Enhancing Sales Strategies In Prime Market Retail Business Using Tuned Gradient Boosting

Abstract

In the retail sector, comprehending customer behavior and employing effective customer segmentation is pivotal for refining marketing strategies and augmenting profits. This study delves into predictive modeling for customer segmentation at Prime Market, a prominent retail entity. The research initially yields a classification error rate of 25.10% by employing Gradient Boosting for customer classification. However, through meticulous parameter tuning, this rate dramatically improves to 8.6%, achieving an impressive accuracy of 91.4%. This refined model furnishes invaluable insights into Prime Market's customer segments, enabling the customization of marketing tactics and strategic business approaches. Armed with these insights, Prime Market can make data-driven decisions to enhance customer segmentation accuracy, better comprehend customer preferences, and pinpoint potential avenues for revenue growth. Leveraging advanced data analytics and predictive modeling empowers Prime Market to maintain a competitive edge and deliver its clientele a personalized, gratifying shopping experience.