Optimizing Online Retail: Uncovering Opportunities with Unsupervised Learning Techniques

This project explores the application of unsupervised learning techniques to analyze an online retail transaction dataset. The primary objectives are to gain insights into customer behavior, product relationships, and opportunities for improving marketing strategies, product recommendations, and inventory management in the online retail business. This dataset comprises comprehensive information, including transactions, items, and consumers, across 24 countries, with the majority of sales occurring in the United Kingdom. Interesting trends in client geography, sales trends over time, and the presence of outliers in quantity and unit pricing are identified through exploratory data analysis. Examples of unsupervised learning techniques are K-means clustering, DBSCAN, fuzzy C-means, and Spectral Clustering. Researchers addressed the problem statements using these techniques. Different client groups, depending on purchase habits and top-selling products, are identified within every segment of product analysis and customer segmentation using K-Means. Information on product linkages and unusual buying patterns was offered when products were clustered using DBSCAN, and outliers were identified. Product association analysis and customer segmentation, using fuzzy C-Means, visualized the found clusters and assessed the ideal segment count. Depending on the country of origin and total sales of consumers, Spectral Clustering was used for geographic-based customer segmentation. The performance of the clustering models was evaluated using Silhouette Scores and Davies-Bouldin Indices, with Fuzzy C-Means demonstrating the highest clustering quality. The insights gained from this analysis can be leveraged by online retail businesses to enhance marketing strategies, product recommendations, cross-selling, and inventory management, ultimately improving the overall customer experience.