User Segmentation with RFM Analysis and K-Means Clustering for Play Store Database via Machine Learning

The objective of this work is to classify users into distinct categories based on their usage habits and activity on the Play Store. By employing RFM (Recency, Frequency, Monetary) analysis, we aim to understand how recently and frequently users interact with the Play Store, as well as their monetary value to the platform. Using these RFM metrics, we apply the K-means clustering algorithm to group users into meaningful clusters. In this work, we present a novel user segmentation method that combines RFM analysis with K-means clustering within a Play Store database. This approach allows businesses to gain deeper insights into their user base and adjust their strategies accordingly. By automatically segmenting users based on their RFM scores using the K-means clustering algorithm, targeted marketing activities can be more effectively implemented. We demonstrate the efficacy of our approach through testing on an actual Play Store dataset, illustrating how enterprises can leverage machine learning methodologies to enhance user segmentation and customer relationship management. Our method effectively identifies significant user categories, enabling companies to refine their strategies and cultivate more enduring customer relationships in the digital marketplace.