Value of Personalized Pricing in Customer Retention

Abstract

Recent technological advances enable firms to engage more effectively with customers who reveal their intent to churn. When consumers disclose such intentions, firms can offer targeted discounts, a form of personalized pricing, to encourage retention. Prior literature, however, shows that broad use of personalized pricing for all returning customers can intensify competition and erode profits in competitive markets. We show that these adverse effects are substantially mitigated when personalized prices are offered selectively, i.e., only after firms identify consumers who are likely to churn. Analyzing a retention-focused personalized pricing strategy, we find three main results. First, personalized pricing increases firm profits only when the proportion of disclosers is relatively low; although it raises margins on retained customers, it also intensifies first-period competition. Second, consumer surplus and social welfare improve when the share of disclosers is high, because higher disclosure induces stronger price reductions and greater retention. Third, firms often have incentives to invest in personalized pricing capabilities, but mutual investment can generate a Prisoner’s Dilemma: Even with low investment costs, simultaneous adoption may strengthen competition and reduce industry profits. These results emphasize the importance of selective targeting and timing when deploying personalized pricing for retention and offer guidance for firms and policymakers considering such investments.

Keywords

Customer retention / personalized pricing / behavior-based pricing / duopoly competition

References

[1] Amaldoss W, He C. The charm of behavior-based pricing: When consumers’ taste is diverse and the consideration set is limited. Journal of Marketing Research, 2019, 56(5): 767-790.

[2] Ben-Rhouma T, Zaccour G. Optimal marketing strategies for the acquisition and retention of service subscribers. Management Science, 2018, 64(6): 2609-2627.

[3] Buell R W, Campbell D, Frei F X. Are self-service customers satisfied or stuck?. Production and Operations Management, 2010, 19(6): 679-697.

[4] Buell R W, Choi M. Improving customer compatibility with tradeoff transparency. Management Science, 2025, 71(2): 1335-1355.

[5] Campbell D, Frei F. Cost structure, customer profitability, and retention implications of self-service distribution channels: Evidence from customer behavior in an online banking channel. Management Science, 2010, 56(1): 4-24.

[6] Cao K, Choi T. Optimal trade-in return policies: Is it wise to be generous?. Production and Operations Management, 2022, 31(3): 1309-1331.

[7] Choe C, King S, Matsushima N. Pricing with cookies: Behavior-based price discrimination and spatial competition. Management Science, 2018, 64(12): 5669-5687.

[8] Choudhary V, Ghose A, Mukhopadhyay T, Rajan U. Personalized pricing and quality differentiation. Management Science, 2005, 51(7): 1120-1130.

[9] Conitzer V, Taylor C R, Wagman L. Hide and seek: Costly consumer privacy in a market with repeat purchases. Marketing Science, 2012, 31(2): 277-292.

[10] Cui R, Lu Z, Sun T, Golden J M. Sooner or later? Promising delivery speed in online retail. Manufacturing & Service Operations Management, 2024, 26(1): 233-251.

[11] Dong Y, Yao Y, Cui T H. When acquisition spoils retention: Direct selling vs. delegation under CRM. Management Science, 2011, 57(7): 1288-1299.

[12] Fudenberg D, Tirole J. Customer poaching and brand switching. RAND Journal of Economics, 2000, 31(4): 634-657.

[13] Fudenberg D, Villas-Boas J M. Behavior-based price discrimination and customer recognition. Handbook on Economics and Information Systems, 2006, 1: 377-436.

[14] Hajihashemi B, Sayedi A, Shulman J D. The perils of personalized pricing with network effects. Marketing Science, 2022, 41(3): 477-500.

[15] Jing B. Customer recognition in experience vs. inspection good markets. Management Science, 2016, 62(1): 216-224.

[16] Jing B. Behavior-based pricing, production efficiency, and quality differentiation. Management Science, 2017, 63(7): 2365-2376.

[17] Jullien B, Reisinger M, Rey P. Personalized pricing and distribution strategies. Management Science, 2023, 69(3): 1687-1702.

[18] Kim Y, Cui H, Zhu Y. Behavior-based pricing under informed privacy consent: Unraveling autonomy paradox. Marketing Science, 2025, 44(6): 1362-1380.

[19] Krishna A, Feinberg F M, Zhang Z J. Should price increases be targeted? Pricing power and selective vs. across-the-board price increases. Management Science, 2007, 53(9): 1407-1422.

[20] Kwon K, Kim C. How to design personalization in a context of customer retention: Who personalizes what and to what extent?. Electronic Commerce Research and Applications, 2012, 11(2): 101-116.

[21] Laussel D, Resende J. When is product personalization profit-enhancing? A behavior-based discrimination model. Management Science, 2022, 68(12): 8872-8888.

[22] Li K J, Jain S. Behavior-based pricing: An analysis of the impact of peer-induced fairness. Management Science, 2016, 62(9): 2705-2721.

[23] Li K J. Behavior-based quality discrimination. Manufacturing & Service Operations Management, 2021, 23(2): 425-436

[24] Li X, Li K J. Beating the algorithm: Consumer manipulation, personalized pricing, and big data management. Manufacturing & Service Operations Management, 2023, 25(1): 36-49.

[25] Liu Y, Zhang Z J. Research note–The benefits of personalized pricing in a channel. Marketing Science, 2006, 25(1): 97-105.

[26] Lu Q, Matsushima N. Personalized pricing when consumers can purchase multiple items. Journal of Industrial Economics, 2024, 72(4): 1507-1524.

[27] Mehra A, Bala R, Sankaranarayanan R. Competitive behavior-based price discrimination for software upgrades. Information Systems Research, 2012, 23(1): 60-74.

[28] Montes R, Sand-Zantman W, Valletti T. The value of personal information in online markets with endogenous privacy. Management Science, 2019, 65(3): 1342-1362.

[29] Pan Y, Zinkhan G M. Exploring the impact of online privacy disclosures on consumer trust. Journal of Retailing, 2006, 82(4): 331-338.

[30] Pattabhiramaiah A, Overby E, Xu L. Spillovers from online engagement: How a newspaper subscriber’s activation of digital paywall access affects her retention and subscription revenue. Management Science, 2022, 68(5): 3528-3548.

[31] Pazgal A, Soberman D. Behavior-based discrimination: Is it a winning play, and if so, when?. Marketing Science, 2008, 27(6): 977-994.

[32] Retana G F, Forman C, Wu D J. Proactive customer education, customer retention, and demand for technology support: Evidence from a field experiment. Manufacturing & Service Operations Management, 2016, 18(1): 34-50.

[33] Rhee K, Thomadsen R. Behavior-based pricing in vertically differentiated industries. Management Science, 2017, 63(8): 2729-2740.

[34] Rhodes A, Zhou J. Personalized pricing and competition. American Economic Review, 2024, 114(7): 2141-2170.

[35] Shin J, Sudhir K. A customer management dilemma: When is it profitable to reward one’s own customers?. Marketing Science, 2010, 29(4): 671-689.

[36] Sriram S, Chintagunta P K, Manchanda P. Service quality variability and termination behavior. Management Science, 2015, 61(11): 2739-2759.

[37] Subramanian U, Raju J S, Zhang Z J. The strategic value of high-cost customers. Management Science, 2014, 60(2): 494-507.

[38] Thisse J F, Vives X (1988). On the strategic choice of spatial price policy. American Economic Review 122–137.

[39] Villas-Boas J M. Price cycles in markets with customer recognition. RAND Journal of Economics, 2004, 35(3): 486-501.

[40] Wang X, Li X, Kopalle P K (2023). When does it pay to invest in pricing algorithms? Production and Operations Management: 1–18.

[41] Wang Y, Lu L X, Shi P. Does customer email engagement improve profitability? Evidence from a field experiment in subscription service retailing. Manufacturing & Service Operations Management, 2022, 24(5): 2703-2721.

[42] Zhang J. The perils of behavior-based personalization. Marketing Science, 2011, 30(1): 170-186.

[43] Zhang X, Dearden J A, Yao Y. Let them stay or let them go? Online retailer pricing strategy for managing stockouts. Production and Operations Management, 2022, 31(11): 4173-4190.

[44] Yoganarasimhan H, Barzegary E, Pani A. Design and evaluation of optimal free trials. Management Science, 2023, 69(6): 3220-3240.

[45] Zhang Z, Nan G, Li M, Tan Y. Competitive entry of information goods under quality uncertainty. Management Science, 2022, 68(4): 2869-2888.

[46] Zhang J, Li K J. Retention or acquisition? Behavior-based quality disclosure. Management Science, 2025, 71(10): 8510-8525.

RIGHTS & PERMISSIONS

Systems Engineering Society of China and Springer-Verlag GmbH Germany

Just Accepted

This article has successfully passed peer review and final editorial review, and will soon enter typesetting, proofreading and other publishing processes. The currently displayed version is the accepted final manuscript. The officially published version will be updated with format, DOI and citation information upon launch. We recommend that you pay attention to subsequent journal notifications and preferentially cite the officially published version. Thank you for your support and cooperation.