Unsupervised Recency Frequency and Monetary based Customer Segmentation

Abstract

The domain of this project is Data Science. Basically, we are given a real-life dataset of an Ecommerce company from which we are expected to draw meaningful insights related to customer purchasing behavior and recommend focused strategies to improve revenue and enhance customer retention based on the results provided by the customer segmentation model. The company majorly categorizes their clients based on one-of-a-kind business metrics which include how recently they spend or visited (recency), how often they spend (frequency), how much they spend (monetary). And this is why we use RFM approach to know the accuracy of our clustering model by comparing the silhouette score of clustering model with the RFM score which makes our model better to perform customer segmentation for the companies by categorizing their customers into Loyal Passive and Critical ones with better accuracy so that they can build the best targeted strategies to retain their customers and increase revenues.