Data-Driven Customer Segmentation Using K-Means and PCA: Leveraging Meteorological and Behavioral
Keywords:
Weather-responsive segmentation, Machine learning, K-means clustering, Principal Component Analysis, Autonomous learning, E-commerce analytics, Meteorological data, Consumer behavior, Predictive analytics, Data-driven marketingAbstract
In today's competitive market, effective customer segmentation is essential for businesses to refine marketing strategies, optimize resources, and enhance customer satisfaction. This study introduces the Weather-Responsive Segmentation (WRS) framework by integrating autonomous K-means clustering with Principal Component Analysis (PCA) while incorporating real-time meteorological data. Our approach achieves 27.8% higher segmentation accuracy (Silhouette Score: 0.524 vs 0.410) and 23.4% improved campaign conversion rates compared to traditional demographic-only methods. Traditional segmentation relies primarily on demographic and behavioral data, but our method adds a new dimension by considering weather-related factors that significantly impact consumer behavior.
Using K-means, we identified four distinct customer segments and applied PCA for data visualization and dimensionality reduction. The inclusion of meteorological variables enhances segmentation accuracy and relevance, providing businesses with more actionable insights. By incorporating external environmental factors, this approach offers a deeper understanding of customer groups and enables more precise, data-driven marketing strategies.
This research contributes to the field by demonstrating the added value of weather-based segmentation in consumer analysis, offering businesses a novel perspective to optimize marketing efforts and improve decision-making.
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