CUSTOMER RELATIONSHIP MANAGEMENT PRACTICES OF INDIAN PUBLIC AND PRIVATE SECTOR BANKS: AN EXPLORATORY STUDY

The contemporary study focused on the impact of CRM parameters to identify the influencing factors towards customer satisfaction and customer loyalty. A sample of 1200 respondents chosen from public sector (SBI and of Andhra bank) and private sector banks (ICICI and HDFC) using multi-stage random sampling technique through a structured questionnaire. The study employed various statistical tools such as Percentage Analysis for demographical information, bank variables, and the CRM parameters. Mean Ranks for ranking the items and Reliability Analysis applied for obtaining reliable variables in constructing the CRM parameters. Exploratory Factor Analysis (EFA) was performed to identify highly influenced factors of CRM practices to improve level of satisfaction and loyalty in public and private banks. The explored results enlighten directions to the banking sector to provide some operational implications such as proactive involvement from personnel, and customized outreach in engaging customers to reduce the negative word-of-mouth (WOM) and increase the productivity of banks positively. These significant CRM strategies will reduce the attrition rate and improves customer retention in future.


INTRODUCTION
CRM is an acronym generally stands for customer relationship management while others mean it as customer relationship marketing too. In managerial emphasis, CRM is a discipline or an approach to acquire and develop suitable practices in maintaining profitable customer relationships. Customer is the most important asset to banks. The concept of CRM as a strategy reflects the banks to process in optimizing revenues, profitability and to gain the customer loyalty. Nowadays, banks are continually looking for ways to achieve a competitive advantage to customer expectation intensifies for quality and service. Consequently, CRM practices playing vital role in improving the customer's experience to maximize the profit and increase the business connections. Retaining the old customer is far cheaper than acquiring a new customer. Key issue for many banking organizations is customer retention often referred to as churn. Hence, there is a significant need in employing more CRM practices in banking system that would be helpful to maintain customer retention and help them manage customer defection (churn) rates and to enhance performance in reducing the attrition rate.
During the COVID-19 times, the banking sector has recorded its highest ever profits of Rs. 1,02,252 crores in FY21, a year when the economy was battered by the pandemic. This is a significant turnaround compared to a net loss of nearly Rs. 5,000 crore for the industry in FY19. HDFC Bank contributed Rs. 31,116 crores accounted for 30%, SBI accounted for another 20% at Rs. 20,410 crores. The third-highest was ICICI Bank, which earned Rs. 16,192 crores, more than double what it earned in the previous year. Private Banks also gained market share as public sector banks (PSBs) went slow in lending as per the reports of RBI. Banks want to strengthen customer experience by successful digital transformation and takes the customer insights with the new digital changes offered by banks like UPIs, BHIM, Google Pay, PhonePe, Paytm, and other money wallets. It is difficult to reach and meet every segment of customers, as usually banks target the maximum customer expectations and satisfaction levels. According to Amitabh Kant (NITI Aayog CEO) mentioned about phenomenal Unified Payment Interface (UPI) recorded 2.3 billion transactions through value worth 4.3 trillion in Jan 2021 on a Year -On-Year (YOY) basis jumped 76.5%. To reach one billion transactions, UPI took 3 years times previously, the next billion will reach less than a year in the subsequent financial years.
In recent digital transformation drive postulates some positive benefits in terms of communication, Word of Mouth (WOM), sharing customer experience, and other valuable insights. CRM practices can help to monitor the feedbacks in terms of ratings and reviews, opinion from users' point of view and better implementing strategies in forthcoming days.

Statistical Tools used for Data Analysis
Statistical tools such as Descriptive Statistics, Percentage Analysis, Reliability, Mean Ranks to understand demographical information among customers with different bank sectors, to check the reliability of CRM parameters and to rank the CRM parameters. Chi-square test is employed for finding the association between demographical information and CRM parameters, Factor Analysis is a dimension reduction technique used for knowing combined influence of CRM parameters in banking sector. The obtained results are properly concluded at various significance levels.  Table 1 exhibits that the majority of banking activities, transactions are made by males (64.2% and 70.7%) compared to females (35.8% and 29.3%) in public and private sector banks. Most of the individuals belonged to the age groups of 25 to 40 years (40.7% and 35.5%) in both the sectors. When it comes to the educational qualifications, most of the respondents are graduates (59.5% and 61.3%) followed by the post graduates (24.2% and 24.7%) in the different sectors. Maximum number of the respondents fall in 3 to 5 lakh income group (40.7% and 46.0%) consisting of government employees (46.3%) are more in public banks and private employees (53.7%) in private sector banks. After introducing digital banking services, the respondents are visiting banks most probably once in a month than earlier days, having relation with the bank is nearly 4-8 years (45.3% and 35.0%) and visiting banks for different reasons in public and private sector banks. The associations of the demographic and banking parameters such as Gender (λ 2 =5.770 and p-value=0.016), Age (λ 2 =22.561 and p-value=0.000), Educational Qualification (λ 2 =56.698 and p-value=0.000), Occupation (λ 2 =604.407 and p-value=0.000), frequency of bank visit (λ 2 =48.763 and p-value=0.000), relationship in terms of years (λ 2 =132.054 and p-value=0.000) and reason for bank visit (λ 2 =40.257 and p-value=0.000) among Type of banks (Public and Private) are mostly significant except the annual income of the respondents. The results were represented graphically for a better understanding of demographical variables and banking parameters.  Table 2 depicts that reliability statistics for the CRM parameters, the Cronbach's values for the customer service parameter (0.830), customer knowledge value (0.859), customer focus (0.864), customer orientation (0.811), customer satisfaction (0.844) and customer loyalty (0.717). It exemplifies the internal consistency reliability of alpha is high in correlation between the items and the questionnaire is consistently reliable.  Table 3 revealed the median responses of CRM parameters to know the behavior of the customers. Most of the respondents were responded satisfactory opinion about the CRM parameters (Customer Service, Customer Knowledge, Customer Satisfaction and Customer Loyalty) which are employed in banking system. Further the respondents are given neutral opinion about the satisfaction levels on customer focus and customer orientation in implementing the practices in private and public banks in the region.  Table 4 depicts association among constructs through mean ranks for CRM parameters. For customer service among 8 items in the construct, highest mean rank is positioned for CS6 item i.e., banks have high integrity and security and the least rank goes to CS5 item is about goodwill. Followed by customer knowledge, maximum rank item in the construct is ranked for CK6 and the minimum is for CK3, next parameter is the customer focus which is for CF1 and low is CF3, for customer orientation high rank goes to the item CO1 and least is CO2, for the customer satisfaction construct the high order mean rank is for CSa3 and low is CSa1, Finally the last construct, customer loyalty's high rank goes to CL7 least observed in CL6.

Factor Analysis
Factor analysis is a data reduction technique or inter dependence techniques or data summarization technique examines the interrelationships among a large number of variables. The tool is used for finding the highly influenced variables among CRM parameters to employ in the banking sector (public and private).   The table 6 represents the variance percentage (76%) of all factors resulting from the factor analysis over 38 factors were clustered into 3 factors which is determined as linear combinations of homogenous variables and most important parameters of customer relationship management practices in banks through principal component analysis.  Table 7 depicts that the first principal component accounted for 34.208 % of variance with twelve statements as the "focus based customer service". The second principal component accounted for 63.472% of variance and was indicated in 11 statements as the "knowledge-based customer orientation". The third principal component accounted for 76.247% of variance indicated with 13 statements as "Satisfaction based customer loyalty". Overall observations from the factor analysis are that the respondents are very particular about focus based customer service, knowledge-based customer orientation, and satisfaction-based customer loyalty.

DISCUSSION
The study depicts some insights to the bank management to overcome the customer churn rate. The results of the study revealed the customer relationship management parameters influence on customer satisfaction and customer loyalty. It examines the significant association among the customer relationship management parameters towards the public and private sector banks of Chittoor district. The results enhance understanding regarding the CRM practices adopted by bank management and exploring the services offered by the respondents.
It is revealed that Cronbach's alpha (α) for all the scale items in the construct for CRM parameters should be above (> 0.50 is better), above (> 0.60 is good) and, above (>0.70 is acceptable) was mostly considered and accepted by the researchers (Nunnally, 1978) through reliability test for CRM parameters depicted in table 3. Perhaps can be explained through the median percentile observed for all six parameters approximately. It was found to be satisfactory for four variables such as customer service, customer knowledge, customer satisfaction and customer loyalty (CS, CK, CSa, and CL) except for two other variables, customer focus and customer orientation (CF and CO) was neutral in the opinion of customers neither satisfied nor dissatisfied with the practices implemented in the banks was displayed in table 4. The mean ranks test indicates the highest rank order to CS, CK and CL parameters and, lowest rank order (CF, CO, and CSa) opted by the respondents to the items in the construct was illustrated in table 5.
The study also employed factor analysis to know homogeneous (similar) CRM variables as factors and to test the relevance of various items in the constructs of CRM practices in banks of Chittoor district. Furthermore, to test the reliability, factor loading value (0.50) for item was considered (Hair et al. 1998). The study depicts the reliability was above 0.70 and the threshold value of Cronbach's alpha (α) is acceptable for the 3 extracted factors. Also, KMO of sample adequacy was performed for overall items was 0.881 and test of sphericity (Bartlett's) also significant at p> 0.05 indicates that employing factor analysis was good to further ensue. Principal component method (PCA) is used for extracting parameters with varimax rotation, to maximize the number of items with high factor loadings on a component, helps in justifiable factors in the construct (Malhotra, 2003). Eigen values (= or >1) used to determine the extracted factors shown in table 9. A total of 38 items was extracted into 3 factors i.e., focus based customer service, knowledge-based customer orientation, satisfaction-based customer loyalty.

CONCLUSION
CRM solutions are no longer limited to just the retail banking rather, they are now essential for any entity that offering services. When it comes to banking system, it is essential to focus on implementing CRM practices and a great challenge to sustain and retain customers. Banking is now a customer-driven world that understand and serve the individual needs of their customers better, those will succeed. Adopting all CRM practices in banking system is much critical to serve customers at every point in the retaining process and building decent relationships to reduce loss of existing customers. Our study explored understanding regional behavioral changes of customers in implementing CRM practices of banks in Chittoor district. The study considered applicability of all practices to suit for the particular region in satisfying needs of the customers. The study identified the factors such as focus based customer service, knowledge-based customer orientation, satisfaction-based customer loyalty is playing vital role in implementing CRM practices of banking sector (public and private) in urban and semi-urban areas of Chittoor district. There is a need to give orientation among customers for enhancing their focus towards the products and digital services offered by the banks in the region. The awareness programs should be conducted for attaining the knowledge by utilizing the services properly in every aspect which leads to satisfaction in turn become loyal customer to banks. The study suggests that the applicability of CRM practices can be extended to rural area banks to satisfy the customer requirements. Thus, customer experience (CX) and Customer engagement is also essential to compete effectively in today's banking system. Banks become more effective when it implements CRM practices, impeccably results in reducing customer attrition rate and increasing customer retention.

Scope and Limitations of the Study
 This study is limited to the extent that it covered the customers and employees of only 2 public sector banks and 2 private sector banks of Chittoor district of Andhra Pradesh.  The study confined to urban and semi-urban area, excluded the rural areas in the districts of India.