IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v67y2014i1p2751-2758.html
   My bibliography  Save this article

Data accuracy's impact on segmentation performance: Benchmarking RFM analysis, logistic regression, and decision trees

Author

Listed:
  • Coussement, Kristof
  • Van den Bossche, Filip A.M.
  • De Bock, Koen W.

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.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jbrese:v:67:y:2014:i:1:p:2751-2758
    DOI: 10.1016/j.jbusres.2012.09.024
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148296312002615
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jbusres.2012.09.024?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. YongSeog Kim & W. Nick Street & Gary J. Russell & Filippo Menczer, 2005. "Customer Targeting: A Neural Network Approach Guided by Genetic Algorithms," Management Science, INFORMS, vol. 51(2), pages 264-276, February.
    2. Cortiñas, Mónica & Chocarro, Raquel & Villanueva, María Luisa, 2010. "Understanding multi-channel banking customers," Journal of Business Research, Elsevier, vol. 63(11), pages 1215-1221, November.
    3. Akaah, Ishmael P. & Korgaonkar, Pradeep K. & Lund, Daulatram, 1995. "Direct marketing attitudes," Journal of Business Research, Elsevier, vol. 34(3), pages 211-219, November.
    4. Gabriel R. Bitran & Susana V. Mondschein, 1996. "Mailing Decisions in the Catalog Sales Industry," Management Science, INFORMS, vol. 42(9), pages 1364-1381, September.
    5. G. V. Kass, 1980. "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 119-127, June.
    6. Crone, Sven F. & Lessmann, Stefan & Stahlbock, Robert, 2006. "The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing," European Journal of Operational Research, Elsevier, vol. 173(3), pages 781-800, September.
    7. Morganosky, Michelle A. & Fernie, John, 1999. "Mail Order Direct Marketing in the United States and the United Kingdom: Responses to Changing Market Conditions," Journal of Business Research, Elsevier, vol. 45(3), pages 275-279, July.
    8. K. Coussement & D. Van Den Poel, 2008. "Integrating the Voice of Customers through Call Center Emails into a Decision Support System for Churn Prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/502, Ghent University, Faculty of Economics and Business Administration.
    9. Geng Cui & Man Leung Wong & Hon-Kwong Lui, 2006. "Machine Learning for Direct Marketing Response Models: Bayesian Networks with Evolutionary Programming," Management Science, INFORMS, vol. 52(4), pages 597-612, April.
    10. William H. DeLone & Ephraim R. McLean, 1992. "Information Systems Success: The Quest for the Dependent Variable," Information Systems Research, INFORMS, vol. 3(1), pages 60-95, March.
    11. 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.
    12. Ko, Eunju & Kim, Sook Hyun & Kim, Myungsoo & Woo, Ji Young, 2008. "Organizational characteristics and the CRM adoption process," Journal of Business Research, Elsevier, vol. 61(1), pages 65-74, January.
    13. Merrilees, Bill & Miller, Dale, 2010. "Brand morphing across Wal-Mart customer segments," Journal of Business Research, Elsevier, vol. 63(11), pages 1129-1134, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. 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.
    3. 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, logis," Journal of Retailing and Consumer Services, Elsevier, vol. 80(C).
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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).
    10. Arno de Caigny & Kristof Coussement & Koen de Bock, 2020. "Leveraging fine-grained transaction data for customer life event predictions," Post-Print hal-02507998, HAL.
    11. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bose, Indranil & Chen, Xi, 2009. "Quantitative models for direct marketing: A review from systems perspective," European Journal of Operational Research, Elsevier, vol. 195(1), pages 1-16, May.
    2. 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.
    3. Fan, Zhi-Ping & Sun, Minghe, 2015. "Behavior-aware user response modeling in social media: Learning from diverse heterogeneous dataAuthor-Name: Chen, Zhen-Yu," European Journal of Operational Research, Elsevier, vol. 241(2), pages 422-434.
    4. David Olson & Qing Cao & Ching Gu & Donhee Lee, 2009. "Comparison of customer response models," Service Business, Springer;Pan-Pacific Business Association, vol. 3(2), pages 117-130, June.
    5. I. Albarrán & P. Alonso-González & J. M. Marin, 2017. "Some criticism to a general model in Solvency II: an explanation from a clustering point of view," Empirical Economics, Springer, vol. 52(4), pages 1289-1308, June.
    6. Bas Donkers & Richard Paap & Jedid‐Jah Jonker & Philip Hans Franses, 2006. "Deriving target selection rules from endogenously selected samples," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(5), pages 549-562, July.
    7. Mustak, Mekhail & Salminen, Joni & Plé, Loïc & Wirtz, Jochen, 2021. "Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda," Journal of Business Research, Elsevier, vol. 124(C), pages 389-404.
    8. 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.
    9. 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.
    10. Legohérel, Patrick & Hsu, Cathy H.C. & Daucé, Bruno, 2015. "Variety-seeking: Using the CHAID segmentation approach in analyzing the international traveler market," Tourism Management, Elsevier, vol. 46(C), pages 359-366.
    11. Roland T. Rust & Ming-Hui Huang, 2014. "The Service Revolution and the Transformation of Marketing Science," Marketing Science, INFORMS, vol. 33(2), pages 206-221, March.
    12. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Causal Machine-Learning Approach," Papers 2103.10251, arXiv.org, revised Sep 2021.
    13. Lessmann, Stefan & Voß, Stefan, 2009. "A reference model for customer-centric data mining with support vector machines," European Journal of Operational Research, Elsevier, vol. 199(2), pages 520-530, December.
    14. do Valle, Patrícia Oom & Pintassilgo, Pedro & Matias, António & André, Filipe, 2012. "Tourist attitudes towards an accommodation tax earmarked for environmental protection: A survey in the Algarve," Tourism Management, Elsevier, vol. 33(6), pages 1408-1416.
    15. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Machine-Learning Approach," CESifo Working Paper Series 9037, CESifo.
    16. Celal Hakan Kagnicioglu & Mune Mogol, 2014. "Implementation of Chaid Algorithm: A Hotel Case," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 3(4), pages 42-51, October.
    17. Ralf Elsner & Manfred Krafft & Arnd Huchzermeier, 2003. "Optimizing Rhenania's Mail-Order Business Through Dynamic Multilevel Modeling (DMLM)," Interfaces, INFORMS, vol. 33(1), pages 50-66, February.
    18. Danijel Bratina & Armand Faganel, 2023. "Using Supervised Machine Learning Methods for RFM Segmentation: A Casino Direct Marketing Communication Case," Tržište/Market, Faculty of Economics and Business, University of Zagreb, vol. 35(1), pages 7-22.
    19. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    20. Baumgartner, Bernhard & Hruschka, Harald, 2005. "Allocation of catalogs to collective customers based on semiparametric response models," European Journal of Operational Research, Elsevier, vol. 162(3), pages 839-849, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jbrese:v:67:y:2014:i:1:p:2751-2758. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jbusres .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.