A BEHAVIORAL LRFS FRAMEWORK FOR CUSTOMER SEGMENTATION IN E-COMMERCE
Keywords:
Behavior-based segmentation, E-commerce, LRFS model, Customer analytics, Spending analysis, Customer relationship management (CRM)Abstract
The LRFS (Length, Recency, Frequency, and Spending) model is employed to categorize online shoppers based on product and engagement in this paper. The objective of the investigation is to enhance personalized marketing strategies, identify profitable customer segments, and increase customer retention and profitability. Customer classification is determined by the most recent purchase, frequency of purchases, platform, and total expenditures, as determined by LRFS transactional data. Advanced data analysis and clustering can be employed to identify consumer trends. Resource allocation and advertising targeting are advantageous to businesses. The research determined that the LRFS model for behavior-based segmentation can assist businesses in the development of data-driven growth strategies for the highly competitive e-commerce market and in the prediction of customer purchases.
