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

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@article{1709109,
      author = {Akinbode, Azeez Kunle, Taiwo, Kamorudeen Abiola, Uchenna Evans-Anoruo},
      title = {Customer Lifetime Value Modeling for E-commerce Platforms Using Machine Learning and Big Data Analytics: A Comprehensive Framework for the US Market},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {7},
      number = {6},
      pages = {565-577},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709109.pdf},
      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},
      month = {December},
  }