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Tackling the Retailer Decision Maze: Which Brands to Discount, How Much, When and Why?

Author

Listed:
  • Gerard J. Tellis

    (University of Southern California)

  • Fred S. Zufryden

    (University of Southern California)

Abstract

We propose a model that seeks the optimal timing and depth of retail discounts with the optimal timing and quantity of the retailer's order over multiple brands and time periods. The model is based on an integration of consumer decisions in purchase incidence, brand choice and quantity with the dynamics of household and retail inventory. The major contribution of the model is that it shows how the optimum depth and timing of discount varies with key demand characteristics such as consumer stockpiling, loyalty, response to the marketing mix, and segmentation. In addition, the optima also vary with key supply characteristics such as retail margins, depth and frequency of manufacturer deals, retail inventory, and retagging costs. The most valuable contribution of the model is that it can provide an optimal discount strategy for multiple brands over multiple time periods. The optimization model runs on a user-friendly personal computer program. An application based on UPC scanner data illustrates the model's uses. Sensitivity analyses of the optimization model under alternative scenarios reveal novel insights as to how optimal discounts vary as a function of the key demand and supply characteristics.

Suggested Citation

  • Gerard J. Tellis & Fred S. Zufryden, 1995. "Tackling the Retailer Decision Maze: Which Brands to Discount, How Much, When and Why?," Marketing Science, INFORMS, vol. 14(3), pages 271-299.
  • Handle: RePEc:inm:ormksc:v:14:y:1995:i:3:p:271-299
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    File URL: http://dx.doi.org/10.1287/mksc.14.3.271
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    References listed on IDEAS

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    Cited by:

    1. Suzuki, Yoshinori & Pautsch, Gregory R., 2005. "A vehicle replacement policy for motor carriers in an unsteady economy," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(5), pages 463-480, June.
    2. William P. Putsis, 1999. "Empirical analysis of competitive interaction in food product categories," Agribusiness, John Wiley & Sons, Ltd., vol. 15(3), pages 295-311.
    3. Vincent R. Nijs & Shuba Srinivasan & Koen Pauwels, 2007. "Retail-Price Drivers and Retailer Profits," Marketing Science, INFORMS, vol. 26(4), pages 473-487, 07-08.
    4. Kim, Namwoon & Srivastava, Rajendra K. & Han, Jin K., 2001. "Consumer decision-making in a multi-generational choice set context," Journal of Business Research, Elsevier, vol. 53(3), pages 123-136, September.
    5. Sourav Ray & Haipeng (Allan) Chen & Mark E. Bergen & Daniel Levy, 2006. "Asymmetric Wholesale Pricing: Theory and Evidence," Marketing Science, INFORMS, vol. 25(2), pages 131-154, 03-04.
    6. Kopalle Praveen K & Neslin Scott A, 2003. "The Economic Viability of Frequency Reward Programs in a Strategic Competitive Environment," Review of Marketing Science, De Gruyter, vol. 1(1), pages 1-41, August.
    7. Jorge Silva-Risso & Irina Ionova, 2008. "—A Nested Logit Model of Product and Transaction-Type Choice for Planning Automakers' Pricing and Promotions," Marketing Science, INFORMS, vol. 27(4), pages 545-566, 07-08.
    8. Praveen K. Kopalle & Carl F. Mela & Lawrence Marsh, 1999. "The Dynamic Effect of Discounting on Sales: Empirical Analysis and Normative Pricing Implications," Marketing Science, INFORMS, vol. 18(3), pages 317-332.
    9. Jorge M. Silva-Risso & Randolph E. Bucklin & Donald G. Morrison, 1999. "A Decision Support System for Planning Manufacturers' Sales Promotion Calendars," Marketing Science, INFORMS, vol. 18(3), pages 274-300.
    10. Kusum L. Ailawadi & Bari A. Harlam, 2009. "—Retailer Promotion Pass-Through: A Measure, Its Magnitude, and Its Determinants," Marketing Science, INFORMS, vol. 28(4), pages 782-791, 07-08.
    11. J. Miguel Villas-Boas & Russell S. Winer, 1999. "Endogeneity in Brand Choice Models," Management Science, INFORMS, vol. 45(10), pages 1324-1338, October.
    12. Foekens, Eijte W. & S.H. Leeflang, Peter & Wittink, Dick R., 1998. "Varying parameter models to accommodate dynamic promotion effects," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 249-268, November.
    13. Ma, Shaohui & Fildes, Robert, 2017. "A retail store SKU promotions optimization model for category multi-period profit maximization," European Journal of Operational Research, Elsevier, vol. 260(2), pages 680-692.
    14. K. Sudhir, 2001. "Structural Analysis of Manufacturer Pricing in the Presence of a Strategic Retailer," Marketing Science, INFORMS, pages 244-264.
    15. Robert W. Palmatier & Srinath Gopalakrishna & Mark B. Houston, 2006. "Returns on Business-to-Business Relationship Marketing Investments: Strategies for Leveraging Profits," Marketing Science, INFORMS, vol. 25(5), pages 477-493, September.
    16. Jie Zhang & Lakshman Krishnamurthi, 2004. "Customizing Promotions in Online Stores," Marketing Science, INFORMS, vol. 23(4), pages 561-578, June.
    17. Venkatesh Shankar & Ruth N. Bolton, 2004. "An Empirical Analysis of Determinants of Retailer Pricing Strategy," Marketing Science, INFORMS, vol. 23(1), pages 28-49, May.
    18. Anett Weber & Winfried J. Steiner & Stefan Lang, 2017. "A comparison of semiparametric and heterogeneous store sales models for optimal category pricing," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(2), pages 403-445, March.
    19. Kumar, V. & Pereira, Arun, 1997. "Assessing the Competitive Impact of Type, Timing, Frequency, and Magnitude of Retail Promotions," Journal of Business Research, Elsevier, vol. 40(1), pages 1-13, September.
    20. Guyt, Jonne, 2015. "Consumer choice models on the effect of promotions in retailing," Other publications TiSEM c310f652-d725-4764-aac7-b, Tilburg University, School of Economics and Management.

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