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Identifying Demand Effects in a Large Network of Product Categories

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  • Gelper, Sarah
  • Wilms, Ines
  • Croux, Christophe

Abstract

Planning marketing mix strategies requires retailers to understand within- as well as cross-category demand effects. Most retailers carry products in a large variety of categories, leading to a high number of such demand effects to be estimated. At the same time, we do not expect cross-category effects between all categories. This paper outlines a methodology to estimate a parsimonious product category network without prior constraints on its structure. To do so, sparse estimation of the Vector AutoRegressive Market Response Model is presented. We find that cross-category effects go beyond substitutes and complements, and that categories have asymmetric roles in the product category network. Destination categories are most influential for other product categories, while convenience and occasional categories are most responsive. Routine categories are moderately influential and moderately responsive.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jouret:v:92:y:2016:i:1:p:25-39
    DOI: 10.1016/j.jretai.2015.05.005
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    4. Luca Barbaglia & Christophe Croux & Ines Wilms, 2017. "Volatility spillovers and heavy tails: a large t-Vector AutoRegressive approach," Working Papers of Department of Decision Sciences and Information Management, Leuven 590528, KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven.
    5. Alain Hecq & Marie Ternes & Ines Wilms, 2021. "Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions," Papers 2102.11780, arXiv.org, revised Mar 2022.
    6. Matteo Barigozzi & Marc Hallin, 2017. "A network analysis of the volatility of high dimensional financial series," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(3), pages 581-605, April.
    7. Gür Ali, Özden & Gürlek, Ragıp, 2020. "Automatic Interpretable Retail forecasting with promotional scenarios," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1389-1406.
    8. Chenarides, Lauren & Gomez, Miguel I. & Richards, Timothy J. & Yonezawa, Koichi, 2021. "Retail Markups and Discount Store Entry," 2021 Annual Meeting, August 1-3, Austin, Texas 313348, Agricultural and Applied Economics Association.
    9. Lauren Chenarides & Miguel I. Gómez & Timothy J. Richards & Koichi Yonezawa, 2024. "Retail Markups and Discount-Store Entry," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 64(1), pages 147-181, February.
    10. Agarwal, Manoj K. & Ma, Zecong & Park, Chang Hee & Zheng, Yilong, 2022. "The impact of a manufacturer’s financial liquidity on its market strategies and pricing and promotion decisions in retail grocery markets," Journal of Business Research, Elsevier, vol. 142(C), pages 844-857.
    11. 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.
    12. Keun Hee Lee & Mali Abdollahian & Sergei Schreider & Sona Taheri, 2023. "Supply Chain Demand Forecasting and Price Optimisation Models with Substitution Effect," Mathematics, MDPI, vol. 11(11), pages 1-28, May.
    13. Matteo Barigozzi & Marc Hallin, 2015. "Networks, Dynamic Factors, and the Volatility Analysis of High-Dimensional Financial Series," Working Papers ECARES ECARES 2015-34, ULB -- Universite Libre de Bruxelles.
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