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Optimal acquisition and retention strategies in a duopoly model of competition

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  • Chang, Shuhua
  • Zhang, Zhaowei
  • Wang, Xinyu
  • Dong, Yan

Abstract

We propose an optimal customer relationship marketing policy in a duopolistic market, where each firm’s market share depends on the CRM decisions of its own and of its competitors. The evolution process of the number of customers served by each firm is governed by a differential equation in which customer acquisition and retention processes are considered. A differential game model is presented to determine the optimal acquisition and retention expenditures of players. We derive a Nash equilibrium of a duopolistic differential game using the Hamilton–Jacobi–Bellman equations. Our results can be summarized mainly by the following three points. First, the optimal acquisition and retention expenditure strategies depend on each firm’s marginal customer equity, but not on the market share or the number of customers. Second, in response to the variation of the firm’s parameters, if its acquisition effectiveness is greater than its retention effectiveness, the firm would take action by making the same investment decision as its rival’s; instead, if its acquisition effectiveness is lower than its retention effectiveness, its rival would take action by making the different investment decision from the firm’s. Last, besides a mature product market, we also consider a market with remaining potential. In this case, a firm’s marginal customer equity can be decomposed into two components: the marginal customer equity from its own and from its rival’s. We show that the firm’s optimal investments in retention and acquisition are both positively related with a weighted difference between two components of the marginal customer equity.

Suggested Citation

  • Chang, Shuhua & Zhang, Zhaowei & Wang, Xinyu & Dong, Yan, 2020. "Optimal acquisition and retention strategies in a duopoly model of competition," European Journal of Operational Research, Elsevier, vol. 282(2), pages 677-695.
  • Handle: RePEc:eee:ejores:v:282:y:2020:i:2:p:677-695
    DOI: 10.1016/j.ejor.2019.09.044
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    References listed on IDEAS

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    1. Berger, Paul D. & Bechwati, Nada Nasr, 2001. "The allocation of promotion budget to maximize customer equity," Omega, Elsevier, vol. 29(1), pages 49-61, February.
    2. A. Prasad & S. P. Sethi, 2004. "Competitive Advertising Under Uncertainty: A Stochastic Differential Game Approach," Journal of Optimization Theory and Applications, Springer, vol. 123(1), pages 163-185, October.
    3. Hsiu-Yuan Tsao, 2013. "Budget allocation for customer acquisition and retention while balancing market share growth and customer equity," Marketing Letters, Springer, vol. 24(1), pages 1-11, March.
    4. Steffen Jørgensen & Simon-Pierre Sigué, 2015. "Defensive, Offensive, and Generic Advertising in a Lanchester Model with Market Growth," Dynamic Games and Applications, Springer, vol. 5(4), pages 523-539, December.
    5. Yanwu Yang & Daniel Zeng & Yinghui Yang & Jie Zhang, 2015. "Optimal Budget Allocation Across Search Advertising Markets," INFORMS Journal on Computing, INFORMS, vol. 27(2), pages 285-300, May.
    6. M. Calciu, 2008. "Numeric decision support to find optimal balance between customer acquisition and retention spending," Post-Print hal-00323717, HAL.
    7. Erickson, Gary M., 2011. "A differential game model of the marketing-operations interface," European Journal of Operational Research, Elsevier, vol. 211(2), pages 394-402, June.
    8. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    9. Erickson, Gary M., 2012. "Transfer pricing in a dynamic marketing-operations interface," European Journal of Operational Research, Elsevier, vol. 216(2), pages 326-333.
    10. Vardit Landsman & Moshe Givon, 2010. "The diffusion of a new service: Combining service consideration and brand choice," Quantitative Marketing and Economics (QME), Springer, vol. 8(1), pages 91-121, March.
    11. Huang, Jian & Leng, Mingming & Liang, Liping, 2012. "Recent developments in dynamic advertising research," European Journal of Operational Research, Elsevier, vol. 220(3), pages 591-609.
    12. Martín-Herrán, Guiomar & McQuitty, Shaun & Sigué, Simon Pierre, 2012. "Offensive versus defensive marketing: What is the optimal spending allocation?," International Journal of Research in Marketing, Elsevier, vol. 29(2), pages 210-219.
    13. 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.
    14. Islam, Towhidul & Fiebig, Denzil G, 2001. "Modelling the Development of Supply-Restricted Telecommunications Markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(4), pages 249-264, July.
    15. Meade, Nigel & Islam, Towhidul, 2006. "Modelling and forecasting the diffusion of innovation - A 25-year review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 519-545.
    16. Dipak Jain & Vijay Mahajan & Eitan Muller, 1991. "Innovation Diffusion in the Presence of Supply Restrictions," Marketing Science, INFORMS, vol. 10(1), pages 83-90.
    17. Robert C. Blattberg & John Deighton, 2010. "Manage Marketing by the Customer Equity Test," World Scientific Book Chapters, in: Greg M Allenby (ed.), Perspectives On Promotion And Database Marketing The Collected Works of Robert C Blattberg, chapter 13, pages 205-213, World Scientific Publishing Co. Pte. Ltd..
    18. Sungjoon Nam & Puneet Manchanda & Pradeep K. Chintagunta, 2010. "The Effect of Signal Quality and Contiguous Word of Mouth on Customer Acquisition for a Video-on-Demand Service," Marketing Science, INFORMS, vol. 29(4), pages 690-700, 07-08.
    19. Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.
    20. Ekinci, Yeliz & Ülengin, Füsun & Uray, Nimet & Ülengin, Burç, 2014. "Analysis of customer lifetime value and marketing expenditure decisions through a Markovian-based model," European Journal of Operational Research, Elsevier, vol. 237(1), pages 278-288.
    21. 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.
    22. Prins, R. & Verhoef, P.C., 2007. "Marketing Communication Drivers of Adoption Timing of a New E-Service among Existing Customers," ERIM Report Series Research in Management ERS-2007-018-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
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