Analysis of Cart Abandonment Behavior in E-Commerce

  • Authors

    • Ken Iverson Professor, Harvard University, Canada. Author

    DOI:

    https://doi.org/10.67228/3071642X/IJCFDE-2023PI6F1K

    Published 03-03-2023

  • E-Commerce, Shopping Cart Abandonment, Customer Behavior Analysis, Machine Learning, Predictive Analytics, Consumer Decision-Making, Clickstream Analysis, Purchase Intention, Recommendation Systems, Digital Marketing, Customer Retention, Data Mining

    Issue

    Vol. 6 No. 1 (2023)

    Section

    Articles

    How to Cite

    Iverson, K. (2023). Analysis of Cart Abandonment Behavior in E-Commerce. International Journal of Commerce, Finance and Digital Economy, 6(1), 01-17. https://doi.org/10.67228/3071642X/IJCFDE-2023PI6F1K

  • Abstract

    E-commerce has transformed online shopping by offering greater convenience and personalized experiences, but shopping cart abandonment remains a major challenge, with global abandonment rates often exceeding 65%. This study presents a machine learning-based framework to analyze customer behavior and predict cart abandonment using factors such as browsing patterns, session duration, pricing, shipping costs, checkout complexity, website usability, payment security, and personalized recommendations. The framework employs data preprocessing, feature engineering, and supervised learning algorithms to identify high-risk abandonment sessions and support proactive engagement strategies. Model performance is evaluated using Accuracy, Precision, Recall, F1-score, and AUC-ROC. The findings indicate that unexpected shipping costs, complex checkout procedures, mandatory account creation, slow website performance, and limited product information significantly increase abandonment, while streamlined checkout, transparent pricing, secure payment options, personalized offers, and recommendation systems improve conversion rates. Overall, the proposed approach enables e-commerce businesses to reduce cart abandonment, enhance customer retention, and improve decision-making through AI-driven predictive analytics.

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