PRFM Model Developed for the Separation of Enterprise Customers Based on the Distribution Companies of Various Goods and Services مقاله

نویسنده مسئول: Hajmohamad، Mohammad Mahdi ؛

نویسنده: Rahimi، Narges ؛ Sasanizaded، Behzad ؛

Journal of System Management Autumn 2020, Volume 6 - Number 3 رتبه A (دانشگاه آزاد/ISC (‎23 صفحه - از 77 تا 99 )

کلیدواژه ها: Clustering RFM model PRFM Model WARD'S Method Customer's Value

چکیده:

In this study, a new model of combining variables affecting the classification of customers is introduced which is based on a distribution system of goods and services. Given the problems that the RFM model has in various distribution systems, a new model for resolving these problems is presented. The core of this model is the older RFM. The new model that has been proposed as PRFM, consists of four dimensions: Profit margins (P) , time period from customer's last purchase (R) , Frequency of transactions (F) and the Monetary Value (M). Adding variable (P) makes a huge change in customer clustering and classification systems and makes it more optimized for future planning. For review and approval, the model was implemented in one of the largest and most diversified distribution companies in Iran. Using Ward's clustering, the optimal number of clusters was prepared and entered by hierarchical clustering and based on Euclidian distance customers are clustered and separated. One of the most important results of this study is introducing a new model and resolving the problems of the old RFM model in determining customer's value.

خلاصه ماشینی:

The new model that has been proposed as PRFM, consists of four dimensions: Profit margins (P), time period from customer's last purchase (R), Frequency of transactions (F) and the Monetary Value (M). At the end, valuable and profitable customers of the bank were divided into 8 clusters based on customer lifetime value using RFM and their characteristics were analyzed (Sohrabi & Khanlari, 2007). Wu used RFM and multi-mean clustering method to analyze customer value in an industrial equipment manufacturing company After data 2 Analytic Hierarchy Process preparation, customer were divided into 6 clusters using multi-mean clustering method based on RFM indicators then customer characteristics were analyzed in form of clusters and customer lifetime value analysis and recommendations for the use of proper promotion programs with various customer segments were suggested (Wu, Chang, & Lo, 2009). Lee used a two-stage clustering method to analyze the customer characteristics of a textile factory Cluster analysis base in the mentioned study was extended RFM model. After data preparation, the optimal number of clusters was determined by using Ward 2 and the customers were divided into 5 clusters based on multi-mean method and the characteristics of each cluster were analyzed based on scoring RFM (Lee, 1981). Ultimately, the company's customers were divided into 8 clusters based on lifetime value using RFM model and their characteristics were analyzed. Customer lifetime value analysis and clustering are among the techniques mentioned in data mining models that have been proposed in recent years and are considered by many researchers as the basis for classification of customers.

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