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Customer Relationship Management in Banking Sector and A Model Design for Banking Performance Enhancement

 

Dr. R. Karuppasamy M.Com., MBA, M.Phil., Ph.D., PLME

 Director, Management Studies, SNS College of Technology, Coimbatore, Tamilnadu, India

 

and

 

R. Lumina Julie M.B.A., M.Phil.,

Assistant Professor, Sri Krishna Arts and Science College, Coimbatore, Tamilnadu, India.

 

“The easiest kind of relationship is with ten thousand people, the hardest is with one.

 

-  Joan Baez

 

INTRODUCTION

 

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. 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 analytical 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. 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.

 

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.

 

A MODEL DESIGN FOR CRM AT GARANTI BANK

 

Garanti Bank, one of the leading banks in Turkey was 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.

 

DATA WAREHOUSE AND DATA MINING

 

The Data warehouse is the core of any decision support 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. 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 develops. 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. Data Mining analyses are not carried out directly on the Data Warehouse, but on the Analytical Datamart 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 are investing heavily in the human resources needed to develop these analyses. The Analytical Datamart 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. The Analytical Data mart is customer centric and contains the following data

 

1. Demographic (age, sex, cultural level, marital status, etc.)

2. Ownership of bank's product/services

3. product/services usage (balance, transactions, etc.)

4. Global variables: profit, cost, risk, assets, liabilities

5. Relationship with the bank: segment, portfolio, etc.

 

CONCLUSIONS

 

Results obtained by extensive usage of customer data to develop and apply Relational Marketing have convinced the Garanti Bank to proceed along the line undertaken. As lists of customers eligible for four very important banking product/services are available, as above described, the following actions are now being deployed:

 

1. Extension of promotions to a larger customer population by having sales people in the branches contacting progressively 15,000 customers.

 

2. Targeted campaigns through Internet and the call center for customers actively using one or both of these innovative channels for their banking operations. The same approach is now being extended to small and medium businesses and to commercial customers. Moreover the analytical and strategic CRM cycle is being completed by developing an application analyzing customers' attrition and deploying strategies to reduce it.

 

REFERENCES

 

1.      Alis, O.F., Karakurt, E., & Melli, P., 2000, Data Mining for Database Marketing at Garanti Bank, Proceeding of the International Seminar "Data Mining 2000", WIT Publications.

2.      Beckett-Camarata, E.J., Camarata, M.R., Barker, R.T., 1998, Integrating Internal and External Customer Relationships Through Relationship Management: A Strategic Response to a Changing Global Environment, Journal of Business Research, 41, 71-81.

3.      Berry, M.J.A. & Linoff G.S., 2000, Mastering Data Mining: The Art and Science of Customer Relationship Management, John Wiley & Sons, Inc.

4.      Cabena, P., Choi H.H., Kim I.S., Otsuka S., Reinschmidt J. & Saarenvirta G., 1999, Intelligent Miner for Data Applications Guide, IBM Redbooks, SG24-5252-00.

5.      Cook, W.D., & Hababou, M., 2001, Sales Performance Measurement in Bank Branches, Omega, 29, 299 –307.

6.      Couldwell, C., 1998, A Data Day Battle, Computing, 21 May, 64–66.

7.      Hosking, J.R.M., Pednault, E. P. D. & Sudan, M., 1997, A statistical perspective on data minin, Future Generation Computer Systems, 13, 17-134.

8.      Mihelis, G., Grigoroudis, E., Siskos, Y., Politis, Y., & Malandrakis, Y., 2001, Customer Satisfaction Measurement in the Private Bank Sector, European Journal of Operational Research, 347-360.

9.      Peppard, J., 2000, Customer Relationship Management (CRM) in Financial Services,

10.  European Management Journal, Vol. 18, No. 3, pp. 312–327,

11.  Peppers, D., & Rogers, M., 1995, A New Marketing Paradigm, Planning Review,23(2), 14–18.

12.  Ryals, L., & Knox, S., 2001, Cross-Functional Issues in the Implementation of Relationship Marketing Through Customer Relationship Management, European Management Journal, Vol. 19, No. 5, pp. 534–542.

13.  Yli-Renko, H., Sapienza, H.J., Hay, M., 2001, The Role of Contractual Governance Flexibility in Realizing the Outcomes of Key Customer Relationships, Journal of Business Venturing, 16, 529–555.

14.  Yuan, S.T., & Chang, W.L., 2001, Mixed-Initiative Synthesized Learning Approach For Web-Based CRM, Expert Systems with Applications, 20, 187-200.