IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2506.22711.html
   My bibliography  Save this paper

Potential Customer Lifetime Value in Financial Institutions: The Usage Of Open Banking Data to Improve CLV Estimation

Author

Listed:
  • Jo~ao B. G. de Brito
  • Rodrigo Heldt
  • Cleo S. Silveira
  • Matthias Bogaert
  • Guilherme B. Bucco
  • Fernando B. Luce
  • Jo~ao L. Becker
  • Filipe J. Zabala
  • Michel J. Anzanello

Abstract

Financial institutions increasingly adopt customer-centric strategies to enhance profitability and build long-term relationships. While Customer Lifetime Value (CLV) is a core metric, its calculations often rely solely on single-entity data, missing insights from customer activities across multiple firms. This study introduces the Potential Customer Lifetime Value (PCLV) framework, leveraging Open Banking (OB) data to estimate customer value comprehensively. We predict retention probability and estimate Potential Contribution Margins (PCM) from competitor data, enabling PCLV calculation. Results show that OB data can be used to estimate PCLV per competitor, indicating a potential upside of 21.06% over the Actual CLV. PCLV offers a strategic tool for managers to strengthen competitiveness by leveraging OB data and boost profitability by driving marketing efforts at the individual customer level to increase the Actual CLV.

Suggested Citation

  • Jo~ao B. G. de Brito & Rodrigo Heldt & Cleo S. Silveira & Matthias Bogaert & Guilherme B. Bucco & Fernando B. Luce & Jo~ao L. Becker & Filipe J. Zabala & Michel J. Anzanello, 2025. "Potential Customer Lifetime Value in Financial Institutions: The Usage Of Open Banking Data to Improve CLV Estimation," Papers 2506.22711, arXiv.org.
    • Handle: RePEc:arx:papers:2506.22711
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2506.22711
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Heldt, Rodrigo & Silveira, Cleo Schmitt & Luce, Fernando Bins, 2021. "Predicting customer value per product: From RFM to RFM/P," Journal of Business Research, Elsevier, vol. 127(C), pages 444-453.
    2. Gupta, Shaphali & Ramachandran, Divya, 2021. "Emerging Market Retail: Transitioning from a Product-Centric to a Customer-Centric Approach," Journal of Retailing, Elsevier, vol. 97(4), pages 597-620.
    3. Peter S. Fader & Bruce G. S. Hardie & Ka Lok Lee, 2005. "“Counting Your Customers” the Easy Way: An Alternative to the Pareto/NBD Model," Marketing Science, INFORMS, vol. 24(2), pages 275-284, August.
    4. Sami Ben Jabeur & Salma Mefteh-Wali & Jean-Laurent Viviani, 2024. "Forecasting gold price with the XGBoost algorithm and SHAP interaction values," Annals of Operations Research, Springer, vol. 334(1), pages 679-699, March.
    Full references (including those not matched with items on IDEAS)

    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. Julia Wamsler & Denis Vuckovac & Martin Natter & Alexander Ilic, 2024. "Live shopping promotions: which categories should a retailer discount to shoppers already in the store?," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 46(1), pages 135-174, March.
    2. Mei, Yupeng & Jing, Kunpeng, 2024. "Customer concentration and management earnings forecast," Finance Research Letters, Elsevier, vol. 65(C).
    3. van Oest, Rutger & Knox, George, 2011. "Extending the BG/NBD: A simple model of purchases and complaints," International Journal of Research in Marketing, Elsevier, vol. 28(1), pages 30-37.
    4. Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.
    5. Chen, Yanyan & Mandler, Timo & Meyer-Waarden, Lars, 2021. "Three decades of research on loyalty programs: A literature review and future research agenda," Journal of Business Research, Elsevier, vol. 124(C), pages 179-197.
    6. Nina Angelovska, 2021. "Analysis Of Customer Activity, The Importance Of Timing For Effective Marketing Actions: Case Of Group Buying Site, Grouper," UTMS Journal of Economics, University of Tourism and Management, Skopje, Macedonia, vol. 12(2), pages 156-170.
    7. Jian Wang & Murtaza Das & Amar Duggasani, 2016. "Predicting and measuring customer lifetime values for apartment tenants," Zeitschrift für Immobilienökonomie (German Journal of Real Estate Research), Springer;Gesellschaft für Immobilienwirtschaftliche Forschung e. V., vol. 2(2), pages 103-120, October.
    8. Zekhov, M. & Pilnik, N. & Radionov, S., 2025. "Constructing a set of equilibrium prices in a generalized distributed markets model," Journal of the New Economic Association, New Economic Association, vol. 67(2), pages 25-44.
    9. Jerath, Kinshuk & Fader, Peter S. & Hardie, Bruce G.S., 2016. "Customer-base analysis using repeated cross-sectional summary (RCSS) data," European Journal of Operational Research, Elsevier, vol. 249(1), pages 340-350.
    10. Alina Ferecatu & Arnaud Bruyn & Prithwiraj Mukherjee, 2024. "Silently killing your panelists one email at a time: The true cost of email solicitations," Journal of the Academy of Marketing Science, Springer, vol. 52(4), pages 1216-1239, July.
    11. Bargoni, Augusto & Santoro, Gabriele & Messeni Petruzzelli, Antonio & Ferraris, Alberto, 2024. "Growth hacking: A critical review to clarify its meaning and guide its practical application," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    12. Albers, Sönke, 2012. "Optimizable and implementable aggregate response modeling for marketing decision support," International Journal of Research in Marketing, Elsevier, vol. 29(2), pages 111-122.
    13. Park, Chang Hee & Park, Young-Hoon & Schweidel, David A., 2014. "A multi-category customer base analysis," International Journal of Research in Marketing, Elsevier, vol. 31(3), pages 266-279.
    14. 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.
    15. 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.
    16. Reutterer, Thomas & Platzer, Michael & Schröder, Nadine, 2021. "Leveraging purchase regularity for predicting customer behavior the easy way," International Journal of Research in Marketing, Elsevier, vol. 38(1), pages 194-215.
    17. Ho Thanh & Nguyen Suong & Nguyen Huong & Nguyen Ngoc & Man Dac-Sang & Le Thao-Giang, 2023. "An Extended RFM Model for Customer Behaviour and Demographic Analysis in Retail Industry," Business Systems Research, Sciendo, vol. 14(1), pages 26-53, September.
    18. Valendin, Jan & Reutterer, Thomas & Platzer, Michael & Kalcher, Klaudius, 2022. "Customer base analysis with recurrent neural networks," International Journal of Research in Marketing, Elsevier, vol. 39(4), pages 988-1018.
    19. Rohit Tandon & Arnab Chakraborty & Girish Srinivasan & Manav Shroff & Ahmar Abdullah & Bharathan Shamasundar & Ritwik Sinha & Suresh Subramanian & Dave Hill & Prasanna Dhore, 2013. "Hewlett Packard: Delivering Profitable Growth for HPDirect.com Using Operations Research," Interfaces, INFORMS, vol. 43(1), pages 48-61, February.
    20. Yan Yan & Nicholas Resnick, 2024. "A high-performance turnkey system for customer lifetime value prediction in retail brands," Quantitative Marketing and Economics (QME), Springer, vol. 22(2), pages 169-192, June.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:arx:papers:2506.22711. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.