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A Dynamic Model of Cross-Category Competition: Theory, Tests and Applications

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  • Bandyopadhyay, Subir

Abstract

Recent marketing studies use scanner data to diagnose the influence of a change in a brand's marketing mix on other brands in the same category. A few studies also use scanner data to model inter-category effects between substitutes (e.g., tea and coffee) or complements (e.g., tea and sugar). No study models the dynamic effects of cross-category competition though.

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  • Bandyopadhyay, Subir, 2009. "A Dynamic Model of Cross-Category Competition: Theory, Tests and Applications," Journal of Retailing, Elsevier, vol. 85(4), pages 468-479.
    • Handle: RePEc:eee:jouret:v:85:y:2009:i:4:p:468-479
    DOI: 10.1016/j.jretai.2009.05.001
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    References listed on IDEAS

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    1. Magid M. Abraham & Leonard M. Lodish, 1993. "An Implemented System for Improving Promotion Productivity Using Store Scanner Data," Marketing Science, INFORMS, vol. 12(3), pages 248-269.
    2. Magid M. Abraham & Leonard M. Lodish, 1987. "Promoter: An Automated Promotion Evaluation System," Marketing Science, INFORMS, vol. 6(2), pages 101-123.
    3. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    4. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    5. Fair, Ray C, 1979. "An Analysis of the Accuracy of Four Macroeconometric Models," Journal of Political Economy, University of Chicago Press, vol. 87(4), pages 701-718, August.
    6. Marcel Corstjens & Peter Doyle, 1981. "A Model for Optimizing Retail Space Allocations," Management Science, INFORMS, vol. 27(7), pages 822-833, July.
    7. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    8. Srinivasan, S. & Pauwels, K.H. & Hanssens, D.M. & Dekimpe, M.G., 2002. "Do Promotions Benefit Manufacturers, Retailers or Both?," ERIM Report Series Research in Management ERS-2002-21-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    9. Sims, Christopher A, 1972. "Money, Income, and Causality," American Economic Review, American Economic Association, vol. 62(4), pages 540-552, September.
    10. Litterman, Robert, 1986. "Forecasting with Bayesian vector autoregressions -- Five years of experience : Robert B. Litterman, Journal of Business and Economic Statistics 4 (1986) 25-38," International Journal of Forecasting, Elsevier, vol. 2(4), pages 497-498.
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    Cited by:

    1. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    2. Jonathan Z. Zhang & Chun-Wei Chang, 2021. "Consumer dynamics: theories, methods, and emerging directions," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 166-196, January.
    3. Gür Ali, Özden & Gürlek, Ragıp, 2020. "Automatic Interpretable Retail forecasting with promotional scenarios," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1389-1406.
    4. Barros, Carlos P. & Gil-Alana, Luis A. & Wanke, Peter, 2016. "Energy production in Brazil: Empirical facts based on persistence, seasonality and breaks," Energy Economics, Elsevier, vol. 54(C), pages 88-95.
    5. Gelper, Sarah & Wilms, Ines & Croux, Christophe, 2016. "Identifying Demand Effects in a Large Network of Product Categories," Journal of Retailing, Elsevier, vol. 92(1), pages 25-39.
    6. Zhou, Chenxi & Yu, Jibin, 2023. "Does it pay to withdraw marketing metrics disclosure? An empirical study of retailers’ cessation of monthly comparable-store sales," Journal of Business Research, Elsevier, vol. 156(C).
    7. Ma, Shaohui & Fildes, Robert & Huang, Tao, 2016. "Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information," European Journal of Operational Research, Elsevier, vol. 249(1), pages 245-257.
    8. Ma, Yu & Seetharaman, P.B. & Narasimhan, Chakravarthi, 2012. "Modeling Dependencies in Brand Choice Outcomes Across Complementary Categories," Journal of Retailing, Elsevier, vol. 88(1), pages 47-62.
    9. Huang, Tao & Fildes, Robert & Soopramanien, Didier, 2014. "The value of competitive information in forecasting FMCG retail product sales and the variable selection problem," European Journal of Operational Research, Elsevier, vol. 237(2), pages 738-748.
    10. Jiang, Yuanchun & Shang, Jennifer & Liu, Yezheng & May, Jerrold, 2015. "Redesigning promotion strategy for e-commerce competitiveness through pricing and recommendation," International Journal of Production Economics, Elsevier, vol. 167(C), pages 257-270.
    11. Tomasz Brzęczek, 2020. "Optimisation of product portfolio sales and their risk subject to product width and diversity," Review of Managerial Science, Springer, vol. 14(5), pages 1009-1027, October.
    12. Tomasz Brzęczek, 2016. "Products demand and substitution modelling and estimation for microdata," Ekonomia journal, Faculty of Economic Sciences, University of Warsaw, vol. 44.

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