BEHAVIOR-BASED SEGMENTATION OF ONLINE SHOPPERS USING LRFS MODEL

Businesses need to learn more about their customers in order to provide them with more personalized shopping experiences as e-commerce continues to grow. Although traditional segmentation methods are commonly employed, they fail to consider the nuances of human behavior, resulting in overly generalized marketing approaches. The LRFS model is described in this research. It uses four primary criteria—lifestyle, recency, frequency, and expenditure—to categorize consumers according to the products they purchase. Lifestyle factors, such as interests and personal preferences, impact customers' interactions with online retailers. The three metrics of recency, expenditure, and frequency provide light on the regularity of client interactions, the velocity of transaction completion, and the total amount of money spent. The model distinguishes between the various types of shoppers by utilizing hierarchical clustering and K-means. Businesses can use this segmentation to target specific groups with more relevant campaigns, product recommendations, and promotions. Evidence from actual online purchases demonstrates that the LRFS model improves engagement and makes accurate predictions. Businesses can increase their success and customer retention rates by learning to adapt to each customer's unique behavior. Digital marketing can be more effective with data-driven segmentation, according to the document. Maintaining a competitive market position via the use of models such as LRFS will be crucial as online shopping continues to expand.