Keywords

Technology, Customer relationship, Customer choice, Indian banks.

Introduction

The global banking industry has undergone significant changes in recent years, and technology has played a vital role in shaping the industry. In a globalised and highly competitive environment and increasing customer expectations, it is a challenge for banks to survive and retain their customers ( (Al-dmour et.al 2019) With the growing number of tech-savvy customers, banks are increasingly leveraging technology to provide better customer experiences and build customer choice. With technology, banks are providing better customer experiences and trying to build customer loyalty (Kamath et.al, 2003) The forces like globalization and increasing consumer expectations are encouraging banks to perform better and forcing banks to deliver superior service, retain customers and improve the profitability of the banks (Abu-Shanab&Anagreh (2015), Al-Abdullat & Dababneh (2018), Owusu et.al (2019). The business focus has taken a shift from purely banking perspective to a customer perspective. Banks have started to use technology applications to improve customer satisfaction. The banks are finding new ways of not only attracting new customers but also retaining them and converting them into loyal customers. The emerging advanced technologies provide banks with new opportunities to improve customer services and achieve a competitive advantage offering web-based services to identify customers' preferences and needs.

The banks have started adopting digital transformation to address the evolving customer needs. The banks strive to deliver similar experience across all platforms like net banking, mobile banking, and physical branch. The banking sector is using technology in the following ways to increase client choice: Mobile banking has taken over as a crucial aspect of banking. Clients can now carry out a variety of operations using their mobile devices. Globally smartphone use is on the rise so mobile banking is now easier. Another crucial component of banking is online banking. Customers now have easier access to financial services because they can conduct transactions from the comfort of their own homes thanks to online banking. Contactless payments have significantly increased in popularity because of the pandemic. Many contactless payment solutions, including mobile payments, QR code payments, and others, are now provided by banks. Transactions are now safer and easy for customers thanks to contactless payments. Banks are using virtual assistants known as chatbots which can respond to client questions and offer fixes for their issues and offer clients customer care. Because they can respond to numerous requests at once and are accessible 24 hours a day, chatbots have gained popularity. Banks are now leveraging customer data to provide personalized recommendations to their customers. Based on the customer's transaction history and preferences, banks can recommend products and services that are relevant to them. This helps customers to make informed decisions and build customer loyalty. Therefore, in the era of substantial growth of technology and the proliferation of the internet, new paradigm has evolved, customer relationship management.

Customer Relationship Management using technology is a strategy that banks are using to manage and analyse customer interactions and data throughout the customer lifecycle, with the goal of improving customer satisfaction, retention, and profitability. It can be leveraged by banks to personalize their interactions with customers. By analysing customer data, banks can understand customer preferences and tailor their communication and product offerings to meet individual customer needs. This can lead to better customer experiences and increased customer loyalty. This can help banks identify opportunities to cross-sell products and services to existing customers. By analysing customer data, banks can identify products and services that are relevant to specific customers and make targeted offers which can help banks to increase revenue and profitability. Banks can provide better customer service by giving customers a comprehensive view of customer interactions across different channels. Banks can identify areas where customers are experiencing issues and address those issues quickly and effectively. Banks can create targeted marketing campaigns that are more likely to resonate with customers. By analysing customer data, banks can identify customer segments that are more likely to respond to specific marketing messages and create campaigns that are tailored to those segments. It can provide banks with valuable insights into customer behaviour and preferences. By analysing customer data, banks can identify trends, patterns, and opportunities that can help them improve their products, services, and customer experiences.

Many studies have reviewed the impact of customer relationship management using technology on customer choice, but the studies are limited to only developed countries. Very few studies have focused on the increasing usage of technology in banking industry in the emerging economies like India. Research needs to be done to explore how banks are leveraging technology in banks and its impact on customers’ choice. The market life cycle, the strategies adopted by emerging economy like India is different compared to developed economics (Khan et.al (2020). The governmental policies and norms are also different for emerging economies and developed nations. The critical questioning approach is required to identify the suitability of those studies to Indian context. There are few studies in Indian scenario which captures the efficiency of technology in influencing customer choices in the Indian banking sector (Kaur et.al (2021), Sharma et.al (2020). Hence this study shows focus on leveraging technology and its impact on the customer choice in the Indian context.

Literature Review

The initial phase of using technology for customer relationship practices is to provide information to customers (Anderson (2002). The data is backed up by banks to resolve customer concerns i.e. whenever the customer’s requests for any services through web can be registered and customers can be contacted. The main aim of this practice is maintaining the relationship with existing customers and get new customers, by enabling web services like optimizing search engine, web banners, and improving navigation on the website. Giustiniani & Ross (2008) offers an in-depth analysis and discusses that banks can accomplish personalized services by contacting customers and providing customized services. According to a study he conducted before the advent of the internet in a big way identified significant deficiencies in two-way communication between customers and service providers. The main characteristics of pre-execution practices are:

1] Technology allows banks to quickly offer customized product propositions based on customer feedback and past behaviour.

2] By automating market offers, banks can reach new customers who may not have been reached through traditional methods.

3] To target limited resources towards high value prospects while minimizing waste.

Customization is another significant aspect of pre-execution activities. Customers can set preferences in advance that are automatically recorded so that banks can deliver better services to their customers. The search engines are widely essential elements of pre-execution. "Website Preferential" and "Alternative Channels" are the approaches in e-banking services. Customers want "Website Preferential" features because they make it easier for them to get customised services based on their preferences. Every time a customer visits a website, they can modify their preferences. "Alternative Channels" preferences show that the website has other ways for users to get in touch with banks like toll-free number, fax, e-mail and voice-enabled services. Customers can use search engines to locate the information they need to access the services they need on a website. Feinberg & Kadam (2002) identified list of qualities in pre- execution activities which include

• New customers introduction: The website should have an introduction page to help new users navigate it. This page will have all the information they need to successfully use the banks' services.

• Online chatting: This feature gives customers the ability to chat with bank representatives in real time. This strategy allows customers to communicate with banks via online chat and obtain additional services and information.

• Navigation Ease: The password-protected pages that banks offer on their websites are simple for customers to access. The bank lets the customer access the necessary data, and then the customer can make a list for easy navigation. This also gives the bank a chance to keep track of everything the customer did on the bank's website up until that point. This makes it possible for the bank to develop relationships with customers.

Based on the customer perception, customer request can be placed about web banking. The recent advancements like chat bots minimise the role of intermediaries to solve the customer queries. The real-time interface has improved banks' efficiency to retain the customer and improved productivity.

Shagirabanu & Sheik Abdullah (2021) have demonstrated the importance of loyalty programs to make the customers of the bank feel special. Bhat & Darzi (2016) study emphasise the importance of conducting online surveys. The surveys will bring an understanding of customer preferences and customer perception to the banks. Banks could also get to know the changes in perception of customers over a period. Feinberg et.al (2002) further mention that the banks portal can be customized based on the requirements of customers. All their preferences can be captured and presented even before the customer entered the portal.

“Call centres” and “Centre for Internet Security” (CISs) play a significant part in banking industry. The developments coming up now like wireless facility, online contact, voice enabled services and video chatting have held may concerns alongside supplementary later technologies, for instance, phone, fax, and electronic data interchange giving a new concern for Centre of Internet Security, because these technologies are responsible for connecting to customers. The purpose of this approach is to provide user friendly and hassle-free services enhancing the safety.

The different types of internets enabled customer relationship practices to provide good customer services used by banks are “Automatic call answering machines” to give better services. This facility will automatically redirect calls to the service centres and the service agents can prioritize calls to favour high-profile customers, cutting longer waiting time for customers.

“Interactive Voice Reply” that provides 24X7 all 365 days services of routing calls established on customers' reply typed on the phone keypads. These requests permit call switching without human interaction. The latest advancement enables customers to verbally talk to the live services without typing the messages with the help of phone also. Every customer services organisation ought to encompass instruments to monitor the presentation of service.

Other instruments contain Computer telephony integration, which is the integration of data alongside phones using these applications and service cyber-agents that is utilized to resolve customers’ queries at single point of time.

Kalaiarasi & Mugunthan (2021) study talks about handling queries and providing answers to the customer through websites. While Ahmed et.al (2022) study supports that the available characteristics will solve the problem and sometime customers resolve the problems by themselves with the help of online chatting facility.

The online availability to handle queries, tracking order and online resolving facility over the websites are considered to be important characteristics of customer relationship management practices as rated by the customers (Singh & Chauhan (2018). The online survey is another method to help and monitor customer feedback and evaluation of the banking services. Use of tracking order method gives possibilities to follow the orders of the customers and online users can share their experience under this forum.

Rozita, (2012) studies show the success of technology in customer relationship management in banking is information availability to the customers on the website while browsing. The websites of the banks offer customized technique that allows customer to filter the contents based on their preference. Subsequently visits of the customer to the website they can view their preferences easily. According to Yoon et al (2006) the customer can be given information through e-mails which acts as an important tool for interaction between banks and customers.

The key word during the search helps the customer to locate the required information quickly and easily.

Customer relationship is a three-step process. Pre execution stage, execution and post execution stage.

Many studies have been undertaken to investigate on pre-execution stage. The factors like website preference, ease of navigation, website compatibility are the practices identified in the pre-execution stage of customer relationship (Rust et.al (2001), Liu et.al (2008), Mittal & Kumar (2001), Zineldin, M (2005) Nysveen (2001). suggest that pre-execution of customer relationship management are raised to activities performed by the customer prior to using the website. For instance, registration process and having a password, browsing done by the customer with the password on protected web page and make online availability secured website.

Joung et al (2018) gives more insights on pre-execution characteristics of customer relationship management. They can be divided into five parts: (a) Presentation of website that refers to looks of the website, the way the information is presented though pictures and images (b) Information accessibility indicating ease for the customer finding services and their pricing information ; (c) Ease of search by allowing customer to quickly retrieve the desired information; (d) Quality of Information generally refers to accuracy, content, and providing updated information on the website (e) Loyalty schemes provided by the banks that make the customer visit the site again and use their services. Loyalty enables the customer to get points on every transaction performed by the customer. Based on the points accumulated rewards are offered like discounts, price cut, and some special benefits.

The study undertaken by (Liu &Han, (2007)) shows website safety and privacy as the main factors that affect the decision of customer while executing the transaction through website of the banks. The banks must reduce perceived risk by building adequate confidence in the customers. Banks should be able to provide feeling of safety for the customer while performing online transactions. It is important for banks that customers must feel that using the website is a simple, secure and reliable for transactions. Positive customer perspective is a significant contributing factor to the success of any bank. Hence, every transaction must be observed as a beginning point towards building a continuing relationship (Wang & Head (2007).

There are studies done on post execution practices (Seyedian & Luo (2004), Park & Kim (2003), Yang & Peterson (2004) suggest three elements: handling queries, resolving queries and online availability. Consequently, as suggested by Mahshid et.al (2002) banking firms must persuade customers for discussing their problems and using the customer feedback for improving both banking services and products. This phase is to enhance customer satisfaction after sale, so that the right relationship is built with the customer having a right customer perspective towards the web services. The activities performed during the post execution practices are handling post transaction issues, handling queries and disputes and so on. The website should provide the facility to support the complaints of the customers and providing feedback. The pursuing factors in post-execution practices are handling queries, resolving queries and online availability.

Past studies identify that customer relationship using technology has an impact on profits making and customer loyalty in the banks but there are not many studies in Indian banking context. This paper bridges the gap and examines how banks are leveraging technology to build customer relationship and influence customer choice in the banking industry

Data Analysis

To examine how banks in India are leveraging technology to build customer relationship and influence customer choice in the banking industry variables which in the pre, during and post implementation stage from the past studies were taken. Confirmatory Factor Analysis (CFA) is carried out in all the three phases pre, during and post implementation of Customer Relationship Management in banks. The factors which influence the customer choice in these three phases are identified through factor loadings. Questionnaire is prepared and customers have filled the questionnaire. Convenient sample is adopted for this study. The data is entered into SPSS after the survey and factor analysis done. The responses, total of 415 (75.4%) respondents, have filled and completed the survey.

Descriptive Statistics for Demographic Variables

The research used descriptive statistics for analysing and presenting customer's demographic characteristics of this study using the frequencies and percentage. The Table 1 shows the details of demographic characteristics namely gender the sample constitutes majority of male respondents 67.7% and female constitute 32.2%. The age of the respondents in the sample (19.3%) fall in age bracket of 18 – 24 years but majority of the respondents (49.6%) fall in the age bracket 25-31years. 53.3% of the respondents are mostly holding bachelor’s degree and the average of monthly income of 38.6% respondent mostly ranges between Rs 40,000 to 60,000 per month. 36.6% of the customers are loyal towards the bank as most of these respondents are customers of the bank for 5 years or more. All the respondents confirmed that they are using website for banking services resulted to 100% and 51.8% of respondents are using the facility more than three years. 49.9% of respondents utilise the banking website at least six to ten times monthly.

Table 1 Demographic Characteristics of the Customers
Items	Classification	Frequency	Percentage %
Age	18-24 yrs 25-31 yrs 32-38 yrs 39-45 yrs 46-52 yrs 53 & above Total	80 206 123 5 1 Nil 415	19.3 49.6 29.6 1.2 0.2 Nil 100%
Occupation	Student Self-employed Professional Homemaker Retired Total	74 77 258 5 1 415	17.8 18.6 62.2 1.2 0.2 100%
Education	Not completed school Diploma degree Under graduate Post graduate Total	8 37 221 149 415	1.9 8.9 53.3 35.9 100%
Gender	Male Female Total	281 134 415	67.7 32.2 100%
Income	Up to Rs. 20,000 Rs .20,000-Rs.40,000 Rs. 40,000-Rs.60,000 Rs. 60,000-Rs.80,000 Rs. 80,000-Rs.1,00,000 Rs. 1,00,000 & above Total Missing 53	28 121 160 51 Nil 2 362 -	6.7 29.2 38.6 12.6 Nil 0.5 87.2% -
No. of Years	More than 1 yr More than 3yrs More than 5yrs More than 6yrs Total	47 135 152 81 415	11.3 32.5 36.6 19.5 100%
Use website services	Yes No	415	100%
No. of times use web services	Less than one year 1-3yrs 4-6yrs More than 6 yrs Total	71 215 116 13 415	17.1 51.8 28.0 3.1 100%
No. of times use web bank(month)	1 to 5 times 6 to 10 times 11 to 15 times Over 15 times Total	111 207 83 14 415	26.7 49.9 20.0 3.4 100%
No. of times use web bank(branch)	Never 1 to 3 times 3 to 5 times 6 to 8 times Over 8 times Total	158 203 40 14 Nil 415	38.1 48.9 9.6 3.4 Nil 100%

The Confirmatory Factor Analysis (CFA)

CFI (comparative fit index) as well as non-Normed fit index are considered to evaluate the model fit index. The acceptable values should be above 0.90 for model fit, basically ranging values between 0.91 to 1, to be close fit. Further, RMSEA (Root Mean Square Error of Approximation) is considered the value which is less than 0.08 is good for model fit, chi-square value less than 0.05, chi-square, degree of freedom that minimum discrepancy should be less than 0.05 for accepting the model fit. (Stevens, 2002) after the model fit, analysis have been carried out. The reliability is tested through Cronbach Alpha. Cronbach’s alpha reliability coefficient values greater than 0 .70 is considered.

Pre-Execution Practices and Measurement Model

AMOS 20.0 is used in evaluating the model and based on the sample data the construct validity test is performed for the survey questionnaire [45]. The 10 variables of pre-execution practices are measured and further these 10 variables have been divided into subscales base on the nature of its transaction, thus for pre-execution practices there are three subscales to determine for instance, website preferential, ease of navigation, and website compatibility and these variables are allocated based on the nature of practices to their respective construct. χ2 (63) = 187.391, p =0.0001, GFFI =0.93, AGFI=0.91, RMSEA=0.069, close fit =0.0309, these are the value obtained at the first stage, which filed to fit the model, Hence the variables inadequately fitting have been removed to do this first the hypothetical model is developed based on the result or values the modification of the model is performed with the help of modification indices and determined the variables having greater standardized residual value, then the software is run to have suitable model for this study, that is CFA. The Table 2 shows the entire variable and its construct for pre-execution practices of customer relationship management.

Table 2 Allocation of Variables (Pre-Execution)
Pre execution variables / practices	Questions
Website Preferential	My bank's web site offers customized preferential services
	My bank have a vibrant strategy to meet customer requirement through its website.
	I find my website has personalized features which are very useful that makes bank’s website more users friendly.
	The transactions can be customized for me which is allowed by bank’s website.
Ease  of Navigation	My bank’s site offers good navigation features
	The information needed precisely and quickly is provided by my bank's website.
	I use always the 'Sign in’ or 'Log in' feature on the website.
Website Compatibility	The “Sign in” or “Log in” speed is good
	I feel safe using “Sign in” or “Log in” feature for my website transactions.
	The bank’s website make me feel, I am the part of it

The hypothetical model for pre execution practices are presented. The pre execution characteristics are measured by website preferential, ease of navigation, and website compatibility. The reliability test is performed; the standardised regression weights and goodness-of-fit statistics are presented in Tables 3 & 4. The standardized regression weights are greater than 0.70 which is acceptable. On basis of Cronbach’s alpha value is 0.86, all the items appears to be consistent and reliable which indicates that all the items with respect to their construct are reliable Further CFI, RMSEA, AGFI and TLI are calculated for the goodness-of-model fit. The estimated values are shown in the Table 2, all the values support the model fit for the three constructs of pre execution practices, the exhibits all the values or data that support the goodness of model fit. The table 4 shows the standardized loading values of Coefficients, Reliability, and Correlation for Pre execution practices.

Table 3 Allocation of Variables and their Constructs (Pre-Execution)
Pre execution variables / practices (α=.721)	Questions	Correlation	Standardised  loading
Website Preferential	My bank's web site offers customized preferential services	0.389	0.82
	My bank have a vibrant strategy to meet customer requirement through its website.	0.421	0.92
	I find my website has personalized features which are very useful that makes bank’s website more users friendly.	0.417	0.90
Ease  of Navigation	My bank’s site offers good navigation features	0.434	0.90
	The information needed precisely and quickly is provided by my bank's website.	0.342	0.89
	I use always the 'Sign in’ or 'Log in' feature on the website.	0.391	0.87
Website Compatibility	The “Sign in” or “Log in” speed is good	0.328	0.90
	I feel safe using “Sign in” or “Log in” feature for my website transactions.	0.371	0.89
	The bank’s website make me feel, I am the part of it	0.332	0.91

Table 4 Pre Initial Index and Modified Model Fit
Model fit Index	χ2	df	χ2/DF (CMIN/DF)	P value	RMEA	GFI	AGFI	TLI	CFI
Initial model fit	141.14	52	4.312	.000	0.072	0.916	0.95	0.92	0.860
Modified model fit	98.56	32	3.492	.000	0.052	0.937	0.97	0.95	0.901

The poor fit variables or items were removed from the model in the beginning after measuring the hypothetical model and the estimation is done on the using modified index for the entire variables by identifying greater standardized residual value. After the estimation one variable or item have be removed and the subsequent model with the CFA is shown along with the modified one is presented.

During-Execution Practices and Measurement Model

Table 5 is the measurement scale for the during execution practices that includes nine variables or items. Further these variables are divided and assigned to their respective construct namely: customized interface, website safety, and transaction performance.

Table 5 During Execution Practices and Standardized Loading
During execution Practices	Questions
Customized Interface	The product and service can be customized as per my need with the help of bank’s website
	I have a tailor-made interface to get the services I need
	I get motivated to use banks website because of service customization
Website  Safety	I feel for the success of the web banking customization of services is important factor.
	My personal information is not misused by my bank's web site
	The bank’s website keep private information is safe and kept secured from unauthorized access.
Transaction Performance	My bank’s website has different payment options and is clearly stated.
	To use or visit again the bank’s website performance of the transaction and different methods of payment are important factors for me
	Are you satisfied with the performance of the website while carrying out a transaction?

The goodness-of-fit statistics for the three constructs is presented in the table 6. The measurement items weights are exceeding 0.80, thus provides evidence for supporting convergent validity. On basis of Cronbach’s alpha value is 0.70, all the items appears to be consistent and reliable which indicates that all the items with respect to their construct are reliable, which are exhibited in Table 7, the estimates of model fit indices: CFI= 0.960, RMEA= 0.052, and AGFI= 0.97, GFI=0.937, support goodness of model fit. The measurement model shows that the three items in their respective constructs are reliable measures of during execution practices.

Table 6 Reliability Coefficients, Correlation, and Standardized Loadings of During Execution Practices
During execution Practices(α=.705)	Questions	Correlation	Standardised  loading
Customized Interface	The product and service can be customized as per my need with the help of bank’s website	0.365	0.82
	I have a tailor-made interface to get the services I need	0.377	0.91
Website  Safety	I feel for the success of the web banking customization of services is important factor.	0.410	0.82
	My personal information is not misused by my bank's web site	0.548	0.82
	The bank’s website keep private information is safe and kept secured from unauthorized access.	0.513	0.92
Transaction Performance	My bank’s website has different payment options and is clearly stated.	0.349	0.82
	To use or visit again the bank’s website performance of the transaction and different methods of payment are important factors for me	0.323	0.92

Table 7 During Execution-Initial Index and Modified Model Fit
Model fit Index	χ2	DF	χ2/DF (CMIN/DF)	p value	RMEA	GFI	AGFI	TLI	CFI
Initial model fit	110.90	24	4.621	.000	0.074	0.946	0.98	0.92	.880
Modified model fit	98.56	32	3.492	.000	0.052	0.937	0.97	0.95	.960

The Modified Model of the during-execution practices are presented. Customer interface, website safety and transaction performance are the three factor influences the customer choice.

Post – Execution Practices and Measurement Model

This scale is for post execution practices consisting of three constructs where all the nine variables for each construct is measured by three subscales and each subscale is measured by three variables or items for instance handling queries, resolving queries and online availability. The details have been shown in the Table 8.

Table 8 Allocation of Variables and their Constructs (Post Execution Practices)
Post-transaction characteristics	Questions
Handling Queries	I found Frequently Asked Questions (FAQs) section helpful during net banking
	I always use FAQs to explore the bank website.
	The customer gets appropriate information provided by the when the problem occurs by bank’s website.
Resolving Queries	The online transaction problems are resolved quickly by my bank.
	Online complaining form is variable to contact bank if any problems occurs
	The bank’s website has online feedback facility
Online Availability	I mostly use feedback form provided by the bank website
	The online customer service representatives available on the bank’s website
	To use website effectively and easily the bank’s website provides all the vital information

For the post execution practices the estimated values for standardized regression, reliability and goodness of model fit are shown in the Table 9. The initial values estimated for the model fit and the variables which showed the poor fit were removed and further estimation is undertaken, the weights were greater than 0.80 for validity acceptance of the variables. The Cronbach’s alpha value is 0.731 hence the variables are consistent and reliable for all the items under their respective construct which has been presented in the Table 10. The supporting estimation for goodness of model fit for instance, RMEA=0.053, CFI=0.920, and AGFI=0.921 are shown, respectively.

Table 9 Reliability Coefficients, Correlation, and Standardized Loadings of Post Execution Practices
Post-transaction characteristics(α=.731)	Questions	Correlations	Standardised  loading
Handling Queries	I found Frequently Asked Questions (FAQs) section helpful during net banking	0.248	0.86
	I always use FAQs to explore the bank website.	0.298	0.83
	The customer gets appropriate information provided by the when the problem occurs by bank’s website.	0.361	0.80
Resolving Queries	The online transaction problems are resolved quickly by my bank.	0.367	0.89
	Online complaining form is variable to contact bank if any problems occurs	0.424	0.82
	The bank’s website has online feedback facility	0.552	0.83
Online Availability	I mostly use feedback form provided by the bank website	0.386	0.88
	The online customer service representatives available on the bank’s website	0.415	0.78

Table 10 Post- Initial Index and Modified Model Fit
Model fit Index	χ2	DF	χ2/DF (CMIN/DF)	p- value	RMEA	GFI	AGFI	TLI	CFI
Initial model fit	147.710	54	6.155	.000	0.072	0.928	0.905	0.951	.901
Modified model fit	71.28	24	2.556	.000	0.053	0.970	0.921	0.969	.920

The factors influencing customer choice during three phases firstly before the implementation of technology, secondly during the implementation and post implementation. In the pre execution stage website preferential in terms of website personalisation and customisation is to be focussed, during the execution stage transaction performance and website safety and post execution stage resolving queries needs to be focussed by banks as per the factor loadings.

Implications and Future Research direction

Banks are leveraging technology to strengthen relationship with customer and influence customer choice. The banks analyse interactions with the customers throughout the customer lifecycle. This improves the experience customer has with the bank which helps in increasing customer retention and loyalty. By using data analytics and customer profiling, companies gain valuable insights into customer behavior, preferences, and needs, which can be implemented in the marketing and sales strategies improving sales. Based on the previous studies the banks focus on customer choice factors in three phases: Pre- execution, During execution and Post execution. In the pre-execution stage the factors of customer choice are Website Preferential, Ease of Navigation, and Website Compatibility to prefer a bank service. The influencing customer choice factors in the during- execution phase are Customized Interface, Website Safety, and Transaction Performance. Handling Queries, Resolve Queries and Online Availability are the significant customer choice factors in the post-execution. Confirmatory Factor Analysis is done based on the factors. Banks can understand the customers’ needs and expectations and implement appropriate business strategies to be the preferred bank of the customer. The findings of this research will help the banks to improve the experience which the customer have and understand the needs and preferences thoroughly. Banks can offer tailor made solutions or services to customers generating more revenue and leading to higher profits. The banks can build a strong relationship with the customers increasing the loyalty and retaining the customer in competitive environment. Banks can use these inputs and gain insights into the customer needs and preferences which help serving them better. Banks with the help of customer profiling can engage them better leading to higher revenue. Usage of technology like chat bots are leading to greater efficiency and better customer feedback. The emerging technologies like artificial intelligence and bock chain technology has an impact on CRM practices and strategies.

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Received: 14-Jul-2025, Manuscript No. AMSJ-25-16075; Editor assigned: 15-Jul-2025, PreQC No. AMSJ-25-16075(PQ); Reviewed: 16- Aug-2025, QC No. AMSJ-25-16075; Revised: 26-Aug-2025, Manuscript No. AMSJ-25-16075(R); Published: 06-Sep-2025