Data Mining Using RFM Analysis

Birant, Derya

doi:10.5772/13683

Cited by 49 publications

(16 citation statements)

References 17 publications

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“…Prioritization, Behavioral Segmentation Methods help retailers making smarter decisions on how to best allocate budget and resources, time by identifying profitable and high-value consumers segments and initiatives with the utmost potential business impact [44], [45]. 4. Performance, the segmentation helps retailer Monitor sales growth patterns and changes in key consumers segments over specific time to track performance against the retailer's goals.…”

Section: Customer Segmentationmentioning

confidence: 99%

“…Simplicity, marketing analysis can use the RFM model effectively with the need for sophisticated analytical software or data scientists. Intuitively, the output generated by the RFM segmentation is easy to understand [4], [24].…”

Section: Rfm With Customer Lifetime Value (Ltv)mentioning

confidence: 99%

“…Cluster 1 ranked first and best segment of consumers. Consumers in this cluster are high in Recency (4), Frequency (4), and strong purchase power with a monetary score(4). Consumers in this cluster have an increasing average purchase power during the period of analysis.…”

mentioning

confidence: 93%

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New Market Segmentation Methods Using Enhanced (Rfm), Clv, Modified Regression and Clustering Methods

Yoseph¹,

AlMalaily²

2019

IJCSIT

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Behavioral segmentation is considered as one of the most important concepts of modern marketing. Traditional customer segmentation models require months of analytical work, resulting in discrete consumers' insights that are outdated to match the dynamic body of the consumers they are meant to represent. Personalization and consumer experience are make or break factors for the retail industry. This study looks towards maximizing Consumer Lifetime Value (LTV) to accommodates the dynamics in consumer shopping behavior for a medium size retailer. using (LTV) matricto investigate behavioral changes in the consumer shopping history gaining knowledge from behavioral and demographic variables stored in POS database converted into RFM dataset format. In addition, this study applies soft clustering Fuzzy C-Means (FCM) and hard clustering Expectation Maximization (EM) algorithms to classify individual consumers exhibit similar purchase history into specific groups. For measuring the algorithms accuracy, weuse cluster quality assessment (CQA). The CQA shows EM algorithm scales much better than Fuzzyy C-Means algorithm with its ability to assign good initial points in the smaller dataset.

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Section: Customer Segmentationmentioning

confidence: 99%

Section: Rfm With Customer Lifetime Value (Ltv)mentioning

confidence: 99%

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New Market Segmentation Methods Using Enhanced (Rfm), Clv, Modified Regression and Clustering Methods

Yoseph¹,

AlMalaily²

2019

IJCSIT

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“…RFM is a useful market ing technique to improve customer seg mentation. It is used for analyzing customer behavior such as how recently a customer has purchased (recency), how often the customer purchases (frequency), and how much the customer spends (monetary) [22]. According to Table 1, the average recency, frequency and monetary of the segments are totally different.…”

Section: Data Preparationmentioning

confidence: 99%

Performance Analysis of Classification Methods and Alternative Linear Programming Integrated with Fuzzy Delphi Feature Selection

Izadi¹,

Ranjbarian²,

Ketabi³

et al. 2013

IJITCS

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Abstract-Among various statistical and data mining discriminant analysis proposed so far for group classification, linear programming discriminant analysis have recently attracted the researchers' interest. This study evaluates mult i-group discriminant linear programming (MDLP) for classificat ion problems against well-known methods such as neural networks, support vector machine, and so on. MDLP is less complex co mpared to other methods and does not suffer fro m local optima. However, somet imes classification becomes infeasible due to insufficient data in databases such as in the case of an Internet Service Prov ider (ISP) small and med iu m-sized market considered in this research. This study proposes a fuzzy Delphi method to select and gather required data. The results show that the performance of MDLP is better than other methods with respect to correct classification, at least for small and medium-sized datasets.

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“…For analyzing the main factors like technology and employee's professional skill that affect Customers satisfaction degree, a decision tree model was used by Mei-Ping Xie and Wei-Ya Zhao in 2010 [8]. In 2009 Derya Birant discovered data mining using RFM analysis tasks, including clustering, classification and association rule mining, to provide market intelligence [9].…”

Section: Related Workmentioning

confidence: 99%

An Efficient Multi-set HPID3 Algorithm based on RFM Model

Rani¹,

Mishra²,

Diwedi³

2013

IJCA

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Data mining is a latest emerging technique, which is mainly used to inspect large database in order to discover hidden knowledge and information about customers' behaviors. With the increasing contest in the retail industry, the main focus of superstore is to classify valuable customers accurately and quickly among the large volume of data. The decision tree algorithm is a more general data classification function algorithm based on machine learning. In this paper the concept of Recency, Frequency and Monetary is introduced, which is usually used by marketing investigators to develop marketing strategies, to find important patterns. Conventional ID3 algorithm is modified by horizontally splitting the sample of customer purchasing RFM dataset and then classification rules are discovered to predict future customer behaviors by matching pattern. The dataset has been accessed from blood transfusion service center and has 5 attributes and 748 instances. The experimental result shows that the proposed HPID3 is more effective than conventional ID3 in terms of accuracy and processing speed.

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Data Mining Using RFM Analysis

Cited by 49 publications

References 17 publications

New Market Segmentation Methods Using Enhanced (Rfm), Clv, Modified Regression and Clustering Methods

New Market Segmentation Methods Using Enhanced (Rfm), Clv, Modified Regression and Clustering Methods

Performance Analysis of Classification Methods and Alternative Linear Programming Integrated with Fuzzy Delphi Feature Selection

An Efficient Multi-set HPID3 Algorithm based on RFM Model

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