APPLICATION OF RFM MODEL ON CUSTOMER SEGMENTATION IN DIGITAL MARKETING

Abstract

Customers play a pivotal role in the success of any business. The ability to attract and retain the right clientele, who consistently engage with a company's products and services, hinges on a thorough understanding of their purchasing behavior. Successful businesses tailor their offerings to meet the unique requirements and preferences of their customers. Utilizing marketing analysis tools, such as the RFM model, facilitates the segmentation of customers based on distinct parameters, enabling the identification of high-value customers through factors like purchase behavior. In addressing this critical aspect of customer-centric strategies,
our research introduces an innovative early purchase prediction framework. This framework aims to assess the likelihood of a potential customer purchasing soon. Leveraging a combination of a decision tree classifier and a gradient-enhancing classification approach, we tackled the classification challenge using a dataset obtained from statistadata.com, focusing on digital market dynamics. Our findings reveal that the gradient-enhancing classifier model outperformed others, demonstrating an impressive accuracy of 93% and an AUC (Area Under the Curve) of 0.98. This research not only contributes to the advancement of predictive
modeling in the realm of customer behavior but also underscores the practical application of such models in digital marketing strategies. As businesses navigate the intricacies of customer engagement, our proposed framework offers valuable insights to enhance decision-making processes and optimize marketing efforts.