IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v181y2007i1p436-452.html
   My bibliography  Save this article

Improved customer choice predictions using ensemble methods

Author

Listed:
  • van Wezel, Michiel
  • Potharst, Rob

Abstract

No abstract is available for this item.

Suggested Citation

  • van Wezel, Michiel & Potharst, Rob, 2007. "Improved customer choice predictions using ensemble methods," European Journal of Operational Research, Elsevier, vol. 181(1), pages 436-452, August.
    • Handle: RePEc:eee:ejores:v:181:y:2007:i:1:p:436-452
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377-2217(06)00390-0
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

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

    References listed on IDEAS

    as
    1. Peter M. Guadagni & John D. C. Little, 1983. "A Logit Model of Brand Choice Calibrated on Scanner Data," Marketing Science, INFORMS, vol. 2(3), pages 203-238.
    2. van Wezel, M.C. & Potharst, R., 2005. "Improved customer choice predictions using ensemble methods," Econometric Institute Research Papers EI 2005-08, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. Hu, Michael Y. & Tsoukalas, Christos, 2003. "Explaining consumer choice through neural networks: The stacked generalization approach," European Journal of Operational Research, Elsevier, vol. 146(3), pages 650-660, May.
    4. Gurumurthy Kalyanaram & Russell S. Winer, 1995. "Empirical Generalizations from Reference Price Research," Marketing Science, INFORMS, vol. 14(3_supplem), pages 161-169.
    5. 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.
    6. Winer, Russell S, 1986. "A Reference Price Model of Brand Choice for Frequently Purchased Products," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 13(2), pages 250-256, September.
    7. Franses,Philip Hans & Paap,Richard, 2010. "Quantitative Models in Marketing Research," Cambridge Books, Cambridge University Press, number 9780521143653.
    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. T H Moon & S Y Sohn, 2010. "Technology credit scoring model considering both SME characteristics and economic conditions: The Korean case," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(4), pages 666-675, April.
    2. Cang, Shuang & Yu, Hongnian, 2014. "A combination selection algorithm on forecasting," European Journal of Operational Research, Elsevier, vol. 234(1), pages 127-139.
    3. Y Hayashi & M-H Hsieh & R Setiono, 2009. "Predicting consumer preference for fast-food franchises: a data mining approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(9), pages 1221-1229, September.
    4. Koen W. de Bock, 2017. "The best of two worlds: Balancing model strength and comprehensibility in business failure prediction using spline-rule ensembles," Post-Print hal-01588059, HAL.
    5. Höppner, Sebastiaan & Stripling, Eugen & Baesens, Bart & Broucke, Seppe vanden & Verdonck, Tim, 2020. "Profit driven decision trees for churn prediction," European Journal of Operational Research, Elsevier, vol. 284(3), pages 920-933.
    6. Farid Bagheri & Diego Reforgiato Recupero & Espen Sirnes, 2023. "Leveraging Return Prediction Approaches for Improved Value-at-Risk Estimation," Data, MDPI, vol. 8(8), pages 1-22, August.
    7. Chen, Shiuann-Shuoh & Choubey, Bhaskar & Singh, Vinay, 2021. "A neural network based price sensitive recommender model to predict customer choices based on price effect," Journal of Retailing and Consumer Services, Elsevier, vol. 61(C).
    8. K. W. De Bock & D. Van Den Poel, 2011. "An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 11/717, Ghent University, Faculty of Economics and Business Administration.
    9. Lessmann, Stefan & Sung, Ming-Chien & Johnson, Johnnie E.V. & Ma, Tiejun, 2012. "A new methodology for generating and combining statistical forecasting models to enhance competitive event prediction," European Journal of Operational Research, Elsevier, vol. 218(1), pages 163-174.
    10. Tripathi Manas & Kumar Saurabh & Inani Sarveshwar Kumar, 2021. "Exchange Rate Forecasting Using Ensemble Modeling for Better Policy Implications," Journal of Time Series Econometrics, De Gruyter, vol. 13(1), pages 43-71, January.
    11. Abellán, Joaquín & Masegosa, Andrés R., 2010. "An ensemble method using credal decision trees," European Journal of Operational Research, Elsevier, vol. 205(1), pages 218-226, August.
    12. 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.
    13. Tran, Duc-Hoc & Luong, Duc-Long & Chou, Jui-Sheng, 2020. "Nature-inspired metaheuristic ensemble model for forecasting energy consumption in residential buildings," Energy, Elsevier, vol. 191(C).
    14. Ngai, Eric W.T. & Wu, Yuanyuan, 2022. "Machine learning in marketing: A literature review, conceptual framework, and research agenda," Journal of Business Research, Elsevier, vol. 145(C), pages 35-48.
    15. Haven, Emmanuel & Liu, Xiaoquan & Shen, Liya, 2012. "De-noising option prices with the wavelet method," European Journal of Operational Research, Elsevier, vol. 222(1), pages 104-112.

    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. 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.
    2. van Wezel, M.C. & Potharst, R., 2005. "Improved customer choice predictions using ensemble methods," Econometric Institute Research Papers EI 2005-08, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. Priya Jha-Dang, 2006. "A Review of Psychological Research on Consumer Promotions and a New Perspective Based on Mental Accounting," Vision, , vol. 10(3), pages 35-43, July.
    4. Necati Tereyağoğlu & Peter S. Fader & Senthil Veeraraghavan, 2018. "Multiattribute Loss Aversion and Reference Dependence: Evidence from the Performing Arts Industry," Management Science, INFORMS, vol. 64(1), pages 421-436, January.
    5. Yasemin Boztug & Lutz Hildebrandt, 2005. "An empirical test of theories of price valuation using a semiparametric approach, reference prices, and accounting for heterogeneity," SFB 649 Discussion Papers SFB649DP2005-057, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    6. Kopalle, Praveen K. & Kannan, P.K. & Boldt, Lin Bao & Arora, Neeraj, 2012. "The impact of household level heterogeneity in reference price effects on optimal retailer pricing policies," Journal of Retailing, Elsevier, vol. 88(1), pages 102-114.
    7. David R. Bell & James M. Lattin, 2000. "Looking for Loss Aversion in Scanner Panel Data: The Confounding Effect of Price Response Heterogeneity," Marketing Science, INFORMS, vol. 19(2), pages 185-200, May.
    8. Tülin Erdem & Michael Katz & Baohong Sun, 2010. "A simple test for distinguishing between internal reference price theories," Quantitative Marketing and Economics (QME), Springer, vol. 8(3), pages 303-332, September.
    9. Alam Kazmi, Syed Hasnain, 2015. "Developments in Promotion Strategies Review on Psychological Streams of Consumers," MPRA Paper 65424, University Library of Munich, Germany, revised 05 May 2015.
    10. Bernhard Baumgartner & Daniel Guhl & Thomas Kneib & Winfried J. Steiner, 2018. "Flexible estimation of time-varying effects for frequently purchased retail goods: a modeling approach based on household panel data," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(4), pages 837-873, October.
    11. Moon, Sangkil & Voss, Glenn, 2009. "How do price range shoppers differ from reference price point shoppers?," Journal of Business Research, Elsevier, vol. 62(1), pages 31-38, January.
    12. van Oest, Rutger, 2013. "Why are Consumers Less Loss Averse in Internal than External Reference Prices?," Journal of Retailing, Elsevier, vol. 89(1), pages 62-71.
    13. Guhl, Daniel & Baumgartner, Bernhard & Kneib, Thomas & Steiner, Winfried J., 2018. "Estimating time-varying parameters in brand choice models: A semiparametric approach," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 394-414.
    14. Gonul, Fusun F., 1998. "Estimating price expectations in the OTC medicine market: An application of dynamic stochastic discrete choice models to scanner panel data," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 41-56, November.
    15. 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.
    16. David R. Bell & Jeongwen Chiang & V. Padmanabhan, 1999. "The Decomposition of Promotional Response: An Empirical Generalization," Marketing Science, INFORMS, vol. 18(4), pages 504-526.
    17. Wang, Fei & Yuan, Yu & Lu, Liangdong, 2021. "Dynamical prediction model of consumers’ purchase intentions regarding anti-smog products during smog risk: Taking the information flow perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    18. Biondi, Beatrice & Cornelsen, Laura & Mazzocchi, Mario & Smith, Richard, 2020. "Between preferences and references: Asymmetric price elasticities and the simulation of fiscal policies," Journal of Economic Behavior & Organization, Elsevier, vol. 180(C), pages 108-128.
    19. Chen, Xiao & Wu, Tian & Zheng, Rui & Guo, Xiaoxian, 2018. "How vehicle market is segmented and influenced by subsidy policy: A theoretical study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 776-782.
    20. Zainurin Bin Dahar & Husam A. Kokash & Fadilah Sulaiman, 2017. "Can Price Sway Children Preference? Preliminary Experiment among Malay Children," International Review of Management and Marketing, Econjournals, vol. 7(1), pages 492-498.

    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:eee:ejores:v:181:y:2007:i:1:p:436-452. 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/eor .

    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.