LTVHub: A Modular Tool for Customer Lifetime Value Calculation with Support for Multiple Models

• Maria Eduarda de Pinho Braga Federal University of Viçosa (UFV)
• João M. A. M. Ramos Federal University of Viçosa (UFV) / Manna Team
• Fabrício A. Silva Federal University of Viçosa (UFV) / Manna Team https://orcid.org/0000-0002-0713-0583
• Linnyer B. R. Aylon State University of Maringá (UEM) / Manna Team

DOI: https://doi.org/10.5753/sbbd_estendido.2025.247672

Abstract

The Customer Lifetime Value (CLV) is an essential metric for identifying the most valuable customers, enabling comprehensive profit estimations. Furthermore, it allows companies to tailor their services to meet customer expectations, thereby improving the quality of the relationship between the consumer and the business. Despite its advantages, CLV is not widely adopted, and applying it across different contexts presents several challenges, particularly regarding the data required. The goal of this work is to provide a tool that allows CLV estimation for different users in a visually intuitive, modular, extensible, and flexible manner, delivering a robust prediction of the expected CLV.

Keywords: LTV, Machine Learning, CLV

References

Berger, P. D. and Nasr, N. I. (1998). Customer lifetime value: Marketing models and applications. Journal of Interactive Marketing, 12(1):17–30.

Fader, P. S., Hardie, B. G., and Lee, K. L. (2005). “counting your customers” the easy way: An alternative to the pareto/nbd model. Marketing science, 24(2):275–284.

Fader, P. S. and Hardie, B. G. S. (2013). The gamma-gamma model of monetary value. Marketing Science Institute Working Paper, 2:1–9.

Jain, D. and Singh, S. S. (2002). Customer lifetime value research in marketing: A review and future directions. Journal of Interactive Marketing, 16(2):34–46.

Popa, A.-L., Sasu, D. V., and Tarcza, T. M. (2021). Investigating the importance of customer lifetime value in modern marketing – a literature review. Annals of the Faculty of Economics, 30(2):410–416.

Qismat, T. and Feng, Y. (2020). Comparison of classical rfm models and machine learning models in clv prediction. Master thesis, BI Norwegian Business School, Oslo, Norway. GRA 19703, Master of Science.

Ramos, J. and Silva, F. (2024). A solution for predicting the customer lifetime value of different market segments. In Anais do XII Symposium on Knowledge Discovery, Mining and Learning, pages 81–88, Porto Alegre, RS, Brasil. SBC.

Schmittlein, D. C., Morrison, D. G., and Colombo, R. (1987). Counting your customers: Who are they and what will they do next? Management Science, 33(1):1–24.

Ullah, A., Mohmand, M. I., Hussain, H., Johar, S., Khan, I., Ahmad, S., Mahmoud, H. A., and Huda, S. (2023). Customer analysis using machine learning-based classification algorithms for effective segmentation using recency, frequency, monetary, and time. Sensors, 23(6):3180.

Venkatesan, R. and Kumar, V. (2004). A customer lifetime value framework for customer selection and resource allocation strategy. Journal of Marketing, 68(4):106–125.