Articles
| Open Access |
DOI:
https://doi.org/10.55640/ijcsis/Volume09Issue12-02
HARNESSING MACHINE LEARNING MODELS FOR ACCURATE CUSTOMER LIFETIME VALUE PREDICTION: A COMPARATIVE STUDY IN MODERN BUSINESS ANALYTICS
Md Parvez Ahmed , Master of Science in Information Technology, Washington University of Science and Technology, USA Ashim Chandra Das , Master of Science in Information Technology, Washington University of Science and Technology, USA Pinky Akter , Master Of Science in Information Technology, Westcliff University, USA Sanjida Nowshin Mou , Department of Management Science and Quantitative Methods, Gannon University, USA Sanjida Akter Tisha , Master of Science in Information Technology, Washington University of Science and Technology, USA Farhan Shakil , Master’s in Cybersecurity Operations, Webster University, Saint Louis, MO, USA Pritom Das , College of Computer Science, Pacific States University, Los Angeles, CA, USA Md Khalilor Rahman , MBA in Business Analytics, Gannon university, Erie, Pennsylvania, USA Adib Ahmed , Department of Management Science and Quantitative Methods, Gannon University, USAAbstract
This study evaluated multiple machine learning models to predict customer lifetime value (CLV), including ensemble methods, linear regression, and deep learning architectures. Our comparative analysis demonstrated that ensemble models, such as Random Forest and Gradient Boosting, provided a robust balance of accuracy and interpretability, while deep learning approaches excelled in capturing complex data patterns but required higher computational resources. The results highlight the trade-offs businesses must consider in selecting models based on data availability, scalability, and interpretability. The insights from this research enable businesses to identify high-value customers effectively, optimize marketing strategies, and drive sustainable profitability.
Keywords
Customer Lifetime Value, Machine Learning, Model Comparison, Predictive Analytics
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Copyright (c) 2024 Md Parvez Ahmed, Ashim Chandra Das, Pinky Akter, Sanjida Nowshin Mou, Sanjida Akter Tisha, Farhan Shakil, Pritom Das, Md Khalilor Rahman
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