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<ArticleSet>
<Article>
<Journal>
				<PublisherName>University of Isfahan</PublisherName>
				<JournalTitle>New Marketing Research Journal</JournalTitle>
				<Issn>2228-7744</Issn>
				<Volume>6</Volume>
				<Issue>1</Issue>
				<PubDate PubStatus="epublish">
					<Year>2016</Year>
					<Month>05</Month>
					<Day>21</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Segmentation of the Customers of Iran Banking System in terms of Customer Expectations and Perceived Value of Banking Services Using Data Mining Techniques</ArticleTitle>
<VernacularTitle>Segmentation of the Customers of Iran Banking System in terms of Customer Expectations and Perceived Value of Banking Services Using Data Mining Techniques</VernacularTitle>
			<FirstPage>201</FirstPage>
			<LastPage>220</LastPage>
			<ELocationID EIdType="pii">20661</ELocationID>
			
<ELocationID EIdType="doi">10.22108/nmrj.2016.20661</ELocationID>
			
			<Language>FA</Language>
<AuthorList>
<Author>
					<FirstName>Sorour</FirstName>
					<LastName>Farokhi</LastName>
<Affiliation>Islamic Azad University, North Branch</Affiliation>

</Author>
<Author>
					<FirstName>Babak</FirstName>
					<LastName>Teimourpoor</LastName>
<Affiliation></Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2016</Year>
					<Month>11</Month>
					<Day>21</Day>
				</PubDate>
			</History>
		<Abstract>In a highly competitive market of financial institutions and also public and private banks, banks try to be the preferred choice of customers for their banking services by maximizing their customers’ satisfaction. The goal of this research is to introduce a mechanism for identification and segmentation of banking customers in accordance with their preferences into 3 key strategic directions, i.e. operational excellence, product leadership, and customer intimacy. To achieve this goal, it is required to detect important factors for the above-mentioned strategic directions and conduct a survey about them, subsequently. After the process of data collection, the next step is to classify customers in this regard and align the banks&amp;#39; operations with their customers’ expectations. This research is based on the result of a survey about 24 factors related to the importance of banks’ characteristics from the viewpoints of customers as well as their satisfaction from the primary banks’ services. Data were analyzed using exploratory factor analysis for the purpose of dimensionality reduction in order to extract 5 new metrics based on the mentioned 24 factors, and also Two-step and K-Means clustering algorithms were conducted and measured clustering metrics. As a result of the above clustering, customers were divided into 3 segments, which can lead the banks to align their operations and services in accordance with their customers’ preferences.</Abstract>
			<OtherAbstract Language="FA">In a highly competitive market of financial institutions and also public and private banks, banks try to be the preferred choice of customers for their banking services by maximizing their customers’ satisfaction. The goal of this research is to introduce a mechanism for identification and segmentation of banking customers in accordance with their preferences into 3 key strategic directions, i.e. operational excellence, product leadership, and customer intimacy. To achieve this goal, it is required to detect important factors for the above-mentioned strategic directions and conduct a survey about them, subsequently. After the process of data collection, the next step is to classify customers in this regard and align the banks&amp;#39; operations with their customers’ expectations. This research is based on the result of a survey about 24 factors related to the importance of banks’ characteristics from the viewpoints of customers as well as their satisfaction from the primary banks’ services. Data were analyzed using exploratory factor analysis for the purpose of dimensionality reduction in order to extract 5 new metrics based on the mentioned 24 factors, and also Two-step and K-Means clustering algorithms were conducted and measured clustering metrics. As a result of the above clustering, customers were divided into 3 segments, which can lead the banks to align their operations and services in accordance with their customers’ preferences.</OtherAbstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Customers Expectation</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">value proposition</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Perceived values</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Expletory Factor analysis</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Clustering</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://nmrj.ui.ac.ir/article_20661_26bb626a31f70ab86b523c99501e2ef5.pdf</ArchiveCopySource>
</Article>
</ArticleSet>
