IDEAS home Printed from https://ideas.repec.org/a/pal/jofsma/v21y2016i1d10.1057_fsm.2015.25.html
   My bibliography  Save this article

Optimal introductory pricing for new financial services

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/fsm.2015.25
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1057/fsm.2015.25?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Mercedes Esteban-Bravo & José Múgica & Jose Vidal-Sanz, 2005. "Optimal Duration of Magazine Promotions," Marketing Letters, Springer, vol. 16(2), pages 99-114, April.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    11. 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.
    12. 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.
    13. 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.
    14. Shlomo Kalish, 1985. "A New Product Adoption Model with Price, Advertising, and Uncertainty," Management Science, INFORMS, vol. 31(12), pages 1569-1585, December.
    15. Bruce Robinson & Chet Lakhani, 1975. "Dynamic Price Models for New-Product Planning," Management Science, INFORMS, vol. 21(10), pages 1113-1122, June.
    16. 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.
    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.
    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. 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.

    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. Chaab, Jafar & Zaccour, Georges, 2024. "Dynamic pricing in the presence of social externalities and reference-price effect," Omega, Elsevier, vol. 122(C).
    2. Wenjing Shen & Izak Duenyas & Roman Kapuscinski, 2014. "Optimal Pricing, Production, and Inventory for New Product Diffusion Under Supply Constraints," Manufacturing & Service Operations Management, INFORMS, vol. 16(1), pages 28-45, February.
    3. Nejad, Mohammad G. & Amini, Mehdi & Babakus, Emin, 2015. "Success Factors in Product Seeding: The Role of Homophily," Journal of Retailing, Elsevier, vol. 91(1), pages 68-88.
    4. Samuel Sale, R. & Mesak, Hani I. & Inman, R. Anthony, 2017. "A dynamic marketing-operations interface model of new product updates," European Journal of Operational Research, Elsevier, vol. 257(1), pages 233-242.
    5. Ashkan Negahban & Jeffrey S. Smith, 2018. "A joint analysis of production and seeding strategies for new products: an agent-based simulation approach," Annals of Operations Research, Springer, vol. 268(1), pages 41-62, September.
    6. Ferreira, Kevin D. & Lee, Chi-Guhn, 2014. "An integrated two-stage diffusion of innovation model with market segmented learning," Technological Forecasting and Social Change, Elsevier, vol. 88(C), pages 189-201.
    7. Muller, Eitan & Peres, Renana, 2019. "The effect of social networks structure on innovation performance: A review and directions for research," International Journal of Research in Marketing, Elsevier, vol. 36(1), pages 3-19.
    8. Shun-Chen Niu, 2006. "A Piecewise-Diffusion Model of New-Product Demands," Operations Research, INFORMS, vol. 54(4), pages 678-695, August.
    9. Xiao, Yu & Han, Jingti, 2016. "Forecasting new product diffusion with agent-based models," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 167-178.
    10. Elmar Kiesling & Markus Günther & Christian Stummer & Lea Wakolbinger, 2012. "Agent-based simulation of innovation diffusion: a review," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(2), pages 183-230, June.
    11. Abedi, Vahideh Sadat, 2019. "Compartmental diffusion modeling: Describing customer heterogeneity & communication network to support decisions for new product introductions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    12. Hongmin Li, 2020. "Optimal Pricing Under Diffusion-Choice Models," Operations Research, INFORMS, vol. 68(1), pages 115-133, January.
    13. Laciana, Carlos E. & Rovere, Santiago L. & Podestá, Guillermo P., 2013. "Exploring associations between micro-level models of innovation diffusion and emerging macro-level adoption patterns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(8), pages 1873-1884.
    14. Wei-yu Kevin Chiang, 2012. "Supply Chain Dynamics and Channel Efficiency in Durable Product Pricing and Distribution," Manufacturing & Service Operations Management, INFORMS, vol. 14(2), pages 327-343, April.
    15. Alexei Parakhonyak & Nick Vikander, 2019. "Optimal Sales Schemes for Network Goods," Management Science, INFORMS, vol. 65(2), pages 819-841, February.
    16. Barnes, Belinda & Southwell, Darren & Bruce, Sarah & Woodhams, Felicity, 2014. "Additionality, common practice and incentive schemes for the uptake of innovations," Technological Forecasting and Social Change, Elsevier, vol. 89(C), pages 43-61.
    17. Karsten Kieckhäfer & Thomas Volling & Thomas Stefan Spengler, 2014. "A Hybrid Simulation Approach for Estimating the Market Share Evolution of Electric Vehicles," Transportation Science, INFORMS, vol. 48(4), pages 651-670, November.
    18. Chang, Byeong-Yun & Li, Xu & Kim, Yun Bae, 2014. "Performance comparison of two diffusion models in a saturated mobile phone market," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 41-48.
    19. Wu, Xiang & (Yale) Gong, Yeming & Xu, Haoxuan & Chu, Chengbin & Zhang, Jinlong, 2017. "Dynamic lot-sizing models with pricing for new products," European Journal of Operational Research, Elsevier, vol. 260(1), pages 81-92.
    20. A. Negahban & J.S. Smith, 2016. "The effect of supply and demand uncertainties on the optimal production and sales plans for new products," International Journal of Production Research, Taylor & Francis Journals, vol. 54(13), pages 3852-3869, July.

    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:pal:jofsma:v:21:y:2016:i:1:d:10.1057_fsm.2015.25. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave.com .

    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.