Advancing machine learning frameworks for customer retention and propensity modeling in E-Commerce Platforms
1 Globacom Nigeria Limited.
2 Independent Researcher, Canada.
3 Independent Researcher, Texas, USA.
4 Independent Researcher, Lagos, Nigeria.
5 Amstek Nigeria Limited.
6 CISCO, Nigeria.
Review Article
GSC Advanced Research and Reviews, 2023, 14(02), 191–203.
Article DOI: 10.30574/gscarr.2023.14.2.0017
Publication history:
Received on 03 December 2022; revised on 20 February 2023; accepted on 24 February 2023
Abstract:
In the highly competitive e-commerce landscape, retaining customers and predicting their behaviors are critical for sustainable growth. This review explores the advancement of machine learning (ML) frameworks for customer retention and propensity modeling, emphasizing their transformative potential in e-commerce platforms. Customer retention is vital, as retaining an existing customer is significantly more cost-effective than acquiring a new one. Propensity modeling further complements retention strategies by predicting customer actions, such as purchases or churn, enabling businesses to tailor their marketing efforts effectively. Advanced ML techniques, including deep learning, reinforcement learning, and natural language processing (NLP), are reshaping how businesses approach these challenges. These models leverage diverse data sources, such as transaction history, browsing behavior, and customer feedback, to identify actionable insights. Key advancements in feature engineering, real-time data processing, and hyperparameter optimization have enhanced the accuracy and scalability of these frameworks, making them indispensable for e-commerce platforms handling vast datasets. Despite these advancements, challenges persist, such as ensuring data privacy, addressing the interpretability of complex models, and achieving real-time scalability. Successful implementations, including case studies from leading e-commerce platforms, demonstrate the potential of ML to improve customer engagement, increase lifetime value, and drive business growth. Looking forward, integrating AI and ML for enhanced personalization, leveraging real-time predictive analytics, and addressing ethical considerations like bias and fairness are crucial for advancing these frameworks. This review provides a comprehensive overview of state-of-the-art ML techniques for customer retention and propensity modeling, highlighting their practical applications and future prospects in e-commerce. By adopting these innovations, e-commerce platforms can achieve a competitive edge, fostering long-term customer loyalty and business success.
Keywords:
Advancing machine learning; Modeling; E-commerce platforms; Frameworks
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Copyright information:
Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
