Analysing Customer Satisfaction Towards Personal Loans: Evidence from Banking Industry
Divya1, Deepti Aggrawal2 and Adarsh Anand1,*
1Department of Operational Research, University of Delhi, Delhi 110007, India
2USME, Delhi Technological University, Delhi 110095, India
E-mail: divya@or.du.ac.in; deeptiaggrawal@dtu.ac.in; adarsh.anand86@gmail.com
*Corresponding Author
Received 17 April 2024; Accepted 21 May 2024
Abstract
With an emphasis on banking industry, this study intends to examine customer satisfaction levels of those who have taken out personal loans from banking institutions. Customer satisfaction is a crucial indicator of success and longevity in the banking industry. This study explores the specifics of customer satisfaction among recipients of personal loans; wherein, the primary goal is to use operational research methods to analyse the intricate topography of customer satisfaction in the personal loan sector. The study’s secondary objective is to demonstrate how factor analysis, a well-known dimensionality reduction method, may combine these disparate traits into a condensed set of essential components. The work presents practical insights to guide strategic choices intended to improve customer satisfaction and foster long-term growth in the banking industry, focusing on Banking sector.
Keywords: Banking sector, customer satisfaction, factor analysis, marketing research, personal loan.
1 Introduction
The banking sector provides individuals and businesses with vital financial services, making it a pillar of contemporary economies. Personal loans are critical to these services, providing customers access to money for various uses, including debt reduction, home improvement, education, and medical costs [1]. Understanding and evaluating consumer satisfaction with these financial products is crucial for banks to stay competitive and sustain long-term relationships with their clients as the demand for personal loans rises [2]. Customer satisfaction is a crucial indicator of how successfully a bank meets or exceeds its clients’ requirements and expectations. Customer satisfaction in the context of personal loans includes several factors, such as the simplicity of the application process, interest rates, loan terms, level of customer service, openness, and general borrowing experience. The efficiency of banking services can be better understood by looking at customer satisfaction with personal loans. This will enable institutions to customize their products to suit the needs of their clientele better and increase retention rates. In the context of company strategy and consumer-centric methodologies, there is a close relationship between marketing research and customer satisfaction [3].
Customer satisfaction is the result of providing goods and services that meet or exceed the consumer’s desires and expectations [4]. Customer satisfaction is a concept in the marketing research field, so it is crucial to understand what marketing research is. Marketing research is essential for comprehending customer preferences, behaviours, and perceptions [5]. Marketing research specifies the information required to address many issues, like designing the method for collecting information, managing and implementing the data collection process, analysing the results, and communicating the findings and their implications [6]. It systematically gathers, records, and analyses data about marketing products and service issues. Marketing research aims to identify and assess how changing elements of the marketing mix impact customer behaviour [7]. The term is commonly interchanged with market research; however, expert practitioners may wish to distinguish between market research is concerned specifically with markets while marketing research is concerned specifically about marketing processes. Also, this process is often partitioned into two sets of categorical pairs: target market, Consumer marketing research, and Business-to-business (B2B) marketing research, or by methodological approach, Qualitative marketing research, and Quantitative marketing research [8]. Consumer marketing research is a form of applied sociology that concentrates on understanding consumers’ preferences, attitudes, and behaviours in a market-based economy. It aims to understand the effects and comparative success of marketing campaigns. Arthur Nielsen pioneered consumer marketing research as a statistical science with the founding of the AC Nielsen Company in 1923 [9].
1.1 Concept of Customer Satisfaction
A fundamental idea in marketing and business management is that customer satisfaction is the level of fulfilment or contentment that consumers feel after interacting with a business, service, or product [10]. It includes their thoughts, emotions, and assessments about several facets of their experience, such as the Caliber of the goods, the promptness of the service, and their general interaction with the business.
Important facets of the customer satisfaction notion consist of:
• Expectations vs. Experience: The degree to which a customer’s experience meets or surpasses their initial expectations is a common way to measure customer satisfaction. When a product or service exceeds clients’ expectations, they will probably be pleased. If, on the other hand, the experience doesn’t live up to expectations, disappointment could result. Customers’ perceptions of quality are influenced by their demands, experiences, and preferences [11]. Subjective elements like dependability, effectiveness, beauty, and value for money are important in determining quality. When goods or services match their expectations for quality, customers are most likely to be happy.
• Service Delivery: The way goods or services are delivered can greatly impact customer satisfaction. Prompt and courteous service, customized attention, efficient problem resolution, and general attentiveness influence positive customer experiences and better satisfaction levels. Experiences with the brand at different touchpoints during the consumer journey shape impressions of it [12].
• Emotional Engagement: Satisfaction with customers results from both rational and emotional factors. Positive feelings like joy, delight, and trust foster stronger emotional ties between consumers and brands and are linked to high satisfaction levels [13]. Engaging customers emotionally increases their advocacy and loyalty, encouraging repeat business and positive word-of-mouth referrals.
• Loyalty and Retention: Happy consumers are more likely to stick with a brand over time and make repeat purchases of its goods or services [14]. Additionally, they are more likely to tell others about the company, which helps with customer acquisition and retention initiatives. Consequently, establishing and sustaining client happiness is essential to long-term company success.
• Continuous Improvement: Due to shifting consumer tastes, industry developments, and competitive pressures, customer satisfaction is dynamic and prone to change. Companies must use performance indicators, market research, and feedback channels to monitor and evaluate customer satisfaction. Businesses may improve customer satisfaction and keep a competitive edge by recognizing areas for improvement and making necessary adjustments to meet changing needs [15]. Customer satisfaction measures how well a client’s wants and expectations are satisfied when contacting a business, service, or product.
The banking industry’s primary goal is to offer financial services and solutions that satisfy customers’ requirements and expectations, and customer satisfaction is intrinsically linked to this goal [16].
1.2 Banking Sector
In the banking sector, many factors contribute to customer satisfaction: service quality, product selection, ease of use, accessibility, and overall customer experience. Maintaining long-term connections with their client and thriving in today’s competitive landscape need banks to understand and improve customer satisfaction [17]. The calibre of services offered is one of the main factors influencing client satisfaction in the banking industry. This includes how quickly bank personnel respond to you, how smoothly transactions go, how accurate the information is, and how professionally all of the bank’s employees act. Banks must meet various consumer needs, from simple savings and checking accounts to more complicated financial products, including loans, mortgages, and investment possibilities. Customers’ satisfaction levels can be raised by offering creative and personalized products at transparent and upfront competitive prices. In the banking industry, accessibility and convenience are also significant determinants of client happiness [18]. Customers now demand smooth online and mobile banking experiences with features like simple account access, bill payment, fund transfers, and round-the-clock customer service, thanks to the development of digital banking technologies. User-friendly interfaces, ATM networks, and branch availability all add to the convenience and happiness of customers. Additionally, relationship management and tailored communication can significantly impact client satisfaction in the banking industry. Proactive relationship managers, tailored advice, and attentive service can foster customers’ trust and loyalty, ultimately increasing their satisfaction with the bank [19].
Customer satisfaction is a complex topic in the banking industry that includes a range of factors such as product choices, ease, trust, and customized relationships. Banks are better positioned to build enduring connections, encourage client loyalty, and prosper in a market that is becoming increasingly competitive when they prioritize comprehending and meeting their customers’ needs and preferences. To analyse customer satisfaction with personal loans, the author used factor analysis as a tool. Factor analysis offers a strong foundation for comprehending the fundamental aspects of client contentment and locating workable solutions for raising satisfaction levels inside and outside the banking industry. Factor analysis identifies discrete client groups with comparable satisfaction patterns, which banks can use to segment their customer base better. By customizing their product offers and marketing methods to the distinct requirements and tastes of various client segments, banks can increase customer retention and loyalty through segmentation. In conclusion, factor analysis and marketing research are essential elements of strategic decision-making in the banking industry. Banks can gain valuable insights into the factors that drive customer satisfaction, optimize their offerings, and improve overall customer experiences by utilizing factor analysis techniques and marketing research methodologies. This can lead to developing long-term relationships and a competitive advantage in the market. The study employs OR methods and factor analysis to obtain practical insights to guide strategic choices intended to improve customer satisfaction and foster long-term growth in the banking industry, focusing on Yes Bank.
The rest of the study has been systematized as follows: Section 2 gives an extensive literature review on customer satisfaction in the banking sector. Sections 3 and 3.1 describe the Research Methodology of this study. Section 3.2 interprets the research methodology used, which is Factor Analysis. The final results of this study are described in Section 4. The author has concluded this study in Section 5, and future scopes and limitations of the study are also discussed in the Sections 5.1 and 5.2.
2 Literature Review
This section has extensively reviewed the literature on customer satisfaction in the banking sector to understand the comprehensive construction and research objective. The banking sector mainly relies on customer service engagement and perspective, and customers, the backbone of the economy, have a significant role in determining their satisfaction.
Parasuraman et al. [20] presented a multi-item scale technique for measuring customer perception in the service industry in their study. They have emphasized the importance of studying customer perception and satisfaction. Hanif et al. [21] studied various factors that affect customer satisfaction. Singh et al. [22] empirically studied customer satisfaction and universal banks. The customer perception and satisfaction towards home loans have been analysed by Chaudhary and Janjhua [23]. Dawar [24] identified critical factors influencing customer satisfaction in banking using factor analysis. Out of the initial 22 attributes, factor analysis was reduced to 6 factors. Linking the banking industry with loan procedure variables, Jain et al. [25] studied the impact of demographic characteristics on customer satisfaction. Customer relationship management is based on increasing customer satisfaction analysed by Tao [26]. In the same year, Kapur et al. [27] presented a study for evaluation based on the expectations of customers and their satisfaction. Using logistic regression in 2016, Anand and Bansal predicted customer satisfaction and dissatisfaction. Begum et al. [28] used a confirmatory factor analysis to determine customer satisfaction with corporate loans. Later, Anand et al. [29] quantified the adopters as satisfied users and dissatisfied customers. In the year 2021, Tien et al. [30] studied customer satisfaction at a bank and used factor analysis for dimension reduction. Agag et al. [31] studied marketing analytics to understand the relationship between customer agility and customer satisfaction. These studies offer a solid framework for examining consumer satisfaction with personal loans in the banking industry. This aligns with the study’s goals of employing operational research (OR) to evaluate customer satisfaction with a specific focus on Yes Bank.
3 Research Methodology
For this study, the authors conducted a survey on customers of Yes Bank at Hudson Lane, Kingsway Camp, New Delhi-110009, India, in the age group of 21–60 years, to analyse how satisfied they are with the services of the bank under consideration. They were intentionally chosen as this market is vital, easily influenced by its surroundings, and good at buying power. Two hundred eleven customers were administered randomly, and the area where questionnaires were distributed was the University of Delhi.
Table 1 is a representation of the customers as per their educational/ qualification level:
Table 1 Frequency table of customers’ qualification
| Education Level | Count of Education Level |
| Doctorate | 6 |
| High School | 4 |
| Other | 6 |
| Postgraduate | 43 |
| Secondary School | 5 |
| Undergraduate | 146 |
| Grand Total | 210 |
In this study, customers were asked to rate their satisfaction with various attributes connected to the dissemination of personal loans. Apart from the different demographic questions, they rated various attributes from 1 to 5, with one being the least satisfied and 5 being the highest satisfaction with a particular attribute.
The authors calculated the weighted mean of those attributes to determine which attributes have the most impact on making the brand more qualitative and valuable among customers. The table below showcases the number of persons catered in the different categories based on their occupation.
Table 2 Occupation-wise division of the customers
| Occupation | Count of Education Level |
| Business | 33 |
| Government Employee | 62 |
| Home Maker | 4 |
| Other | 23 |
| Private Job | 70 |
| Retired | 1 |
| Self Employed | 18 |
| Grand Total | 211 |
Table 2 shows that most of the respondents are private sector employees, followed by the government sector, and so on.
Table 3 describes the various reasons why particular loans have been taken from the bank.
Here, it can be seen that most of the respondents took a loan to finance a new venture after that for medical emergencies, debt consolidation, and so on.
Table 3 Reasons for availing of personal loans
| Purpose for Availing of Personal Loan | Count of Education Level |
| Debt consolidation | 35 |
| Finance a new venture | 52 |
| Home remodelling | 33 |
| Medical Emergencies | 38 |
| Other | 25 |
| Vacation Costs | 6 |
| Wedding Expenses | 22 |
| Grand Total | 211 |
After fetching useful information from the demographics, the next step is moving from demography toward dimensionality reduction. As described above, the various attributes under consideration are presented to the customers, and they are asked to rate them based on their satisfaction level. Before moving toward the analysis, it is essential to understand factor analysis. In the next section, the author briefly describes the methodology used – Factor Analysis.
3.1 Factor Analysis
A statistical method called factor analysis is used to find underlying dimensions or factors that account for patterns of correlations between a group of observed data. Factor analysis can be used in customer satisfaction to determine the main aspects influencing overall satisfaction and the underlying structure of variables connected to satisfaction.
The following actions can be conducted to perform factor analysis on customer satisfaction data:
• Step 1: Data collection: Compile information on a range of customer satisfaction factors, including product characteristics, the calibre of the services, cost, ease of use, and overall experience. Feedback forms, interviews, and surveys can all be used to get this information.
• Step 2: Choose the variables (or survey questions) that best capture the many facets of customer satisfaction. These variables should encompass a broad range of satisfaction parameters and be pertinent to the area of interest.
• Step 3: Data Preparation: Ensure the data satisfies the requirements of factor analysis, including continuous variables, a sizable enough sample size, and no problems with multicollinearity. It can be necessary to attribute or exclude missing data.
• Step 4: Factor Extraction: Use methods for factor extraction, such as joint factor analysis or principal component analysis (PCA), to find the underlying factors that account for the patterns of correlations between the variables. The aim is to reduce the number of dimensions in the data while maintaining the maximum amount of information.
• Step 5: Factor Rotation: After extracting the factors, use rotation techniques like varimax or rotation to create a more straightforward and understandable factor structure. Rotation makes it simpler to see how variables and factors relate to one another.
• Step 6: Factor Interpretation: To understand the results, look at the factor loadings, which show how strongly and in which direction each variable is related to the underlying factors. High-loading variables on a given factor are thought to be closely related to that factor.
• Step 7: Naming & Labelling: Based on the analysis of factor loadings, give the extracted factors that represent the fundamental aspects of customer satisfaction appropriate names or labels. These labels should reflect each component’s variables’ general themes or traits.
• Step 8: Assessment of Validity and Reliability: Determine the extracted variables’ internal consistency and construct validity to assess their validity and reliability. The reliability of the factors can be evaluated using methods like Cronbach’s alpha, and the model fit and validity of the factor structure can be examined using confirmatory factor analysis (CFA).
• Step 9: Application and Interpretation: Apply the final factor structure to customer satisfaction and use the insights gained to improve customer experiences. The factors identified have the potential to impact strategic choices about product creation, marketing tactics, improving customer service, and general business operations.
The authors have used 16 variables to perform a factor analysis to analyse customer satisfaction with personal loans. Table 4 represents the descriptive statistics performed on these 16 variables.
Table 4 Descriptive statistics
| Mean | Std. Deviation | Analysis N | |
| Debt to Income Ratio | 3.50 | 1.319 | 208 |
| Top Up Loan | 3.74 | 1.155 | 208 |
| Employment History | 3.39 | 1.506 | 208 |
| Efficient Staff | 3.30 | 1.040 | 208 |
| Monthly installment | 3.41 | 1.201 | 208 |
| Rate of Interest | 4.09 | 1.147 | 208 |
| Time taken for the verification | 3.13 | 1.062 | 208 |
| Credit Score (CIBIL Score) | 3.72 | 1.448 | 208 |
| E-Verification | 3.53 | 0.962 | 208 |
| Penalty fees | 2.99 | 1.026 | 208 |
| Loan Repayment | 2.89 | 1.331 | 208 |
| Following RBI Guidelines | 3.23 | 0.994 | 208 |
| Facility of loan extension | 3.77 | 1.033 | 208 |
| Quick Loan Approval facility | 3.54 | 1.289 | 208 |
| Documentation Process | 3.08 | 1.166 | 208 |
| Low File Closure Charge | 3.23 | 1.356 | 208 |
But before proceeding with factor analysis, we need to test the KMO and Bartlett’s tests to determine the data’s suitability. This test measures sampling adequacy for each variable in the model and for the complete model. The statistic measures the proportion of variance among variables that might be common variance. The lower the proportion, the more suitable the collected data is for factor analysis.
As a rule of thumb for interpreting the statistics:
• KMO values between 0.8 and 1 indicate the sampling is adequate.
• KMO values less than 0.6 indicate inadequate sampling, and that remedial action should be taken. Some authors put this value at 0.5, so use your judgment for values between 0.5 and 0.6.
• KMO values close to zero mean there are large partial correlations compared to the sum of correlations.
Table 5 KMO and Bartlett’s test
| Kaiser-Meyer-Olkin Measure of Sampling Adequacy | 0.825 | |
| Bartlett’s Test of Sphericity | Approx. Chi-Square | 1035.129 |
| df | 120 | |
| Sig. | 0.001 | |
For the collected data, the KMO value is 0.825, which can be seen in Table 5, which falls into the excellent range, so we can be confident that the sample size is adequate for factor analysis. Bartlett’s test is a statistic used to examine the hypothesis that the variables are uncorrelated in the population. From the data, we have the KMO value of 0.825 0.5. Thus, factor analysis is appropriate for this case. Hence, we can further proceed with the analysis.
The foundation of factor analysis is to study the various variables under a limited set of technical definitions, which can then be presented to management. Around 16 different variables were considered, and the relevant queries regarding their satisfaction level were sent to the customers. Some findings are presented below in Table 6.
| Initial | Extraction | |
| Debt to Income Ratio | 1.000 | 0.584 |
| Top Up Loan | 1.000 | 0.472 |
| Employment History | 1.000 | 0.607 |
| Efficient Staff | 1.000 | 0.694 |
| Monthly installment | 1.000 | 0.644 |
| Rate of Interest | 1.000 | 0.762 |
| Time taken for the verification | 1.000 | 0.504 |
| Credit Score (CIBIL Score) | 1.000 | 0.419 |
| E-Verification | 1.000 | 0.679 |
| Penalty fees | 1.000 | 0.684 |
| Loan Repayment | 1.000 | 0.584 |
| Following RBI Guidelines | 1.000 | 0.637 |
| Facility of loan extension | 1.000 | 0.537 |
| Quick Loan Approval facility | 1.000 | 0.575 |
| Documentation Process | 1.000 | 0.399 |
| Low File Closure Charge | 1.000 | 0.316 |
Table 7 Total variance explained
| Initial Eigenvalues | Extraction Sums of Squared Loadings | Rotation Sums of Squared Loadings | |||||
| Component | Total | % of Variance | Cumulative % | Total | % of Variance | Cumulative % | Total |
| 1 | 6.402 | 40.011 | 40.011 | 6.402 | 40.011 | 40.011 | 5.709 |
| 2 | 1.283 | 8.019 | 48.030 | 1.283 | 8.019 | 48.030 | 1.334 |
| 3 | 1.222 | 7.637 | 55.667 | 1.222 | 7.637 | 55.667 | 4.289 |
| 4 | 1.012 | 6.328 | 61.995 | 1.012 | 6.328 | 61.995 | 1.222 |
| 5 | 0.816 | 5.097 | 67.093 | ||||
| 6 | 0.776 | 4.851 | 71.944 | ||||
| 7 | 0.713 | 4.459 | 76.402 | ||||
| 8 | 0.694 | 4.339 | 80.741 | ||||
| 9 | 0.561 | 3.509 | 84.250 | ||||
| 10 | 0.545 | 3.403 | 87.653 | ||||
| 11 | 0.443 | 2.766 | 90.419 | ||||
| 12 | 0.397 | 2.479 | 92.899 | ||||
| 13 | 0.375 | 2.347 | 95.245 | ||||
| 14 | 0.312 | 1.953 | 97.198 | ||||
| 15 | 0.255 | 1.596 | 98.794 | ||||
| 16 | 0.193 | 1.206 | 100.000 | ||||
The amount of variance in each variable that is accounted for is shown in this table. Estimates of the variance in each variable accounted for by all components or factors are known as initial communalities. Estimates of the variance in each variable that the components account for are known as extraction communalities. The factors explain all variance, and there is only one communality prior to extraction because there are precisely as many factors as there are variables. Unfortunately, some of the components are eliminated following extraction, which results in the loss of some information. While they can only account for a portion of the variance in the data, the retained components cannot fully explain all of it. Following that, four elements are removed and kept (as shown in Table 7); the communality in the above table is 0.58 for the Debt-to-income ratio, 0.47 for Top-Up loan, and so on. Four factors have been retrieved from the 16 variables; factors with eigenvalues greater than one are presumed to be extracted. The above table (all values are called communality in the above table) shows that the communality is 40% for variable 1, 48% for variable 2, and so on after the factors are extracted and retained. Finding the extracted variables, their Eigenvalues, and the cumulative percentage of variance is the first step in evaluating the results. The cumulative percentage column shows that the four factors extracted—information from the 16 original variables—account for 55% of the variance overall.
Table 8 represents the Rotated Component Matrix, and Table 9 represents the Component Matrix. These tables are required to interpret or summarize the results. These tables report the factor loadings for each variable on the factors or components after rotation. Each number represents the partial correlation between the item and the rotated factor.
Table 8 Rotated component matrix
| Component | Component | Component | Component | |
| 1 | 2 | 3 | 4 | |
| Debt to Income Ratio | 0.720 | |||
| Top Up Loan | 0.590 | |||
| Employment History | 0.745 | |||
| Efficient Staff | 0.773 | |||
| Monthly installment | 0.557 | |||
| Rate of Interest | 0.786 | |||
| Time taken for the verification | 0.598 | |||
| Credit Score CIBIL Score | 0.585 | |||
| E- Verification | 0.752 | |||
| Penalty fees | 0.810 | |||
| Loan Repayment | 0.752 | |||
| Follow RBI Guidelines | 0.731 | |||
| Facility of loan extension | 0.718 | |||
| Quick Loan Approval | 0.601 | |||
| Documentation Process | ||||
| Low Foreclosure Charge | 0.537 | |||
| Extraction Method: Principal Component Analysis. | ||||
| Rotation Method: Varimax with Kaiser Normalization. | ||||
| a. Rotation converged in 7 iterations. | ||||
Table 9 Component transformation matrix
| Component | 1 | 2 | 3 | 4 |
| 1 | 0.816 | 0.345 | 0.442 | 0.140 |
| 2 | -0.381 | 0.915 | -0.050 | 0.122 |
| 3 | -0.244 | -0.202 | 0.325 | 0.891 |
| 4 | -0.360 | -0.049 | 0.835 | -0.414 |
| Extraction Method: Principal Component Analysis. | ||||
| Rotation Method: Varimax with Kaiser Normalization. | ||||
The authors are able to reduce the 16 variables to 4 factors that are free from redundancy. After completing the statistical computations factoring, the next step is interpreting these factors. This is achieved by inspecting the pattern of high and low loading of each of the factors on the variables.
3.2 Interpretation of Factor Analysis
The results of those above require some discussion. From Table 7 of the total variance explained, it can be seen that the total 16 attributes can be clubbed under the four factors in totality. They can be given specific names to aid the management so that instead of studying the 16 attributes separately, only four factors can be worked upon. Based on the values obtained in the rotated component matrix, around eight attributes can be clubbed under one canopy based on the values of the factor loadings.
Factor 1: Loan processing efficiency
| Attributes | Factor loading |
| Efficient staff | 0.773 |
| Time taken for verification | 0.598 |
| E-Verification | 0.752 |
| Penalty fees | 0.810 |
| Follow RBI Guidelines | 0.731 |
| Quick loan Approval | 0.601 |
| Low foreclosure charge | 0.537 |
Factor 1 mainly comprises the personal loan processing efficiency attributes influencing the respondents to take a loan. All the attributes have high factor loading. The attribute ‘Penalty fees’ has the highest factor loading of 0.810, indicating that this factor influences this factor the most.
Similarly, other factors can be created and named accordingly.
Factor 2: Revolving Credit
| Attributes | Factor loading |
| Top-Up Loan | 0.590 |
| Rate of Interest | 0.786 |
| Facility of loan extension | 0.718 |
Factor 2 mainly comprises the Revolving Credit attributes of the personal loan that influence the respondents to purchase. All the attributes have high factor loading. The attribute ‘Rate of Interest’ has the highest factor loading of 0.78, which indicates that this attribute influences this factor the most.
Factor 3: Financial Liabilities and Employment History
| Attributes | Factor Loading |
| Debt to income ratio | 0.720 |
| Employment history | 0.745 |
| Credit Score | 0.585 |
Factor 3 comprises mainly the Financial and employment attributes of the personal loan that influence the respondents’ purchase. All the attributes have higher factor loading values. The attribute ‘Employment History’ has the highest factor loading of 0.74, which indicates that this attribute influences this factor the most. All the attributes within this factor have positive loading.
Factor 4: Payment Mode
| Attributes | Factor loading |
| Monthly installment | 0.557 |
| Loan Repayments | 0.752 |
Factor 4 mainly comprises the Payment attributes of the personal loan that influence the respondents to purchase. All the attributes have high factor loading. The attribute ‘Loan Repayments’ has the highest factor loading of 0.752, indicating that this factor influences this factor the most.
4 Result Discussion
The profile of the related respondents was recorded, wherein it can be seen that out of the 211 customers interviewed for this survey, 75.71% were found to be male, and 25.29% were found to be female. It can be seen that the majority of the loan wishers here are male as loan requirements for business purposes to finance a new venture are more demanded by male people and for medical emergencies, too. To analyze the age buckets under which the customers that were interviewed were falling, the age profiling of the respondents was done. It came out that around 59% of the respondents were falling under the age criteria of 31-40 years, making it the majority, whereas 29% were under 21–30 years, and the rest were under 8.53% and 2.37%, respectively. This shows that most of the customers have proper knowledge about the loan scheme they will take. An educated person makes wiser choices and is aware of the market scenarios. Here, the education level of the respondents showed the results depicting the level of qualification that respondents had. It was seen that 69% of the respondents were graduated and fell under the majority section, with 20% of the customers being postgraduate. This shows that most customers are educated and know about market scenarios well.
4.1 Implications for Practice
The study’s conclusions about the banking sector’s customer satisfaction with personal loans have important practical implications. First off, banks may better serve their customers by customizing their services by knowing the important factors that influence customer happiness, such as interest rates, loan conditions, and the calibre of customer care. To improve customer delivery, this can entail updating loan products, expediting the application process, or funding employee training. Second, pinpointing areas of low customer satisfaction gives banks useful information for enhancing their operations. Banks may proactively improve their service offerings and cultivate customer happiness and loyalty by using feedback tools to gain a deeper understanding of client preferences or addressing frequent pain points encountered during the loan application process.
5 Conclusion
The primary purpose of this study is to understand customer satisfaction with Personal loans with reference to Yes Bank. This study found that employees in small ventures or private jobs mostly take personal loans. In most cases, customers are aware of the loan schemes through the telephone from the bank. Most of the respondents have taken loans of 5-10 lakhs. Furthermore, the reason for choosing the respective bank is that the respondent is already a bank customer due to competitive interest rates. Nowadays, Yes Bank takes only one week to sanction the loan. Most customers pay the EMI through Electronic Clearance Services (ECS). Most importantly, Yes Bank charges low for the foreclosure loan. The staff is also efficient in the bank, following RBI guidelines and providing a top-up loan facility with a low-interest rate.
5.1 Limitations
In this study, only exploratory factor analysis was done to summarize the variable. Further, confirmatory factor analysis can be done to delve into the study. The study was only conducted in the Delhi region, so the limited study area may affect the conclusions. Hence, this study can be extended by increasing the sample size, and subsequently, the analysis will be much more appropriate, and results may improve.
5.2 Future Scope
There is an enormous amount of scope for further research and application in the area of customer satisfaction with personal loans in the banking sector. Longitudinal analyses could be carried out to monitor changes in satisfaction over time and identify developing trends and possible areas for development. Furthermore, dividing up the clientele according to different criteria, such as behaviour or demographics, may offer a more in-depth understanding of the disparate needs of separate groups, guiding customized banking tactics. Cross-cultural comparisons could clarify how cultural differences affect satisfaction levels and help banks provide effective services to diversified customers. In light of the current digital landscape, banks looking to maximize their digital services may find it crucial to examine how technology adoption affects consumer satisfaction with digital lending procedures. Additionally, examining how customer experience, contentment, and service quality interact may help identify areas for development that have the biggest effects on consumer perceptions.
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Biographies
Divya received a bachelor’s degree in Mathematics from the University of Delhi in 2020, and a master’s degree in operational research from the University of Delhi in 2022, and she is currently pursuing a doctorate degree in Department of Operational Research from the University of Delhi.
Deepti Aggrawal is currently working as Assistant Professor at USME, Delhi Technological University, India. She obtained her PhD degree from Department of Operational Research, University of Delhi. She was Operations Manager in Axis Bank till she joined as a research scholar in the Department of Operational Research in 2011. Her Research areas include Marketing and Software Reliability. She is a life member of SREQOM and has publications in journals of national and international repute.
Adarsh Anand did his doctorate in the area of Innovation Diffusion Modeling in Marketing and Software Reliability Assessment. Presently he is working as an Associate Professor in the Department of Operational Research, University of Delhi (INDIA). He has been conferred with Young Promising Researcher in the field of Technology Management and Software Reliability by Society for Reliability Engineering, Quality and Operations Management (SREQOM) in 2012. He is a lifetime member of the Society for Reliability Engineering, Quality and Operations Management (SREQOM). He is also on the editorial board of International Journal of System Assurance and Engineering management (Springer). He has Guest edited several Special Issues for Journals of international repute. He has edited two books namely: “System Reliability Management (Solutions and Technologies)” and “Recent Advancements in Software Reliability Assurance” under the banner of Taylor and Francis (CRC – Press). He has publications in journals of national and international repute. His research interest includes modeling innovation adoption and successive generations in marketing, software reliability growth modelling and social media analysis.
Journal of Graphic Era University, Vol. 12_1, 1–22.
doi: 10.13052/jgeu0975-1416.12110
© 2024 River Publishers