Customer Lifetime Value Modeling for E-commerce Platforms Using Machine Learning and Big Data Analytics: A Comprehensive Framework for the US Market

• Author(s): Akinbode, Azeez Kunle ; Taiwo, Kamorudeen Abiola ; Uchenna Evans-Anoruo
• Paper ID: 1709109
• Page: 565-577
• Published Date: 31-12-2023
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 7 Issue 6 December-2023

Abstract

Customer Lifetime Value (CLV) modeling has emerged as a critical component for sustainable growth in the competitive US e-commerce landscape. This study presents a comprehensive framework for implementing machine learning and big data analytics to enhance CLV prediction accuracy and strategic decision-making. Through analysis of data from major US e-commerce platforms including Amazon, Shopify merchants, and direct-to-consumer brands, we demonstrate how advanced analytical techniques can improve CLV prediction accuracy by up to 34% compared to traditional methods. Our research introduces a hybrid modeling approach combining RFM analysis, cohort-based modeling, and ensemble machine learning algorithms, validated through real-world case studies from the US market. The findings reveal that personalized CLV models significantly outperform generic approaches, with implications for customer acquisition strategies, retention programs, and revenue optimization.

Keywords

Customer Lifetime Value, E-commerce, Machine Learning, Big Data Analytics, Predictive Modeling, Customer Analytics

Citations

IRE Journals:
Akinbode, Azeez Kunle , Taiwo, Kamorudeen Abiola , Uchenna Evans-Anoruo "Customer Lifetime Value Modeling for E-commerce Platforms Using Machine Learning and Big Data Analytics: A Comprehensive Framework for the US Market" Iconic Research And Engineering Journals Volume 7 Issue 6 2023 Page 565-577

IEEE:
Akinbode, Azeez Kunle , Taiwo, Kamorudeen Abiola , Uchenna Evans-Anoruo "Customer Lifetime Value Modeling for E-commerce Platforms Using Machine Learning and Big Data Analytics: A Comprehensive Framework for the US Market" Iconic Research And Engineering Journals, 7(6)