Leveraging Data-Driven Decision Making for E-Commerce Growth: A Machine Learning Framework

This paper illustrates the pivotal role of K-means clustering in shaping effective e-commerce strategies by identifying customer segments with similar buying behaviors. This leads to targeted marketing, personalized product recommendations, and optimized pricing. The elbow method is employed to enhance cluster count determination. Through K-Means clustering analysis, color-coded points highlight distinct customer groups, aiding strategic decision-making for price and location predictions. Logistic regression, a vital supervised learning algorithm, accurately predicts categorical outcomes like customer purchasing behavior, achieving a 97% accuracy on test data. The Support Vector Machine (SVM) and Random Forest models attain remarkable accuracies of 99 and 100%, respectively. Ural’s integration of machine learning and data analysis has resulted in improved customer understanding, personalized offerings, and optimized inventory management. This strategy significantly boosted sales, emphasizing the value of continuous monitoring and adjustments. In summary, this paper underscores how data-driven techniques enhance e-commerce strategies, fostering better customer experiences, increased revenue, and a competitive advantage.