IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v18y1999i3p274-300.html
   My bibliography  Save this article

A Decision Support System for Planning Manufacturers' Sales Promotion Calendars

Author

Listed:
  • 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)

Abstract

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
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.18.3.274
    Download Restriction: no

    References listed on IDEAS

    as
    1. Varian, Hal R, 1980. "A Model of Sales," American Economic Review, American Economic Association, vol. 70(4), pages 651-659, September.
    2. Sang Yong Kim & Richard Staelin, 1999. "Manufacturer Allowances and Retailer Pass-Through Rates in a Competitive Environment," Marketing Science, INFORMS, vol. 18(1), pages 59-76.
    3. 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.
    4. 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.
    5. Jagmohan S. Raju & V. Srinivasan & Rajiv Lal, 1990. "The Effects of Brand Loyalty on Competitive Price Promotional Strategies," Management Science, INFORMS, vol. 36(3), pages 276-304, March.
    6. Duncan Simester, 1997. "Note. Optimal Promotion Strategies: A Demand-Sided Characterization," Management Science, INFORMS, vol. 43(2), pages 251-256, February.
    7. Stephen J. Hoch & David A. Schkade, 1996. "A Psychological Approach to Decision Support Systems," Management Science, INFORMS, vol. 42(1), pages 51-64, January.
    8. Narasimhan, Chakravarthi, 1988. "Competitive Promotional Strategies," The Journal of Business, University of Chicago Press, vol. 61(4), pages 427-449, October.
    9. Scott A. Neslin & Caroline Henderson & John Quelch, 1985. "Consumer Promotions and the Acceleration of Product Purchases," Marketing Science, INFORMS, vol. 4(2), pages 147-165.
    10. Richard A. Colombo & Donald G. Morrison, 1989. "Note—A Brand Switching Model with Implications for Marketing Strategies," Marketing Science, INFORMS, vol. 8(1), pages 89-99.
    11. Rajiv Lal, 1990. "Price Promotions: Limiting Competitive Encroachment," Marketing Science, INFORMS, vol. 9(3), pages 247-262.
    12. Magid M. Abraham & Leonard M. Lodish, 1987. "Promoter: An Automated Promotion Evaluation System," Marketing Science, INFORMS, vol. 6(2), pages 101-123.
    13. Chakravarthi Narasimhan, 1984. "A Price Discrimination Theory of Coupons," Marketing Science, INFORMS, vol. 3(2), pages 128-147.
    14. Joseph E. Stiglitz, 1977. "Monopoly, Non-linear Pricing and Imperfect Information: The Insurance Market," Review of Economic Studies, Oxford University Press, vol. 44(3), pages 407-430.
    15. Imran S. Currim & Linda G. Schneider, 1991. "A Taxonomy of Consumer Purchase Strategies in a Promotion Intensive Environment," Marketing Science, INFORMS, vol. 10(2), pages 91-110.
    16. 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.
    17. Lakshman Krishnamurthi & S. P. Raj, 1988. "A Model of Brand Choice and Purchase Quantity Price Sensitivities," Marketing Science, INFORMS, vol. 7(1), pages 1-20.
    18. Scott A. Neslin & Stephen G. Powell & Linda Schneider Stone, 1995. "The Effects of Retailer and Consumer Response on Optimal Manufacturer Advertising and Trade Promotion Strategies," Management Science, INFORMS, vol. 41(5), pages 749-766, May.
    19. Roland T. Rust & Duncan Simester & Roderick J. Brodie & V. Nilikant, 1995. "Model Selection Criteria: An Investigation of Relative Accuracy, Posterior Probabilities, and Combinations of Criteria," Management Science, INFORMS, vol. 41(2), pages 322-333, February.
    20. Jeuland, Abel P & Narasimhan, Chakravarthi, 1985. "Dealing-Temporary Price Cuts-by Seller as a Buyer Discrimination Mechanism," The Journal of Business, University of Chicago Press, vol. 58(3), pages 295-308, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Erjen van Nierop & Dennis Fok & Philip Hans Franses, 2008. "Interaction Between Shelf Layout and Marketing Effectiveness and Its Impact on Optimizing Shelf Arrangements," Marketing Science, INFORMS, vol. 27(6), pages 1065-1082, 11-12.
    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.
    5. Prasad A. Naik & Kalyan Raman & Russell S. Winer, 2005. "Planning Marketing-Mix Strategies in the Presence of Interaction Effects," Marketing Science, INFORMS, vol. 24(1), pages 25-34, June.
    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.
    14. repec:eee:jbrese:v:83:y:2018:i:c:p:215-228 is not listed on IDEAS
    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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormksc:v:18:y:1999:i:3:p:274-300. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Mirko Janc). General contact details of provider: http://edirc.repec.org/data/inforea.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.