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Optimal introductory pricing for new financial services

Author

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  • Mohammad G Nejad

    (Gabelli School of Business, Fordham University)

  • Sertan Kabadayi

Abstract

Financial services institutions often provide special introductory prices to new customers who sign up for their services such as credit cards, credit monitoring services and online stock trading. Despite their prevalence, the decision to provide introductory prices to new customers entails challenges for decision makers. Providing small incentives may not perceptibly affect the adoption of the service while providing a large incentive leads to the loss of revenue and profits. As a result, the effectiveness of such activities on firm profitability remains largely unexplored. This study seeks to address this gap in the literature by exploring optimal introductory pricing of a financial service. Employing agent-based simulation experiments, we find that offering introductory discounts significantly increases a firm’s net present value (NPV) of profits. Moreover, the findings suggest the amount of discount and the duration of time that a new customer receives the discount are critical factors in determining the NPV of profits. The research and managerial implications are discussed.

Suggested Citation

  • Mohammad G Nejad & Sertan Kabadayi, 2016. "Optimal introductory pricing for new financial services," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 21(1), pages 34-50, March.
    • Handle: RePEc:pal:jofsma:v:21:y:2016:i:1:d:10.1057_fsm.2015.25
    DOI: 10.1057/fsm.2015.25
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    References listed on IDEAS

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    1. Rand, William & Rust, Roland T., 2011. "Agent-based modeling in marketing: Guidelines for rigor," International Journal of Research in Marketing, Elsevier, vol. 28(3), pages 181-193.
    2. Mercedes Esteban-Bravo & José Múgica & Jose Vidal-Sanz, 2005. "Optimal Duration of Magazine Promotions," Marketing Letters, Springer, vol. 16(2), pages 99-114, April.
    3. Grewal, Dhruv & Ailawadi, Kusum L. & Gauri, Dinesh & Hall, Kevin & Kopalle, Praveen & Robertson, Jane R., 2011. "Innovations in Retail Pricing and Promotions," Journal of Retailing, Elsevier, vol. 87(S1), pages 43-52.
    4. Annamaria Lusardi, 2008. "Financial Literacy: An Essential Tool for Informed Consumer Choice?," NFI Working Papers 2008-WP-13, Indiana State University, Scott College of Business, Networks Financial Institute.
    5. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    6. Goldenberg, Jacob & Libai, Barak & Muller, Eitan, 2010. "The chilling effects of network externalities," International Journal of Research in Marketing, Elsevier, vol. 27(1), pages 4-15.
    7. Margaret S. Trench & Shane P. Pederson & Edward T. Lau & Lizhi Ma & Hui Wang & Suresh K. Nair, 2003. "Managing Credit Lines and Prices for Bank One Credit Cards," Interfaces, INFORMS, vol. 33(5), pages 4-21, October.
    8. 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.
    9. Yadav, Manjit S, 1994. "How Buyers Evaluate Product Bundles: A Model of Anchoring and Adjustment," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 21(2), pages 342-353, September.
    10. Frank M. Bass, 2004. "Comments on "A New Product Growth for Model Consumer Durables The Bass Model"," Management Science, INFORMS, vol. 50(12_supple), pages 1833-1840, December.
    11. Bass, Frank M, 1980. "The Relationship between Diffusion Rates, Experience Curves, and Demand Elasticities for Consumer Durable Technological Innovations," The Journal of Business, University of Chicago Press, vol. 53(3), pages 51-67, July.
    12. Amini, Mehdi & Wakolbinger, Tina & Racer, Michael & Nejad, Mohammad G., 2012. "Alternative supply chain production–sales policies for new product diffusion: An agent-based modeling and simulation approach," European Journal of Operational Research, Elsevier, vol. 216(2), pages 301-311.
    13. Uri Benzion & Amnon Rapoport & Joseph Yagil, 1989. "Discount Rates Inferred from Decisions: An Experimental Study," Management Science, INFORMS, vol. 35(3), pages 270-284, March.
    14. Trichy V. Krishnan & Frank M. Bass & Dipak C. Jain, 1999. "Optimal Pricing Strategy for New Products," Management Science, INFORMS, vol. 45(12), pages 1650-1663, December.
    15. Shlomo Kalish, 1985. "A New Product Adoption Model with Price, Advertising, and Uncertainty," Management Science, INFORMS, vol. 31(12), pages 1569-1585, December.
    16. Bruce Robinson & Chet Lakhani, 1975. "Dynamic Price Models for New-Product Planning," Management Science, INFORMS, vol. 21(10), pages 1113-1122, June.
    17. Insik Min & Jong-Ho Kim, 2003. "Modeling Credit Card Borrowing: A Comparison of Type I and Type II Tobit Approaches," Southern Economic Journal, John Wiley & Sons, vol. 70(1), pages 128-143, July.
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    Cited by:

    1. Conor B. Hamill & Raad Khraishi & Simona Gherghel & Jerrard Lawrence & Salvatore Mercuri & Ramin Okhrati & Greig A. Cowan, 2023. "Agent-based Modelling of Credit Card Promotions," Papers 2311.01901, arXiv.org, revised Nov 2023.

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