IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v60y2014i8p2002-2019.html
   My bibliography  Save this article

Balancing Acquisition and Retention Spending for Firms with Limited Capacity

Author

Listed:
  • Anton Ovchinnikov

    (Darden Graduate School of Business, University of Virginia, Charlottesville, Virginia 22903)

  • Béatrice Boulu-Reshef

    (Darden Graduate School of Business and Department of Economics, University of Virginia, Charlottesville, Virginia 22903)

  • Phillip E. Pfeifer

    (Darden Graduate School of Business, University of Virginia, Charlottesville, Virginia 22903)

Abstract

This paper discusses the interaction between revenue management and customer relationship management for a firm that operates in a customer retention situation but faces limited capacity. We present a dynamic programming model for how the firm balances investments in customer acquisition and retention, as well as retention across multiple customer types. We characterize the optimal policy and discuss how the policy changes depending on capacity limitations. We then contrast the modeling results with those of a behavioral experiment in which subjects acted as managers making acquisition and retention decisions. In the modeling part of the paper, we introduce a concept of the value of an incremental customer (VIC), and show that when capacity is unlimited, VIC equals customer lifetime value (CLV), but when capacity is limited, VIC is much smaller and changes dynamically depending on the number of customers and their mix. As a result, the optimal spending is constant and depends on CLV for the firms with unlimited capacity, but changes dynamically and is generally unrelated to CLV when capacity is limited. In the experimental part, we introduce a concept of conditional optimality for the analysis of state-dependent decisions. Applying this concept to our data, we document a number of decision biases, specifically the subjects' tendency to overspend on retaining high-value customers and underspend on lower-value customers retention and acquisition. We show that providing CLV information exacerbates these biases and leads to a loss of net revenue when capacity is limited, but providing information about the marginal costs of acquisition and retention eliminated these biases and increases net revenue.Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2013.1842 . This paper was accepted by Yossi Aviv, operations management.

Suggested Citation

  • Anton Ovchinnikov & Béatrice Boulu-Reshef & Phillip E. Pfeifer, 2014. "Balancing Acquisition and Retention Spending for Firms with Limited Capacity," Management Science, INFORMS, vol. 60(8), pages 2002-2019, August.
    • Handle: RePEc:inm:ormnsc:v:60:y:2014:i:8:p:2002-2019
    DOI: 10.1287/mnsc.2013.1842
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.2013.1842
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mnsc.2013.1842?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Pfeifer, Phillip E. & Ovchinnikov, Anton, 2011. "A Note on Willingness to Spend and Customer Lifetime Value for Firms with Limited Capacity," Journal of Interactive Marketing, Elsevier, vol. 25(3), pages 178-189.
    2. Goker Aydin & Serhan Ziya, 2009. "Technical Note---Personalized Dynamic Pricing of Limited Inventories," Operations Research, INFORMS, vol. 57(6), pages 1523-1531, December.
    3. Sunil Gupta & Valarie Zeithaml, 2006. "Customer Metrics and Their Impact on Financial Performance," Marketing Science, INFORMS, vol. 25(6), pages 718-739, 11-12.
    4. J. Neil Bearden & Ryan O. Murphy & Amnon Rapoport, 2008. "Decision Biases in Revenue Management: Some Behavioral Evidence," Manufacturing & Service Operations Management, INFORMS, vol. 10(4), pages 625-636, June.
    5. Francis de Véricourt & Miguel Sousa Lobo, 2009. "Resource and Revenue Management in Nonprofit Operations," Operations Research, INFORMS, vol. 57(5), pages 1114-1128, October.
    6. Sam Aflaki & Ioana Popescu, 2014. "Managing Retention in Service Relationships," Management Science, INFORMS, vol. 60(2), pages 415-433, February.
    7. Daniel Kahneman & Jack L. Knetsch & Richard H. Thaler, 1991. "Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias," Journal of Economic Perspectives, American Economic Association, vol. 5(1), pages 193-206, Winter.
    8. James E. Smith & Kevin F. McCardle, 2002. "Structural Properties of Stochastic Dynamic Programs," Operations Research, INFORMS, vol. 50(5), pages 796-809, October.
    9. Michael Lewis, 2005. "Research Note: A Dynamic Programming Approach to Customer Relationship Pricing," Management Science, INFORMS, vol. 51(6), pages 986-994, June.
    10. Roland T. Rust & Tuck Siong Chung, 2006. "Marketing Models of Service and Relationships," Marketing Science, INFORMS, vol. 25(6), pages 560-580, 11-12.
    11. Matthew J. Drake & Serhan Duran & Paul M. Griffin & Julie L. Swann, 2008. "Optimal timing of switches between product sales for sports and entertainment tickets," Naval Research Logistics (NRL), John Wiley & Sons, vol. 55(1), pages 59-75, February.
    12. Mak, Vincent & Rapoport, Amnon & Gisches, Eyran J., 2012. "Competitive dynamic pricing with alternating offers: Theory and experiment," Games and Economic Behavior, Elsevier, vol. 75(1), pages 250-264.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. So Yeon Chun & Dan A. Iancu & Nikolaos Trichakis, 2020. "Loyalty Program Liabilities and Point Values," Manufacturing & Service Operations Management, INFORMS, vol. 22(2), pages 257-272, March.
    2. Philipp Afèche & Mojtaba Araghi & Opher Baron, 2017. "Customer Acquisition, Retention, and Service Access Quality: Optimal Advertising, Capacity Level, and Capacity Allocation," Manufacturing & Service Operations Management, INFORMS, vol. 19(4), pages 674-691, October.
    3. So Yeon Chun & Anton Ovchinnikov, 2019. "Strategic Consumers, Revenue Management, and the Design of Loyalty Programs," Management Science, INFORMS, vol. 65(9), pages 3969-3987, September.
    4. Johannes Habel & Sascha Alavi & Nicolas Heinitz, 2023. "A theory of predictive sales analytics adoption," AMS Review, Springer;Academy of Marketing Science, vol. 13(1), pages 34-54, June.
    5. Ascarza, & Neslin, & Netzer, & Lemmens, Aurélie & Anderson, Zachery & Fader, Peter S. & Gupta, S. & Hardie, B.G.S. & Libai, Barak & Neal, David & Provost, Foster, 2018. "In pursuit of enhanced customer retention management : Review, key issues, and future directions," Other publications TiSEM 28a90d28-6daf-42f1-bd8e-e, Tilburg University, School of Economics and Management.
    6. Mehrdad Memarpour & Erfan Hassannayebi & Navid Fattahi Miab & Ali Farjad, 2021. "Dynamic allocation of promotional budgets based on maximizing customer equity," Operational Research, Springer, vol. 21(4), pages 2365-2389, December.
    7. Eva Ascarza & Scott A. Neslin & Oded Netzer & Zachery Anderson & Peter S. Fader & Sunil Gupta & Bruce G. S. Hardie & Aurélie Lemmens & Barak Libai & David Neal & Foster Provost & Rom Schrift, 2018. "In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(1), pages 65-81, March.
    8. Klein, Robert & Kolb, Johannes, 2015. "Maximizing customer equity subject to capacity constraints," Omega, Elsevier, vol. 55(C), pages 111-125.
    9. Ben Ali, M. & D’Amours, S. & Gaudreault, J. & Carle, M-A., 2018. "Configuration and evaluation of an integrated demand management process using a space-filling design and Kriging metamodeling," Operations Research Perspectives, Elsevier, vol. 5(C), pages 45-58.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Philipp Afèche & Mojtaba Araghi & Opher Baron, 2017. "Customer Acquisition, Retention, and Service Access Quality: Optimal Advertising, Capacity Level, and Capacity Allocation," Manufacturing & Service Operations Management, INFORMS, vol. 19(4), pages 674-691, October.
    2. Sam Aflaki & Ioana Popescu, 2014. "Managing Retention in Service Relationships," Management Science, INFORMS, vol. 60(2), pages 415-433, February.
    3. Mercedes Esteban-Bravo & Jose M. Vidal-Sanz & Gökhan Yildirim, 2014. "Valuing Customer Portfolios with Endogenous Mass and Direct Marketing Interventions Using a Stochastic Dynamic Programming Decomposition," Marketing Science, INFORMS, vol. 33(5), pages 621-640, September.
    4. Lewis, Michael & Whitler, Kimberly A. & Hoegg, JoAndrea, 2013. "Customer Relationship Stage and the Use of Picture-Dominant versus Text-Dominant Advertising: A Field Study," Journal of Retailing, Elsevier, vol. 89(3), pages 263-280.
    5. Andrés Musalem & Yogesh V. Joshi, 2009. "—How Much Should You Invest in Each Customer Relationship? A Competitive Strategic Approach," Marketing Science, INFORMS, vol. 28(3), pages 555-565, 05-06.
    6. Kohsuke Matsuoka, 2020. "Exploring the interface between management accounting and marketing: a literature review of customer accounting," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 31(3), pages 157-208, September.
    7. Vincent Mak & Amnon Rapoport & Eyran J. Gisches & Jiaojie Han, 2014. "Purchasing Scarce Products Under Dynamic Pricing: An Experimental Investigation," Manufacturing & Service Operations Management, INFORMS, vol. 16(3), pages 425-438, July.
    8. Vincent Mak & Amnon Rapoport & Eyran J. Gisches, 2018. "Dynamic Pricing Decisions and Seller-Buyer Interactions under Capacity Constraints," Games, MDPI, vol. 9(1), pages 1-23, February.
    9. Chen, Kee-Kuo & Ho, Hui-Ping & Chang, Ching-Ter, 2015. "Estimating attributes importance for container shipping industry by closing the listening gap with maximum convergent validity," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 79(C), pages 145-163.
    10. Mohammed Al-Hitmi & Salman Ahmad & Atif Iqbal & Sanjeevikumar Padmanaban & Imtiaz Ashraf, 2018. "Selective Harmonic Elimination in a Wide Modulation Range Using Modified Newton–Raphson and Pattern Generation Methods for a Multilevel Inverter," Energies, MDPI, vol. 11(2), pages 1-16, February.
    11. Timothy L. Keiningham & Lerzan Aksoy & Bruce Cooil & Tor Wallin Andreassen, 2008. "—Net Promoter, Recommendations, and Business Performance: A Clarification on Morgan and Rego," Marketing Science, INFORMS, vol. 27(3), pages 531-532, 05-06.
    12. Gregory DeCroix & Xiaoyang Long & Jordan Tong, 2021. "How Service Quality Variability Hurts Revenue When Customers Learn: Implications for Dynamic Personalized Pricing," Operations Research, INFORMS, vol. 69(3), pages 683-708, May.
    13. Gisches, Eyran J. & Qi, Hang & Becker, William J. & Rapoport, Amnon, 2021. "Strategic retailers and myopic consumers: Competitive pricing of perishable goods," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 92(C).
    14. Sunil Mithas & M. S. Krishnan & Claes Fornell, 2016. "Research Note—Information Technology, Customer Satisfaction, and Profit: Theory and Evidence," Information Systems Research, INFORMS, vol. 27(1), pages 166-181, March.
    15. Peter Seele & Claus Dierksmeier & Reto Hofstetter & Mario D. Schultz, 2021. "Mapping the Ethicality of Algorithmic Pricing: A Review of Dynamic and Personalized Pricing," Journal of Business Ethics, Springer, vol. 170(4), pages 697-719, May.
    16. Roland T. Rust & Ming-Hui Huang, 2014. "The Service Revolution and the Transformation of Marketing Science," Marketing Science, INFORMS, vol. 33(2), pages 206-221, March.
    17. Gupta, Sunil, 2009. "Customer-Based Valuation," Journal of Interactive Marketing, Elsevier, vol. 23(2), pages 169-178.
    18. Seung Hyun Kim & Tridas Mukhopadhyay, 2011. "Determining Optimal CRM Implementation Strategies," Information Systems Research, INFORMS, vol. 22(3), pages 624-639, September.
    19. Baohong Sun, 2006. "—Technology Innovation and Implications for Customer Relationship Management," Marketing Science, INFORMS, vol. 25(6), pages 594-597, 11-12.
    20. Tarek Ben Rhouma & Georges Zaccour, 2018. "Optimal Marketing Strategies for the Acquisition and Retention of Service Subscriber," Management Science, INFORMS, vol. 64(6), pages 2609-2627, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormnsc:v:60:y:2014:i:8:p:2002-2019. See general information about how to correct material in RePEc.

    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 CitEc recognized a bibliographic reference but did not link an item in RePEc 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.