Customer Relationship Mamagement in Banking Sector
Today, many businesses such as banks, insurance companies, and other service providers realize the importance of Customer Relationship Management (CRM) and its potential to help them acquire new customers, retain existing ones and maximize their lifetime value. At this point, close relationship with customers will require a strong coordination between IT and marketing departments to provide a long-term retention of selected customers,The role of Customer Relationship Management in banking sector is very important 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.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
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2. Customer Relationship Management In literature, many definitions were given to describe CRM. The main difference among these definitions is technological and relationship aspects of CRM. Some authors from marketing background emphasize technological side of CRM while the others considers IT perspective of CRM. From marketing aspect, CRM is defined by [Couldwell 1998] as “.. a combination of business process and technology that seeks to understand a company’s customers from the perspective of who they are, what they do, and what they are like”. Technological definition of CRM was given as “.. the market place of the future is undergoing a technology-driven metamorphosis” [Peppers and Rogers 1995]. Consequently, IT and marketing departments must work closely to implement CRM efficiently.
3. CRM Objectives in Banking Sector The idea of CRM is that it helps businesses use technology and human resources gain insight into the behavior of customers and the value of those customers. If it works as hoped, a business can: provide better customer service, make call centers more efficient, cross sell products more effectively, help sales staff close deals faster, simplify marketing and sales processes, discover new customers, and increase customer revenues.It doesn’t happen by simply buying software and installing it. For CRM to be truly effective, an organization must first decide what kind of customer information it is looking for and it must decide what it intends to do with that information.
For example, many financial institutions keep track of customers’ life stages in order to market appropriate banking products like mortgages or IRAs to them at the right time to fit their needs. Next, the organization must look into all of the different ways information about customers comes into a business, where and how this data is stored and how it is currently used. One company, for instance, may interact with customers in a myriad of different ways including mail campaigns, Web sites, brick-and-mortar stores, call centers, mobile sales force staff and marketing and advertising efforts. Solid CRM systems link up each of these points.
This collected data flows between operational systems (like sales and inventory systems) and analytical systems that can help sort through these records for patterns. Company analysts can then comb through the data to obtain a holistic view of each customer and pinpoint areas where better services are needed. In CRM projects, following data should be collected to run process engine: 1) Responses to campaigns, 2) Shipping and fulfillment dates, 3)Sales and purchase data, 4) Account information, 5) Web registration data, 6) Service and support records, 7) Demographic data, 8) Web sales data.
CRM at Indian Banks Indian banks have recorded a phenomenal growth in the past decade with the initiation of Economic Reforms. The banks, both Public and Private, have transformed themselves into profit-oriented business organizations besides playing a developmental role in the economy. In an attempt to be more profitable, the banks have become competitive and more customer-oriented. This new orientation has compelled them to take a more pragmatic approach for conducting the business. In the backdrop of this scenario, the study reviewed implementation of Customer Relationship Management (CRM) and the impact of CRM on service quality and customer retention in ten public and private sector banks of India.
It was found that the Private Sector Banks have been able to implement the CRM practices more effectively as compared to their Public Sector counterparts. This fact has further been corroborated by the findings of the service quality level being provided by these banks. Further, it was observed both the public and private sector banks scored the least on responsiveness and empathy factors. Public Sector Banks have fared better in terms of reliability and assurance whereas the Private Sector Banks have fared better in terms of tangibility, reliability and assurance.
A Model Design for CRM At Garanti Bank(Turkey)
Garanti Bank, one of the leading banks in Turkey were looking at new ways to enhance its customer potential and service quality. Electronic means of banking have proved a success in acquiring new customer groups until the end of 2001. After then, a strategic decision was made to re-engineer their core business process in order to enhance the bank’s performance by developing strategic lines. Strategic lines were given in order to meet the needs of large Turkish and multinational corporate customers, to expand commercial banking business, to focus expansion in retail banking and small business banking, to use different delivery channels while growing, and to enhance operating efficiency though investments in technology and human resources
To support this strategy Garanti Bank has implemented a number of projects since 1992 regarding branch organization, processes and information systems. The administration burden in the branches has been greatly reduced and centralized as much as possible in order to leave a larger room to marketing and sales. The BPR projects have been followed by rationalizing and modernizing the operational systems and subsequently by the introduction of innovative channels: internet banking, call center and self-servicing. In parallel, usage of technology for internal communication: intranet, e-mail, workflow and management reporting have become widespread.
4.1.C R M Development To be prepared to the changing economic conditions and, in particular, to a rapidly decreasing inflation rate scenario Garanti Bank has started timely to focus on developing a customer relationship management (CRM) system. The total number of customers is presently around two millllions, but an increase to roughly three millions is foreseen as mergings with Osmanli Bank and Koferzbank are achieved and the present growth targets are reached.
The importance for the bank of managing the relationhips with their customers has been the drive of the joint projects that have been developed with IBM in the last three years. During the projects a number of crucial technological and architecture choices have been made to implement the entire process. Realizing the importance of customer information availability the first of these projects has focussed on the problem of routinely collecting and cleansing data. The project has been undertaken by the bank with the spirit that has characterized the whole CRM development. The project has promoted a massive involvment of the branches, namely of the portfolio managers and campaigns have been launched for popularizing among branch staff the importance of gathering and maintaining reliable customer data.
Another set of methods have been tested for customer not included in portfolios (pool customers), such as mailing or distributing questionnaires in the branches or using automatic teller machines (ATM) and the call center. Methods for data checking and testing have been developed to be routinely employed by the bank’s staff. Results obtained are very good: for portfolio customers data available are respectively 98% for the commercial ones and 85% for the retail ones. For pool customers availability goes down to 65%: this is a well-known phenomenon due to the loose relationship with the latter customers.
4.2. Data Warehouse and Data Mining The Data warehouse is the core of any decision suppport system and hence of the CRM. In implementing its Data Warehouse Garanti Bank has selected an incremental approach, where the development of information systems is integrated with the business strategy. Instead of developing a complete design of a corporate Data Warehouse before implementing it, the bank has decided to develop a portion of the Data Warehouse to be used for customer relationship management and for the production of accurate and consistent management reports. Here we are not concerned with the latter goal, but are concentrating on the former.
The Data Warehouse has been designed according to the IBM BDW (Banking Data Warehouse) model, that has been developed as a consequence of the collaboration between IBM and many banking customers. The model is currently being used by 400 banks worldwide. The Garanti Bank Data Warehouse is regularly populated both from operational systems and from intermediate sources obtained by partial preprocessing of the same raw data.
Figure 1. The process of Relational Marketing
It includes customers’ demographic data, product ownership data and transaction data or, more generally product usage data as well as risk and profitability data. Most data are monthly averages and today’s historical depth is 36 months starting from 1/1/1999 to 12/31/2001. As new data are produced they are placed temporarily in an intermediate, from which they are preprocessed and transferred to the warehouse.
The importance of the Data Warehouse stems from the analysis of Figure 1. As a result of strategic decisions customer analysis is carried out by using data continuously updated as well the analytical methods and tools to be described later on. The CRM group analyzes results obtained and designs action plans, such as campaigns, promotions, special marketing initiatives, etc. Plans developed are then implemented by means of the several channels used by the bank to reach customers. Evaluation or results completes the cycle. The results become an integral part of the description of the bank-customer relationship in the warehouse.
The learning cycle is thus complete and results obtained can be reused in future analyses and in future marketing plans. It is easy to understand that the Data Warehouse cannot actually be built ‘once for all’ but is a kind of living structure continuously enriched and updated as the Relational Marketing activity developes. OLAP (On Line Application Programming) analyses are developed by means of Business Object in its web version. CRM analysts use this tool to issue complex SQL queries on the Data Warehouse or on the Analytical Datamart and carry out mono and bivariate statistics on the whole customers’ population or on selected groups. Figure 2 shows general structure of Relational Marketing Activity.
Figure 2.The Relational Marketing process is supported by a computing infrastructure where many software packages are integrated with the bank’s information system. Data Mining analyses are not carried out directly on the Data Warehouse, but on the Analytical Data mart by means of the software package IBM Intelligent Miner [Cabena et.al. 1999], using as a computing and data server the same mainframe where the Data Warehouse resides. Garanti Bank believes these tools and methodologies are a powerful competitive weapon and is investing heavily in the human resources needed to develop these analyses.
The Analytical Data mart is derived from the Data Warehouse through the following steps: 1)Raw data processing: data selection, data extraction, and data verification and rectification 2) Data modeling and variable preprocessing: variable selection, new variable creation, variable statistics, variable discretization. The above processing, based on traditional data analysis, is strictly dependent on the investigated process; new variable creation, for instance, is intended to aggregate information contained in the raw data into more expressive variables. A simple example is the number of credit transaction on current account, that contains much of the information contained in the individual transactions, but is easier to analyze and represent. Variable discretization, based on the distribution of the original variables, is intended to generate categorical variables that better express the physical reality of the problem under investigation.