IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v18y1999i3p317-332.html
   My bibliography  Save this article

The Dynamic Effect of Discounting on Sales: Empirical Analysis and Normative Pricing Implications

Author

Listed:
  • Praveen K. Kopalle

    (Amos Tuck School of Business Administration, Dartmouth College, Hanover, New Hampshire 03755)

  • Carl F. Mela

    (Fugua School of Business, Duke University, Durham, North Carolina, 27708)

  • Lawrence Marsh

    (Department of Economics, University of Notre Dame, Notre Dame, Indiana 46556)

Abstract

Baseline sales measure what retail sales would be in the absence of a promotion (Abraham and Lodish 1993), and models that measure baseline sales are widely used by managers to assess the profitability of promotions (Bucklin and Gupta 1999–this issue). Estimates of baseline sales and promotional response are typically independent of past promotional activity, even though there is evidence to suggest that increased discounting reduces off-promotion sales and increases the percentage of purchases made on deal (e.g., Krishna 1994). As a result, models that do not consider dynamic promotional effects can mislead managers to overpromote. Given the widespread use of “static” models to evaluate the efficacy of promotions, it is particularly desirable to calibrate a dynamic brand sales model and use it to establish an optimal course of action. Accordingly, we develop a descriptive dynamic brand sales model and use it to determine normative price promotion strategies. Our descriptive approach consists of estimating a varying-parameter sales response model. Letting model parameters vary with past discounting activity accommodates the possibility that market response changes with firms' discounting policies. In the normative model, we use the estimates obtained in the descriptive model to determine optimal retailer and manufacturer prices over time. The results of the descriptive model indicate that promotions have positive contemporaneous effects on sales accompanied by negative future effects on baseline sales. The results of the normative model suggest that the higher-share brands in our data tend to overpromote while the lower-share brands do not promote frequently enough. We project that the use of our model could improve manufacturers' profits by as much as 7% to 31%. More generally, the normative results indicate that i) if deals become more effective in the current period, i.e., if consumers are more price sensitive, promotions should be used more frequently; and ii) as the negative dynamic effect of discounts on sales increases, the optimal level of discounting should go down. Without our approach, it would be difficult to make this trade-off exact. Finally, we demonstrate that these dynamic effects provide another perspective to the marketing literature regarding the existence of promotions.

Suggested Citation

  • 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.
  • Handle: RePEc:inm:ormksc:v:18:y:1999:i:3:p:317-332
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.18.3.317
    Download Restriction: no

    References listed on IDEAS

    as
    1. Kamel Jedidi & Carl F. Mela & Sunil Gupta, 1999. "Managing Advertising and Promotion for Long-Run Profitability," Marketing Science, INFORMS, vol. 18(1), pages 1-22.
    2. 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.
    3. Peter S. Fader & James M. Lattin & John D. C. Little, 1992. "Estimating Nonlinear Parameters in the Multinomial Logit Model," Marketing Science, INFORMS, vol. 11(4), pages 372-385.
    4. Timothy W. McGuire & Richard Staelin, 1983. "An Industry Equilibrium Analysis of Downstream Vertical Integration," Marketing Science, INFORMS, vol. 2(2), pages 161-191.
    5. Gurumurthy Kalyanaram & Russell S. Winer, 1995. "Empirical Generalizations from Reference Price Research," Marketing Science, INFORMS, vol. 14(3_supplem), pages 161-169.
    6. Abel P. Jeuland & Steven M. Shugan, 1988. "Note—Channel of Distribution Profits When Channel Members Form Conjectures," Marketing Science, INFORMS, vol. 7(2), pages 202-210.
    7. Keane, Michael, 1997. "Current Issues in Discrete Choice Modeling," MPRA Paper 52515, University Library of Munich, Germany.
    8. David Besanko & Sachin Gupta & Dipak Jain, 1998. "Logit Demand Estimation Under Competitive Pricing Behavior: An Equilibrium Framework," Management Science, INFORMS, vol. 44(11-Part-1), pages 1533-1547, November.
    9. 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.
    10. Ruth N. Bolton, 1989. "The Relationship Between Market Characteristics and Promotional Price Elasticities," Marketing Science, INFORMS, vol. 8(2), pages 153-169.
    11. 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.
    12. Kadiyali, Vrinda & Vilcassim, Naufel J & Chintagunta, Pradeep K, 1996. "Empirical Analysis of Competitive Product Line Pricing Decisions: Lead, Follow, or Move Together?," The Journal of Business, University of Chicago Press, vol. 69(4), pages 459-487, October.
    13. Aradhna Krishna, 1994. "The Impact of Dealing Patterns on Purchase Behavior," Marketing Science, INFORMS, vol. 13(4), pages 351-373.
    14. Ram C. Rao, 1991. "Pricing and Promotions in Asymmetric Duopolies," Marketing Science, INFORMS, vol. 10(2), pages 131-144.
    15. 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.
    16. 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.
    17. 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.
    18. Eric A. Greenleaf, 1995. "The Impact of Reference Price Effects on the Profitability of Price Promotions," Marketing Science, INFORMS, vol. 14(1), pages 82-104.
    19. Richard Bellman, 1957. "On a Dynamic Programming Approach to the Caterer Problem--I," Management Science, INFORMS, vol. 3(3), pages 270-278, April.
    20. Eunkyu Lee & Richard Staelin, 1997. "Vertical Strategic Interaction: Implications for Channel Pricing Strategy," Marketing Science, INFORMS, pages 185-207.
    21. Rajiv Lal, 1990. "Price Promotions: Limiting Competitive Encroachment," Marketing Science, INFORMS, vol. 9(3), pages 247-262.
    22. Magid M. Abraham & Leonard M. Lodish, 1987. "Promoter: An Automated Promotion Evaluation System," Marketing Science, INFORMS, vol. 6(2), pages 101-123.
    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. Michis Antonis A, 2009. "Regression Analysis of Marketing Time Series: A Wavelet Approach with Some Frequency Domain Insights," Review of Marketing Science, De Gruyter, vol. 7(1), pages 1-43, July.
    2. Koen Pauwels & Shuba Srinivasan & Philip Hans Franses, 2007. "When Do Price Thresholds Matter in Retail Categories?," Marketing Science, INFORMS, vol. 26(1), pages 83-100, 01-02.
    3. repec:bbz:fcpbbr:v:1:y:2004:i:2:p:103-117 is not listed on IDEAS
    4. Tülin Erdem & Susumu Imai & Michael Keane, 2003. "Brand and Quantity Choice Dynamics Under Price Uncertainty," Quantitative Marketing and Economics (QME), Springer, vol. 1(1), pages 5-64, March.
    5. Marshall Freimer & Dan Horsky, 2008. "Try It, You Will Like It—Does Consumer Learning Lead to Competitive Price Promotions?," Marketing Science, INFORMS, vol. 27(5), pages 796-810, 09-10.
    6. Tao Chen & Baohong Sun & Vishal Singh, 2009. "An Empirical Investigation of the Dynamic Effect of Marlboro's Permanent Pricing Shift," Marketing Science, INFORMS, vol. 28(4), pages 740-758, 07-08.
    7. 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.
    8. Vincent R. Nijs & Marnik G. Dekimpe & Jan-Benedict E.M. Steenkamps & Dominique M. Hanssens, 2001. "The Category-Demand Effects of Price Promotions," Marketing Science, INFORMS, vol. 20(1), pages 1-22, September.
    9. Fleischmann, M. & Hall, J.M. & Pyke, D.F., 2003. "Smart Pricing: Linking Pricing Decisions with Operational Insights," ERIM Report Series Research in Management ERS-2004-001-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    10. 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.
    11. 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.
    12. 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.
    13. Sheu, Jiuh-Biing, 2011. "Marketing-driven channel coordination with revenue-sharing contracts under price promotion to end-customers," European Journal of Operational Research, Elsevier, vol. 214(2), pages 246-255, October.
    14. Ataman, B.M., 2007. "Managing brands," Other publications TiSEM 462dcbba-2ac1-46d1-a61c-f, Tilburg University, School of Economics and Management.
    15. Baohong Sun & Jinhong Xie & H. Henry Cao, 2004. "Product Strategy for Innovators in Markets with Network Effects," Marketing Science, INFORMS, vol. 23(2), pages 243-254, October.
    16. repec:eee:jbrese:v:76:y:2017:i:c:p:189-200 is not listed on IDEAS
    17. Csilla Horváth & Dennis Fok, 2013. "Moderating Factors of Immediate, Gross, and Net Cross-Brand Effects of Price Promotions," Marketing Science, INFORMS, vol. 32(1), pages 127-152, July.
    18. Kogan, Konstantin & Herbon, Avi, 2008. "A supply chain under limited-time promotion: The effect of customer sensitivity," European Journal of Operational Research, Elsevier, vol. 188(1), pages 273-292, July.
    19. Eric Anderson & Nanda Kumar, 2007. "Price competition with repeat, loyal buyers," Quantitative Marketing and Economics (QME), Springer, vol. 5(4), pages 333-359, December.
    20. Srinivasan, S. & Pauwels, K.H. & Hanssens, D.M. & Dekimpe, M.G., 2002. "Do Promotions Benefit Manufacturers, Retailers or Both?," ERIM Report Series Research in Management ERS-2002-21-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    21. P. B. Seetharaman, 2004. "Modeling Multiple Sources of State Dependence in Random Utility Models: A Distributed Lag Approach," Marketing Science, INFORMS, vol. 23(2), pages 263-271, April.
    22. Benchekroun, Hassan & Martín-Herrán, Guiomar & Taboubi, Sihem, 2009. "Could myopic pricing be a strategic choice in marketing channels? A game theoretic analysis," Journal of Economic Dynamics and Control, Elsevier, vol. 33(9), pages 1699-1718, September.
    23. Fleischmann, M. & Hall, J.M. & Pyke, D.F., 2005. "A Dynamic Pricing Model for Coordinated Sales and Operations," ERIM Report Series Research in Management ERS-2005-074-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

    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:317-332. 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.