Customer Behavior Analysis Using Rough Set Approach

The customer relationship management (CRM) is a business methodology used to build long term profitable customers by analyzing customer needs and behaviors. The customer behavior is analyzed by choosing important attributes in the customer database. The customers are then segmented into groups accor...

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Autores principales:	Dhandayudam,Prabha, Krishnamurthi,Ilango
Lenguaje:	English
Publicado:	Universidad de Talca 2013
Materias:	Clustering Customer relationship management K-means LEM2 Rough set theory Rule induction RFM RFMP
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-18762013000200003
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spelling	oai:scielo:S0718-187620130002000032018-10-12Customer Behavior Analysis Using Rough Set ApproachDhandayudam,PrabhaKrishnamurthi,Ilango Clustering Customer relationship management K-means LEM2 Rough set theory Rule induction RFM RFMP The customer relationship management (CRM) is a business methodology used to build long term profitable customers by analyzing customer needs and behaviors. The customer behavior is analyzed by choosing important attributes in the customer database. The customers are then segmented into groups according to their attribute values. The rules are generated using rule induction algorithms to describe the customers in each group. These rules can be used by the entrepreneur to predict the behavior of their new customers and to vary the attraction process for existing customers. In this paper a new rule algorithm has been proposed based on the concepts of rough set theory. Its performance has been compared with LEM2 (Learning from Examples Module, version 2) algorithm, an existing rough set based rule induction algorithm. Real data set of the customer transaction is used for analysis. Recency(R), Frequency (F), Monetary (M) and Payment (P) are the attributes chosen for analyzing customer data. The proposed algorithm on average achieves 0.439% increase in sensitivity, 0.007% increase in specificity, 0.151% increase in accuracy, 0.014% increase in positive predictive value, 0.218% increase in negative predictive value and 0.228% increase in F-measure when compared to LEM2 algorithm.info:eu-repo/semantics/openAccessUniversidad de TalcaJournal of theoretical and applied electronic commerce research v.8 n.2 20132013-08-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-18762013000200003en10.4067/S0718-18762013000200003
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Clustering Customer relationship management K-means LEM2 Rough set theory Rule induction RFM RFMP
spellingShingle	Clustering Customer relationship management K-means LEM2 Rough set theory Rule induction RFM RFMP Dhandayudam,Prabha Krishnamurthi,Ilango Customer Behavior Analysis Using Rough Set Approach
description	The customer relationship management (CRM) is a business methodology used to build long term profitable customers by analyzing customer needs and behaviors. The customer behavior is analyzed by choosing important attributes in the customer database. The customers are then segmented into groups according to their attribute values. The rules are generated using rule induction algorithms to describe the customers in each group. These rules can be used by the entrepreneur to predict the behavior of their new customers and to vary the attraction process for existing customers. In this paper a new rule algorithm has been proposed based on the concepts of rough set theory. Its performance has been compared with LEM2 (Learning from Examples Module, version 2) algorithm, an existing rough set based rule induction algorithm. Real data set of the customer transaction is used for analysis. Recency(R), Frequency (F), Monetary (M) and Payment (P) are the attributes chosen for analyzing customer data. The proposed algorithm on average achieves 0.439% increase in sensitivity, 0.007% increase in specificity, 0.151% increase in accuracy, 0.014% increase in positive predictive value, 0.218% increase in negative predictive value and 0.228% increase in F-measure when compared to LEM2 algorithm.
author	Dhandayudam,Prabha Krishnamurthi,Ilango
author_facet	Dhandayudam,Prabha Krishnamurthi,Ilango
author_sort	Dhandayudam,Prabha
title	Customer Behavior Analysis Using Rough Set Approach
title_short	Customer Behavior Analysis Using Rough Set Approach
title_full	Customer Behavior Analysis Using Rough Set Approach
title_fullStr	Customer Behavior Analysis Using Rough Set Approach
title_full_unstemmed	Customer Behavior Analysis Using Rough Set Approach
title_sort	customer behavior analysis using rough set approach
publisher	Universidad de Talca
publishDate	2013
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-18762013000200003
work_keys_str_mv	AT dhandayudamprabha customerbehavioranalysisusingroughsetapproach AT krishnamurthiilango customerbehavioranalysisusingroughsetapproach
_version_	1714202212220010496

Customer Behavior Analysis Using Rough Set Approach

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