Almost all the papers on market segmentation modeling using retail transaction data reported in the literatures deal with finding groupings of customers. This paper proposes the application of clustering techniques on finding groupings of retailers who use the Electronic Funds Transfer at Point Of Sale (EFTPOS) facilities of a major bank in Australia in their businesses. The RFM (Recency, Frequ...

Data mining has a tremendous contribution for researchers to extract the hidden knowledge and information which have been inherited in the raw data. This study has proposed a brand new and practical fuzzy analytic network process (FANP) based weighted RFM (Recency, Frequency, Monetary value) model for application in K-means algorithm for auto insurance customers’ segmentation. The developed met...

Many online service providers use a recommendation system to assist their customers' decision-making by generating recommendations. Accordingly, this paper proposes new for tourism customers make reservations hotels with the features they need, saving time and increasing impact of personalized hotel This combined collaborative content-based filtering approaches created hybrid system. Two datase...

For superior decision making, the mining of interesting patterns and rules becomes one of the most indispensible tasks in today’s business environment. Although there have been many successful customer relationship management (CRM) applications based on sequential pattern mining techniques, they basically assume that the importance of each customer are the same. Many studies in CRM show that no...

This study aimed at providing a systematic method to analyze the characteristics of customers’ purchasing behavior in order to improve the performance of customer relationship management system. For this purpose, the improved model of LRFM (including Length, Recency, Frequency, and Monetary indices) was utilized which is now a more common model than the basic RFM model apt for analyzing the cus...

In a highly competitive medical industry, hospitals can continue to create values and advantages using data mining technologies identify patients’ needs provide the services needed by various patients. This research focuses on outpatients in center Taiwan adopts recency, frequency, monetary (RFM) model, self-organizing maps, K-means method construct set of exploration procedures so that hospita...

Purpose: This research aims to do customer segmentation in retail companies by implementing the Recency Frequency Monetary (RFM) K-Means cluster model and algorithm optimized Elbow method.Methods: study uses several methods. The RFM method was chosen segment customers because it is one of optimal methods for segmenting customers. easy interpret, implement, fast convergence, adapt, but lacks sen...

With the increase of living standards and the sustainable changing patterns of people’s lives, nowadays, hairdressing services have been widely used by people. This paper adopts data mining techniques by combining self-organizing maps (SOM) and K-means methods to apply in RFM (recency, frequency, and monetary) model for a hair salon in Taiwan to segment customers and develop marketing strategie...

In this paper, we try to validate existing theory on and develop additional insight into repeat-purchase behavior in a direct marketing setting by means of an illuminating case study. The case involves the detection and qualification of the most relevant RFM (Recency, Frequency and Monetary) variables, using a neural network wrapper as our input pruning method. Results indicate that elimination...