New RFM+WL Model for Effective Customer Segmentation using Machine Learning

Customer segmentation is a powerful tool for exploring customer insights to tailor
their marketing strategies and improve customer retention. Traditional RFM (Recency,
Frequency, Monetary) analysis provides a strong basis for segmenting customers based on
purchase behavior. However, by incorporating customer “Lifetime” i.e., recurring value as an
additional feature and machine learning techniques, businesses can gain deeper insights and
create more precise customer segments. This study explores a novel approach that combines
RFM +WL and machine learning to optimize customer segmentation. By applying clustering
algorithms, we can identify distinct customer segments based on their RFM+WL attributes.
This refined segmentation enables businesses to prioritize high-value customers, personalize
marketing campaigns, and implement target retention strategies. The proposed approach offers
a powerful tool for companies to maximize customer lifetime and drive sustainable growth.