Segmenting Walmart Customers for Personalized Marketing Strategies Using MiniBatchKMeans Clustering and Decision Trees: An Analysis of Purchasing Behavior - Agung Dharmawan Buchdadi (Journal of Digital Market and Digital Currency, 2024)
This study explores the application of MiniBatchKMeans clustering and decision tree analysis to segment Walmart customers for personalized marketing strategies. Using a dataset of 550,068 customer transactions, including variables such as User_ID, Product_ID, Gender, Age, Occupation, City_Category, Stay_In_Current_City_Years, Marital_Status, Product_Category, and Purchase, we identified five distinct customer segments. These segments were characterized by unique demographic and purchasing behaviors. Segment 1 included older customers (mean age: 55+) with high and consistent spending, primarily on premium products. Segment 2 comprised middle-aged customers (mean age: 36-45) with moderate to high spending levels, favoring household and family-related products. Segment 3 consisted of young adults (mean age: 18-25) with variable purchasing patterns, focusing on low to mid-range priced items. Segment 4 included young families (mean age: 26-35) with significant spending on a variety of products, and Segment 5 featured middle-aged to older customers (mean age: 46-55) with steady but moderate spending habits. The MiniBatchKMeans clustering algorithm effectively handled the large dataset, identifying clear customer segments. Decision tree analysis provided insights into the key features driving each segment, with Purchase amount, Age, and Occupation being the most significant. The decision tree model achieved an accuracy of 100%, with precision, recall, and f1-scores of 1.00 for all segments, indicating robust classification. These findings have significant implications for personalized marketing strategies. For instance, premium product promotions can be directed at high-spending older customers, while family-oriented discounts and bundles can be tailored for young families. Digital marketing efforts can be optimized to engage younger segments through social media and personalized recommendations. This study highlights the importance of data-driven decision-making in retail, emphasizing the need for continuous data collection and analysis to stay competitive. Future research should incorporate datasets from different retail contexts and explore alternative clustering techniques and additional features to provide a more holistic view of customer segmentation.