Product Sales Optimization with DBSCAN Clustering Algorithm on e-commerce Dataset

This research aims to optimize product sales strategies in e-commerce businesses by utilizing the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm. DBSCAN is applied to a customer transaction dataset to group customers into segments based on their purchasing patterns, represented by the Quantity and UnitPrice variables. The clustering results demonstrate that DBSCAN effectively identifies customer segments with distinct characteristics, such as loyalty, price sensitivity, and product preferences. This information is then used to design targeted sales strategies, including personalized product recommendations, relevant promotional offers, and efficient inventory management. Implementing DBSCAN-based optimization strategies is expected to improve sales, profitability, and customer satisfaction. This research highlights the effectiveness of the DBSCAN clustering algorithm as a valuable tool for understanding customer behavior and optimizing sales strategies in e-commerce businesses.