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Modeling consideration sets and brand choice using artificial neural networks

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  • Vroomen, Bjorn
  • Hans Franses, Philip
  • van Nierop, Erjen

Abstract

The concept of consideration sets makes brand choice a two-step process. House-holds first construct a consideration set which not necessarily includes all available brands and conditional on this set they make a final choice. In this paper we put forward a parametric econometric model for this two-step process, where we take into account that consideration sets usually are not observed. It turns out that our model is an artificial neural network, where the consideration set corresponds with the hidden layer. We discuss representation, parameter estimation and inference. We illustrate our model for the choice between six detergent brands and show that the model improves upon a one-step multinomial logit model, in terms of fit and out-of-sample forecasting.
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Suggested Citation

  • Vroomen, Bjorn & Hans Franses, Philip & van Nierop, Erjen, 2004. "Modeling consideration sets and brand choice using artificial neural networks," European Journal of Operational Research, Elsevier, vol. 154(1), pages 206-217, April.
  • Handle: RePEc:eee:ejores:v:154:y:2004:i:1:p:206-217
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    References listed on IDEAS

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    1. Dasgupta, Chanda Ghose & Dispensa, Gary S. & Ghose, Sanjoy, 1994. "Comparing the predictive performance of a neural network model with some traditional market response models," International Journal of Forecasting, Elsevier, vol. 10(2), pages 235-244, September.
    2. Hruschka, Harald, 1993. "Determining market response functions by neural network modeling: A comparison to econometric techniques," European Journal of Operational Research, Elsevier, vol. 66(1), pages 27-35, April.
    3. Hausman, Jerry A & Wise, David A, 1978. "A Conditional Probit Model for Qualitative Choice: Discrete Decisions Recognizing Interdependence and Heterogeneous Preferences," Econometrica, Econometric Society, vol. 46(2), pages 403-426, March.
    4. Zhang, Guoqiang & Y. Hu, Michael & Eddy Patuwo, B. & C. Indro, Daniel, 1999. "Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis," European Journal of Operational Research, Elsevier, vol. 116(1), pages 16-32, July.
    5. 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.
    6. Manrai, Ajay K. & Andrews, Rick L., 1998. "Two-stage discrete choice models for scanner panel data: An assessment of process and assumptions," European Journal of Operational Research, Elsevier, vol. 111(2), pages 193-215, December.
    7. 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.
    8. A. Stewart Fotheringham, 1988. "Note—Consumer Store Choice and Choice Set Definition," Marketing Science, INFORMS, vol. 7(3), pages 299-310.
    9. Chintagunta, Pradeep K & Prasad, Alok R, 1998. "An Empirical Investigation of the "Dynamic McFadden" Model of Purchase Timing and Brand Choice: Implications for Market Structure," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(1), pages 2-12, January.
    10. Chiang, Jeongwen & Chib, Siddhartha & Narasimhan, Chakravarthi, 1998. "Markov chain Monte Carlo and models of consideration set and parameter heterogeneity," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 223-248, November.
    11. Franses, Philip Hans & Draisma, Gerrit, 1997. "Recognizing changing seasonal patterns using artificial neural networks," Journal of Econometrics, Elsevier, vol. 81(1), pages 273-280, November.
    12. Swanson, Norman R & White, Halbert, 1995. "A Model-Selection Approach to Assessing the Information in the Term Structure Using Linear Models and Artificial Neural Networks," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 265-275, July.
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    Cited by:

    1. repec:pal:jorsoc:v:60:y:2009:i:9:d:10.1057_palgrave.jors.2602646 is not listed on IDEAS
    2. repec:rnd:arimbr:v:2:y:2011:i:4:p:162-172 is not listed on IDEAS
    3. Hauser, John R., 2014. "Consideration-set heuristics," Journal of Business Research, Elsevier, vol. 67(8), pages 1688-1699.
    4. 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.

    More about this item

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • M - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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