Exploring Hypermarket Product Segmentation using Machine Learning Approach in Muscat City, Oman
DOI:
https://doi.org/10.53840/myjict9-1-187Abstract
The Omani retail industry faces various issues, including identifying patterns in customer behavior and categorizing sold products, analyzing the value of products, and segmenting these products for marketing purposes. In addition, the wide range of products and the differences in consumer purchasing patterns across various product categories, as well as the challenges in precisely monitoring and assigning sales to customers. This research aims to explore the product segmentation at one hypermarket located in Muscat, Sultanate of Oman. Based on consumers' purchase histories, recency frequency monetary value (RFM) Analysis was used to project a product sales trend and to provide recommendations to stakeholders for how to enhance digital platforms. Using methods from unsupervised machine learning, this study constructed models employing K-means and Fuzzy C-means algorithms for hard clustering, and the density-based clustering algorithm (DBSCAN) for soft clustering. Product segmentation procedures were performed to gauge the three algorithms' relative performance. The findings revealed that the DBSCAN method had the best performance, scoring 0.89 across all three clusters, while the K-means algorithm scored 0.724 and the Fuzzy C-means strategy scored 0.702. The results may be used to shed light on client buying habits and provide data-driven suggestions for enhancing stakeholders' digital platforms. In preventing issues like inventory loss, stockouts, and overstock, businesses might benefit from delivering customized suggestions based on consumers' purchasing behavior history. Improved marketing, stock control, and customer satisfaction are all possible benefits from this study.
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