Customer Segmentation with RFM Analysis using ML Models

The technique of examining the attributes of customers and creating groups based on some specific characteristics is known as customer segmentation. Customer segmentation is a methodology employed to analyze the characteristics of consumers and subsequently categorize them into distinct groups based on specified features. Customer segmentation is a widely employed approach for discerning the identities of the most devoted patrons as well as those that exhibit infrequent engagement with the platform. This study examines a new methodology for consumer segmentation by combining Recency, Frequency, Monetary (RFM) information with sophisticated Machine Learning (ML) models. Through the utilization of transactional data, the calculation of RFM scores is employed, which enables to get valuable insights into client behavior pertaining to the aspects of purchase recency, frequency, and monetary value. The scores play a crucial role in the training of K-means clustering and other related algorithms. The existing machine learning models exhibit potential for development. The study performed has a strong and dependable machine learning model that operates with high efficiency and delivers consistent performance. This is achieved by including the RFM scores of all consumers into the model. The model that has been provided has superior efficiency and robustness compared to all other models, resulting in a notable improvement in silhouette score which comes out to be 0.954319641633133, thus making it 53.8% more efficient then the previously built one. This achievement is particularly noteworthy when considering the conventional machine learning models that have previously been employed.