UTILIZING ARTIFICIAL INTELLIGENCE FOR CUSTOMER BEHAVIOR ANALYSIS IN E-COMMERCE LOGISTICS OPERATIONS

Abstract

This research aims to: 1) study customer behavior patterns in e-commerce logistics systems, 2) explore the application of AI in analyzing customer behavior data, and 3) evaluate the impact of AI on the efficiency of e-commerce logistics systems. The research focuses on the application of Artificial Intelligence (AI) in analyzing customer behavior data to enhance the efficiency of logistics in e-commerce businesses. This is a quantitative research study with a sample size of 400 sets. The statistical analysis methods used include percentages, standard deviations, as well as statistical tests like Correlation, Regression, and ANOVA to test the research hypotheses.

          The findings of the research are as follows 1) AI helps analyze customer behavior and predict behavior trends accurately, assisting in the improvement of marketing strategies and service planning. Despite challenges in processing large data, AI is widely accepted for improving services and marketing strategies. 85% of companies using AI to forecast customer behavior trends reported a significant improvement in their marketing strategies. 2) AI increases the speed and accuracy of product delivery by calculating the best routes and predicting product locations. This helps optimize logistics resource management, reduce costs, and enhance efficiency. AI usage in route optimization reduced delivery time by 20-30% and reduced shipping costs by 15-20%. 3) The results of hypothesis testing show that AI in analyzing customer behavior improves logistics system efficiency, with a correlation coefficient of 0.711, indicating a moderate to strong relationship between AI usage for customer behavior analysis and logistics system efficiency. This enables more effective logistics management and planning.