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The Category-Demand Effects of Price Promotions

Author

Listed:
  • Vincent R. Nijs

    (Catholic University of Leuven, Naamsestraat 69, 3000 Leuven, Belgium)

  • Marnik G. Dekimpe

    (Catholic University of Leuven, Naamsestraat 69, 3000 Leuven, Belgium)

  • Jan-Benedict E.M. Steenkamps

    (Tilburg University, PO Box 90153, 5000 LE Tilburg, The Netherlands)

  • Dominique M. Hanssens

    (The Anderson Graduate School of Management, University of California, Los Angeles, California 90095-1481)

Abstract

Although price promotions have increased in both commercial use and quantity of academic research over the last decade, most of the attention has been focused on their effects on brand choice and brand sales. By contrast, little is known about the conditions under which price promotions expand short-run and long-run category demand, even though the benefits of category expansion can be substantial to manufacturers and retailers alike. This paper studies the category-demand effects of consumer price promotions across 560 consumer product categories over a 4-year period. The data describe national sales in Dutch supermarkets and cover virtually the entire marketing mix, i.e., prices, promotions, advertising, distribution, and new-product activity. We focus on the estimation of main effects (i.e., the dynamic category expansive impact of price promotions) as well as the moderating effects of marketing intensity and competition (both conduct and structure) on short- and long-run promotional effectiveness. The research design uses modern multivariate time-series analysis to disentangle short-run and long-run effects. First, we conduct a series of unit-root tests to determine whether or not category demand is stationary or evolving over time. The results are incorporated in the specification of vector-autoregressive models with exogenous variables (VARX models). The impulse-response functions derived from these VARX models provide estimates of the short- and long-term effects of price promotions on category demand. These estimates, in turn, are used as dependent variables in a series of second-stage regressions that assess the explanatory power of marketing intensity and competition. Several model validation tests support the robustness of the empirical findings. We present our results in the form of empirical generalizations on the main effects of price promotions on category demand in the short and the long run and through statistical tests on how these effects change with marketing intensity and competition. The findings generate an overall picture of the power and limitations of consumer price promotions in expanding category demand, as follows. Category demand is found to be predominantly stationary, either around a fixed mean or a deterministic trend. Although the total net short-term effects of price promotions are generally strong, with an average elasticity of 2.21 and a more conservative median elasticity of 1.75, they rarely exhibit persistent effects. Instead, the effects dissipate over a time period lasting approximately 10 weeks on average, and their long-term impact is essentially zero. By contrast, the successful introduction of new products into a category is more frequently associated with a permanent category-demand increase. Several moderating effects on price-promotion effectiveness exist. More frequent promotions increase their effectiveness, but only in the short run. The use of nonprice advertising reduces the category-demand effects of price promotions, both in the short run and in the long run. Competitive structure matters as well: The less oligopolistic the category, the smaller the short-run effectiveness of price promotions. At the same time, we find that the dominant form of competitive reaction, either in price promotion or in advertising, is no reaction. Short-run category-demand effectiveness of price promotions is lower in categories experiencing major new-product introductions. Finally, both the short- and long-run price promotion effectiveness is higher in perishable product categories. The paper discusses several managerial implications of these empirical findings and suggests various avenues for future research. Overall, we conclude that the power of price promotions lies primarily in the preservation of the status quo in the category.

Suggested Citation

  • 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.
  • Handle: RePEc:inm:ormksc:v:20:y:2001:i:1:p:1-22
    DOI: 10.1287/mksc.20.1.1.10197
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    References listed on IDEAS

    as
    1. Jagmohan S. Raju, 1992. "The Effect of Price Promotions on Variability in Product Category Sales," Marketing Science, INFORMS, vol. 11(3), pages 207-220.
    2. 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.
    3. 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.
    4. Robert C. Blattberg & Richard Briesch & Edward J. Fox, 1995. "How Promotions Work," Marketing Science, INFORMS, vol. 14(3_supplem), pages 122-132.
    5. Perron, Pierre, 1989. "The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis," Econometrica, Econometric Society, vol. 57(6), pages 1361-1401, November.
    6. 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.
    7. Perron, Pierre, 1990. "Testing for a Unit Root in a Time Series with a Changing Mean," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 153-162, April.
    8. repec:bla:jecsur:v:12:y:1998:i:5:p:533-72 is not listed on IDEAS
    9. João L. Assunção & Robert J. Meyer, 1993. "The Rational Effect of Price Promotions on Sales and Consumption," Management Science, INFORMS, vol. 39(5), pages 517-535, May.
    10. Ram C. Rao & Ramesh V. Arjunji & B. P. S. Murthi, 1995. "Game Theory and Empirical Generalizations Concerning Competitive Promotions," Marketing Science, INFORMS, vol. 14(3_supplem), pages 89-100.
    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. Anil Kaul & Dick R. Wittink, 1995. "Empirical Generalizations About the Impact of Advertising on Price Sensitivity and Price," Marketing Science, INFORMS, vol. 14(3_supplem), pages 151-160.
    13. Abhik Roy & Dominique M. Hanssens & Jagmohan S. Raju, 1994. "Competitive Pricing by a Price Leader," Management Science, INFORMS, vol. 40(7), pages 809-823, July.
    14. Marnik G. Dekimpe & Dominique M. Hanssens, 1995. "Empirical Generalizations About Market Evolution and Stationarity," Marketing Science, INFORMS, vol. 14(3_supplem), pages 109-121.
    15. Pierre Perron, 1994. "Trend, Unit Root and Structural Change in Macroeconomic Time Series," Palgrave Macmillan Books, in: B. Bhaskara Rao (ed.), Cointegration, chapter 4, pages 113-146, Palgrave Macmillan.
    16. G. Dekimpe, Marnik & Hanssens, Dominique M. & Silva-Risso, Jorge M., 1998. "Long-run effects of price promotions in scanner markets," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 269-291, November.
    17. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    18. Hall, Alastair R, 1994. "Testing for a Unit Root in Time Series with Pretest Data-Based Model Selection," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(4), pages 461-470, October.
    19. Johansen, Soren & Juselius, Katarina, 1990. "Maximum Likelihood Estimation and Inference on Cointegration--With Applications to the Demand for Money," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 52(2), pages 169-210, May.
    20. Jurgen A. Doornik & David F. Hendry & Bent Nielsen, 1998. "Inference in Cointegrating Models: UK M1 Revisited," Journal of Economic Surveys, Wiley Blackwell, vol. 12(5), pages 533-572, December.
    21. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
    22. Pagoulatos, Emilio & Sorensen, Robert, 1986. "What determines the elasticity of industry demand?," International Journal of Industrial Organization, Elsevier, vol. 4(3), pages 237-250, September.
    23. Cogger, Ko, 1981. "A Time-Series Analytic Approach To Aggregation Issues In Accounting Data," Journal of Accounting Research, Wiley Blackwell, vol. 19(2), pages 285-298.
    24. Marnik G. Dekimpe & Dominique M. Hanssens, 1995. "The Persistence of Marketing Effects on Sales," Marketing Science, INFORMS, vol. 14(1), pages 1-21.
    25. Charlotte H. Mason, 1990. "New Product Entries and Product Class Demand," Marketing Science, INFORMS, vol. 9(1), pages 58-73.
    26. Naufel J. Vilcassim & Vrinda Kadiyali & Pradeep K. Chintagunta, 1999. "Investigating Dynamic Multifirm Market Interactions in Price and Advertising," Management Science, INFORMS, vol. 45(4), pages 499-518, April.
    27. Frank M. Bass, 1995. "Empirical Generalizations and Marketing Science: A Personal View," Marketing Science, INFORMS, vol. 14(3_supplem), pages 6-19.
    28. Pradeep K. Chintagunta, 1993. "Investigating Purchase Incidence, Brand Choice and Purchase Quantity Decisions of Households," Marketing Science, INFORMS, vol. 12(2), pages 184-208.
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