IDEAS home Printed from https://ideas.repec.org/p/ems/eureir/7038.html
   My bibliography  Save this paper

Solving and interpreting binary classification problems in marketing with SVMs

Author

Listed:
  • Bioch, J.C.
  • Groenen, P.J.F.
  • Nalbantov, G.I.

Abstract

Marketing problems often involve inary classification of customers into ``buyers'' versus ``non-buyers'' or ``prefers brand A'' versus ``prefers brand B''. These cases require binary classification models such as logistic regression, linear, and quadratic discriminant analysis. A promising recent technique for the binary classification problem is the Support Vector Machine (Vapnik (1995)), which has achieved outstanding results in areas ranging from Bioinformatics to Finance. In this paper, we compare the performance of the Support Vector Machine against standard binary classification techniques on a marketing data set and elaborate on the interpretation of the obtained results.

Suggested Citation

  • Bioch, J.C. & Groenen, P.J.F. & Nalbantov, G.I., 2005. "Solving and interpreting binary classification problems in marketing with SVMs," Econometric Institute Research Papers EI 2005-46, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:7038
    as

    Download full text from publisher

    File URL: https://repub.eur.nl/pub/7038/ei2005-46.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Franses,Philip Hans & Paap,Richard, 2010. "Quantitative Models in Marketing Research," Cambridge Books, Cambridge University Press, number 9780521143653, October.
    2. 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.
    3. Makoto Abe, 1995. "A Nonparametric Density Estimation Method for Brand Choice Using Scanner Data," Marketing Science, INFORMS, vol. 14(3), pages 300-325.
    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. Andrey Zahariev & Mikhail Zveryаkov & Stoyan Prodanov & Galina Zaharieva & Petko Angelov & Silvia Zarkova & Mariana Petrova, 2020. "Debt management evaluation through Support Vector Machines: on the example of Italy and Greece," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 7(3), pages 2382-2393, March.

    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. Makoto Abe & Yasemin Boztug & Lutz Hildebrandt, 2004. "Investigating the competitive assumption of Multinomial Logit models of brand choice by nonparametric modeling," Computational Statistics, Springer, vol. 19(4), pages 635-657, December.
    2. Potharst, R. & van Rijthoven, M. & van Wezel, M.C., 2005. "Modeling brand choice using boosted and stacked neural networks," Econometric Institute Research Papers EI 2005-05, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. Risselada, Hans & Verhoef, Peter C. & Bijmolt, Tammo H.A., 2010. "Staying Power of Churn Prediction Models," Journal of Interactive Marketing, Elsevier, vol. 24(3), pages 198-208.
    4. Polo, Yolanda & Sese, F. Javier & Verhoef, Peter C., 2011. "The Effect of Pricing and Advertising on Customer Retention in a Liberalizing Market," Journal of Interactive Marketing, Elsevier, vol. 25(4), pages 201-214.
    5. Edwin Van Gameren & Michiel Ras & Evelien Eggink & Ingrid Ooms, 2005. "The demand for housing services in the Netherlands," ERSA conference papers ersa05p327, European Regional Science Association.
    6. Saiful Anwar & A.M Hasan Ali, 2018. "ANNs-BASED EARLY WARNING SYSTEM FOR INDONESIAN ISLAMIC BANKS," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 20(3), pages 325-342, January.
    7. Franses, Philip Hans, 2006. "Forecasting in Marketing," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 18, pages 983-1012, Elsevier.
    8. Shao, Wei & Lye, Ashley & Rundle-Thiele, Sharyn, 2009. "Different strokes for different folks: A method to accommodate decision -making heterogeneity," Journal of Retailing and Consumer Services, Elsevier, vol. 16(6), pages 495-501.
    9. Clarijs, P. & Hogeling, B. & Franses, Ph.H.B.F. & Heij, C., 2007. "Evaluation of survey effects in pre-election polls," Econometric Institute Research Papers EI 2007-50, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    10. Wiktor Adamowicz & David Bunch & Trudy Cameron & Benedict Dellaert & Michael Hanneman & Michael Keane & Jordan Louviere & Robert Meyer & Thomas Steenburgh & Joffre Swait, 2008. "Behavioral frontiers in choice modeling," Marketing Letters, Springer, vol. 19(3), pages 215-228, December.
    11. Sibdari, Soheil & Pyke, David F., 2010. "A competitive dynamic pricing model when demand is interdependent over time," European Journal of Operational Research, Elsevier, vol. 207(1), pages 330-338, November.
    12. Romina Gambacorta & Maria Iannario, 2012. "Statistical models for measuring job satisfaction," Temi di discussione (Economic working papers) 852, Bank of Italy, Economic Research and International Relations Area.
    13. Abdelfatah Ichou, 2010. "Modelling the Determinants of Job Creation: Microeconometric Models Accounting for Latent Entrepreneurial Ability," Scales Research Reports H201018, EIM Business and Policy Research.
    14. Fabio Luis Marques dos Santos & Paolo Tecchio & Fulvio Ardente & Ferenc Pekár, 2021. "User Automotive Powertrain-Type Choice Model and Analysis Using Neural Networks," Sustainability, MDPI, vol. 13(2), pages 1-15, January.
    15. Hruschka, Harald & Fettes, Werner & Probst, Markus, 2004. "An empirical comparison of the validity of a neural net based multinomial logit choice model to alternative model specifications," European Journal of Operational Research, Elsevier, vol. 159(1), pages 166-180, November.
    16. repec:dgr:rugsom:99b35 is not listed on IDEAS
    17. Richard Paap, 2002. "What are the advantages of MCMC based inference in latent variable models?," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 56(1), pages 2-22, February.
    18. Abe, Makoto & Boztuæg, Yasemin & Hildebrandt, Lutz, 2000. "Investigation of the stochastic utility maximization process of consumer brand choice by semiparametric modeling," SFB 373 Discussion Papers 2000,84, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    19. Armstrong, J. Scott & Brodie, Roderick J., 1999. "Forecasting for Marketing," MPRA Paper 81690, University Library of Munich, Germany.
    20. Steven M. Ramsey & Jason S. Bergtold, 2021. "Examining Inferences from Neural Network Estimators of Binary Choice Processes: Marginal Effects, and Willingness-to-Pay," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1137-1165, December.
    21. Bas Donkers & Peter Verhoef & Martijn Jong, 2007. "Modeling CLV: A test of competing models in the insurance industry," Quantitative Marketing and Economics (QME), Springer, vol. 5(2), pages 163-190, June.

    More about this item

    Statistics

    Access and download statistics

    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:ems:eureir:7038. 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: RePub (email available below). General contact details of provider: https://edirc.repec.org/data/feeurnl.html .

    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.