Document Type : Original Article

Authors

1 Assistant Prof, Industrial Management, Faculty of Administrative Sciences and Economics, Management, Vali-e-Asr University of Rafsanjan, Iran (VRU),

2 Associate Prof, Industrial Management, Faculty of Administrative Sciences and Economics, Management, Vali-e-Asr University of Rafsanjan, Iran (VRU).

3 M.A Student, Industrial Management, Faculty of Administrative Sciences and Economics, Management, Vali-e-Asr University of Rafsanjan, Iran (VRU).

4 M.A Student, Department of Computer engineering, Iran University Of Science and Technology, Tehran, Iran.

10.52547/jimp.2023.227929.1378

Abstract

Today,when the competition between marketing companies and retail stores to attract new customers and retain old customers is at its peak, every company tries to consider the approach of customer segmentation and recommending the right products for each customer to excel. Generally, customers decide to buy goods based on their basic needs and relative needs. Salespeople play an important role in influencing customers in the real market. So the recommendation engine is nothing but a good and automated seller. The product recommendation system has various applications in various industries, both manufacturing, and service. One of the motivations for using a product recommendation system is to encourage customers to buy other products. In this article, we present a method for recommending products to customers using the k-means clustering algorithm and the RFM model, the number of repetitions of the customer's purchase, and the monetary value of the purchase, and a method based on customer purchase records to segment customers. And then recommend the product. To verify the performance of the proposed system, we conduct experiments with a dataset collected from the Digikala. TheResults show that the results of clustering in terms of R, F, and M characteristics were obtained more for cluster number zero, so the price of the product suggested for cluster number zero (loyal customers) is higher than other clusters, which can be used to persuade Loyal customers used it to buy goods at a higher price according to the conditions, and special discounts were also considered for customers.

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