Develop a recommender system based on a novel approach to the RFM customer segmentation model (A Case Study of GreenWeb Co.)

Along with the development of the digital economy, companies providing web hosting services and e-commerce infrastructure are increasing. Therefore, today, segmenting customers and recommending suitable products have become critical strategies for maintaining a competitive edge. With the rapid growth of online shopping, customers often make purchasing decisions based on their needs and desires. Salesforces can play a crucial role in influencing customers' decisions, so making a product recommendation system is essential. Such a system has various applications and can encourage customers to purchase additional products. In this study, we present a method for recommending products to customers that utilize the RFM (Recency, Frequency, Monetary) model to segment customers into eight classes and make interesting recommendations. To evaluate the performance of the proposed system, we conducted experiments using data collected from GreenWeb Co., This company mainly offers services such as Cloud Hosting, Web Applications, UI/UX Design, Mobile Applications, and online integrative services. The results show that after implementing the proposed recommender system, the average purchase amount of customers in ۸ RFM label categories has increased by about ۱۳.۷۹%. At the same time, the number of tickets per customer has decreased by ۹.۶۸%, and as a result, the workload and cost of products have decreased., which represents loyal customers. Therefore, similar companies can use this solution to encourage customers to purchase higher-priced products to increase sales and customer satisfaction.Along with the development of the digital economy, companies providing web hosting services and e-commerce infrastructure are increasing. Therefore, today, segmenting customers and recommending suitable products have become critical strategies for maintaining a competitive edge. With the rapid growth of online shopping, customers often make purchasing decisions based on their needs and desires. Salesforces can play a crucial role in influencing customers' decisions, so making a product recommendation system is essential. Such a system has various applications and can encourage customers to purchase additional products. In this study, we present a method for recommending products to customers that utilize the RFM (Recency, Frequency, Monetary) model to segment customers into eight classes and make interesting recommendations. To evaluate the performance of the proposed system, we conducted experiments using data collected from GreenWeb Co., This company mainly offers services such as Cloud Hosting, Web Applications, UI/UX Design, Mobile Applications, and online integrative services. The results show that after implementing the proposed recommender system, the average purchase amount of customers in ۸ RFM label categories has increased by about ۱۳.۷۹%. At the same time, the number of tickets per customer has decreased by ۹.۶۸%, and as a result, the workload and cost of products have decreased., which represents loyal customers. Therefore, similar companies can use this solution to encourage customers to purchase higher-priced products to increase sales and customer satisfaction.

کلیدواژه ها:

RFM ، Customer segmentation ، Recommendation system ، Behavioral analysis