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Tourism Management Perspectives, 2016 This study focuses on profiling profitable hotel customers by RFM analysis, which is a data mining technique. In RFM analysis, Recency, Frequency and Monetary indicators are employed for discovering the nature of the customers.
Aslihan Dursun, Meltem Caber
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Customer Analysis Using the RFM Methodology in A Dental Clinic
Proceedings of the 2023 9th International Conference on Industrial and Business Engineering, 2023Digital transformation and data collection are needs that are emerging in companies, including in the dental clinic "Red Odontológica de Lima" which will be taken as the case of this study. Customer retention is essential in business management, given the investment required to attract new customers, so customer satisfaction and loyalty are critical to
Melani Daniela Pablo Felix +2 more
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Clustering Algorithms and RFM Analysis Performed on Retail Transactions
2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 2020This paper investigates how clustering algorithms and Recency, Frequency, and Monetary value (RFM) analysis can be performed on online transactions to provide strategies for customer purchasing behaviors. Along with performing RFM analysis on the retail dataset, clustering algorithms such as Mean-shift, Density-Based Spatial Clustering of Applications ...
Yash Parikh, Eman Abdelfattah
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Customer clustering using RFM analysis
2018 26th Signal Processing and Communications Applications Conference (SIU), 2018In this study, customers' behaviors are determined by detecting natural clusterings using existing reservation and customer data. We also customize their services and sales strategies according to these behaviors. The basic characteristics that provide these existing heuristics have been extracted by the decision tree approach after the K-means is ...
Muhammet Pakyürek +5 more
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RFM analysis for detecting future core technology
Proceedings of the 2012 ACM Research in Applied Computation Symposium, 2012RFM is a simple and powerful method to provide a framework for understanding and quantifying customer behavior based on purchase in marketing field. The purpose of this study is to demonstrate that RFM analysis can be effectively used for predicting future core technologies.
Dohyun Kim +4 more
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Estimating customer lifetime value based on RFM analysis of customer purchase behavior: Case study
Procedia Computer Science, 2011 Since the increased importance is placed on customer equity in today’s business environment, many firms are focusing on the notion of customer loyalty and profitability to increasing market share. Building successful customer relationship management (CRM)
Kiyana Zolfaghar, Somayeh Alizadeh
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RFM-Based Customer Analysis and Product Recommendation System
2021In this current Covid-19 pandemic scenario, most of the supermarkets and retail stores that are present even in small towns have started giving out products online. This system classifies the customers into categories based on their Recency, Frequency and Monetary (RFM) values and recommends products to them to make sure the potentially valuable ...
Rahul Krishnan, Prashant R. Nair
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Fuzzy RFM Analysis: An Application in E-Commerce
2020RFM (recency, frequency, monetary) analysis is an essential tool for customer segmentation, which is very important for marketing, communication, and even operations management activities. RFM is a widely adopted segmentation tool since it can be accomplished by using purchase transactions.
Basar Oztaysi, Mert Kavi
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Customer stratification theory and value evaluation—analysis based on improved RFM model
Journal of Intelligent & Fuzzy Systems, 2021Scientific customer stratification method can help enterprises identify valuable customers, thus effectively improving the operating profit of enterprises. However, current customer stratification methods have not considered the impact of cost to service (CTS) on customer value (such as the RFM model). In this paper, K-mean clustering method is adopted
Yi Zong, Hao Xing
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Optimization of RFM’s Structure Based on PSO Algorithm and Figure Condition Analysis
IEEE Geoscience and Remote Sensing Letters, 2018Rational function model (RFM) faces difficulty in extracting accurate geometric information from remotely sensed images, which is mainly due to the problems of overparameterization and ill-posedness. These problems can be addressed via variable selection methods, in which an optimum subset of rational polynomial coefficients is identified via an ...
Sayyed Hamed Alizadeh Moghaddam +2 more
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