Customized Bundle Pricing for Information Goods: A Nonlinear Mixed-Integer Programming Approach
This paper proposes using nonlinear mixed-integer programming to solve the customized bundle-pricing problem in which consumers are allowed to choose up to N goods out of a larger pool of J goods. Prior work has suggested that this mechanism has attractive features for the pricing of information and other low-marginal cost goods. Although closed-form solutions exist for this problem for certain cases of consumer preferences, many interesting scenarios cannot be easily handled without a numerical solution procedure. In this paper, we investigate the efficiency gains created by customized bundling over the alternatives of pure bundling or individual sale under different assumptions about customer preferences and firm cost structure, as well as the potential loss of efficiency caused by pricing with incomplete information about consumer reservation values. Our analysis suggests that customized bundling enhances sellers' profits and enhances welfare when consumers do not place positive values on all goods, and that this consumer characteristic is much more important than the shape of the valuation distribution in determining the optimal pricing scheme. We also find that customized bundling outperforms both pure bundling and individual sale in the presence of incomplete information, and that customized bundling still outperforms other simpler pricing schemes even when exact consumer valuations are not known ex ante.
Volume (Year): 54 (2008)
Issue (Month): 3 (March)
|Contact details of provider:|| Postal: 7240 Parkway Drive, Suite 300, Hanover, MD 21076 USA|
Web page: http://www.informs.org/
More information through EDIRC
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Jeffrey K. MacKie-Mason & Juan F. Riveros & Robert S. Gazzale, "undated". "Pricing and Bundling Electronic Information Goods: Field Evidence," Department of Economics Working Papers 2000-01, Department of Economics, Williams College.
- Schmalensee, Richard, 1984. "Gaussian Demand and Commodity Bundling," The Journal of Business, University of Chicago Press, vol. 57(1), pages 211-230, January.
- R. Venkatesh & Wagner Kamakura, 2003. "Optimal Bundling and Pricing under a Monopoly: Contrasting Complements and Substitutes from Independently Valued Products," The Journal of Business, University of Chicago Press, vol. 76(2), pages 211-232, April.
- Marshall L. Fisher, 1981. "The Lagrangian Relaxation Method for Solving Integer Programming Problems," Management Science, INFORMS, vol. 27(1), pages 1-18, January.
- David S. Sibley & Padmanabhan Srinagesh, 1997. "Multiproduct Nonlinear Pricing with Multiple Taste Characteristics," RAND Journal of Economics, The RAND Corporation, vol. 28(4), pages 684-707, Winter.
- R. Preston McAfee & John McMillan & Michael D. Whinston, 1989. "Multiproduct Monopoly, Commodity Bundling, and Correlation of Values," The Quarterly Journal of Economics, Oxford University Press, vol. 104(2), pages 371-383.
- Chuang, John Chung-I & Sirbu, Marvin A., 1999. "Optimal bundling strategy for digital information goods: network delivery of articles and subscriptions," Information Economics and Policy, Elsevier, vol. 11(2), pages 147-176, July.
- Salinger, Michael A, 1995. "A Graphical Analysis of Bundling," The Journal of Business, University of Chicago Press, vol. 68(1), pages 85-98, January.
- A. Michael Spence, 1980. "Multi-Product Quantity-Dependent Prices and Profitability Constraints," Review of Economic Studies, Oxford University Press, vol. 47(5), pages 821-841.
- Kamel Jedidi & Sharan Jagpal & Puneet Manchanda, 2003. "Measuring Heterogeneous Reservation Prices for Product Bundles," Marketing Science, INFORMS, vol. 22(1), pages 107-130, July.
- Lorin M. Hitt & Pei-yu Chen, 2005. "Bundling with Customer Self-Selection: A Simple Approach to Bundling Low-Marginal-Cost Goods," Management Science, INFORMS, vol. 51(10), pages 1481-1493, October.
- Shugan, Steven M, 1980. " The Cost of Thinking," Journal of Consumer Research, Oxford University Press, vol. 7(2), pages 99-111, Se.
- Yannis Bakos & Erik Brynjolfsson, 1999.
"Bundling Information Goods: Pricing, Profits, and Efficiency,"
INFORMS, vol. 45(12), pages 1613-1630, December.
- Yannis Bakos & Erik Brynjolfsson, 1997. "Bundling Information Goods: Pricing, Profits and Efficiency," Working Paper Series 199, MIT Center for Coordination Science.
- William James Adams & Janet L. Yellen, 1976. "Commodity Bundling and the Burden of Monopoly," The Quarterly Journal of Economics, Oxford University Press, vol. 90(3), pages 475-498.
- McAfee, R. Preston & McMillan, John, 1988. "Multidimensional incentive compatibility and mechanism design," Journal of Economic Theory, Elsevier, vol. 46(2), pages 335-354, December.
When requesting a correction, please mention this item's handle: RePEc:inm:ormnsc:v:54:y:2008:i:3:p:608-622. 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)
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 references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link 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 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.