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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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    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Gür Ali, Özden & Gürlek, Ragıp, 2020. "Automatic Interpretable Retail forecasting with promotional scenarios," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1389-1406.
    7. 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.
    8. Tomasz Brzęczek, 2016. "Products demand and substitution modelling and estimation for microdata," Ekonomia journal, Faculty of Economic Sciences, University of Warsaw, vol. 44.
    9. 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.
    10. 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).
    11. 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.
    12. 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.

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