Customer profiling with k-means clustering and product recommendation with market basket analysis for strategy marketing MSMEs - Al Ayubi, Ahmad; Achmadi, Hendra (Institute of Computer Science (IOCS), 2024)
This research aims to develop a data-driven marketing strategy to increase consumer purchase interest in XYZ Hijab. This small and medium-sized Muslim fashion enterprise experienced a sales decline of 65.2% in 2022 and 2023. Utilizing the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology and employing Python programming language with Google Colab, this study combines RFM-D analysis and K-means clustering for customer segmentation, as well as Market Basket Analysis (MBA) for product bundling strategies. The study uses sales transaction data from December 9, 2023, to January 8, 2024. The analysis results indicate that the optimal RFM-D model uses four customer clusters: Superstar Customers (50.37%), Golden Customers (31.92%), Typical Customers (17.65%), and Dormant Customers (0.06%). The MBA identifies 11 product association rules that can be utilized for bundling strategies. The recommended marketing strategies include exclusive loyalty programs for top customers and tailored promotions for potential and dormant customers. Implementing these strategies will increase customer retention and revenue for XYZ Hijab.