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Competitive Personalized Pricing

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  • Zhijun Chen
  • Chongwoo Choe
  • Noriaki Matsushima

Abstract

We study a duopoly model where each firm chooses personalized prices for its targeted consumers, who can be active or passive in identity management. Active consumers can bypass price discrimination and have access to the price offered to non-targeted consumers, which passive consumers cannot. When all consumers are passive, personalized pricing leads to intense competition and total industry profit lower than that under the Hotelling equilibrium. But market is always fully covered. Active consumers raise the firm's cost of serving non-targeted consumers, which softens competition. When firms have sufficiently large and non-overlapping target segments, active consumers enable firms to extract full surplus from their targeted consumers through perfect price discrimination. With active consumers, firms also choose not to serve the entire market when the commonly non-targeted market segment is small. Thus active identity management can lead to lower consumer surplus and lower social welfare. We also discuss the regulatory implications for the use of consumer information by firms as well as the implications for management.

Suggested Citation

  • Zhijun Chen & Chongwoo Choe & Noriaki Matsushima, 2018. "Competitive Personalized Pricing," ISER Discussion Paper 1023, Institute of Social and Economic Research, Osaka University.
  • Handle: RePEc:dpr:wpaper:1023
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    1. Amit Pazgal & David Soberman, 2008. "Behavior-Based Discrimination: Is It a Winning Play, and If So, When?," Marketing Science, INFORMS, vol. 27(6), pages 977-994, 11-12.
    2. Monika Schnitzer, 1994. "Dynamic Duopoly with Best-Price Clauses," RAND Journal of Economics, The RAND Corporation, vol. 25(1), pages 186-196, Spring.
    3. Rodrigo Montes & Wilfried Sand-Zantman & Tommaso Valletti, 2019. "The Value of Personal Information in Online Markets with Endogenous Privacy," Management Science, INFORMS, vol. 65(3), pages 1342-1362, March.
    4. Greg Shaffer & Z. John Zhang, 2002. "Competitive One-to-One Promotions," Management Science, INFORMS, vol. 48(9), pages 1143-1160, September.
    5. Greg Shaffer & Z. John Zhang, 1995. "Competitive Coupon Targeting," Marketing Science, INFORMS, vol. 14(4), pages 395-416.
    6. Belleflamme, Paul & Vergote, Wouter, 2016. "Monopoly price discrimination and privacy: The hidden cost of hiding," Economics Letters, Elsevier, vol. 149(C), pages 141-144.
    7. Yuxin Chen & Ganesh Iyer, 2002. "Research Note Consumer Addressability and Customized Pricing," Marketing Science, INFORMS, vol. 21(2), pages 197-208, November.
    8. Drew Fudenberg & Jean Tirole, 2000. "Customer Poaching and Brand Switching," RAND Journal of Economics, The RAND Corporation, vol. 31(4), pages 634-657, Winter.
    9. Krista J. Li & Sanjay Jain, 2016. "Behavior-Based Pricing: An Analysis of the Impact of Peer-Induced Fairness," Management Science, INFORMS, vol. 62(9), pages 2705-2721, September.
    10. Esteves, Rosa-Branca, 2010. "Pricing with customer recognition," International Journal of Industrial Organization, Elsevier, vol. 28(6), pages 669-681, November.
    11. Thisse, Jacques-Francois & Vives, Xavier, 1988. "On the Strategic Choice of Spatial Price Policy," American Economic Review, American Economic Association, vol. 78(1), pages 122-137, March.
    12. Dirk Bergemann & Alessandro Bonatti, 2015. "Selling Cookies," American Economic Journal: Microeconomics, American Economic Association, vol. 7(3), pages 259-294, August.
    13. Alessandro Acquisti & Hal R. Varian, 2005. "Conditioning Prices on Purchase History," Marketing Science, INFORMS, vol. 24(3), pages 367-381, May.
    14. Drew Fudenberg & David M. Kreps & David K. Levine, 2008. "On the Robustness of Equilibrium Refinements," World Scientific Book Chapters, in: Drew Fudenberg & David K Levine (ed.), A Long-Run Collaboration On Long-Run Games, chapter 5, pages 67-93, World Scientific Publishing Co. Pte. Ltd..
    15. Juanjuan Zhang, 2011. "The Perils of Behavior-Based Personalization," Marketing Science, INFORMS, vol. 30(1), pages 170-186, 01-02.
    16. Yuxin Chen & Chakravarthi Narasimhan & Z. John Zhang, 2001. "Consumer Heterogeneity and Competitive Price-Matching Guarantees," Marketing Science, INFORMS, vol. 20(3), pages 300-314, June.
    17. Chongwoo Choe & Stephen King & Noriaki Matsushima, 2017. "Pricing with Cookies: Behavior-Based Price Discrimination and Spatial Competition," Monash Economics Working Papers 07-17, Monash University, Department of Economics.
    18. Vidyanand Choudhary & Anindya Ghose & Tridas Mukhopadhyay & Uday Rajan, 2005. "Personalized Pricing and Quality Differentiation," Management Science, INFORMS, vol. 51(7), pages 1120-1130, July.
    19. Rosa-Branca Esteves & Joana Resende, 2016. "Competitive Targeted Advertising with Price Discrimination," Marketing Science, INFORMS, vol. 35(4), pages 576-587, July.
    20. Mark Armstrong & Robert Porter (ed.), 2007. "Handbook of Industrial Organization," Handbook of Industrial Organization, Elsevier, edition 1, volume 3, number 1.
    21. J. Miguel Villas-Boas, 2004. "Price Cycles in Markets with Customer Recognition," RAND Journal of Economics, The RAND Corporation, vol. 35(3), pages 486-501, Autumn.
    22. Yuk‐fai Fong & Qihong Liu, 2011. "Loyalty Rewards Facilitate Tacit Collusion," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 20(3), pages 739-775, September.
    23. Vincent Conitzer & Curtis R. Taylor & Liad Wagman, 2012. "Hide and Seek: Costly Consumer Privacy in a Market with Repeat Purchases," Marketing Science, INFORMS, vol. 31(2), pages 277-292, March.
    24. Anindya Ghose & Vidyanand Choudhary & Tridas Mukhopadhyay & Uday Rajan, 2002. "Personalized Pricing and Quality Differentiation on the Internet," Review of Marketing Science Working Papers 2-1-1005, Berkeley Electronic Press.
    25. Yongmin Chen, 1997. "Paying Customers to Switch," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 6(4), pages 877-897, December.
    26. Thomas E. Cooper, 1986. "Most-Favored-Customer Pricing and Tacit Collusion," RAND Journal of Economics, The RAND Corporation, vol. 17(3), pages 377-388, Autumn.
    27. Chongwoo Choe & Stephen King & Noriaki Matsushima, 2018. "Pricing with Cookies: Behavior-Based Price Discrimination and Spatial Competition," Management Science, INFORMS, vol. 64(12), pages 5669-5687, December.
    28. Toshihiro Matsumura & Noriaki Matsushima, 2015. "Should Firms Employ Personalized Pricing?," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 24(4), pages 887-903, October.
    29. Verhoef, Peter C. & Lemon, Katherine N. & Parasuraman, A. & Roggeveen, Anne & Tsiros, Michael & Schlesinger, Leonard A., 2009. "Customer Experience Creation: Determinants, Dynamics and Management Strategies," Journal of Retailing, Elsevier, vol. 85(1), pages 31-41.
    30. Curtis R. Taylor, 2004. "Consumer Privacy and the Market for Customer Information," RAND Journal of Economics, The RAND Corporation, vol. 35(4), pages 631-650, Winter.
    31. Jackson, Matthew O. & Rodriguez-Barraquer, Tomas & Tan, Xu, 2012. "Epsilon-equilibria of perturbed games," Games and Economic Behavior, Elsevier, vol. 75(1), pages 198-216.
    32. J. Miguel Villas-Boas, 1999. "Dynamic Competition with Customer Recognition," RAND Journal of Economics, The RAND Corporation, vol. 30(4), pages 604-631, Winter.
    33. Stole, Lars A., 2007. "Price Discrimination and Competition," Handbook of Industrial Organization, in: Mark Armstrong & Robert Porter (ed.), Handbook of Industrial Organization, edition 1, volume 3, chapter 34, pages 2221-2299, Elsevier.
    34. Peitz, Martin & Waldfogel, Joel, 2012. "The Oxford Handbook of the Digital Economy," OUP Catalogue, Oxford University Press, number 9780195397840.
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    More about this item

    JEL classification:

    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
    • L5 - Industrial Organization - - Regulation and Industrial Policy

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