting Consumer Choice," Marketing Science, INFORMS, vol. 16(4), pages 370-391.
- King, Gary & Zeng, Langche, 2001. "Logistic Regression in Rare Events Data," Political Analysis, Cambridge University Press, vol. 9(2), pages 137-163, January.
- Lemmens, A. & Croux, C., 2006. "Bagging and boosting classification trees to predict churn," Other publications TiSEM d5cb664d-5859-44db-a621-e, Tilburg University, School of Economics and Management.
- Paul H. Lee, 2014. "Resampling Methods Improve the Predictive Power of Modeling in Class-Imbalanced Datasets," IJERPH, MDPI, vol. 11(9), pages 1-14, September.
- K. Coussement & D. Van Den Poel, 2006.
"Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparing Two Parameter-Selection Techniques,"
Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium
06/412, Ghent University, Faculty of Economics and Business Administration.
- K. Coussement & D. van den Poel, 2008. "Churn prediction in subscription services: an application of support vector machines while comparing two parameter-selection techniques," Post-Print hal-00788096, HAL.
- Susan Brudvig & Michael J. Brusco & J. Dennis Cradit, 2019. "Joint selection of variables and clusters: recovering the underlying structure of marketing data," Journal of Marketing Analytics, Palgrave Macmillan, vol. 7(1), pages 1-12, March.
- Ying Liu & Sudha Ram & Robert F. Lusch & Michael Brusco, 2010. "Multicriterion Market Segmentation: A New Model, Implementation, and Evaluation," Marketing Science, INFORMS, vol. 29(5), pages 880-894, 09-10.
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- Maria Petrescu & Anjala S. Krishen, 2023. "Mapping 2022 in Journal of Marketing Analytics: what lies ahead?," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(1), pages 1-4, March.
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Keywords
Strategic market segmentation; Typing tools; Imbalance correction methods; Classification;All these keywords.
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