Skip to main content
SpringerLink
Log in
Book cover

International Conference on Auctions, Market Mechanisms and Their Applications

AMMA 2011: Auctions, Market Mechanisms, and Their Applications pp 40–52Cite as

A Framework for Automated Bundling and Pricing Using Purchase Data

  • Conference paper

Abstract

We present a framework for automatically suggesting high-profit bundle discounts based on historical customer purchase data. We develop several search algorithms that identify profit-maximizing prices and bundle discounts. We introduce a richer probabilistic valuation model than prior work by capturing complementarity, substitutability, and covariance, and we provide a hybrid search technique for fitting such a model to historical shopping cart data. As new purchase data is collected, it is integrated into the valuation model, leading to an online technique that continually refines prices and bundle discounts. To our knowledge, this is the first paper to study bundle discounting using shopping cart data. We conduct computational experiments using our fitting and pricing algorithms that demonstrate several conditions under which offering discounts on bundles can benefit the seller, the buyer, and the economy as a whole. One of our main findings is that, in contrast to products typically suggested by recommender systems, the most profitable products to offer bundle discounts on appear to be those that are occasionally purchased together and often separately.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Adams, W.J., Yellen, J.L.: Commodity bundling and the burden of monopoly. Quarterly Journal of Economics 90(3), 475–498 (1976)

    Article  Google Scholar 

  2. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: VLDB (1994)

    Google Scholar 

  3. Armstrong, M.: Multiproduct nonlinear pricing. Econometrica 64(1), 51–75 (1996)

    Article  MATH  Google Scholar 

  4. Bakos, Y., Brynjolfsson, E.: Bundling information goods: Pricing, profits, and efficiency. Management Science 45(12), 1613–1630 (1999)

    Article  MATH  Google Scholar 

  5. Balcan, M.-F., Blum, A., Mansour, Y.: Item pricing for revenue maximization. In: ACM EC (2008)

    Google Scholar 

  6. Benisch, M., Sadeh, N., Sandholm, T.: A theory of expressiveness in mechanisms. In: AAAI (2008)

    Google Scholar 

  7. Benisch, M., Sadeh, N., Sandholm, T.: Methodology for designing reasonably expressive mechanisms with application to ad auctions. In: IJCAI (2009)

    Google Scholar 

  8. Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. In: SIGMOD (1997)

    Google Scholar 

  9. Brooks, C.H., Durfee, E.H.: Toward automated pricing and bundling of information goods. In: Knowledge-based Electronic Markets (2000)

    Google Scholar 

  10. Bulut, Z., Grler, I., Sen, A.: Bundle pricing of inventories with stochastic demand. European Journal of Operational Research 197(3), 897–911 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  11. Chu, C.S., Leslie, P., Sorensen, A.: Nearly optimal pricing for multiproduct firms. NBER Working Paper 13916 (2008)

    Google Scholar 

  12. Conitzer, V., Sandholm, T., Santi, P.: Combinatorial auctions with k-wise dependent valuations. In: AAAI (2005)

    Google Scholar 

  13. Dansby, R.E., Conrad, C.: Commodity bundling. The American Economic Review 74(2), 377–381 (1984)

    Google Scholar 

  14. Guruswami, V., Hartline, J.D., Karlin, A.R., Kempe, D., Kenyon, C., McSherry, F.: On profit-maximizing envy-free pricing. In: SODA (2005)

    Google Scholar 

  15. Hanson, W., Martin, R.K.: Optimal bundle pricing. Management Science 36(2), 155–174 (1990)

    Article  MATH  Google Scholar 

  16. Hitt, L.M., Chen, P.-Y.: Bundling with customer self-selection: A simple approach to bundling low-marginal-cost goods. Management Science 51(10), 1481–1493 (2005)

    Article  MATH  Google Scholar 

  17. Jedidi, K., Jagpal, S., Manchanda, P.: Measuring heterogeneous reservation prices for product bundles. Marketing Science 22(1), 107–130 (2003)

    Article  Google Scholar 

  18. Jeziorski, P., Segal, I.: What makes them click: Empirical analysis of consumer demand for internet search advertising. Mimeo (June 2009)

    Google Scholar 

  19. Kephart, J.O., Brooks, C.H., Das, R., MacKie-Mason, J.K., Gazzale, R., Durfee, E.H.: Pricing information bundles in a dynamic environment. In: ACM EC (2001)

    Google Scholar 

  20. Preston McAfee, R., McMillan, J., Whinston, M.D.: Multiproduct monopoly, commodity bundling, and correlation of values. Quarterly Journal of Economics 104(2), 371–383 (1989)

    Article  MathSciNet  Google Scholar 

  21. Ostrovsky, M., Schwarz, M.: Reserve prices in internet advertising auctions: A field experiment. In: ACM EC (2011)

    Google Scholar 

  22. Rusmevichientong, P., Van Roy, B., Glynn, P.W.: A nonparametric approach to multiproduct pricing. Operations Research 54(1), 82–98 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  23. Sandholm, T.: Expressive commerce and its application to sourcing: How we conducted $35 billion of generalized combinatorial auctions. AI Magazine 28(3), 45–58 (2007)

    MathSciNet  Google Scholar 

  24. Schmalensee, R.: Gaussian demand and commodity bundling. The Journal of Business 57(1), S211–S230 (1984)

    Article  Google Scholar 

  25. US Census Bureau. Statistical abstract of the United States, wholesale & retail trade: Online retail sales (2010), http://www.census.gov/

  26. Varian, H.R.: The non-parametric approach to demand analysis. Econometrica 50, 945–974 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  27. Venkatesh, R., Kamakura, W.: Optimal bundling and pricing under a monopoly: Contrasting complements and substitutes from independently valued products. Journal of Business 76(2), 211–231 (2003)

    Article  Google Scholar 

  28. Walsh, W., Boutilier, C., Sandholm, T., Shields, R., Nemhauser, G., Parkes, D.: Automated channel abstraction for advertising auctions. In: AAAI (2010)

    Google Scholar 

  29. Wu, S.-Y., Hitt, L.M., Chen, P.-Y., Anandalingam, G.: Customized bundle pricing for information goods: A nonlinear mixed-integer programming approach. Management Science 54(3), 608–622 (2008)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science, Carnegie Mellon University, USA

    Michael Benisch & Tuomas Sandholm

Authors
  1. Michael Benisch
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tuomas Sandholm
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Harvard Business School, Soldier Field Road, 2173, Boston, MA, USA

    Peter Coles

  2. Department of Computer Science, Rensselaer Polytechnic Institute, 110, 8th St, 12180-3590, Troy, NY, USA

    Sanmay Das

  3. Yahoo! Research, 111 West 40th St., 10018, New York, NY, USA

    Sébastien Lahaie

  4. Department of Computer Science, Rensselaer Polytechnic Institute, 110, 8th St., 12180, Troy, NY, USA

    Boleslaw Szymanski

Rights and permissions

Reprints and permissions

Copyright information

© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering

About this paper

Cite this paper

Benisch, M., Sandholm, T. (2012). A Framework for Automated Bundling and Pricing Using Purchase Data. In: Coles, P., Das, S., Lahaie, S., Szymanski, B. (eds) Auctions, Market Mechanisms, and Their Applications. AMMA 2011. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 80. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30913-7_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-30913-7_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30912-0

  • Online ISBN: 978-3-642-30913-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation