Enhancing Personalized Marketing with Customer Lifetime Value Models

Shashank Shekhar Katyayan

Eli Broad College of Business, Michigan State University

Bogue St, East Lansing, MI 48824

United States

shashank17@outlook.com

Dr. Shakeb Khan

Research Supervisor

Maharaja Agrasen Himalayan Garhwal University

Uttarakhand

omgoeldec2@gmail.com

Abstract – Personalized marketing has emerged as a critical strategy for businesses to enhance customer engagement and loyalty. In this context, Customer Lifetime Value (CLV) models play a pivotal role by providing a quantitative assessment of a customer’s potential financial contribution over their relationship with a business. This paper explores innovative approaches to integrating CLV models with personalized marketing strategies, highlighting the synergistic benefits of data-driven decision-making and tailored customer experiences. The use of machine learning algorithms, coupled with real-time analytics and behavioral data, can help a business determine high-value customers, optimize resource allocation, and design highly customized campaigns that resonate with individual preferences. The research emphasizes the role of predictive analytics and segmentation techniques in building dynamic, value-based marketing strategies. In addition, it considers some of the challenges of dealing with data privacy issues, model accuracy, and scalability and presents practical solutions to these problems. The findings show that improving personalized marketing with robust CLV models does not only maximize profitability but also builds long-term customer relationships in a competitive marketplace.

Keywords –  Customer Lifetime Value, personalized marketing, predictive analytics, machine learning, customer segmentation, data-driven strategies, customer engagement, marketing optimization, behavioral data, profitability.

References

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• Sreeprasad Govindankutty, Ajay Shriram Kushwaha. (2024). The Role of AI in Detecting Malicious Activities on Social Media Platforms. International Journal of Multidisciplinary Innovation and Research Methodology, 3(4), 24–48. Retrieved from https://ijmirm.com/index.php/ijmirm/article/view/154. 
• Srinivasan Jayaraman, S., and Reeta Mishra. (2024). Implementing Command Query Responsibility Segregation (CQRS) in Large-Scale Systems. International Journal of Research in Modern Engineering and Emerging Technology (IJRMEET), 12(12), 49. Retrieved December 2024 from http://www.ijrmeet.org. 
• Jayaraman, S., & Saxena, D. N. (2024). Optimizing Performance in AWS-Based Cloud Services through Concurrency Management. Journal of Quantum Science and Technology (JQST), 1(4), Nov(443–471). Retrieved from https://jqst.org/index.php/j/article/view/133. 
• Abhijeet Bhardwaj, Jay Bhatt, Nagender Yadav, Om Goel, Dr. S P Singh, Aman Shrivastav. Integrating SAP BPC with BI Solutions for Streamlined Corporate Financial Planning. Iconic Research And Engineering Journals, Volume 8, Issue 4, 2024, Pages 583-606. 
• Pradeep Jeyachandran, Narrain Prithvi Dharuman, Suraj Dharmapuram, Dr. Sanjouli Kaushik, Prof. (Dr.) Sangeet Vashishtha, Raghav Agarwal. Developing Bias Assessment Frameworks for Fairness in Machine Learning Models. Iconic Research And Engineering Journals, Volume 8, Issue 4, 2024, Pages 607-640. 
• Bhatt, Jay, Narrain Prithvi Dharuman, Suraj Dharmapuram, Sanjouli Kaushik, Sangeet Vashishtha, and Raghav Agarwal. (2024). Enhancing Laboratory Efficiency: Implementing Custom Image Analysis Tools for Streamlined Pathology Workflows. Integrated Journal for Research in Arts and Humanities, 4(6), 95–121. https://doi.org/10.55544/ijrah.4.6.11
• Jeyachandran, Pradeep, Antony Satya Vivek Vardhan Akisetty, Prakash Subramani, Om Goel, S. P. Singh, and Aman Shrivastav. (2024). Leveraging Machine Learning for Real-Time Fraud Detection in Digital Payments. Integrated Journal for Research in Arts and Humanities, 4(6), 70–94. https://doi.org/10.55544/ijrah.4.6.10