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Data Accuracy's Impact on Segmentation Performance: Benchmarking RFM Analysis, Logistic Regression, and Decision Trees

Author

Listed:
  • K. Coussement

    (LEM - Lille - Economie et Management - Université de Lille, Sciences et Technologies - CNRS - Centre National de la Recherche Scientifique)

  • F.A.M. van den Bossche
  • K.W. de Bock

    (LEM - Lille - Economie et Management - Université de Lille, Sciences et Technologies - CNRS - Centre National de la Recherche Scientifique)

Abstract

Companies greatly benefit from knowing how problems with data quality influence the performance of segmentation techniques and which techniques are more robust to these problems than others. This study investigates the influence of problems with data accuracy – an important dimension of data quality – on three prominent segmentation techniques for direct marketing: RFM (recency, frequency, and monetary value) analysis, logistic regression, and decision trees. For two real-life direct marketing data sets analyzed, the results demonstrate that (1) under optimal data accuracy, decision trees are preferred over RFM analysis and logistic regression; (2) the introduction of data accuracy problems deteriorates the performance of all three segmentation techniques; and (3) as data becomes less accurate, decision trees retain superior to logistic regression and RFM analysis. Overall, this study recommends the use of decision trees in the context of customer segmentation for direct marketing, even under the suspicion of data accuracy problems.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • 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.
    • Handle: RePEc:hal:journl:hal-00788060
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    2. Chen, Yanhong & Liu, Luning & Zheng, Dequan & Li, Bin, 2023. "Estimating travellers’ value when purchasing auxiliary services in the airline industry based on the RFM model," Journal of Retailing and Consumer Services, Elsevier, vol. 74(C).
    3. Dolnicar, Sara & Grün, Bettina & Leisch, Friedrich, 2016. "Increasing sample size compensates for data problems in segmentation studies," Journal of Business Research, Elsevier, vol. 69(2), pages 992-999.
    4. Wang, Ying & Lan, Jiahui & Pan, Jialing & Fang, Lin, 2024. "How do consumers’ attitudes differ across their basic characteristics toward live-streaming commerce of green agricultural products: A preliminary exploration based on correspondence analysis, logistic regression and decision tree," Journal of Retailing and Consumer Services, Elsevier, vol. 80(C).
    5. Patel, Ronit & Perret, Jens K. & Samunderu, Eyden, 2022. "Unbundling CRM – A RFMC Perspective," Research Journal for Applied Management (RJAM), International School of Management (ISM), Dortmund, vol. 3(1), pages 37-52.
    6. De Caigny, Arno & Coussement, Kristof & Hoornaert, Steven & Meire, Matthijs, 2025. "Life event-based marketing using AI," Journal of Business Research, Elsevier, vol. 193(C).
    7. Lingfeng Dong & Ting Ji & Jie Zhang, 2022. "Effects of Conversation Politeness on Hiring Decision in Online Labor Markets: An Inverted U-Shaped Relationship Exploration," Sustainability, MDPI, vol. 14(22), pages 1-11, November.
    8. Horvat Ivan & Pejić Bach Mirjana & Merkač Skok Marjana, 2014. "Decision Tree Approach to Discovering Fraud in Leasing Agreements," Business Systems Research, Sciendo, vol. 5(2), pages 61-71, September.
    9. Marco Vriens & Nathan Bosch & Chad Vidden & Jason Talwar, 2022. "Prediction and profitability in market segmentation typing tools," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(4), pages 360-389, December.
    10. Li, Yixin & Hou, Bingzhang & Wu, Yue & Zhao, Donglai & Xie, Aoran & Zou, Peng, 2021. "Giant fight: Customer churn prediction in traditional broadcast industry," Journal of Business Research, Elsevier, vol. 131(C), pages 630-639.
    11. Azarnoush Ansari & Arash Riasi, 2016. "Customer Clustering Using a Combination of Fuzzy C-Means and Genetic Algorithms," International Journal of Business and Management, Canadian Center of Science and Education, vol. 11(7), pages 1-59, June.
    12. Chen, Song & Qiu, Yongqin & Li, Jingmao & Fang, Kan & Fang, Kuangnan, 2023. "Precision marketing for financial industry using a PU-learning recommendation method," Journal of Business Research, Elsevier, vol. 160(C).
    13. Miguel Alves Gomes & Tobias Meisen, 2023. "A review on customer segmentation methods for personalized customer targeting in e-commerce use cases," Information Systems and e-Business Management, Springer, vol. 21(3), pages 527-570, September.
    14. Arno de Caigny & Kristof Coussement & Koen de Bock, 2020. "Leveraging fine-grained transaction data for customer life event predictions," Post-Print hal-02507998, HAL.
    15. Joni Salminen & Mekhail Mustak & Muhammad Sufyan & Bernard J. Jansen, 2023. "How can algorithms help in segmenting users and customers? A systematic review and research agenda for algorithmic customer segmentation," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 677-692, December.

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