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—Structural Demand Estimation with Varying Product Availability

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  • Hernán A. Bruno

    (Erasmus School of Economics, Erasmus University Rotterdam, 3000 DR Rotterdam, The Netherlands)

  • Naufel J. Vilcassim

    (London Business School, London NW1 4SA, United Kingdom)

Abstract

This paper develops a model that extends the traditional aggregate discrete-choice-based demand model (e.g. Berry et al. 1995) to account for varying levels of product availability. In cases where not all products are available at every consumer shopping trip, the observed market share is a convolution of two factors: consumer preferences and the availability of the product in stores. Failing to account for the varying degree of availability would produce incorrect estimates of the demand parameters. The proposed model uses information on aggregate availability to simulate the potential assortments that consumers may face in a given shopping trip. The model parameters are estimated by simulating potential product assortment vectors by drawing multivariate Bernoulli vectors consistent with the observed aggregate level of availability. The model is applied to the UK chocolate confectionery market, focusing on the convenience store channel. We compare the parameter estimates to those obtained from not accounting for varying availability and analyze some of the substantive implications.

Suggested Citation

  • Hernán A. Bruno & Naufel J. Vilcassim, 2008. "—Structural Demand Estimation with Varying Product Availability," Marketing Science, INFORMS, vol. 27(6), pages 1126-1131, 11-12.
    • Handle: RePEc:inm:ormksc:v:27:y:2008:i:6:p:1126-1131
    DOI: 10.1287/mksc.1080.0366
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    References listed on IDEAS

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    6. Sergei Koulayev, 2009. "Estimating demand in search markets: the case of online hotel bookings," Working Papers 09-16, Federal Reserve Bank of Boston.
    7. Hickman, William & Mortimer, Julie Holland, 2016. "Demand Estimation with Availability Variation," SocArXiv qe69j, Center for Open Science.
    8. van Ewijk, Bernadette J. & Gijsbrechts, Els & Steenkamp, Jan-Benedict E.M., 2022. "What drives brands’ price response metrics? An empirical examination of the Chinese packaged goods industry," International Journal of Research in Marketing, Elsevier, vol. 39(1), pages 288-312.
    9. Jun B. Kim & Paulo Albuquerque & Bart J. Bronnenberg, 2010. "Online Demand Under Limited Consumer Search," Marketing Science, INFORMS, vol. 29(6), pages 1001-1023, 11-12.
    10. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2012. "Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content," Marketing Science, INFORMS, vol. 31(3), pages 493-520, May.
    11. In Kyung Kim & Vladyslav Nora, 2020. "Does vertical integration enhance non-price efficiency? Evidence from the movie theater industry," Review of Economic Design, Springer;Society for Economic Design, vol. 24(3), pages 143-170, December.
    12. Lu, Zhentong, 2022. "Estimating multinomial choice models with unobserved choice sets," Journal of Econometrics, Elsevier, vol. 226(2), pages 368-398.
    13. Minha Hwang & Raphael Thomadsen, 2016. "How Point-of-Sale Marketing Mix Impacts National-Brand Purchase Shares," Management Science, INFORMS, vol. 62(2), pages 571-590, February.
    14. Patalinghug, Jason C., 2013. "The Effect of Advertising and In-Store Promotion on the Demand for Chocolate," Working Paper series 159981, University of Connecticut, Charles J. Zwick Center for Food and Resource Policy.
    15. K. Sudhir & Nathan Yang, 2014. "Exploiting the Choice-Consumption Mismatch: A New Approach to Disentangle State Dependence and Heterogeneity," Cowles Foundation Discussion Papers 1941, Cowles Foundation for Research in Economics, Yale University.
    16. Andrés Musalem & Marcelo Olivares & Eric T. Bradlow & Christian Terwiesch & Daniel Corsten, 2010. "Structural Estimation of the Effect of Out-of-Stocks," Management Science, INFORMS, vol. 56(7), pages 1180-1197, July.
    17. Mou, Shandong & Robb, David J. & DeHoratius, Nicole, 2018. "Retail store operations: Literature review and research directions," European Journal of Operational Research, Elsevier, vol. 265(2), pages 399-422.
    18. Jean-Pierre Dubé & Ali Hortaçsu & Joonhwi Joo, 2021. "Random-Coefficients Logit Demand Estimation with Zero-Valued Market Shares," Marketing Science, INFORMS, vol. 40(4), pages 637-660, July.
    19. Boone, Tonya & Ganeshan, Ram & Jain, Aditya & Sanders, Nada R., 2019. "Forecasting sales in the supply chain: Consumer analytics in the big data era," International Journal of Forecasting, Elsevier, vol. 35(1), pages 170-180.
    20. Gonca P. Soysal & Lakshman Krishnamurthi, 2012. "Demand Dynamics in the Seasonal Goods Industry: An Empirical Analysis," Marketing Science, INFORMS, vol. 31(2), pages 293-316, March.

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