Sales Resource Optimization Based on Customer Lifetime Value (CLV): A Data-Driven Dynamic Decision Model

null null

doi:10.70393/6a6374616d.333335

OPEN ACCESS |Research Article ||4 November 2025

Sales Resource Optimization Based on Customer Lifetime Value (CLV): A Data-Driven Dynamic Decision Model

null null

Ym Trading Limited

meisson.lee@gmail.com

Ym Trading Limited, Denver, CO 80202, USA.

* Corresponding Author¹: null null, E-Mail: meisson.lee@gmail.com

Publication

Accepted 2025 October 31 ; Published 2025 November 4

Journal of Computer Technology and Applied Mathematics, 2025, 2(6), 3007-4126.

Abstract

This study proposes a framework for a dynamic decision-making model based on Customer Lifetime Value (CLV) to optimize sales resources. This model prioritizes profitability as a benchmark for customer performance, serving as a guide for effective resource allocation and improvement. The paper presents a data-driven approach that combines real-time data and dynamic analysis of customer behavior for resource allocation and adjustment. This method analyzes enterprise data and customer behavior to inform decisions that maximize resource utilization and enhance sales performance. The core idea is to prioritize high-value customers and allocate more resources accordingly. Furthermore, by categorizing customers into different CLV groups, more informed and targeted decisions are made for each customer group. Ultimately, this improves customer retention, thereby enhancing the enterprise's long-term attractiveness and strategic model.

Keywords

Customer Lifetime Value (CLV) , Dynamic Resource Allocation , Data-Driven Decisions , Sales Optimization .

Metadata

DOI:

10.70393/6a6374616d.333335

ARK:

ark:/40704/JCTAM.v2n6a07

Pages: 44-50

References: 14

Disciplines: Statistical Analysis

Subjects: Data Mining

Cite This Article

APA Style

Unknown Author (2025). Sales resource optimization based on customer lifetime value (clv): a data-driven dynamic decision model. Journal of Computer Technology and Applied Mathematics, 2(6), 44-50. https://doi.org/10.70393/6a6374616d.333335

Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

FUNDING

Not applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

INFORMED CONSENT STATEMENT

Not applicable.

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

AUTHOR CONTRIBUTIONS

Not applicable.

References

1.

Kanchanapoom, K., & Chongwatpol, J. (2023). Integrated customer lifetime value (CLV) and customer migration model to improve customer segmentation. Journal of Marketing Analytics, 11(2), 172-185.

2.

Dandis, A. O., Al Haj Eid, M. B., Robin, R., & Wierdak, N. (2022). An empirical investigation of the factors affecting customer lifetime value. International journal of quality & reliability management, 39(4), 910-935.

3.

Abdolvand, N., Albadvi, A., & Koosha, H. (2021). Customer lifetime value: Literature scoping map, and an agenda for future research. International Journal of Management Perspective.

4.

Mosaddegh, A., Albadvi, A., Sepehri, M. M., & Teimourpour, B. (2021). Dynamics of customer segments: A predictor of customer lifetime value. Expert Systems with Applications, 172, 114606.

5.

Ali, N., & Shabn, O. S. (2024). Customer lifetime value (CLV) insights for strategic marketing success and its impact on organizational financial performance. Cogent Business & Management, 11(1), 2361321.

6.

Almestarihi, R., Ahmad, A. Y., Frangieh, R. H., Abualsondos, I. A., Nser, K. K., & Ziani, A. (2024). Measuring the ROI of paid advertising campaigns in digital marketing and its effect on business profitability.

7.

Venkataramanan, S., Sadhu, A. K. R., Gudala, L., & Reddy, A. K. (2024). Leveraging artificial intelligence for enhanced sales forecasting accuracy: a review of AI-driven techniques and practical applications in customer relationship management systems. Aust. J. Mach. Learn. Res. Appl, 4(1), 267-287.

8.

Ileana, M., Petrov, P., & Milev, V. (2025). Optimizing customer experience by exploiting real-time data generated by IoT and leveraging distributed web systems in CRM systems. IoT, 6(2), 24.

9.

Märtin, C., Bissinger, B. C., & Asta, P. (2023). Optimizing the digital customer journey—Improving user experience by exploiting emotions, personas, and situations for individualized user interface adaptations. Journal of Consumer Behavior, 22(5), 1050-1061.

10.

Eslami, E., Razi, N., Lonbani, M., & Rezazadeh, J. (2024). Unveiling IoT customer behaviour: segmentation and insights for enhanced IoT-CRM strategies: a real case study. Sensors, 24(4), 1050.

11.

Gorment, N. Z., Shanmugam, M., Ibrahim, N., Sugu, R., Dandarawi, T. N. N. T. A., & Ahmad, N. A. (2022). Extending the Role of Customer Relationship Management (CRM) System for an Omnichannel Customer Experience. electronic Journal of Computer Science and Information Technology, 8(1), 1-8.

12.

Kumar, S., Bajpai, V. N., Jha, A. K., & Upadhyay, S. (2024, December). Predictive Analytics for Customer Lifetime Value (CLV) Optimization: Estimating CLV to Inform Strategic Marketing Decisions for Maximizing Profitability. In 2024 13th International Conference on System Modeling & Advancement in Research Trends (SMART) (pp. 431-435). IEEE.

13.

Firmansyah, E. B., Machado, M. R., & Moreira, J. L. R. (2024). How can Artificial Intelligence (AI) be used to manage Customer Lifetime Value (CLV)—A systematic literature review. International Journal of Information Management Data Insights, 4(2), 100279.

14.

Curiskis, S., Dong, X., Jiang, F., & Scarr, M. (2023). A novel approach to predicting customer lifetime value in B2B SaaS companies. Journal of Marketing Analytics, 11(4), 587-601.

PUBLISHER'S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Copyright © 2025 The Author(s). Published by Southern United Academy of Sciences.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.