A dashboard for online pricing

California Management Review

Volumn 50, Issue 1, 2007, Pages

  Baye, Michael R ^a   Gatti, J Rupert J ^b   Kattuman, Paul ^c   Morgan, John ^d  

^a INDIANA UNIVERSITY   (United States)

^b TRINITY COLLEGE   (United Kingdom)

^c UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^d UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 36749032137     PISSN: 00081256     EISSN: None     Source Type: Journal    
DOI: 10.2307/41166424     Document Type: Review

References (17)

1. The Economist, November 20, 1999, p. 112.
2. Baye, Morgan, and Scholten survey 20 different studies that document levels of price dispersion of 20 to 40 percent in online markets in the U.S. and abroad. Michael R. Baye, John Morgan, and Patrick Scholten, 'Information, Search, and Price Dispersion," in T. Hendershott, ed., Economics and Information Systems (Boston, MA: Elsevier, 2006).
3. See, for instance, Hal Varian and Carl Shapiro, Information Rules (Boston, MA: Harvard Business School Press, 1998).
4. Online information can also have spillover effects for pricing in offline markets. See Meghan Busse, Jorge Silva-Risso, and Florian Zettelmeyer, "$1000 Cash Back: The Pass-Through of Auto Manufacturer Promotions," American Economic Review, 96/4 (2006): 1253-1270.
5. See, for instance, "Comparison Search Engines Tested," , November 14, 2006.
6. A standard measure of price sensitivity is the price elasticity of demand: ε = % change in sales ÷ % change in price For instance, a firm with a price elasticity of -4 would enjoy a 4% increase in units sold if it decreased its price by 1%. On the other hand, if that same firm faced a price elasticity of -10, then a 1 % price reduction would increase units sold by 10%. In order to maximize profits the optimal markup factor is simply Optimal Markup Factor = ε ÷ (1 + ε) Thus, if the price elasticity is -4, then the optimal markup factor is 1.33. If consumers are more price-sensitive, such that the elasticity is -10, the optimal markup factor is 1.11. Of course, short-run profit maximization need not be the only objective for pricing policies, but invariably the price sensitivity of consumers remains a key determinant of the optimal markup factor. For a discussion of alternative pricing objectives, see, for example. Christopher Tang, David Bell, and Teck-Hua Ho, "Store Choice and Shopping Behavior: How Price Format Works,' California Management Review, 43/2 (Winter 2001): 56-74;
7. George J. Avlonitis and Kostis A. Indounas, "The Impact of Market Structure on Pricing Objectives of Service Firms," Journal of Product & Brand Management, 13/5 (2004): 343-358.
8. 1. Then the following can be used to obtain a simple estimate of the elasticity of demand for the product: (Equation Presented) For example, a firm that sold 10 units at a price of $100 but only 6 units when it experimented with a $120 price would estimate a price elasticity of ε = -2.75, leading to an optimal markup factor of 1.57.
9. See for example, Walter Baker, Mike Marn, and Craig Zawada, "Price Smarter on the Net," Harvard Business Review, 79/2 (February 2001): 122-127;
10. Eric Brynjolfsson and Xianquan (Michael) Zhang, "Innovation Incentives for Information Goods," in Adam B. Jaffe, Josh Lerner, and Scott Stern, eds., Innovation Policy and the Economy, Vol. 7 (Cambridge, MA: MIT Press, 2006), chapter 4, pp. 99-123.
11. See Greg Shaffer and Z. John Zhang, "Competitive One-to-One Promotions," Management Science, 48/9 (September 2002): 1143-1160.
12. The optimal dynamic wholesale pricing over the product life cycle is a rich topic in itself and not the focus of this article. See Trichy V. Krishnan, Frank M. Bass, and Dipak C. Jain, "Optimal Pricing Strategy for New Products" Management Science, 45/12 (1999): 1650-1663;
13. Harikesh Nair, "Intertemporal Price Discrimination with Forward-Looking Consumers: Application to the U.S. Market for Console Video-Games," Stanford University Graduate School of Business Research Paper No. 1947, 2006.
14. See, for instance, Michael R. Baye, John Morgan, and Patrick Scholten, 'Temporal Price Dispersion: Evidence from an Online Consumer Electronics Market," Journal of Interactive Marketing (2004), Vol. 18 (4), pp. 101-115.
15. See, for instance, Michael Baye et al., "Clicks, Discontinuities, and Firm Demand Online/Working Paper, University of California, Berkeley, December 2006;
16. A.M. Ghose, M. Smith, and R. Telang, "Internet Exchanges for Used Books: An Empirical Analysis of Product Cannibalization and Welfare Impact," Information Systems Research, 17/1 (March 2006): 3-19.
17. The dangers of competing only on price in online markets are discussed by Michael E. Porter, "Strategy and the Internet," Harvard Business Review, 79/3 (March 2001): 62-78.