Comparison of customer response models
Segmentation of customers by likelihood of repeating business is a very important tool in marketing management. A number of approaches have been developed to support this activity. This article reviews basic recency, frequency, and monetary (RFM) methods on a set of data involving the sale of beef products. Variants of RFM are demonstrated. Classical data mining techniques of logistic regression, decision trees, and neural networks are also demonstrated. Results indicate a spectrum of tradeoffs. RFM methods are simpler, but less accurate. Considerations of balancing cell sizes as well as compressing data are examined. Both balancing expected cell densities as well as compressing RFM variables into a value function were found to provide more accurate models. Data mining algorithms were all found to provide a noticeable increase in predictive accuracy. Relative tradeoffs among these data mining algorithms in the context of customer segmentation are discussed. Copyright Springer-Verlag 2009
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 3 (2009)
Issue (Month): 2 (June)
|Contact details of provider:|| Web page: http://www.springer.com|
Web page: http://www.panpacificbusiness.org/
|Order Information:||Web: http://www.springer.com/business/journal/11628|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- Baesens, Bart & Viaene, Stijn & Van den Poel, Dirk & Vanthienen, Jan & Dedene, Guido, 2002. "Bayesian neural network learning for repeat purchase modelling in direct marketing," European Journal of Operational Research, Elsevier, vol. 138(1), pages 191-211, April.
- 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.
When requesting a correction, please mention this item's handle: RePEc:spr:svcbiz:v:3:y:2009:i:2:p:117-130. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla)or (Rebekah McClure)
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 references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.