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Are Sale Signs Less Effective When More Products Have Them?

Author

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  • Eric T. Anderson

    (GSB, The University of Chicago, 1101 East 58th Street, Chicago, Illinois 60637)

  • Duncan I. Simester

    (Sloan School of Management, MIT, E56-305, 38 Memorial Drive, Cambridge, Massachusetts 02139)

Abstract

We analyze data from a variety of sources, including historical data from a women's clothing catalog, a field study in that catalog, survey responses to catalog stimuli, and grocery store data for frozen juice, toothpaste, and tuna. The analysis yields three conclusions. First, sale signs are less effective at increasing demand when more items have them. Second, total category sales are maximized when some but not all products have sale signs. Third, placing a sale sign on a product reduces the perceived likelihood that the product will be available at a lower price in the future, but the effect is smaller when more products have sale signs. By ruling out alternative hypotheses, the findings suggest that moderation of the sale sign effect is in part due to reduced credibility when they are used on more products. The credibility argument is motivated in part by a recent paper that presented an equilibrium model predicting that customers who lack knowledge of market prices rely on point-of-purchase sale signs to help evaluate posted prices (Anderson and Simester 1998). The model predicts that sale signs increase demand but that the increase is smaller when more products have them. This moderating effect regulates how many sale signs stores use and makes customer reliance on these cues an equilibrium strategy. To evaluate whether the number of sale signs moderates their effectiveness we compare demand for items with sale signs when varying the number of sale signs on other products. For this comparison we use two datasets describing demand for products in a women's clothing catalog. The first dataset describes customer orders for the same set of items across three sequential issues of the catalog. The second dataset is a field test conducted by mailing different versions of the catalog to randomly selected customer samples. Reassuringly, although the datasets do not share the same limitations, the findings are consistent and indicate that sale signs are less effective when more products have them. In addition to the credibility explanation there are at least two alternative explanations for this result. First, using a sale sign to make a product more attractive may lead to substitution of demand from other sale items within a store, and so demand may vary even if there is no change in the credibility of the sale signs. To discriminate between the substitution and credibility explanations, we evaluate whether total category demand is maximized when some but not all products in the category have sale signs. The credibility explanation implies that adding another sale sign will eventually decrease total category sales when many items already have sale signs. In contrast, if adding a sale sign to an item leads solely to substitution from other products within (or outside) the category we will not observe a decrease in total category demand. We test the total category sales prediction using grocery store scan data describing total category demand for frozen juice, toothpaste, and canned tuna. The findings reveal that in all three categories there is a significant reduction in aggregate demand. A second alternative explanation is that sale signs focus customer attention on products with these cues. Distinguishing between the attention and credibility explanations is a difficult task. They both imply that sale signs deliver less information when there are too many sale signs. The credibility argument predicts that this occurs because the signs are noticed but not believed. The attention explanation predicts that the signs are less likely to be noticed when attention is diluted by a large number of sale signs. To discriminate between the credibility explanation and this attention effect we use survey measures to evaluate a third hypothesis. This hypothesis predicts both that placing a sale sign on a product reduces the perceived likelihood that the product will be available at a lower price in the next period, and that this effect is smaller when more items have them. In an attempt to control for the attention effect we focus subjects' attention on a set of focal items. Under these conditions it is unlikely that subjects overlook sale signs on these items. More important, the likelihood of overlooking sale signs on the focal items is unlikely to depend on how many other items also have sale signs. The results confirm both that the presence of a sale sign reduced subjects' expectations that an item would be available at a lower price in the future and that this effect is smaller when more items have sale signs.

Suggested Citation

  • Eric T. Anderson & Duncan I. Simester, 2001. "Are Sale Signs Less Effective When More Products Have Them?," Marketing Science, INFORMS, vol. 20(2), pages 121-142, March.
  • Handle: RePEc:inm:ormksc:v:20:y:2001:i:2:p:121-142
    DOI: 10.1287/mksc.20.2.121.10194
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    References listed on IDEAS

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    Cited by:

    1. Leibbrandt, Andreas, 2020. "Behavioral constraints on price discrimination: Experimental evidence on pricing and customer antagonism," European Economic Review, Elsevier, vol. 121(C).
    2. Anderson, Eric & Malin, Benjamin A. & Nakamura, Emi & Simester, Duncan & Steinsson, Jón, 2017. "Informational rigidities and the stickiness of temporary Sales," Journal of Monetary Economics, Elsevier, vol. 90(C), pages 64-83.
    3. Gopal Das & Shailendra Pratap Jain & Durairaj Maheswaran & Rebecca J. Slotegraaf & Raji Srinivasan, 2021. "Pandemics and marketing: insights, impacts, and research opportunities," Journal of the Academy of Marketing Science, Springer, vol. 49(5), pages 835-854, September.
    4. Steven M. Shugan, 2002. "In Search of Data: An Editorial," Marketing Science, INFORMS, vol. 21(4), pages 369-377.
    5. Deng, Yiting & Staelin, Richard & Wang, Wei & Boulding, William, 2018. "Consumer sophistication, word-of-mouth and “False” promotions," Journal of Economic Behavior & Organization, Elsevier, vol. 152(C), pages 98-123.
    6. Berger, A. & Grigoriev, A. & van Loon, J., 2008. "Price strategy implementation," Research Memorandum 035, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    7. Andreas Leibbrandt, 2016. "Behavioral Constraints on Pricing: Experimental Evidence on Price Discrimination and Customer Antagonism," CESifo Working Paper Series 6214, CESifo.
    8. Eric T. Anderson & Duncan I. Simester, 2004. "Long-Run Effects of Promotion Depth on New Versus Established Customers: Three Field Studies," Marketing Science, INFORMS, vol. 23(1), pages 4-20, February.
    9. Lindsey-Mullikin, Joan & Petty, Ross D., 2011. "Marketing tactics discouraging price search: Deception and competition," Journal of Business Research, Elsevier, vol. 64(1), pages 67-73, January.
    10. Jimmy Q. Li & Paat Rusmevichientong & Duncan Simester & John N. Tsitsiklis & Spyros I. Zoumpoulis, 2015. "The Value of Field Experiments," Management Science, INFORMS, vol. 61(7), pages 1722-1740, July.
    11. Meghan R. Busse & Duncan I. Simester & Florian Zettelmeyer, 2010. "“The Best Price You'll Ever Get”: The 2005 Employee Discount Pricing Promotions in the U.S. Automobile Industry," Marketing Science, INFORMS, vol. 29(2), pages 268-290, 03-04.
    12. Bogomolova, Svetlana & Dunn, Steven & Trinh, Giang & Taylor, Jennifer & Volpe, Richard J., 2015. "Price promotion landscape in the US and UK: Depicting retail practice to inform future research agenda," Journal of Retailing and Consumer Services, Elsevier, vol. 25(C), pages 1-11.
    13. Desmet, Pierre & Le Nagard, Emmanuelle & Vinzi, Vincenzo Esposito, 2012. "Refund depth effects on the impact of price-beating guarantees," Journal of Business Research, Elsevier, vol. 65(5), pages 603-608.
    14. Steven Miller & Eric Bradlow & Kevin Dayaratna, 2006. "Closed-form Bayesian inferences for the logit model via polynomial expansions," Quantitative Marketing and Economics (QME), Springer, vol. 4(2), pages 173-206, June.
    15. Guodong (Gordon) Gao & Anandasivam Gopal & Ritu Agarwal, 2010. "Contingent Effects of Quality Signaling: Evidence from the Indian Offshore IT Services Industry," Management Science, INFORMS, vol. 56(6), pages 1012-1029, June.
    16. Weathers, Danny & Swain, Scott D. & Makienko, Igor, 2015. "When and how should retailers rationalize the size and duration of price discounts?," Journal of Business Research, Elsevier, vol. 68(12), pages 2610-2618.
    17. James M. Leonhardt & David Trafimow & Mihai Niculescu, 2017. "Selecting Field Experiment Locations with Archival Data," Journal of Consumer Affairs, Wiley Blackwell, vol. 51(2), pages 448-462, July.
    18. Breugelmans, Els & Campo, Katia, 2011. "Effectiveness of In-Store Displays in a Virtual Store Environment," Journal of Retailing, Elsevier, vol. 87(1), pages 75-89.
    19. Sigué, Simon Pierre, 2008. "Consumer and Retailer Promotions: Who is Better Off?," Journal of Retailing, Elsevier, vol. 84(4), pages 449-460.
    20. Schmidbauer, Eric & Stock, Axel, 2018. "Quality signaling via strikethrough prices," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 524-532.
    21. Eric Anderson & Duncan Simester, 2003. "Effects of $9 Price Endings on Retail Sales: Evidence from Field Experiments," Quantitative Marketing and Economics (QME), Springer, vol. 1(1), pages 93-110, March.
    22. Gázquez-Abad, Juan Carlos & Martínez-López, Francisco J., 2016. "Understanding the impact of store flyers on purchase behaviour: An empirical analysis in the context of Spanish households," Journal of Retailing and Consumer Services, Elsevier, vol. 28(C), pages 263-273.
    23. Meghan R. Busse & Duncan Simester & Florian Zettelmeyer, 2007. ""The Best Price You'll Ever Get" The 2005 Employee Discount Pricing Promotions in the U.S. Automobile Industry," NBER Working Papers 13140, National Bureau of Economic Research, Inc.

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