




































Indian Journal of Finance and Banking 

 Vol. 7, No. 1; 2021 

                                       ISSN 2574-6081   E-ISSN 2574-609X 

Published by CRIBFB, USA 

 

1 

CUSTOMER RELATIONSHIP MANAGEMENT PRACTICES OF 

INDIAN PUBLIC AND PRIVATE SECTOR BANKS: AN 

EXPLORATORY STUDY 

 
Lalitha P S 

Research Scholar    

Department of Management Studies 

Sri Venkateswara University, Tirupati, India 

 E-mail: lalithaparendur@gmail.com 

 

Dr. Kiran Kumar Paidipati 

Assistant Professor 

Department of Statistics, Lady Shri Ram College for Women, 

University of Delhi, New Delhi, India 

E-mail: kirankumar.paidipati@lsr.du.ac.in 

 

Dr. Arvind Kumar 

Professor & Dean 

Atal Bihari Vajpayee School of Management and Entrepreneurship 

Jawaharlal Nehru University, New Delhi, India 

E-mail: kumararvind@mail.jnu.ac.in 

 
ABSTRACT 

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. 

 

Keywords: Customer Experience, Banking Sector, CRM Parameters, Exploratory Factor Analysis, 

Customer Retention. 

 

JEL Classification Codes: G210, G410, O180, D100. 

 

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 

mailto:kirankumar.paidipati@lsr.du.ac.in


https://www.cribfb.com/journal/index.php/ijfb                           Indian Journal of Finance and Banking                        Vol. 7, No. 1; 2021 
 

 

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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.  

 

 
 

Figure 1. Net Profit /Loss of Public and Private Sector banks18 

 

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.   

 
 Figure 2. Importance of CRM  

Communication & 
Customer Insights 

and Trends

Drive a digital 
Transformation

Strengthen 
Customer 

Expereience



https://www.cribfb.com/journal/index.php/ijfb                           Indian Journal of Finance and Banking                        Vol. 7, No. 1; 2021 
 

 

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REVIEW OF LITERATURE 

Several studies explored CRM practices in banking sector which focused on diverse strategies to 

enhance the customer services. Starting from the specified study, loyalty was an attitude-based 

phenomenon influenced by customer relationship management initiatives affinity programs (Mark et al., 

2003). The authors focused on customer service results in a positive customer attitude (Kirmaci, 2012). 

The study assessed the quality of service provide by private sector banks which dominate public sector 

banks in terms of customer service and providing awareness (Singh, 2013). Another study examined 

customers with various personal attributes, found differ in their expectations and perceptions in terms 

of quality, technological innovations usage. It initiates significant impact on technological relationship 

and their perceptions of CRM parameters to customer demographics (Law et al., 2013). The researchers 

observed the mediating role of customer trust among social identity and customer loyalty. Also, 

recognized the momentous influence of relationship marketing to influence positive customers so as it 

increased the identity, reputation, image on customer trust which affects customer loyalty (Nguyen et 

al., 2013).  

The authors investigated positive relationship between CRM practices and customer loyalty in 

banking sector (Anabila & Awunyo, 2013). The study described about services provided by private 

sector banks such as positive customer attitude helps to correct fulfilment of customer expectations, and 

positive perception of staff towards customer services improve the better CRM practices (Vugec et al., 

2017). Another explored the implementation of CRM model, IDIC (Identify, Develop, Interact and 

Customization) was adopted in Nigerian banks to enhance customer retention (Karahan & Kuzu, 2014). 

According to study identified that bank personnel get expertise technically in rendering services to the 

users, which setbacks appropriate strategies to banks efficiency (Khan et al., 2017).  

Further an exploratory study focused on customer perception and customer orientation in the 

banking sector and suggested that inferred technology acts as a customer relation paradigm in engaging 

the customers (Dubey & Sangle, 2019). Another study examined the success factors of CRM which 

includes customer information, system support, quality of service in turn results customer trust and 

retention towards banks financial performance (Al-Dmour et al., 2019). The influence of technological 

advancements in banks reduces customer retention (Bankole et al., 2020). The analytical CRM practices 

like online marketing services for effective communication channels made banks to become more 

customer – centric (Devendran, 2020). Finally, the researchers investigated the practical implications of 

deep learning technologies and their applications includes virtual service assistant, smart image 

processing, face recognition, personalized marketing, audio-video processing, user authorization, 

cybersecurity will save as the one-stop repository for banks and protects the assets, prevents losses from 

frauds. It aims to provide quality service to satisfy customers and reduce customer churn detections 

(Hassani et al., 2020). 

Overall, the studies discussed on CRM parameters such as satisfaction, loyalty and retention etc., 

and effective practices to reduce the churn rates. There is a need of keen understanding about the 

customer behavior towards satisfaction and being loyal in banking sector. Several studies suggested 

certain improvements to be added in CRM practices to maintain the customer retention and reduce 

attrition. Our study will be another feather and mostly focused to study in-depth understanding of 

customer behavior in implementing CRM practices in the banking sector in India.   

 

RESEARCH METHODOLOGY 
The study envisioned in exploring significant CRM parameters in Indian public and private banking 

sector. The study considered rural and urban areas of one of the districts of India to understand the 

consumer behaviour towards implementing the CRM practices. The study designed in collecting 1200 

samples with a structured questionnaire from three revenue divisions in Chittoor district, Andhra 

Pradesh, India using multi-stage sampling technique. The customers were selected from Public (SBI & 

Andhra Bank) and Private (ICICI & HDFC) sector banks of 600 customers each. 

 

 



https://www.cribfb.com/journal/index.php/ijfb                           Indian Journal of Finance and Banking                        Vol. 7, No. 1; 2021 
 

 

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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. Frequency Distribution for Demographic Variables with Type of Banking Sector 

S. 

No. 

Demographic 

Variables 
Attributes 

Public Sector 

Bank 

Private Sector 

Bank 
Test -

Statistic 
N % N % 

1 Gender 

Male 385 64.2% 424 70.7% χ2=5.770 

and    

p=0.016 (S) 
Female 215 35.8% 176 29.3% 

2 Age 

Below 25 years 102 17.0% 163 27.2% 
χ2=22.561 

and  

p=0.000 (S) 

25-40 years 244 40.7% 213 35.5% 

41-55 years 145 24.2% 106 17.7% 

Above 55 years 109 18.2% 118 19.7% 

3 
Educational 

Qualification 

Illiterate / Below 

10th Std  
54 9.0% 0 0.0% 

χ2=66.698 

and 

p=0.000 (S) 

SSC / 

Intermediate 
44 7.3% 84 14.0% 

Graduation 357 59.5% 368 61.3% 

Post-Graduation 

& above 
145 24.2% 148 24.7% 

4 Annual Income 

Below 1 Lakh 42 7.0% 42 2.0% 
χ2=23.541 

and p=0.085 

(NS) 

1-3 lakhs 202 33.7% 202 17.0% 

3-5 lakhs 244 40.7% 244 46.0% 

5-10 lakhs 112 18.7% 112 35.0% 

5 Occupation 

Government 

Employee 
278 46.3% 24 4.0% 

χ2=604.407 

and  

p=0.000 (S) 

Private Employee 18 3.0% 322 53.7% 

Business/Corpora

tes 
81 13.5% 77 12.8% 

Students/Unempl

oyed 
39 6.5% 125 20.8% 

Pensioners/farmer

s/others 
184 30.7% 52 8.7% 

6 
Frequency of 

Bank visit 

Very rare 13 2.2% 98 16.3% 
χ2=48.763 

and 

p=0.000 (S) 

 

Occasionally 71 11.8% 137 22.8% 

Once in a week 38 6.3% 47 7.8% 

Twice in a month 118 19.7% 117 19.5% 

Monthly once 360 60.0% 201 33.5% 

7 

Relationship 

with bank in 

years 

Less than a year 2 0.3% 45 7.5% 

χ2=132.054 

and 

p=0.000 (S) 

1-2years 76 12.7% 80 13.3% 

2-4 years 150 25.0% 148 24.7% 

4-8years 272 45.3% 210 35.0% 

more than 8years 100 16.7% 117 19.5% 



https://www.cribfb.com/journal/index.php/ijfb                           Indian Journal of Finance and Banking                        Vol. 7, No. 1; 2021 
 

 

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STATISTICAL ANALYSIS RESULTS  

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 (λ2=5.770 and p-value=0.016), Age (λ2=22.561 and p-value=0.000), 

Educational Qualification (λ2=56.698 and p-value=0.000), Occupation (λ2=604.407 and p-value=0.000), 

frequency of bank visit (λ2=48.763 and p-value=0.000), relationship in terms of years (λ2=132.054 and 

p-value=0.000) and reason for bank visit (λ2=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. 
 

 
 

 

 
 

 

54
0

44
84

357 368

145 148

0

100

200

300

400

Public Sector Banks Private Sector Banks

Education of respondents among type of Bank Sector

Illiterate/10th std SSC/Intermediate

Graduation Post-Graduation & above

385
424

215
176

0

100

200

300

400

500

Public Sector

Banks

Private Sector

Banks

Gender of respondents among type of Bank Sector

Male

Female

42
12

202

102

244
274

112

212

0

50

100

150

200

250

300

Public Sector

Banks

Private Sector

Banks
Income of respondents among type of Bank Sector

Below 1 lakh

1-3 lakhs

3-5 lakhs

5-10lakhs & above

102
163

244
213

0

100

200

300

Public Sector Banks Private Sector Banks

Age of respondents among type of Bank Sector

Below 25 years 25-40 years

41-55years 55 years above

8 
Reason for 

visiting bank 

Balance Enquiry- 

new A/c 
117 19.5% 89 

14.8% 

χ2=40.257 

and p=0.000 

(S) 

Cash 

Deposit/with 

drawl 

    103 17.2% 103 

17.2% 

Complaints/queri

es 
109 18.2% 108 

18.0% 

Locker 161 26.8% 104 17.3% 

Loan 

Services/others 
62 10.3% 112 

18.7% 

Total 1200 100.0 1200 100.0  

Source: Primary data, where N=No. of Samples, NS= Not Significant, S=Significant.  *p=0.05 Level 

of Significance 



https://www.cribfb.com/journal/index.php/ijfb                           Indian Journal of Finance and Banking                        Vol. 7, No. 1; 2021 
 

 

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Reliability Test for CRM parameters  

Table 2. Reliability Statistics for CRM Parameters 

 

CRM 

Parameters 

Customer 

Service 

Customer 

Knowledge 

Customer 

Focus 

Customer 

Orientation 

Customer 

Satisfaction 

Customer 

Loyalty 

Cronbach’s 

Alpha 

0.830 0.859 0.864 0.811 0.844 0.717 

Source: Primary data 
 

          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. 

 

Median Responses of CRM parameters  

Table 3. Median of CRM Parameters 

 

CRM 

Parameters 

Customer 

Service 

Customer 

Knowledge 

Customer 

Focus 

Customer 

Orientation 

Customer 

Satisfaction 

Customer 

Loyalty 

Median 4.00 4.00 3.00 3.00 4.00 4.00 

Source: Primary data 

  

 
 

 
 

 

 

 

 
 

 
 

278

2418

322

81 77

39

125

184

52

0

50

100

150

200

250

300

350

Public Sector

Banks

Private Sector

Banks

Occupation of respondents among type of Bank Sector

Government Employee

Private Employee

Business/Corporates

Students/Unemployed

Pensioner/Retired

persons

13

98
71

137

38 47

118 117

360

201

0

50

100

150

200

250

300

350

400

Public Sector

Banks

Private Sector

Banks

Frequency of bank visit among type of Bank Sector

Very rare

Occasionally

Once in a week

Twice in a month

Monthly once

2

45

76 80

150 148

272

210

100
117

0

50

100

150

200

250

300

Public Sector

Banks

Private Sector

Banks

Relationship in years among type of Bank Sector

Less than a year 1-2years 2-4 years

4-8years more than 8years

117

89
103 103109 108

161

104

62

112

48

84

0

20

40

60

80

100

120

140

160

180

Public Sector Banks Private Sector Banks

Reason for bank visit among type of Bank Sector

Balance Enquiry/Acc opening Cash Deposit/with drawl

Complaints/queries Locker

Loan Services/Replacements Shares/Currency exchange



https://www.cribfb.com/journal/index.php/ijfb                           Indian Journal of Finance and Banking                        Vol. 7, No. 1; 2021 
 

 

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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.   

 

Mean rankings for CRM parameters  

Table 4. Mean Ranks for CRM Parameters 

 

CRM 

Parameters 

Customer 

Service 

Customer 

Knowledge 

Customer 

Focus 

Customer 

Orientation 

Customer 

Satisfaction 

Customer 

Loyalty 

Mean Rank 5.87  5.16 3.88 2.64 3.26 5.05 

Highest rank 

construct 

CS6 CK6 CF1 CO1 CSa3 CL7 

Mean Rank 3.73 3.27 3.30 2.40 2.78 3.97 

Lowest rank 

construct 

CS5 CK3 CF3 CO2 CSa1 CL6 

Source: Primary data 

 

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).  

 
Table 5. KMO and Bartlett's Test 

 

Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.881 

Bartlett's Test of 

Sphericity 

Approx. Chi-Square 39036.533 

df 703 

Sig. 0.000 

                   Source: Primary data                       Extraction Method: Principal Component Analysis.            

 

Table 5 elucidates measure of sample adequacy was computed through Kaiser-Meyer-Olkin 

(KMO) was 0.881 indicates that the samples are good enough for sampling. Also, the overall correlation 

matrices had been verified with Bartlett Test (approx. χ2 =39036.533 and significant at (p =0.00 < 0.05) 

provided the validity of data.  

 

 

 



https://www.cribfb.com/journal/index.php/ijfb                           Indian Journal of Finance and Banking                        Vol. 7, No. 1; 2021 
 

 

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Table 6. Total Variance Explained 

 

Source: Primary data                                   Extraction Method: Principal Component Analysis. 

 

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. Factors influencing CRM Parameters 

 

Factors CRM Parameters 
Factor 

Loadings 

Eigen 

values 

% 

Variance 

Cronbach’s 

Alpha (α) 

Focus 

based 

Customer 

Service 

 

Services to individual customers 

- CF1 
0.508 

4.189 34.208 0.895 

Beyond customer expectations - 

CF2 
0.666 

Treat the customers with great 

care - CF3 
0.662 

Strengthens emotional bonds - 

CF4 0.828 

Uses customer suggestions - 

CF5 0.487 

Interact with customers - CF6 0.561 

Customized web page - CS1 0.720 

Resolve complaints - CS2 0.846 

Follow up single customer - 

CS3 0.512 

Flexible working hours - CS4 0.737 

Exercise goodwill deal with 

customers - CS5 0.602 

High integrity and security - 

CS6 0.526 

Speedy when counter services - 

CS7 0.726 

Receptiveness-CS8 0.767 

 

 

 

Knowledge 

based 

Access of Information - CK1 0.663 

2.616 29.264 
0.846 

 

Responsiveness - CK2 0.598 

Awareness on CRM 

Programmes - CK3 

0.504 

Reliability - CK4 0.542 

Communication - CK5 0.537 

Component Initial Eigenvalues Rotation Sums of Squared Loadings 

Total % of 

Variance 

Cumulative 

% 

Total % of 

Varianc

e 

Cumulative % 

1 5.376 47.832 47.832 4.189 34.208 34.208 

2 2.751 18.026 65.858 2.616 29.264 63.472 

3 1.249 10.389 76.247 1.372 12.775 76.247 



https://www.cribfb.com/journal/index.php/ijfb                           Indian Journal of Finance and Banking                        Vol. 7, No. 1; 2021 
 

 

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Factors CRM Parameters 
Factor 

Loadings 

Eigen 

values 

% 

Variance 

Cronbach’s 

Alpha (α) 

Customer 

Orientation 

Trust - CK6 0.831 

Assurance - CK7 0.769 

Suitability using CRM 

technology - CO1 

0.708 

Relationship personnel - CO2 0.720 

Prior preference to customer - 

CO3 

0.661 

Customer touchpoints – CO4 0.517 

 

Satisfaction 

based 

Customer 

Loyalty 

Complaints management - CSa1 0.669 

1.372 12.775 0.816 

Sincerity and helpfulness of 

personnel - CSa2 

0.617 

Opinion on services offered by 

bank - CSa3 

0.476 

Establish long term relationship 

- CSa4 

0.698 

Can meet customer expectations 

- CSa5 

0.697 

Changing the bank - CL1 0.857 

Considering loyal customer - 

CL2 

0.729 

Continuing services in future - 

CL3 

0.798 

Use other services offer by bank 

- CL4 

0.767 

Endorsing bank to others - CL5 0.768 

Switch to competitor banks - 

CL6 

0.890 

Trouble in provide service shift 

bank - CL7 

0.896 

First choice among other banks 

area - CL8 

0.791 

Source: Primary data   Extraction Method: Principal Component Analysis.  Rotation - varimax method          

 

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. 



https://www.cribfb.com/journal/index.php/ijfb                           Indian Journal of Finance and Banking                        Vol. 7, No. 1; 2021 
 

 

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 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. 

 



https://www.cribfb.com/journal/index.php/ijfb                           Indian Journal of Finance and Banking                        Vol. 7, No. 1; 2021 
 

 

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REFERENCES 

Anabila, P., & Awunyo-Vitor, D. (2013). Customer relationship management: A key to organizational 

survival and customer loyalty in Ghana’s banking Industry. International Journal of Marketing 

Studies, 5(1), 107-117. 

 

Al-Dmour, H. H., Algharabat, R. S., Khawaja, R., & Al-Dmour, R. H. (2019). Investigating the impact 

of ECRM success factors on business performance. Asia Pacific Journal of Marketing and 

Logistics, 31(1), 1355-5855. 

 

Bankole, O. A., Ogundipe, C. F., Enitilo, O., Ogundepo, Y. O., Oghogho, V. O., & Eguabo, R. O (2020) 

Relationship Management and Customer Retention in the Banking Sector: A Case Study of 

Akure Metropolis, Nigeria. International Journal of Research in Social Science and Humanities 

1(1), 21-28 

 

Dubey, N. K., & Sangle, P. (2019). Customer perception of CRM implementation in banking context. 

Journal of Advances in Management Research, 16(1), 38-63. https://doi.org/10.1108/JAMR-12-

2017-0118  

 

Devendra, G. D. (2020). Current Scenario of Different Type of Services Marketing and Delivery in 

Customer Relationship Management. International Journal of Engineering and Management 

Research, 10(1), 30-32. https://doi.org/10.31033/ijemr.10.1.6 

 

Hassani, H., Huang, X., Silva, E., & Ghodsi, M. (2020). Deep Learning and Implementations in Banking. 

Annals of Data Science, 7(3), 433-446. https://doi.org/10.1007/s40745-020-00300-1 

 

Hair, J., Anderson, R., Tatham, R., & Black, W. (1998). Multivariate Data Analysis, 5th ed., Prentice-

Hall, Upper Saddle River, NJ. 

 

Kirmaci, S. (2012). Customer relationship management and customer loyalty; a survey in the sector of 

banking. International Journal of Business and Social Science, 3(3). 

 

Karahan, M., & Kuzu, Ö. H. (2014). Evaluating of CRM in Banking Sector: A Case Study on Employees 

of Banks in Konya. Procedia-Social and Behavioral Sciences, 109, 6-10. 

 

Khan, H. U., Lalitha, V. M., & Omonaiye, J. F. (2017). Employees' perception as internal customers 

about online services: A case study of banking sector in Nigeria. International Journal of 

Business Innovation and Research, 13(2), 181-202. 

 

Law, A. K., Ennew, C. T., & Mitussis, D. (2013). Adoption of customer relationship management in the 

service sector and its impact on performance. Journal of Relationship Marketing, 12(4), 301-

330. 

 

Mark, D. U., Grahame, R. D., & Kathy, H. (2003). Customer loyalty and customer loyalty programs. 

Journal of Consumer Marketing, 20(4), 294-316. 

 

Malhotra, N.K. (2003). Marketing Research- An Applied Orientation, Pearson Education, Singapore.  

 

Nguyen, N., Leclerc, A., & LeBlanc, G. (2013). The mediating role of customer trust on customer 

loyalty. Journal of Service Science and Management, 6(1), 96-109 

http://dx.doi.org/10.4236/jssm.2013.61010  

 

https://doi.org/10.1108/JAMR-12-2017-0118
https://doi.org/10.1108/JAMR-12-2017-0118
https://doi.org/10.1108/JAMR-12-2017-0118


https://www.cribfb.com/journal/index.php/ijfb                           Indian Journal of Finance and Banking                        Vol. 7, No. 1; 2021 
 

 

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Nunnally, J.C. (1978). Psychometric Theory, New York: McGraw-Hill. 

 

Singh, D. (2013). Service quality and customer satisfaction: a comparative study of an Indian public 

VS private bank. Malaysian Management Journal, 17, 59-75. 

 

Vugec, D. S., Spremić, M., & Bach, M. P. (2017). IT governance adoption in banking and insurance 

sector: Longitudinal case study of cobit use. International Journal for Quality Research, 11(3), 

691-716. 

 

 

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