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A Decision Support System for Planning Manufacturers' Sales Promotion Calendars


  • Jorge M. Silva-Risso

    (J.D. Power and Associates, 30401 Agoura Road, Agoura Hills, California 91301 and Anderson School, University of California at Los Angeles, Los Angeles, California 90095-1481)

  • Randolph E. Bucklin

    (Anderson School, University of California at Los Angeles, Los Angeles, California 90095-1481)

  • Donald G. Morrison

    (Anderson School, University of California at Los Angeles, Los Angeles, California 90095-1481)


A common event in the consumer packaged goods industry is the negotiation between a manufacturer and a retailer of the sales promotion calendar. Determining the promotion calendar involves a large number of decisions regarding levels of temporary price reductions, feature ads, and in-store displays, each executed at the level of individual retail accounts and brand SKUs over several months or a year. Though manufacturers spend much of their marketing budget on trade promotions, they lack decision support systems to address the complexity and dynamics of promotion planning. Previous research has produced insights into how to evaluate the effectiveness of promotional events, but has not addressed the planning problem in a dynamic environment. This paper develops a disaggregate-level econometric model to capture the dynamics and heterogeneity of consumer response. By modeling the purchase incidence (timing), choice and quantity decisions of consumers we decompose total sales into incremental and nonincremental (baseline plus borrowed). The response model forms the basis of a market simulator that permits us to search for the manufacturer's optimal promotion calendar (subject to a set of constraints, some of them imposed by the retailer) via the simulated annealing algorithm. Calendar profits are the net result of the contribution from incremental sales minus the opportunity cost from giving away discounts to nonincremental sales and the fixed costs associated with implementing promotional events (e.g., retagging, features, displays). Incremental sales result from promotion-induced switching, the acceleration and quantity promotion effects on those switchers, increased consumption and the carryover effect from purchase event feedback. We applied our approach to the promotion-planning problem of a large consumer-packaged goods company in a nonperishable, staple product category suggested by company executives (canned tomato sauce). Subject to a retailer pass-through constant rate of 80%, provided to us by the collaborating firm, the optimal promotion calendar produced by the modeling system followed a pattern of frequent and shallow temporary price reductions with no feature or display activity. We also analyze how that result would change under different retailer pass-through scenarios. Our findings indicated that the manufacturer could substantially improve the profitability of its sales promotion activity and that there would be a concurrent positive effect on retailer profit and volume levels. Management reported to us that the insights from the use of the system were implemented in their promotion-planning process and produced positive results. A validation analysis on follow-up data for one market showed that promotion activity could be significantly reduced, as recommended, with no adverse effect on the brand's market share, as predicted. To generalize the model beyond the specific category where it was implemented, we conducted a sensitivity analysis on the profile of the calendar (i.e., frequency, depth, and duration of deals) with respect to changes in market response, competitive activity, and retailer pass-through. First, we found that the optimal depth, frequency, and timing of discounts is stable for price elasticities ranging from near zero to around four (in absolute magnitude). We also found no systematic impact of competitive promotions on the profile of the optimal calendar. For example, variation in competitive activity did not affect the optimal depth or frequency of discounts. Lastly, we found changes in retailer pass-through to have a significant effect on the optimal depth and number of weeks of trade promotion that a manufacturer should offer. This emphasizes the importance to manufacturers of having accurate estimates of pass-through for purposes of promotion budgeting and planning.

Suggested Citation

  • 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.
  • Handle: RePEc:inm:ormksc:v:18:y:1999:i:3:p:274-300

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    References listed on IDEAS

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    2. David R. Bell & Jeongwen Chiang & V. Padmanabhan, 1999. "The Decomposition of Promotional Response: An Empirical Generalization," Marketing Science, INFORMS, vol. 18(4), pages 504-526.
    3. David Besanko & Jean-Pierre Dubé & Sachin Gupta, 2005. "Own-Brand and Cross-Brand Retail Pass-Through," Marketing Science, INFORMS, vol. 24(1), pages 123-137, July.
    4. Greg M. Allenby & Thomas S. Shively & Sha Yang & Mark J. Garratt, 2004. "A Choice Model for Packaged Goods: Dealing with Discrete Quantities and Quantity Discounts," Marketing Science, INFORMS, vol. 23(1), pages 95-108, June.
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    6. 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.
    7. Arnd Huchzermeier & Ananth Iyer & Julia Freiheit, 2002. "The Supply Chain Impact of Smart Customers in a Promotional Environment," Manufacturing & Service Operations Management, INFORMS, vol. 4(3), pages 228-240, November.
    8. Kusum L. Ailawadi & Praveen K. Kopalle & Scott A. Neslin, 2005. "Predicting Competitive Response to a Major Policy Change: Combining Game-Theoretic and Empirical Analyses," Marketing Science, INFORMS, vol. 24(1), pages 12-24, September.
    9. Tian Xia & Richard Sexton, 2010. "Brand or Variety Choices and Periodic Sales as Substitute Instruments for Monopoly Price Discrimination," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 36(4), pages 333-349, June.
    10. Michel Wedel & Jie Zhang & Fred Feinberg, 2015. "Implementing Retail Category Management: a Model-Based Approach to Setting Optimal Markups," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 2(2), pages 165-176, June.
    11. 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.
    12. Randolph E. Bucklin & Sunil Gupta, 1999. "Commercial Use of UPC Scanner Data: Industry and Academic Perspectives," Marketing Science, INFORMS, vol. 18(3), pages 247-273.
    13. Xavier Drèze & David R. Bell, 2003. "Creating Win–Win Trade Promotions: Theory and Empirical Analysis of Scan-Back Trade Deals," Marketing Science, INFORMS, vol. 22(1), pages 16-39, November.
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    15. Foubert, Bram & Gijsbrechts, Els, 2010. "Please or Squeeze? Brand performance implications of constrained and unconstrained multi-item promotions," European Journal of Operational Research, Elsevier, vol. 202(3), pages 880-892, May.
    16. 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.
    17. K. Sudhir, 2001. "Structural Analysis of Manufacturer Pricing in the Presence of a Strategic Retailer," Marketing Science, INFORMS, pages 244-264.
    18. Jie Zhang & Lakshman Krishnamurthi, 2004. "Customizing Promotions in Online Stores," Marketing Science, INFORMS, vol. 23(4), pages 561-578, June.
    19. Joseph Pancras, 2010. "A Framework to Determine the Value of Consumer Consideration Set Information for Firm Pricing Strategies," Computational Economics, Springer;Society for Computational Economics, vol. 35(3), pages 269-300, March.
    20. SPRINGAEL, Johan & VAN NIEUWENHUYSE, Inneke, 2006. "On the sum of independent zero-truncated Poisson random variables," Working Papers 2006011, University of Antwerp, Faculty of Applied Economics.
    21. Martin Natter & Thomas Reutterer & Andreas Mild & Alfred Taudes, 2007. "—An Assortmentwide Decision-Support System for Dynamic Pricing and Promotion Planning in DIY Retailing," Marketing Science, INFORMS, vol. 26(4), pages 576-583, 07-08.
    22. 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.
    23. 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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