|InterJournal Complex Systems, 522
|Manuscript Number: |
Submission Date: 20501
|Customer Relationship Management in Banking Sector and A Model Design for Banking Performance Enhancement|
Subject(s): CX.68, CX.65, CX.06, CX.07
Huge growth of Customer Relationship Management (CRM) is predicted in the banking sector over the next few years. Banks are aiming to increase customer profitability with any customer retention. This paper deals with the role of Customer Relationship Management in banking sector and the need for Customer Relationship Management to increase customer value by using some analitycal methods in CRM applications. CRM is a sound business strategy to identify the bank’s most profitable customers and prospects, and devotes time and attention to expanding account relationships with those customers through individualized marketing, repricing, discretionary decision making, and customized service-all delivered through the various sales channels that the bank uses. In banking sector, relationship management could be defined as having and acting upon deeper knowledge about the customer such as how to find the customer, get to know the customer, keep in touch with the customer, ensure that the customer gets what she/he wishes from service provider, and understand when they are not satisfied and might leave the service provider and act accordingly. This study will underline CRM objectives such as growth, retention and cost reduction. Increasing customers’ product cross-holdings, maximizing the contribution from each customer through time and increasing efficiency are couple of those objectives, which this study will cover. Under this case study, a campaign management in a bank is conducted using data mining tasks such as dependency analysis, cluster profile analysis, concept description, deviation detection, and data visualization. Crucial business decisions with this campaign are made by extracting valid, previously unknown and ultimately comprehensible and actionable knowledge from large databases. The model developed here answers what the different customer segments are, who more likely to respond to a given offer is, which customers are the bank likely to lose, who most likely to default on credit cards is, what the risk associated with this loan applicant is. Finally, a cluster profile analysis is used for revealing the distinct characteristics of each cluster, and for modeling product propensity, which should be implemented in order to increase the sales.
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