CRM Best Practicces - Indian Retail Banking - Copy

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Customer Relationship Management (CRM) Best Practices and

Customer Loyalty

A Study of Indian Retail Banking Sector

Kallol Das

School of Management, International Institute of Information Technology, P-14 Rajiv Gandhi Infotech Park, Hinjawadi, Pune, Maharashtra, India

E-mail: getkdas@gmail.com Jitesh Parmar

Shrimad Rajchandra Institute of Management & Computer Application Gopal Vidyanagar, Bardoli Mahuva Road, Dist. Surat, Gujarat, India

E-mail: jiteshsp@gmail.com Vijay Kumar Sadanand

Bhoj Reddy Engineering College for Women Hyderabad Vinay Nagar Saidabad, Hyderabad - 500059

Andhra Pradesh, India

E-mail: nenuvijay@gmail.com

Abstract

The current study explores the association between deployment of customer relationship management (CRM) best practices and loyalty of profitable customers in Indian retail banking sector. The study comprises two parts. The first part called the CRM best practices survey involves the use of descriptive research design. The second part viz. case study research involves the use of embedded customer loyalty survey. The hypothesis testing based on literal and theoretical replication is done using the concept of pattern matching. The findings reveal that there is no perfect bank, as yet, across the three bank types, which has deployed all the 29 CRM best practices to the fullest extent. The results of literal and theoretical replication done by using pattern matching technique indicates no strong association between deployment of CRM best practices in scheduled commercial banks and loyalty levels of both high and medium relationship value retail customers. The study develops a list of 29 CRM best practices, which may be helpful to the organizations toward achieving comprehensive CRM deployment. The results also imply that going for CRM deployment may not be a profitable strategy for retail banks, particularly in the Indian context.

Keywords: Customer relationship management (CRM), customer loyalty, best practices, retail banking, India

1. Introduction

The current study explores the association between deployment of customer relationship management (CRM) best practices and loyalty of profitable customers in scheduled commercial banks of India with respect to retail banking segment. This is important because a strong positive association will act as a significant motivator to organizations for making larger investments towards deployment of CRM best

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practices. On the other hand, a weak association will fail to provide necessary encouragement to the same organizations for CRM deployment. The paper begins with an introduction to Indian banking industry and delineates the scope of the study. It is followed by a literature review on CRM best practices as well as customer loyalty. The methodology used is discussed in detail followed by the findings and implications.

1.1. Indian Banking

The structure of schedule banks in India in shown in figure 1. The total number of public sector banks (PSBs) stands at 28.

In the category of private banks (PBs), there are 16 banks classified as old private banks (OPBs) which were existing prior to the liberalization of the banking sector. The new private banks (NPBs) were born after 1991-92 with the opening up of this sector to private players. The total number of NPBs, as of 1st May, 2007, is 8. Plus, there are 29 foreign banks (FBs), most of which are limited to the metropolitan cities (RBI, 2006). In all, the total number of scheduled commercial banks as of 1st May, 2007 is 81.

1.2. Retail Banking

Retail banking refers to the dealing of commercial banks with individual customers, both on liabilities and assets sides of the balance sheet (Gopinath, 2005b). Similarly, Sood (2003) defines retail banking as “catering to the multiple banking requirements of individuals relating to deposits, advances and associated services” (p. 9).

1.3. Scope of Study

The current study focuses exclusively on the retail banking segment of scheduled commercial banks. However, within scheduled commercial banks, the regional rural banks are not being covered. These banks are expected to mobilize resources from rural areas and play a significant role in developing agriculture and rural economy (RBI, 2006).

As it is understood today, retail banking is largely an urban phenomenon with a clear objective of increasing the bank’s bottom line (see Gopinath, 2005a). It is very apparent that the regional rural banks are outside the ambit of retail banking.

1.4. Geographic Scope

Many of the 81 scheduled commercial banks in the country have a pan-India presence in terms of branch network. For matters of convenience, the study was restricted to Surat city, where the researcher was based. Another reason for accepting Surat as the place of study is the fact that except for one bank (State Bank of Patiala), the entire segment of PSBs is existing for many years. In the case of PBs, except for four banks, all others have a presence in this city. These four banks are Lord Krishna Bank, Nainital Bank, Ratnakar Bank, and SBI Commercial and International Bank. The merger of Lord Krishna Bank with Centurion Bank of Punjab is on the cards (RBI, 2007). Nainital Bank is in the process of getting merged with Bank of Baroda (PR Domain, 2006) and the future of the other two PBs is uncertain. In the case of FBs, out of the 29 players operating in the country, only 4 players are active in the retail banking segment for more than one year (Chowdhury, 2007), out of which 3 banks are present in Surat (only HSBC Bank is not present). Further, the practices of the banks are consistent across cities/ regions as confirmed by their respective managers and further corroborated by the researcher’s personal observations, thus ensuring that external validity will not be affected.

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Figure 1: Structure of Scheduled Banks in India

Scheduled Banks in India Scheduled Commercial Banks Public Sector Banks Private Banks Foreign Banks Regional Rural Banks

Scheduled Co-operative Banks Scheduled Urban Co-operative Banks Scheduled State Co-operative Banks Old Private Banks New Private Banks

Surat is the ninth largest city of India in terms of population as per 2001 census (Population, 2006). The city is ranked 70th amongst the most populous cities of the world for the year 2006 with an estimated population of 3.9 million (vom Hove, 2006). The city was ranked 131st amongst the wealthiest cities of the world for the year 2005 and is predicted to be the 4th fastest growing city of the world (second in the country) for the period 2006 to 2020 (vom Hove, 2006). All these facts make Surat a very lucrative market for the banks, particularly, in the retail banking segment. This also explains the high representation of scheduled commercial banks in the city.

1.5. Population for the Current Study

The population for the study is defined as the retail banking segment of scheduled commercials banks

based in Surat city with at least one year of commercial operation. The scheduled commercial banks

are into both retail and wholesale (corporate) banking. Retail banking is an area of interest for all the banks today and the study has been restricted only to this segment of the banks for the purpose of focus. Also, it was felt that business organizations need to complete a significant period of operation (in this case, one year) after which the practices can be considered to be well established and suitable for comprehensive description. This line of thought was endorsed by other researchers and marketing practitioners who have provided their support in the conduct of the study. With this definition of the population, one NPB, Yes Bank, had to be excluded from the scope of the study as it had not completed one year of existence as of November 2006 when the data collection phase got over. Similarly, State Bank of Patiala, a PSB, was not studied as it was non-existent in the city as of November 2006.

2. Literature Review

CRM has been a part of marketing literature since more than a decade. Interestingly, there is still much debate over what exactly constitutes CRM (Nevin, 1995; Parvatiyar and Sheth, 2001; Sin et al., 2005). According to Parvatiyar and Sheth (2001), some of the themes represent a narrow functional marketing perspective while others offer a perspective that is broad and paradigmatic in approach and orientation. One example of a narrow perspective is to view CRM as database marketing (Peppers and Rogers, 1995) emphasizing promotional aspects of marketing by leveraging customer databases. Other examples of a narrow approach include electronic marketing (Blattberg and Deighton, 1991) and aftermarketing (Vavra, 1992). Electronic marketing encompasses all marketing efforts supported by information technology while aftermarketing efforts focus on customer bonding after the sale is made.

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On a broader level, CRM may mean customer retention or partnering (Peppers and Rogers, 1993, Vavra, 1992).

In order to develop a comprehensive list of CRM practices, it is essential to identify the key constructs of CRM. In this direction, Sin et al. (2005) have proposed that CRM comprises the following four constructs: Key customer focus, CRM organization, Knowledge management and Technology-based CRM. Each of these is discussed as follows.

Key customer focus

This is all about developing a strong customer focus (Das, 2004; Sheth et al., 2000; Vandermerwe, 2004) and continuously delivering superior value to selected key customers (Parvatiyar and Sheth, 2001) through personalized/ customized offerings (Dyche´, 2002).

CRM organization

It implies organizing the whole organization around CRM, which will lead to considerations like organizational structure, commitment of resources and human resources management (Sin et al., 2005). Knowledge management

Key facets of this construct include learning about customer needs and wants, dissemination and sharing of this knowledge and action (Sin et al., 2005).

Technology-based CRM

Technology plays the role of enabler in CRM deployment (Das, 2004) and allows firms to achieve greater customization and better service at lower cost (Sin et al., 2005).

A review of academic and practitioners’ literature was done to develop a comprehensive list of CRM practices. Please refer appendix I for the practices and their respective chief sources.

Going over to customer loyalty, Oliver (1999) defined it as a deeply held commitment to re-buy or re-patronize a preferred product or service in the future despite situation influence and marketing efforts having the potential to cause switching behaviour. Thus, loyalty has both an attitudinal and behavioural dimension (Day, 1969; Dick and Basu, 1994). Behavioural loyalty will include examples like repeat purchase, word of mouth, etc while attitudinal loyalty will comprise examples like trust or emotional attachment (Baumann et al., 2005).

Further, behavioural loyalty does not necessarily reflect attitudinal loyalty, because there might exist other factors that prevent customers from defecting (Aldlaigan and Buttle, 2005; Liljander and Roos, 2002; Reinartz and Kumar, 2002). Customer loyalty has been additionally related to profit levels (Reichheld and Teal, 1996). Besides, customer loyalty is one of the key objectives of CRM (Das, 2004; Lindgreen, 2004; Parvatiyar and Sheth, 2001; Payne, 2002; Sin et al., 2005).

3. Study Outline

The current study has two parts as mentioned below: a. CRM best practices survey

b. Case study research

Following are the research questions associated with the current study: 1. What are the best practices with regard to CRM deployment?

2. What is the extent of deployment of the CRM best practices in the Indian retail banking sector?

3. What is the association between deployment of CRM best practices and loyalty of profitable retail customers in the Indian retail banking sector?

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The methodology used is discussed in the following sections.

4. Methodology: CRM Best Practices Survey

This survey attempts to measure the extent of deployment of CRM best practices across the retail banking segments of scheduled commercial banks in Surat city. This survey covers all the population elements and can be termed as census study and, therefore, the research design is descriptive (Malhotra, 2006).

4.1. Questionnaire Development

In order to develop a questionnaire comprising the CRM best practices, extensive review of literature was done. Based on the review of literature mentioned earlier, 140 statements were developed each representing a CRM practice. A panel of experts was formed to validate, trim and refine the initial items. The panel consisted of five experts: two academics, who specialized in CRM and services marketing averaging approximately 18 years of experience across teaching, consulting and research; and three marketing practitioners having an average of 13 years of experience in CRM and belonging to the banking industry. A similar process was adapted by McMullan (2005), McMullan and Gilmore (2002), and Sin et al. (2005) in their respective questionnaire development exercises.

The five experts were introduced to the key definitions of CRM (e.g., Parvatiyar & Sheth, 2000; Jackson, 1985; Sin et al., 2005) with an explanation of the meaning of best practice. They were then asked to study the 140 CRM statements and indicate whether each of them is (1) a relevant CRM practice and (2) a CRM best practice. Further, they were also told that many of the statements in the list provided to them might be repetitive and differing only by a shade. In such cases, they were asked to select the most superior/ comprehensive statement/ practice from amongst the similar statements/ practices and reject the others. It was decided that only those statements/ practices will be retained on which there is a complete consensus amongst the judges (see McMullan, 2005; McMullan and Gilmore, 2002; Sin et al., 2005). Using this guideline, 111 statements were rejected and only 29 statements were retained (refer appendix II). The optimum length of scale is debated within the literature with suggestions ranging from 20 to 33 items (Bearden et al., 1993; Raju, 1980). The judges ensured that the scale is of optimum length and also saw to it that all the constructs were properly represented. Each best practice statement was measured using a five-point rating scale anchored by “strongly disagree (1)” and “strongly agree (5)”.

4.2. Validity and Reliability

The CRM best practices questionnaire comprises 29 best practices limited to the domain of CRM as explained by well known researchers (e.g., Parvatiyar and Sheth, 2001; Jackson, 1985; Sin et al., 2005), each of which is different from any other. Factor analysis can be used to determine the broad underlying constructs of a scale. However, it also mandates that the minimum number of observations should be five times the number of variables (Hair et al., 2006). In this study, the actual number of observations (49 observations) is much less than the minimum requirement of 145 observations and, therefore, factor analysis is not feasible. However, since the statements have been generated from an extensive review of academic and practitioner’s literature, it is assumed that construct validity will hold.

In addition, content validity of the scale was evaluated by the panel of judges who found it to be a good scale measuring the extent of deployment of CRM best practices. Further, the questionnaire was pre-tested with a set of five bank managers similar to those targeted to participate in the research. The pre-testing results indicated that the questionnaire was clearly understandable and unambiguous leading to the conclusion that the questionnaire had adequate content validity. Most of the respondents in the pre-testing stage suggested that it would be better if labels carrying appropriate meanings are attached to each of the pointers in the scale. Based on that suggestion, the scale labels were re-designed

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as “strongly disagree (1)”, “disagree (2)”, “neutral (3)”, “agree (4)” and “strongly agree (5)” so as to suggest roughly equal intervals between scale pointers, which were immediately accepted.

Regarding external validity, the findings can be generalized to the population of 49 scheduled commercial banks located in Surat city and selected for the study and only with respect to the retail banking segment. Further, as the practices of each of the banks are largely consistent across cities/ regions, we can safely infer that the findings can be further generalized to the larger Indian retail banking sector.

Reliability was computed using Cronbach’s coefficient alpha for the entire set of 29 best practice statements and was found to be 0.95, which is much higher than the threshold value of 0.65. Therefore, the scale can be considered to be reliable (Nunnaly, 1978).

4.3. Respondents for the Study

None of the banks surveyed is headquartered in Surat city. Therefore, only the bank managers (branch managers/ senior managers/ chief managers) are best fit to comment on the CRM best practices of their respective organizations and they were selected for the administration of the questionnaires.

4.4. Increasing Response – Both Quality and Quantity

The detailed briefing of the survey was given to each of the respondents and the meaning of each item in the best practice questionnaire was well explained. In addition, in order to improve the quality of response, full assurance was promised vide the covering letter with respect to confidentiality of data collected (Cooper and Schindler, 2006). Also, respondents were told not to put their name or signature or anyway reveal their identity anywhere on the questionnaire. Further, the questionnaire was made reader friendly by using good quality paper (85 GSM paper), good quality printing (laser printing) with clear and large fonts. The covering letter also mentioned that the broad findings of the study without the names of the banks would be provided to the participants on request. The personal contact details of the researcher were additionally provided in the covering letter to foster trust.

4.5. Limitations

The current survey provides for data collection from a single respondent, which, however, can affect the findings. A more appropriate alternative would have been to collect data from more than one employee and that too across the three management levels, namely top, middle and lower management. The same was, however, not attempted for reasons of exorbitant cost and time. However, as mentioned earlier, all care was taken to ensure that the responses collected are representative.

5. Findings: CRM Best Practices Survey

5.1. CRM Best Practices Survey: Public Sector Banks

Table 1 shows the performance of PSBs with respect to deployment of CRM best practices. Clearly, Industrial Development Bank of India is the winner with State Bank of India trailing far behind and closely followed by Bank of Baroda. At the other end of the list, United Bank of India has scored the lowest followed by UCO Bank and Punjab & Sind Bank. State Bank of Saurashtra, even though, a part of the State Bank Group, has performed poorly and is one the four banks that have got a mean score of less than 3.00.

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Table 1: Deployment of CRM Best Practices - Public Sector Banks

Sr. No. Name of Bank Mean Std. Dev.

1 Industrial Development Bank of India 3.97 0.98

2 State Bank of India 3.62 0.90

3 Bank of Baroda 3.59 0.78

4 Corporation Bank 3.55 0.91

5 Union Bank of India 3.55 0.99

6 Bank of India 3.52 0.87

7 State Bank of Hyderabad 3.45 0.98

8 State Bank of Mysore 3.41 0.95

9 Oriental Bank of Commerce 3.38 0.98

10 Syndicate Bank 3.38 0.94

11 State Bank of Indore 3.38 0.94

12 State Bank of Travancore 3.38 0.94

13 Canara Bank 3.34 0.94

14 Punjab National Bank 3.34 0.90

15 State Bank of Bikaner & Jaipur 3.34 0.94

16 Vijaya Bank 3.28 1.00

17 Indian Overseas Bank 3.28 0.96

19 Central Bank of India 3.24 0.99

18 Andhra Bank 3.21 0.99

20 Indian Bank 3.14 0.95

21 Allahabad Bank 3.10 0.94

22 Bank of Maharashtra 3.10 0.92

23 Dena Bank 3.00 0.87

24 State Bank of Saurashtra 2.97 0.87

26 Punjab & Sind Bank 2.83 0.89

25 UCO Bank 2.83 0.89

27 United Bank of India 2.76 0.87

Overall Mean 3.29 0.93

5.2. CRM Best Practices Survey: Private Banks

Table 2 shows the performance of PBs. As can be seen, ICICI Bank and HDFC Bank have done very well with Kotak Mahindra Bank and UTI Bank trailing far behind. On the other extreme, City Union Bank, Dhanalakshmi Bank and Catholic Syrian Bank (all being OPBs) are amongst the lowest scorers. The table reveals that there are 8 banks, which have got mean scores less than 3.00.

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Table 2: Deployment of CRM Best Practices - Private Banks

Sr. No. Name of Bank Type Mean Std. Dev.

1 ICICI Bank NPB 4.45 0.78

2 HDFC Bank NPB 4.41 0.63

3 Kotak Mahindra Bank NPB 4.10 0.86

4 UTI Bank NPB 4.07 0.88

6 IndusInd Bank NPB 3.79 1.11

5 ING Vysya Bank OPB 3.76 0.64

7 Centurion Bank of Punjab NPB 3.48 1.02

8 Karur Vysya Bank OPB 3.28 1.14

9 Federal Bank OPB 3.24 1.06

10 Tamilnad Mercentile Bank OPB 3.10 1.01

11 Jammu & Kashmir Bank OPB 3.07 1.00

12 Karnataka Bank OPB 2.97 0.96

13 Development Credit Bank NPB 2.97 0.98

14 South Indian Bank OPB 2.93 0.92

16 Bank of Rajasthan OPB 2.93 0.89

15 Lakshmi Vilas Bank OPB 2.86 0.98

17 City Union Bank OPB 2.79 0.98

18 Dhanalakshmi Bank OPB 2.79 0.86

19 Catholic Syrian Bank OPB 2.55 0.78

Overall Mean 3.34 0.92

5.3. CRM Best Practices Survey: Foreign Banks

Table 3 shows the performance of FBs. It is clearly evident that the three FBs in the retail banking segment are deploying CRM Best Practices to a very high extent. None of the banks within the FB segment has got a mean score less than 4.00.

Table 3: Deployment of CRM Best Practices - Foreign Banks

Sr. No. Name of Bank Mean Std. Dev.

1 ABN AMRO Bank 4.48 0.63

2 Citibank 4.38 0.90

3 Standard Chartered Bank 4.21 0.86

Overall Mean 4.36 0.80

Comparison of Deployment

The PBs are further bifurcated into NPBs and OPBs and their respective mean scores are computed. The results are shown in the table 4. The OPBs are the worst performers in terms of deployment of CRM best practices. This is followed by PSBs, NPBs and FBs respectively.

Table 4: Deployment of CRM Best Practices Across Bank Types

Sr. No. Type of Bank Mean

1 Old Private Banks 3.06

2 Public Sector Banks 3.29

3 New Private Banks 3.90

4 Foreign Banks 4.36

6. Methodology: Case Study Research

The case study research design used in this project is pictorially depicted in figures 2 and 3. As can be seen, case study research designs include the desire to analyze contextual conditions (the retail banking

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division of the bank under investigation) in relation to the “case” – the CRM best practices and the dotted lines between the two indicate that the boundaries between the case and the context are not likely to be sharp. Further, the case study research design comprises an embedded loyalty survey involving the profitable retail customers of the same bank.

In the present case study research design, the method of selection of cases used is extreme or deviant cases (Flyvbjerg, 2006), which enables the testing of propositions/ hypotheses. In the current study, the top scorers in the CRM best practices survey, termed as ‘CRM-strong’ banks, will be identified across PSBs and PBs for conduct of embedded customer loyalty survey. In addition, the banks which have scored lowest in terms of deployment of CRM best practices, termed ‘CRM-weak’ banks, across PSBs and PBs will be similarly identified for the conduct of embedded survey. The FBs did not allow the conduct of the same.

Construct validity will be achieved by use of multiple sources of evidence (Flyvberg, 2006; Yin, 2003). This was done to further ensure that the responses given by the managers to the CRM best practices survey are truly representative.

The use of questionnaires is a major way of increasing the reliability of case study research (Yin, 2003). Subsequently, for doing a multiple-case study (as has been done here), the use of the same questionnaires in conducting the data collection will automatically help in improving the reliability of the study.

For addressing internal validity, pattern matching has been suggested (Yin, 2003). According to Yin (2003), the pattern matching technique comprises literal and theoretical replications, which are useful in testing of propositions/ hypotheses. The theoretical framework needs to state the conditions under which a particular phenomenon is likely to be found (a literal replication) as well as the conditions when it is not likely to be found (a theoretical replication).

In the present research design, both literal and theoretical replication will be done to explore the association between deployment of CRM best practices and loyalty of profitable retail customers. In this direction, the loyalty levels of High and Medium Relationship Value (RV) retail customers from three CRM-strong banks across both PSBs and PBs are measured to achieve literal replication. In order to achieve theoretical replication, the loyalty levels of High and Medium RV retail customers of two CRM-weak banks are measured across both PSBs and PBs.

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Figure 2: Case Study Research Design for Public Sector Banks (PSBs) CONTEXT – CRM-STRONG PSB1 (RETAIL) Case – CRM Best Practices Embedded Unit of Analysis – Profitable Retail Customers (for Loyalty Survey)

CONTEXT – CRM-STRONG PSB 2 (RETAIL) Case – CRM Best Practices Embedded Unit of Analysis – Profitable Retail Customers (for Loyalty Survey)

CONTEXT – CRM-STRONG PSB 3 (RETAIL) Case – CRM Best Practices Embedded Unit of Analysis – Profitable Retail Customers (for Loyalty Survey)

CONTEXT – CRM-WEAK PSB1 (RETAIL) Case – CRM Best Practices Embedded Unit of Analysis – Profitable Retail Customers (for Loyalty Survey)

CONTEXT – CRM-WEAK PSB 2 (RETAIL) Case – CRM Best Practices Embedded Unit of Analysis – Profitable Retail Customers (for Loyalty Survey)

Literal Replication

Theoretical Replication

For the purpose of the current study, High RV retail customers are defined as those customers having an average annual total business of Rs. 1 million ($1 = Rs. 47.77 as of 1st July, 2009) and more with the bank across retail deposits, loans, credit cards, demat, mutual fund, insurance and so forth. Similarly, Medium RV retail customers are defined as those having an average annual total business across different products in the range of Rs. 0.05 to Rs. 0.99 million. Discussions with managers in PSBs and PBs confirmed that these two segments fairly represented high and medium relationship value/ size customers and having high and medium contribution respectively to the bank’s retail segment profitability.

The deployment of best practices is supposed to lead to superior performance with respect to profits, retention, loyalty, and so forth (Camp, 1989). The current study focuses on the impact of deployment of CRM best practices on loyalty of profitable retail customers. Only customer loyalty was selected amongst the various performance indicators because of difficulty in accessing data with respect to other important measures like customer retention and profitability. Further, most of the banks covered in the case study design do not compute profits separately for the retail banking segment. A few banks do so but their definition of retail banking includes small business enterprises/ small and medium scale enterprises as well.

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Figure 3: Case Study Research Design for Private Banks (PBs)

CONTEXT – CRM-STRONG PB1 (RETAIL) Case – CRM Best Practices Embedded Unit of Analysis – Profitable Retail Customers (for Loyalty Survey)

CONTEXT – CRM-STRONG PB 2 (RETAIL) Case – CRM Best Practices Embedded Unit of Analysis – Profitable Retail Customers (for Loyalty Survey)

CONTEXT – CRM-STRONG PB 3 (RETAIL) Case – CRM Best Practices Embedded Unit of Analysis – Profitable Retail Customers (for Loyalty Survey)

CONTEXT – CRM-WEAK PB1 (RETAIL) Case – CRM Best Practices Embedded Unit of Analysis – Profitable Retail Customers (for Loyalty Survey)

CONTEXT – CRM-WEAK PB 2 (RETAIL) Case – CRM Best Practices Embedded Unit of Analysis – Profitable Retail Customers (for Loyalty Survey)

Literal Replication

Theoretical Replication

According to Sin et al. (2005), the CRM efforts are targeted at the profitable customers. Further, these efforts are intended to lead to increased customer loyalty (Das, 2004; Lindgreen, 2004; Parvatiyar and Sheth, 2001; Sin et al., 2005). Therefore, the current study explores the association between the deployment of CRM best practices and loyalty with respect to profitable retail customers. Further, as discussed earlier, the profitable retail customers are classified as High and Medium RV customers making high and medium contribution to the bank’s profitability respectively. Thus, the study will try to explore the veracity of the following two hypotheses using pattern matching technique consisting of literal and theoretical replication.

Hypothesis 1 (H1): There is a strong association between deployment of CRM Best Practices in

scheduled commercial banks and loyalty levels of High Relationship Value retail customers.

Null Hypothesis 1 (Ho1): There is no strong association between deployment of CRM best

practices in scheduled commercial banks and loyalty levels of High Relationship Value retail customers.

Hypothesis 2 (H2): There is a strong association between deployment of CRM Best Practices in

scheduled commercial banks and loyalty levels of Medium Relationship Value retail customers.

Null Hypothesis 2 (Ho2): There is no strong association between deployment of CRM best

practices in scheduled commercial banks and loyalty levels of Medium Relationship Value retail customers.

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As per the pattern matching technique, the data are matched with potential patterns (hypothesis and null hypothesis) to identify the pattern which produces the best match and accordingly either the hypothesis or the null hypothesis is upheld (Campbell, 1975).

6.1. Questionnaire Development

The questionnaire for measuring customer loyalty, targeted at the high and medium RV retail customers, was developed by reviewing existing scales on loyalty. Based on the review, the following items / questions were selected for the final scale, as shown in table 5 along with their corresponding sources. Some of the items were reverse-scored to take care of order bias (Zikmund, 2003). This was particularly more critical in this survey as the researcher had limited role to play in the data collection exercise. The scale items are further classified as items representing attitudinal and behavioural loyalty. The questions were administered using a rating scale with the pointers – “(1) strongly disagree”, “(2) disagree”, “(3) neutral”, “(4) agree”, and “(5) strongly agree”.

Table 5: Operationalising Loyalty

Loyalty Scale Item Source

1. I have never seriously considered changing this bank. (Attitudinal loyalty)

2. I consider myself to be a loyal customer of this bank. (Attitudinal loyalty) (Leverin and Liljander, 2006) 3. I will continue using the services offered by this bank. (Behavioural loyalty)

4. I will use other products/ services offered by this bank in the future. (Behavioural loyalty)

5. I recommend my bank to others. (Behavioural loyalty)

(Zeithaml, Berry, and Parasuraman, 1996) 6. I will switch to a competitor bank that offers more attractive benefits/ interest

rates/ service charges.* (Behavioural loyalty)

7. I will switch to a competitor bank when there are problems with the current bank’s service.* (Behavioural loyalty)

(Parasuraman, Zeithaml, and Berry, 1994 as cited in Wong and Sohal, 2003)

8. I deal with the bank because I want to, not because I have to. (Attitudinal loyalty)

9. Sometimes I get a feeling of being trapped in dealing with the retail bank.* (Attitudinal loyalty)

(Barnes, 1997)

Note: * denotes reverse-scored items

6.2. Validity and Reliability

The items selected for the customer loyalty survey were taken from scales having established validity and reliability. All the same, content validity was checked using two academics and three marketing practitioners and it was found to be a good measure of customer loyalty. Further, the questionnaire was tested on 10 bank customers having characteristics very similar to the targeted audience. The pre-testing results revealed that the scale is easy to understand and unambiguous.

The bank managers advised that the questionnaire be translated into the local language, that is, Gujarati as many of the high and medium RV customers, even though quite wealthy, may not be fully proficient with written English. Therefore, the questionnaire was translated into Gujarati using the method of back translation (Zikmund, 2003). Further, the questionnaires were pre-tested with 10 bank customers having characteristics similar to that of the final target audience. The pre-testing indicated that the translation was perfect and that there was no miscommunication or misinterpretation.

Regarding external validity, since the sampling was non-probability in nature, the findings of the loyalty survey, done separately for each of the selected 10 banks, cannot be fully generalized to the respective banks (Malhotra, 2006). Further, scale reliability was measured using Cronbach’s coefficient of alpha which was computed to be 0.79 indicated that the scale is reliable (Nunnaly, 1978).

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73 6.3. Respondents for the Study

The respondents for the customer loyalty survey comprised the high and medium RV retail customers of the selected PSBs and PBs. Each of the two categories of retail customers has been earlier defined in terms of business contribution. The questionnaires were given to the bank managers for getting it filled up by their High and Medium RV retail customers. For banks having more than one branch, all branches having significant proportion of high and medium RV customers in the retail banking segment were approached for the survey.

The problem of missing data was solved by discarding such questionnaires. At the end, there were 369 fully filled questionnaires comprising respondents across PSBs and PBs totaling 10 banks in all.

6.4. Increasing Response – Quality and Quantity

Random sampling using mail questionnaires could not be attempted because of strict privacy codes prevailing in the banking industry which prevent sharing of customer contact details with outside parties. Also, many banks had reservations regarding allowing any external researcher to conduct survey within the bank premises. Further, since the survey was targeted at the premium retail customers whose visits to the bank premises are infrequent, it was felt that the only way to complete the survey would be through the support and co-operation of the bank managers. The managers of the selected banks were given a set of customer loyalty survey questionnaires with a request to get it self-administered by their High and Medium RV retail customers as and when these customers visit the branch.

The bank managers were clearly instructed to encourage the respondents to present both positive and negative feelings about the bank using the questionnaire. The bank managers were also told that a copy of the findings will be provided to them and it will be upto them to decide whether to share it with the top management. This was done so that they do not get disturbed if the respondents give lower ratings for customer loyalty. Further, they were told that the findings would be helpful to them to get an idea about the current state of customer loyalty. All the bank managers were well educated and having good work experience (in the range of 8 to 15 years) and they well appreciated the need to get unbiased responses. In addition, regular monitoring was done to check the proper conduct of the study. It is hoped that in light of these precautions, the limitations were minimized. Since a strong rapport had already been built with the managers of the selected banks, it can be further expected that the managers gave their full co-operation, thus ensuring that the distortions were minimum.

Regarding the questionnaire, the layout was made simple, attractive and reader-friendly. The number of questions, based on the suggestions of several bank managers, was deliberately kept on the lower side so that the entire questionnaire can be filled up in less than 10 minutes in order to increase customer response.

6.5. Limitations

As discussed earlier, the findings of the loyalty survey cannot be fully generalized. Second, since the questionnaires were mostly filled by the customers in the presence of the bank managers/officials, it is quite possible that the respondents may avoid giving negative responses fearing retaliation. It is also possible that the bank managers might have given the questionnaires only to those customers who have high loyalty towards the bank and that way, again, biasing the results. However, the data collection exercise was very closely monitored to minimize possible distortions.

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7. Findings: Case Study Research

7.1. Customer Loyalty: CRM-Strong Public Sector Banks

This section presents the results of pattern matching with respect to the deployment of CRM best practices versus loyalty levels (of high and medium RV retail customers) of the CRM-strong PSBs, namely, Industrial Development Bank of India, State Bank of India, and Bank of Baroda.

A simple pattern matching (see table 6) shows that as the deployment of CRM best practices decreases from Industrial Development Bank of India (3.97) to State Bank of India (3.62) and Bank of Baroda (3.59), the same pattern is not replicated for loyalty of high RV retail customers. State Bank of India and Bank of Baroda, which trail far behind Industrial Development Bank of India in terms of deployment of CRM Best Practices, enjoy higher loyalty levels of their high RV retail customers. This indicates the lack of strong association between deployment of CRM best practices and loyalty of high RV retail customers.

Table 6: CRM Best Practices’ Deployment Vs. Customer Loyalty (High RV): CRM-Strong Public Sector Banks

CRM Best Practices High Relationship Value

CRM-Strong Public Sector Bank

CRM Score SD n Loyalty Score SD

Industrial Development Bank of India 3.97 0.98 5 3.62 0.52

State Bank of India 3.62 0.90 4 4.64 0.28

Bank of Baroda 3.59 0.78 12 4.38 0.41

Note: n=Nos. of respondents; SD= Standard Deviation

Table 7: CRM Best Practices’ Deployment Vs. Customer Loyalty (Medium RV): CRM-Strong Public Sector Banks

CRM Best Practices Medium Relationship Value

CRM-Strong Public Sector Bank

Industrial Development Bank of India 3.97 0.98 25 3.49 0.70

State Bank of India 3.62 0.90 55 4.27 0.55

Bank of Baroda 3.59 0.78 30 4.05 0.49

Repeating the same pattern matching exercise using medium RV retail customers (see table 7) again indicates the lack of strong association between the deployment of CRM best practices and customer loyalty. Here again, State Bank of India and Bank of Baroda, which have lower deployment of CRM best practices as compared to Industrial Development Bank of India, enjoy higher levels of loyalty of their medium RV retail customers.

7.2. Customer Loyalty: CRM-Weak Public Sector Banks

Table 1 indicated that the following three PSBs scored lowest in the CRM best practices survey. These three banks are (in descending order of mean scores): UCO Bank, Punjab & Sind Bank and United Bank of India. Out of these three banks, only one bank, that is, UCO Bank agreed to participate in the loyalty study. For the purpose of replication, at least one additional bank is needed from the CRM-weak PSB segment. To achieve this, the next lowest scorer, that is, State Bank of Saurashtra (fourth lowest scorer in all) was selected for the study.

The results of loyalty study for both high and medium RV retail customers of CRM-weak PSBs, namely, State Bank of Saurashtra, and UCO Bank are presented in tables 8 and 9.

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Table 8: CRM Best Practices’ Deployment Vs. Customer Loyalty (High RV): CRM-Weak Public Sector Banks

CRM-Weak Public Sector Bank

State Bank of Saurashtra 2.97 0.87 11 3.76 0.74

UCO Bank 2.83 0.85 15 3.96 0.70

The sole purpose of studying the loyalty levels of CRM-weak PSBs is to achieve theoretical replication. In other words, the loyalty levels of retail customers of CRM-weak PSBs should be lesser than that of CRM-strong PSBs. However, a simple pattern matching done across tables 6 and 8 reveals that both State Bank of Saurashtra and UCO Bank enjoy higher loyalty levels of their high RV retail customers as compared to that of Industrial Development Bank of India, the CRM-strong PSB.

Table 9: CRM Best Practices’ Deployment Vs. Customer Loyalty (Medium RV): CRM-Weak Public Sector Banks

CRM-Weak Public Sector Bank

State Bank of Saurashtra 2.97 0.87 28 4.20 0.47

UCO Bank 2.83 0.85 15 3.96 0.46

Similarly, using pattern matching done across tables 7 and 9 has revealed that State Bank of Saurashtra and UCO Bank also enjoy higher levels of loyalty of their medium RV retail customers as compared to that of CRM-strong Industrial Development Bank of India.

Thus, like in the case of literal replication, even theoretical replication indicates the absence of strong association between deployment of CRM best practices and customer loyalty (for the two customer segments studied) with respect to PSBs.

7.3. Customer Loyalty: CRM-Strong Private Banks

This section presents the results of the pattern matching done on deployment of CRM Best Practices versus loyalty of high and medium RV retail customers of CRM-strong PBs as identified by the CRM best practices survey, namely, ICICI Bank, HDFC Bank, and Kotak Mahindra Bank are presented in the tables below.

Table 10: CRM Best Practices’ Deployment Vs. Customer Loyalty (High RV): CRM-Strong Private Banks

CRM-Strong Private Bank

ICICI Bank 4.45 0.78 15 4.07 0.33

HDFC Bank 4.41 0.63 17 4.01 0.48

Kotak Mahindra Bank 4.10 0.86 3 4.04 0.45

Table 11: CRM Best Practices’ Deployment Vs. Customer Loyalty (Medium RV): CRM-Strong Private Banks

CRM-Strong Private Bank

ICICI Bank 4.45 0.78 21 3.42 0.57

HDFC Bank 4.41 0.63 24 3.61 0.61

Kotak Mahindra Bank 4.10 0.86 29 3.41 0.42

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Pattern matching done on table 10 reveals that as the deployment of CRM best practices decreases from ICICI Bank (4.45) to HDFC Bank (4.41) to Kotak Mahindra Bank (4.10), the loyalty levels of high RV retail customers do not follow the same pattern. Similarly, applying pattern matching on table 11 for medium RV retail customers gives similar results. Thus, literal replication indicates lack of strong association between deployment of CRM best practices and loyalty levels of both high and medium RV retail customers for CRM-strong PBs.

7.4. Customer Loyalty: CRM–Weak Private Banks

Table 2 also nominated 3 PBs who scored lowest in the CRM best practices survey. These 3 banks are (in descending order of mean scores): City Union Bank, Dhanalakshmi Bank and Catholic Syrian Bank. Out of these three banks, only two banks, that is, City Union Bank and Catholic Syrian Bank agreed to participate in the study.

The CRM best practices deployment versus loyalty levels of high and medium RV retail customers of City Union Bank and Catholic Syrian Bank are presented in tables 12 and 13.

Table 12: CRM Best Practices’ Deployment Vs. Customer Loyalty (High RV): CRM-Weak Private Banks

CRM-Weak Private Bank

City Union Bank 2.79 0.98 7 4.16 0.48

Catholic Syrian Bank 2.55 0.78 5 3.31 1.02

Just like in the case of PSBs, theoretical replication is attempted also in the case of PBs, which stipulates that the loyalty levels of the CRM-weak PBs should be lower than that of CRM-strong PBs. However, pattern matching reveals that the loyalty levels of City Union Bank are higher than that of ICICI Bank, HDFC Bank and Kotak Mahindra Bank for high RV retail customers (see tables 10 and 12).

Similarly, comparing the patterns emerging from tables 11 and 13 (with respect to medium RV retail customers) reveals that City Union Bank again enjoys higher levels of loyalty as compared to that of ICICI Bank, HDFC Bank and Kotak Mahindra Bank. Further, even in the case of Catholic Syrian Bank, the customer loyalty levels are higher than that of CRM-strong ICICI Bank and Kotak Mahindra Bank.

Table 13: CRM Best Practices’ Deployment Versus Customer Loyalty (Medium RV): CRM-Weak Private

Banks

CRM-Weak Private Bank

City Union Bank 2.79 0.98 23 3.86 0.38

Catholic Syrian Bank 2.55 0.78 25 3.54 0.83

Based on the results of literal and theoretical replication done by using pattern matching technique covering PSBs and PBs, we fail to reject the first null hypothesis (Ho1) which states that there is no

strong association between the deployment of CRM best practices in scheduled commercial banks and loyalty levels of high RV retail customers.

Likewise, we also fail to reject the second null hypothesis (Ho2) which states that there is no

strong association between the deployment of CRM best practices in scheduled commercial banks and loyalty levels of medium RV retail customers.

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Conclusions & Implications

The current paper develops a list of 29 CRM best practices, which may be helpful to the organizations toward achieving comprehensive CRM deployment. The extent of deployment of these best practices was examined across the three bank types. However, the findings revealed that there is no perfect bank, as yet, across the three types, which has deployed all the 29 CRM best practices to the fullest extent. Shortcomings do remain in each of the banks with respect to the deployment of the CRM best practices though the degree of the same varies from bank to bank. Overall, the PSBs, particularly, State Bank of India and Bank of Baroda are much lagging behind their counterparts from other categories regarding deployment of the best practices.

The present study also includes a case study research comprising an embedded exploratory loyalty survey of profitable retail customers of selected banks. The results of literal and theoretical replication done by using pattern matching technique indicates no strong association between deployment of CRM best practices in scheduled commercial banks and loyalty levels of both high and medium RV retail customers. These results are supported by an earlier study by Leverin and Liljander (2006) who found that the implementation of a relationship marketing strategy in a retail bank did not result in the increase of loyalty with respect to the most profitable customer segment. This implies that going for CRM deployment may not be a profitable strategy for retail banks, particularly in an Indian context.

However, more research needs to be done using random samples from both banks as well as other sectors to determine the association between deployment of CRM best practices and loyalty of profitable retail customers.

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Appendix I

CRM Practices Developed From Academic and Practitioners’ Literature

Chief Source

1. My bank has a clearly defined mission and business strategy, driven by customer needs and the performance of customer relationships.

(Das, 2004) 2. Our top management team provides leadership for building and maintaining customer

relationships as a major goal of my bank.

(Sin et al., 2005) 3. The top management shows by its actions that everything begins and ends with customers. (Whiteley, 1995) 4. Our top management team spends much time with key customers. (Hiebeler et al., 1998) 5. Our bank’s culture emphasizes the values of honesty, transparency and fairness. (Zeithaml and Bitner,

2003)

6. We do not make promises to customers that we cannot deliver. (Gronroos, 2006) 7. Our Core Values are emphatic about Relationship Building. (Das, 2004) 8. Our bank’s structure is meticulously designed around our customers. (Daffy, 1999) 9. Our business objective is primarily driven by customer relationships. (Whiteley, 1995) 10. My bank has established clear business goals related to customer acquisition, development,

retention and reactivation.

(Parvatiyar and Sheth, 2001)

11. Our board meetings give a high priority to non-financial success factors such as customer satisfaction, employee satisfaction, etc.

12. Customer metrics are used to facilitate strategy formulation and decision-making. (Hiebeler et al., 1998) 13. Our competitive advantage is based on building and maintaining long-term customer

relationships.

(Hiebeler et al., 1998) 14. My bank is well organized and integrated internally to suit the needs of our customers. (Hiebeler et al., 1998) 15. Customer relationships are the crux of our existence. (Daffy, 1999)

16. My bank embraces CRM for mutual benefits.

17. We do customer segmentation using Customer Lifetime Value (CLV)/ related metrics. (Cokins, 2002) 18. Customer Lifetime Value (CLV) is the essential criterion for key customer selection. (Cokins, 2002) 19. My bank regularly assesses the lifetime value of each customer. (Daffy, 1999) 20. Investments in customer relationships are based on the lifetime value of each customer of

my bank.

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21. We regularly track our investments in customers using metrics like Return on Relationship (RoR) /Return on Customer (RoC)

(Peppers & Rogers, 1995); (Gummesson, 2004)

22. We have a Loyalty program to enhance the Lifetime Value of our Customers. (Cooper et al., 2005) 23. We provide increased customer convenience using a variety of distribution channels. (Hiebeler et al., 1998) 24. We regularly do customer service audit using mystery shoppers, customer comments and

surveys.

(Daffy, 1999) 25. We welcome complaints from customers. (Daffy, 1999) 26. We have formal complaint systems, which cover both written and verbal complaints, and

inform customers of the progress of the complaint.

(Daffy, 1999) 27. We have effective customer recovery strategies including guarantees for service failures. (Zeithaml & Bitner,

2003) 28. We believe that customers are the purpose of all our activities and keep communicating the

same to all our employees and suppliers

(Daffy, 1999) 29. Our senior managers are expected to spend time in customer-contact areas, both observing

and working in customer service jobs.

(Daffy, 1999) 30. Customers can expect exactly when services will be performed. (Sin et al., 2005) 31. My bank has the service resources and excellence to succeed in CRM. (Sin et al., 2005) 32. Our top management is actively involved in understanding, interacting with and marketing

to customers and asking them for feedback and ideas.

(Hiebeler et al., 1998) 33. A team comprising representatives from various groups / departments goes out regularly to

meet with customers.

(Hiebeler et al., 1998) 34. My bank understands individual customer's character, needs and preferences and behaviors

through past interactions with us.

(Sin et al., 2005) 35. We regularly measure and monitor employee satisfaction, loyalty, and commitment. (Daffy, 1999)

36. We regularly measure and monitor customer satisfaction, loyalty, and commitment. (Hiebeler et al., 1998) 37. We use information from customers to design or improve our products or services. (Hiebeler et al., 1998) 38. We never get tired of asking customers for feedback about our performance through as

many means as possible.

(Whiteley, 1995) 39. Customers are offered quick, tangible rewards for completing surveys or requests for

information.

(Hiebeler et al., 1998) 40. We take customer feedback seriously and reply to them. (Whiteley, 1995) 41. Customer and employee feedback is taken using a variety of direct and indirect measures. (Daffy, 1999) 42. Customer feedback is used to create strategies conducive to positive customer perceptions. (Sin et al., 2005) 43. When my bank finds that customers would like to modify a product/service, the

departments involved make concerted efforts to do so.

(Hiebeler et al., 1998) 44. Any changes or actions deemed necessary would be implemented to the benefits of our

customers.

(Sin et al., 2005) 45. We broadcast to the employees the feedback given by our customers. (Sin et al., 2005) 46. We measure and monitor relationship performance and satisfaction for both the bank and

the customer to measure either party’s propensity to continue or terminate the relationship.

47. We respond to customer enquiries and requests in real-time. (Hiebeler et al., 1998) 48. Our systems are designed to make it easy for customers to do business with us. (Hiebeler et al., 1998) 49. Our systems are flexible enough to adapt to customers’ changing needs and wants. (Hiebeler et al., 1998) 50. Our business processes are simple, transparent and well defined. (Hiebeler et al., 1998) 51. Budgets are made on the basis of periodically evaluating the performance of customer

metrics.

(Sin et al., 2005) 52. We analyze the causes of customer defection through exit interviews and lost customer

surveys with the aim to win back those customers who have strong profit potential.

(Hiebeler et al., 1998) 53. We have a differential reward system that rewards customers based upon their profit

/revenue contribution.

(Uncles et al., 2003) 54. Our reward system is designed to prevent downward migration of key customers and to

encourage upward migration of potentially big customers.

(Coyles and Gokey, 2002)

55. We use the concept of ‘Relationship Pricing’ in pricing our different products/ services. (Cram, 1996) 56. We run After-Marketing programs to enhance customer experience and increase emotional

bonding.

(Vavra, 1992 as cited in Parvatiyar and Sheth, 2001)

57. We do co-branding / affinity partnering programs to provide increased value to our customers.

58. We leverage the power of word of mouth by using referral marketing programs. (Parvatiyar and Sheth, 2001)