This document is a research project submitted by Kuldeep Mathur to fulfill requirements for a Master of Business Administration degree from Jiwaji University in Gwalior, India. The project examines the relationship between the 7Ps of the services marketing mix (product, price, place, promotion, people, physical evidence, and process) and customer outcomes in the banking sector in India. The introduction provides background on changes in the Indian banking industry and importance of an effective services marketing mix. The literature review explores previous research on customers, the 7Ps framework, and need to examine the 7Ps strategy in the Indian banking context. The study aims to determine the most important 7Ps factors for creating an appropriate marketing mix strategy from the Indian customer
Customer perception towards banking servicesPriyank Thada
This is my Dissertation Project on Customer Perception on Banking services in India this will help people to do research on Banking sector • The purpose of the study is to explore the basic dimensions of service quality offered by Indian banking industry and its impact on individual customers by using the gap between the customer expectations and perceptions regarding the services offered by banking industry.
The cutting edge for business today is e-commerce.It means dealing in goods and services through the electronic media and internet. On the internet, it relates to a website of the vendor, who sells products or services directly to the customer from the portal using a digital shopping cart or digital shopping basket system and allows payment through credit card, debit card or EFT (Electronic fund transfer) payments. E-commerce or E-business involves carrying on a business with the help of the internet and by using the information technology like Electronic Data Interchange (EDI).
Customer perception towards banking servicesPriyank Thada
This is my Dissertation Project on Customer Perception on Banking services in India this will help people to do research on Banking sector • The purpose of the study is to explore the basic dimensions of service quality offered by Indian banking industry and its impact on individual customers by using the gap between the customer expectations and perceptions regarding the services offered by banking industry.
The cutting edge for business today is e-commerce.It means dealing in goods and services through the electronic media and internet. On the internet, it relates to a website of the vendor, who sells products or services directly to the customer from the portal using a digital shopping cart or digital shopping basket system and allows payment through credit card, debit card or EFT (Electronic fund transfer) payments. E-commerce or E-business involves carrying on a business with the help of the internet and by using the information technology like Electronic Data Interchange (EDI).
Quality of online banking services a comparative study of sbi & icici bankKarishma Prajapati
a research is conducted on the quality of online banking services of SBI & ICICI bank in india. A small survey is conducted with 50 limited respondents.
A study on Comsumer Behaviour towards Online Shopping in MaharashtraGauri Belan
To identify the consumer’ s awareness about online shopping. To analyze the
consumers preferred websites for online shopping. To identify the various factors for influence consumers towards online shopping
A Study of Impact of Customer Satisfaction on Online Shoppingijtsrd
Customer satisfaction is considered important for online shopping. Researching what leads to customer satisfaction has become paramount for online businesses. Thus, the goal of this work was to identify the determinants of customer satisfaction in an online context. In this work, the authors proposed a conceptual model of customer satisfaction in an online context, identifying key factors proposed in previous studies, and hypotheses were developed accordingly. Hypotheses were tested using multiple regression analysis based on a sample of 50 online clients. The work found that customer service, website design, and perceptions of security were largely related to customer satisfaction on the internet. Nyamsuren Bayartogtoh | Gantogtoh Tsogtgerel | Ariuntuya Erdenebaatar "A Study of Impact of Customer Satisfaction on Online Shopping" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29710.pdf Paper URL: https://www.ijtsrd.com/management/management-development/29710/a-study-of-impact-of-customer-satisfaction-on-online-shopping/nyamsuren-bayartogtoh
Quality of online banking services a comparative study of sbi & icici bankKarishma Prajapati
a research is conducted on the quality of online banking services of SBI & ICICI bank in india. A small survey is conducted with 50 limited respondents.
A study on Comsumer Behaviour towards Online Shopping in MaharashtraGauri Belan
To identify the consumer’ s awareness about online shopping. To analyze the
consumers preferred websites for online shopping. To identify the various factors for influence consumers towards online shopping
A Study of Impact of Customer Satisfaction on Online Shoppingijtsrd
Customer satisfaction is considered important for online shopping. Researching what leads to customer satisfaction has become paramount for online businesses. Thus, the goal of this work was to identify the determinants of customer satisfaction in an online context. In this work, the authors proposed a conceptual model of customer satisfaction in an online context, identifying key factors proposed in previous studies, and hypotheses were developed accordingly. Hypotheses were tested using multiple regression analysis based on a sample of 50 online clients. The work found that customer service, website design, and perceptions of security were largely related to customer satisfaction on the internet. Nyamsuren Bayartogtoh | Gantogtoh Tsogtgerel | Ariuntuya Erdenebaatar "A Study of Impact of Customer Satisfaction on Online Shopping" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29710.pdf Paper URL: https://www.ijtsrd.com/management/management-development/29710/a-study-of-impact-of-customer-satisfaction-on-online-shopping/nyamsuren-bayartogtoh
Relationship Marketing Strategies in Banking Sector: A ReviewIJBBR
The paper is review of relationship marketing strategies prevalent in Banking Sector. In this era of mature and intense competitive pressures, it is imperative that banks maintain a loyal customer base. Nowadays, banks realize the importance ofRelationship Marketing. Relationship marketing offers benefits to the banks,
customers as wellas employees of the organization. Relationship Marketing gives the banks way to developmutually beneficial and valuable long term relationships. These long term relationships are further helping banks in reducing operating cost and attracting new customers.
A Study on Customer Relationship Marketing in Banking Sectorpaperpublications3
Abstract: Modern Marketing Philosophy advocates the concept of customer relationship marketing that creates customer delight. In the banking field a unique relationship exists between the customers and the bank. Due to various reasons like financial burdens, risk of failure, marketing inertia etc., many banks are still following the traditional ways of marketing and only few banks are making attempts to adapt customer relationship marketing. The role of custom er relationship marketing is very vital in leading the banks towards high level and volume of profits. So there is a need to study the role of customer relationship marketing in development and promotion of banking sector through the side-lines of the practices, problems and impact of CRM on banking sector all the time.
Customer relationship marketing is an emerging customer innovation focused on growing customer’s
satisfaction, retention and loyalty that will culminate into bank profitability. The paper investigated the impact
of investment in implementing customer relationship marketing on the performance of commercial banks in
Nigeria. The study was carried out in Federal capital territory Abuja,
The main objective of the study is to explore customer relationship marketing as a competitive tool at Best Point Savings and Loans Limited. A cross-sectional research and quantitative approach was adopted for the study. A non-random quota sampling technique was used to select a sample size of 20 staff members. Questionnaires were adopted to collect data from the staff. Data was descriptively analyzed. Findings from the study revealed that Customer Relationship Marketing strategy in Best Point Savings and Loans Limited to create and retain profitable customers
CUSTOMERS SATISFACTION TOWARDS CRM PRACTICES ADHERED BY PUBLIC SECTOR BANKS I...IAEME Publication
In a country, banking sector servers as the foundation on which the pillars of economic growth and development can be constructed. With the major reforms in banking sector, the scenario of bank market has been changed. Target market of banking sector has become highly competitive, dynamic and fragmented. Hence, there is a need for a shift from the transactional marketing strategy to relationship-based marketing strategy in all the banks. The current study attempts to conduct a study of deployment of CRM Practices Adopted by Public Sector Banks in E-Banking Era specific to an Indian Public sector bank, The research objective involves describing how the selected bank is deploying the CRM Best Practices toward building relationships with their customers.
Customer Relationship Management is human function than technology
implementation. Banks need to constantly orient their employees and
vendors towards never losing focus of the customers, technology can be
harnessed to unable the human aspect to function more effectively. A
successful relationship will be one that lowers the business cost, increase
the company revenue and retains profitable relationship, win situation for
the company business and its valuable assets and business customers. The
banking business is becoming more and more complex with the changes
emerging from the liberalisation and globalisation. For new banks,
customer creation is important but established bank retention is much
more necessary with cost effective mechanism. The big benefit of customer
relationship management is the visibility of everything relating to
customers going on in your company. If an organization wants to provide
the better service to customers, it has to be able to manage everything
from complaints to sale opportunities. The core theme of all customer
relationship management and relationship marketing perspectives is to
focus on cooperative, collaborative relationship between the firm and its
customers and look after the marketing factors. Through this paper
researcher want to show the significance of customer relationship
management and also made as effort to explore the benefits of this concept
to banking industry.
Keywords: Customer Relationship Management, Efficiency, Customer Expectation.
Customer relationship managment in banking industryTapasya123
Customer Relationship Management is human function than technology
implementation. Banks need to constantly orient their employees and
vendors towards never losing focus of the customers, technology can be
harnessed to unable the human aspect to function more effectively. A
successful relationship will be one that lowers the business cost, increase
the company revenue and retains profitable relationship, win situation for
the company business and its valuable assets and business customers. The
banking business is becoming more and more complex with the changes
emerging from the liberalisation and globalisation. For new banks,
customer creation is important but established bank retention is much
more necessary with cost effective mechanism. The big benefit of customer
relationship management is the visibility of everything relating to
customers going on in your company. If an organization wants to provide
the better service to customers, it has to be able to manage everything
from complaints to sale opportunities. The core theme of all customer
relationship management and relationship marketing perspectives is to
focus on cooperative, collaborative relationship between the firm and its
customers and look after the marketing factors. Through this paper
researcher want to show the significance of customer relationship
management and also made as effort to explore the benefits of this concept
to banking industry.
Abstract: Customer satisfaction is a key factor in any organization; banks are looking to earn a stable income to our loyal customers. For many years the emergence of the marketing concept and are it to attract customer in the past. The organization can be found and the principle of marketing are not met concern is not attracting customers. The competitiveness of financial markets and financial institution and development banks, and credit, marketing and implementation of marketing strategies to attract customer. The Role of marketing in the banking industry continues to change. For many years the primary focus of bank marketing was public relations. Then the focus shifted to advertising and sales promotion. That was followed by focus on the development of a sales culture.
The Relationship between Customer Knowledge Management, Customer Relationship...inventionjournals
ABSTRACT: This study was aimed to assess the relationship between customer knowledge management and customer relationship management, with organizational innovation and customer loyalty (for consumers and retailers of protein industry in Isfahan province). The statistical population consisted of all employees (1385 people) and customers of nine production and distribution groups of the protein products in Isfahan province. Corresponding to the number in each group, stratified random sampling was made based on the contributions made, and 451 questionnaires were analyzed regarding the number of people in each group. The findings showed that there was a significant correlation between customer loyalty and customer relationship management, customer knowledge management and organizational innovation (P<0.01). The values of effective coefficients; β, showed that for every unit increase in innovation, knowledge management, and customer relationship management, the customer loyalty was increased 0.332, 0.331 and 0.331 units, respectively. According to the results of this study, it could be suggested that the protein industry retailers, must incorporate knowledge based and relationship marketing tools, such as customer relationship management, and customer knowledge management, for implementing customer loyalty strategies. Results of this study showed that organizational innovation should be considered as the first priority for implementing loyalty strategies of the organization.
Most small businesses struggle to see marketing results. In this session, we will eliminate any confusion about what to do next, solving your marketing problems so your business can thrive. You’ll learn how to create a foundational marketing OS (operating system) based on neuroscience and backed by real-world results. You’ll be taught how to develop deep customer connections, and how to have your CRM dynamically segment and sell at any stage in the customer’s journey. By the end of the session, you’ll remove confusion and chaos and replace it with clarity and confidence for long-term marketing success.
Key Takeaways:
• Uncover the power of a foundational marketing system that dynamically communicates with prospects and customers on autopilot.
• Harness neuroscience and Tribal Alignment to transform your communication strategies, turning potential clients into fans and those fans into loyal customers.
• Discover the art of automated segmentation, pinpointing your most lucrative customers and identifying the optimal moments for successful conversions.
• Streamline your business with a content production plan that eliminates guesswork, wasted time, and money.
A.I. (artificial intelligence) platforms are popping up all the time, and many of them can and should be used to help grow your brand, increase your sales and decrease your marketing costs.In this presentation:We will review some of the best AI platforms that are available for you to use.We will interact with some of the platforms in real-time, so attendees can see how they work.We will also look at some current brands that are using AI to help them create marketing messages, saving them time and money in the process. Lastly, we will discuss the pros and cons of using AI in marketing & branding and have a lively conversation that includes comments from the audience.
Key Takeaways:
Attendees will learn about LLM platforms, like ChatGPT, and how they work, with preset examples and real time interactions with the platform. Attendees will learn about other AI platforms that are creating graphic design elements at the push of a button...pre-set examples and real-time interactions.Attendees will discuss the pros & cons of AI in marketing + branding and share their perspectives with one another. Attendees will learn about the cost savings and the time savings associated with using AI, should they choose to.
The digital marketing industry is changing faster than ever and those who don’t adapt with the times are losing market share. Where should marketers be focusing their efforts? What strategies are the experts seeing get the best results? Get up-to-speed with the latest industry insights, trends and predictions for the future in this panel discussion with some leading digital marketing experts.
The session includes a brief history of the evolution of search before diving into the roles technology, content, and links play in developing a powerful SEO strategy in a world of Generative AI and social search. Discover how to optimize for TikTok searches, Google's Gemini, and Search Generative Experience while developing a powerful arsenal of tools and templates to help maximize the effectiveness of your SEO initiatives.
Key Takeaways:
Understand how search engines work
Be able to find out where your users search
Know what is required for each discipline of SEO
Feel confident creating an SEO Plan
Confidently measure SEO performance
For too many years marketing and sales have operated in silos...while in some forward thinking companies, the two organizations work together to drive new opportunity development and revenue. This session will explore the lessons learned in that beautiful dance that can occur when marketing and sales work together...to drive new opportunity development, account expansion and customer satisfaction.
No, this is not a conversation about MQLs and SQLs. Instead we will focus on a framework that allows the two organizations to drive company success together.
SMM Cheap - No. 1 SMM panel in the worldsmmpanel567
Boost your social media marketing with our SMM Panel services offering SMM Cheap services! Get cost-effective services for your business and increase followers, likes, and engagement across all social media platforms. Get affordable services perfect for businesses and influencers looking to increase their social proof. See how cheap SMM strategies can help improve your social media presence and be a pro at the social media game.
10 Video Ideas Any Business Can Make RIGHT NOW!
You'll never draw a blank again on what kind of video to make for your business. Go beyond the basic categories and truly reimagine a brand new advanced way to brainstorm video content creation. During this masterclass you'll be challenged to think creatively and outside of the box and view your videos through lenses you may have never thought of previously. It's guaranteed that you'll leave with more than 10 video ideas, but I like to under-promise and over-deliver. Don't miss this session.
Key Takeaways:
How to use the Video Matrix
How to use additional "Lenses"
Where to source original video ideas
Unleash the power of UK SEO with Brand Highlighters! Our guide delves into the unique search landscape of Britain, equipping you with targeted strategies to dominate UK search engine results. Discover local SEO tactics, keyword magic for UK audiences, and mobile optimization secrets. Get your website seen by the right people and propel your brand to the top of UK searches.
To learn more: https://brandhighlighters.co.uk/blog/top-seo-agencies-uk/
Monthly Social Media News Update May 2024Andy Lambert
TL;DR. These are the three themes that stood out to us over the course of last month.
1️⃣ Social media is becoming increasingly significant for brand discovery. Marketers are now understanding the impact of social and budgets are shifting accordingly.
2️⃣ Instagram’s new algorithm and latest guidance will help us maintain organic growth. Instagram continues to evolve, but Reels remains the most crucial tool for growth.
3️⃣ Collaboration will help us unlock growth. Who we work with will define how fast we grow. Meta continues to evolve their Creator Marketplace and now TikTok are beginning to push ‘collabs’ more too.
Mastering Multi-Touchpoint Content Strategy: Navigate Fragmented User JourneysSearch Engine Journal
Digital platforms are constantly multiplying, and with that, user engagement is becoming more intricate and fragmented.
So how do you effectively navigate distributing and tailoring your content across these various touchpoints?
Watch this webinar as we dive into the evolving landscape of content strategy tailored for today's fragmented user journeys. Understanding how to deliver your content to your users is more crucial than ever, and we’ll provide actionable tips for navigating these intricate challenges.
You’ll learn:
- How today’s users engage with content across various channels and devices.
- The latest methodologies for identifying and addressing content gaps to keep your content strategy proactive and relevant.
- What digital shelf space is and how your content strategy needs to pivot.
With Wayne Cichanski, we’ll explore innovative strategies to map out and meet the diverse needs of your audience, ensuring every piece of content resonates and connects, regardless of where or how it is consumed.
Digital Commerce Lecture for Advanced Digital & Social Media Strategy at UCLA...Valters Lauzums
E-commerce in 2024 is characterized by a dynamic blend of opportunities and significant challenges. Supply chain disruptions and inventory shortages are critical issues, leading to increased shipping delays and rising costs, which impact timely delivery and squeeze profit margins. Efficient logistics management is essential, yet it is often hampered by these external factors. Payment processing, while needing to ensure security and user convenience, grapples with preventing fraud and integrating diverse payment methods, adding another layer of complexity. Furthermore, fulfillment operations require a streamlined approach to handle volume spikes and maintain accuracy in order picking, packing, and shipping, all while meeting customers' heightened expectations for faster delivery times.
Amid these operational challenges, customer data has emerged as an important strategy. By focusing on personalization and enhancing customer experience from historical behavior, businesses can deliver improved website and brand experienced, better product recommendations, optimal promotions, and content to meet individual preferences. Better data analytics can also help in effectively creating marketing campaigns, improving customer retention, and driving product development and inventory management.
Innovative formats such as social commerce and live shopping are beginning to impact the digital commerce landscape, offering new ways to engage with customers and drive sales, and may provide opportunity for brands that have been priced out or seen a downturn with post-pandemic shopping behavior. Social commerce integrates shopping experiences directly into social media platforms, tapping into the massive user bases of these networks to increase reach and engagement. Live shopping, on the other hand, combines entertainment and real-time interaction, providing a dynamic platform for showcasing products and encouraging immediate purchases. These innovations not only enhance customer engagement but also provide valuable data for businesses to refine their strategies and deliver superior shopping experiences.
The e-commerce sector is evolving rapidly, and businesses that effectively manage operational challenges and implement innovative strategies are best positioned for long-term success.
AI-Powered Personalization: Principles, Use Cases, and Its Impact on CROVWO
In today’s era of AI, personalization is more than just a trend—it’s a fundamental strategy that unlocks numerous opportunities.
When done effectively, personalization builds trust, loyalty, and satisfaction among your users—key factors for business success. However, relying solely on AI capabilities isn’t enough. You need to anchor your approach in solid principles, understand your users’ context, and master the art of persuasion.
Join us as Sarjak Patel and Naitry Saggu from 3rd Eye Consulting unveil a transformative framework. This approach seamlessly integrates your unique context, consumer insights, and conversion goals, paving the way for unparalleled success in personalization.
Come learn how YOU can Animate and Illuminate the World with Generative AI's Explosive Power. Come sit in the driver's seat and learn to harness this great technology.
Search Engine Marketing - Competitor and Keyword researchETMARK ACADEMY
Over 2 Trillion searches are made per day in Google search, which means there are more than 2 Trillion visits happening across the websites of the world wide web.
People search various questions, phrases or words. But some words and phrases are searched
more often than others.
For example, the words, ‘running shoes’ are searched more often than ‘best road running
shoes for men’
These words or phrases which people use to search on Google are called Keywords.
Some keywords are searched more often than others. Number of times a keyword is searched
for in a month is called keyword volume.
Some keywords have more relevant results than others. For the phrase “running shoes” we
get more than 80M relevant results, whereas for “best road running shoes for men” we get
only 8.
The former keyword ‘running shoes’ has way more competition from popular websites to
new and small blogs, whereas the latter keyword doesn’t have that much competition. This
search competition for a keyword is called search difficulty of a keyword or keyword
difficulty.
In other words, if the keyword difficulty is ‘low’ or ‘easy’, there won’t be any competition
and if you target such keywords on your site, you can easily rank on the front page of Google.
Some keywords are searched for, just to know or to learn some information about something,
that’s their search intention. For example, “What shoe size should I choose?” or “How to pick
the right shoe size?”
These keywords which are searched just to know about stuff are called informational
keywords. Typically people who are searching this type of keywords are top of a Conversion
funnel.
Conversion funnel is the journey that search visitors go through on their way to an email
subscription or a premium subscription to the services you offer or a purchase of products
you sell or recommend using your referral link.
For some buyers, research is the most important part when they have to buy a product.
Depending on that, their journey either widens or narrows down. These types of buyers are
Researchers and they spend more time with informational keywords.
Conversion is the action you want from your search visitors. Number of conversions that you
get for every 100 search visitors is called Conversion rate.
People who are at different stages of a conversion funnel use different types of keywords.
When most people in the industry talk about online or digital reputation management, what they're really saying is Google search and PPC. And it's usually reactive, left dealing with the aftermath of negative information published somewhere online. That's outdated. It leaves executives, organizations and other high-profile individuals at a high risk of a digital reputation attack that spans channels and tactics. But the tools needed to safeguard against an attack are more cybersecurity-oriented than most marketing and communications professionals can manage. Business leaders Leaders grasp the importance; 83% of executives place reputation in their top five areas of risk, yet only 23% are confident in their ability to address it. To succeed in 2024 and beyond, you need to turn online reputation on its axis and think like an attacker.
Key Takeaways:
- New framework for examining and safeguarding an online reputation
- Tools and techniques to keep you a step ahead
- Practical examples that demonstrate when to act, how to act and how to recover
Top 3 Ways to Align Sales and Marketing Teams for Rapid GrowthDemandbase
In this session, Demandbase’s Stephanie Quinn, Sr. Director of Integrated and Digital Marketing, Devin Rosenberg, Director of Sales, and Kevin Rooney, Senior Director of Sales Development will share how sales and marketing shapes their day-to-day and what key areas are needed for true alignment.
Top 3 Ways to Align Sales and Marketing Teams for Rapid Growth
7Ps’ of Service marketing-Major Research Project
1. School of Studies in Management
Jiwaji University Gwalior
Major Reserach Project
On
“The relationship among the ‘7Ps’ of Service
marketing mix efforts toward customer in Banking
Sectors”
Submitted in partial fulfillment for the award of degree of
Master of Business Administration
of
School of Studies in Management Jiwaji University Gwalior
By
KULDEEP MATHUR
MBA IV SEM.
Roll. No. 16098002
Session 2016-18
June 2018
2. DECLARATION
I, Kuldeep Mathur student of Jiwaji University, Gwalior Batch in MBA,
hereby declare that, this Project Report under the title “7P’s of service marketing
in banking sector” is the record of my original work under the guidance of Dr.
Yogesh Upadhayay, Head of School of Studies in Management. This report
has never been submitted anywhere else for award of any degree or diploma.
Kuldeep Mathur
Roll No:16098002
MBA IV SEM
3. ACKNOWDLEGEMENT
It is a great opportunity & pleasure for me to express my profound gratitude
towards all the individuals who directly or indirectly contributed towards
completion of this report.
Working on this report was a great fun, excitement, challenges and a new
exposure in the field of Marketing. I am in debated to under whose guidance and
concern I am able to bring the report into its real shape.
I am thankful to Dr. Yogesh Upadhyay and all faculty members of Management
Department in providing me useful guidance for the completion of this report. I
convey my gratitude to all those who are directly or indirectly related in the
completion of this project report.
Kuldeep Mathur
Roll No:16098002
MBA IV SEM
4. Table of Content
Chapter Description Page No.
1. Abstract 1
2. Introduction 1
3. Literature review and hypotheses
development
3
3.1. Customer 3
3.2. Service marketing mix 4
3.2.1. Product 5
3.2.2. Price 5
3.2.3. Place 6
3.2.4. Promotion 7
3.2.5. People 7
3.2.6. Physical evidence 8
3.2.7. Process 8
4. Methodology 9
4.1. Measurement instrument 9
4.2. Sampling designing and data collection 9
5. Data analysis and findings 10
5.1. Scale validity and reliability 10
5.2. Structural model analysis 12
5.2.1. Model assessment 12
5.2.2. Main effects and path coefficients 22
6. Discussion and conclusion 22
7. Managerial implication 22
8. Limitations 23
9. References 24
10. Questionnaires 24
6. 1
1. Abstract
The primary aim of the study is to examine the effects of services marketing mix elements on Indian
customer for making the appropriate marketing mix strategy in banking services context. The study is
based on a sample of 72 customers of bank users in India who filled an online questionnaire. The paper
uses confirmatory factor analysis and structural equation modeling to analyse and confirm the conceptual
model proposed in the research. The paper finds that physical evidence, process, place, and people have a
positive and significant effect on customer. The study suggested an appropriate services marketing mix
strategy for Indian customer perspective in the context of banking services. The paper would help the
bankers to create marketing strategies and action plans to retain their existing customers and to attract
new customers. The paper is first of its kind to discuss the effects of ‘7Ps’ of services marketing mix
collectively on Indian customer. The results of the analysis indicated that managing the marketing mix
dimensions of product, price and promotion is of less importance except place than managing interactive
marketing dimensions such as people, physical evidence, and process.
2. Introduction
The competitive climate in the Indian financial market has changed dramatically over
the last few years. The expectations of the customers are changing. Indian banking
sector has also under-gone financial reforms since the 1990s Earlier, banks enjoyed a
protected market. After economic liberalisation, banks were exposed to free market
competition, advanced technological sophistication and changing customer dynamics.
Owing to the globalisation of markets, banking in India is experiencing internal turmoil.
Few Indian banks initiated experimenting with new innovative services by offering
online and mobile banking which provides 24 h service. Private sector banks and
foreign banks have also introduced some new innovative services. Banking firms have
become flatter and customer-centric now.
In recent years, there has been an increasing interest in the service marketing mix which aims
to achieve the maximum outcomes in terms of customer satisfaction and retention that allow
firms, includ- ing banks, to be competitive over time. During the past decade, marketers and
researchers have identified the importance of 7Ps of services marketing and customer
orientation for sustainable compe- titive advantage (Gronroos, 2004). Crisis in banking industry
have shown the need for sustainable and effective service marketing mix strategies. Krasnikov
(2009) suggested that a successful market- ing mix approach can help banks to achieve better
customer service and support, greater efficiency and cost reduction. The major differ- ence
between services marketing mix and regular marketing is that instead of the traditional 4Ps i.e.
product, price, place, and promotion, there are three additional Ps consisting of people, physical
evidence, and process. It means that service marketing mix involves the 7Ps of marketing i.e.
product, price, place, promotion, people, physical evidence, and process. To a certain extent
7. 2
managing services are more complicated then managing products. Products can be
standardised, to standardise a service is far more difficult as there are more input factors
involve, namely, physical evidence, process, and people. There is evidence to suggest that
managing the marketing mix (i.e. product, price, place, and promotion) is of less importance
than managing interactive marketing dimensions, namely, people, processes and physical
evidence. While the literature defines 7Ps of services marketing as being wide in scope and it
encompasses all of the dimensions, some dimensions are of more importance than others. In
such a situation, marketing is no longer a function of its own but rather it becomes part of the
various functions of the firm.
On the other hand, bank deals with providing services to satisfy customers' financial needs
and wants. Banks have to find out the financial needs of the customers and offer the services
which can satisfy those needs. Banks may also require satisfying the customers' financial and
other related needs and wants. The individuals and corporate bodies have certain needs in
relation to money commodity. To satisfy these financial needs, customers want specific
services. Wallis (1997) stated that “customers will seek out those financial products and
services which offer the best value for money”. Different banks offer different benefits by
offering various schemes which can take care of the wants of the customers. Service
marketing mix helps in achieving the organisational objec- tives of the bank. It is the
‘aggregate of functions’ which signify the totality of the marketing activity. This aggregate of
functions is the sum total of all individual activities consisting of an integrated effort to
discover, create, arouse and satisfy customer needs This means that each indivi- dual
function in the banking is a marketing function which con- tributes to the total satisfaction to
customers and the bank should ultimately develop integrated customer orientation approach.
Because firm cannot stay in business so long if it does not attract and hold enough
customers, no matter how efficiently it operates.
The literature review revealed that the concept of marketing mix and additional three P's
of services marketing have been defined by a large number of marketing researchers in
different contexts and along different industries. The importance of research on these P's
strategy is undoubted. However, empirical research on the 7P's of services marketing mix
in banking industry is unfortunately characterised by non-significant, contradictory and
confusing. Banking is such as industry that the degree of flexibility of the service
marketing mix is low, and the initiative of banks that present those services is less than
other industries. In addition, banking sector has been suffering in creating superior
individual service performance and direct relations with their customers (Shin and Elliott,
2001). A fundamental issue facing Indian banking is the question of how to coordinate the
different generic services marketing mix dimen- sions around the Indian customer. The
literature on services marketing strategy provides a magnitude of arguments for both the
standardisation and the adaptation of the different combina- tion of 7Ps in various
financial services (Gronroos, 2004). Many researchers have also focused on a single
analysis of the influence of one marketing mix dimension on a firm's performance.
However, a marketing mix does not result in a single marketing ‘P’ strategy. It may be the
8. 3
interplay of all 7P's elements at the same time. So there is a need to examine the
appropriate services marketing mix strategy for Indian customer perspective in banking
services. Therefore, the present study is expected to contribute to the literature on services
marketing mix as related to Indian customer in banking services. The primary aim of the
current study is to examine the effects of services marketing mix elements on Indian
customer for making the appropriate marketing mix strategy in the context of banking
services.
3. Literature review and hypotheses development
3.1.Customer
It was McCarthy (1960) who clarified that the customer is not a part of the marketing
mix; rather, he should be the target of all marketing efforts (Kotler, 2000). In order to
develop effective marketing strategies, the marketers need first to understand why
customers use services and how they choose among competing service suppliers. What
are their expectations at each step in service delivery? Finally, of course, they should
determine whether the experience of using the service and receiving its benefits has met
customers' expectations and left them satisfied and ready to repurchase in the future.
Indian customers also typically hold similar desired expectations across banking services.
A customer's desired service expectation from banks may be quick, convenient, value
added, low cost, with advanced technology, easy and smooth, safe and reliable through a
modern branch setting. Safe and reliable banking is the primary concern of all customers.
Marketers do not usually need to know the specifics of how physical goods are manufac-
tured—that responsibility belongs to the people who run the factory. However, the
situation is different in services because their customers are often involved in service
production and may have preferences for certain methods of service delivery, so that
marketers must understand the nature of the processes through which services are created
and delivered. Because designing a simple and seamless service delivery process support
firms to reduced the necessary time of delivering the service products. It has an important
role in shaping customers overall perception of service quality evaluation. This strategic
response of a firm can achieve the competitive advantage from its competitors and surpass
the competition. It significantly affects the creation and delivering of superior value,
customer satisfaction, competi- tive advantage, growth opportunity, and profitability of the
firm.
Due to a dynamic business environment, Indian banks have also started to adopt customer-
driven marketing strategies to address the rapid and changing needs of their customers. Thus,
banks have come to realise the importance of differentiating themselves from their
competitors on the basis of superior customer service and relying on effective service
9. 4
marketing mix strategies instead of the traditional banking (Gronroos, 1982). But the first
and most important step in applying any marketing strategy is to have a whole hearted
commitment to customer orientation. This means that the central focus of all the marketing
activities of a bank is customer. As a result, the notion of 7Ps of services marketing mix has
emerged as a key factor in modern banking and their customer analysis. Understanding and
gaining access to India's markets will also require careful analysis of customer perception
regarding services marketing mix.
3.2. Services marketing mix
The concept of the marketing mix was coined by Neil Borden in 1953 and then
formalised in his article ‘The concept of the Marketing mix’. McCarthy (1960)
‘4Ps’- product, price, place, and promotion. Alternative models of marketing mix
were also proposed around the same time. However, McCarthy's four Ps model has
dominated marketing thoughts, particularly in the goods marketing context For
service indus- tries, it was observed that the traditional marketing mix was
inadequate because the original marketing mix was developed for manufacturing
industries. The mar- keting practitioners in the service sector found that the marketing
mix does not address their needs. They observed that the services have certain basic
characteristics which in turn, have marketing implications. For example there is a
problem as regard to maintaining the quality due to lack of standardisation. Also services
cannot be inventoried, patented or transferred. Services are basically different in
comparison to physical products. Therefore, the marketing models and concepts have to
be developed in direction of the service sector. Then, the marketing mix has extended
beyond the 4Ps for marketing of services. The three additional Ps are added to meet the
marketing challenges posed by the characteristics of services such as people, physical
evidence, and process. A number of marketing research studies supplements the
relevance of each of the ‘7Ps’ of services marketing mix.
On the other hand, Indian banking sector has been slow in adopting the modern
marketing knowledge to their advantage. There is no actual realisation that 7Ps of
services marketing can be of use to them. They are not even clear about the scope and
dimensions of marketing, as applicable to banking industry. But the present scenario
is totally changed because of fast-changing customer needs and intense competition
in the banking services. This business environment has created more diversified and
dynamic customer base. Customers now have a lot of options from which to opt, they
can easily switch over from one service provider to other who promises to offer better
services at lower costs. So the focus of banking services now has been completely
shifted from a transactional marketing approach to a customer oriented approach. In
this regard, services marketing mix can be a critical component in running a
successful business in today's economy. Because developing a complete marketing
10. 5
mix is vital for any business. Without it, all efforts to achieve organisational goal are
likely to be haphazard and inefficient. This has resulted in banks becoming
increasingly engaged in marketing and planning activities in order to achieve certain
objectives such as attracting new customers or providing a superior service for high
net-worth clients or retaining valuable customers. These changes in the nature of
marketing activity have repercussions for service marketing mix decision making and
implementation. As marketing activities become more sophisticated in banking
sector, greater attention needs to be directed towards product, price, place,
promotion, people, physical evidence, and process.
3.2.1.Product
Product is anything which is offered to the market for exchange or consumption (Kotler,
2000). In goods marketing, there is a tangible component to which some intangibles like
style, after- sales-service, credit, etc. are integrated. In the case of services, the tangible
component is nil or minimal. A service is a bundle of features and benefits and these
have relevance for a specific target market. Since the products offered to the customers
of a bank are more or less standardised in nature, banks are feeling an increasing need to
design customised products/services to meet customer needs. Value added dimension
includes those features which are embedded in service itself as its characteristics. Bank's
product such as saving accounts, current accounts, fixed deposits, and investment
options are the primary component in this category. The bank marketing litera- ture
indicates that the transaction context in banking services is mainly concerned with
product and then achieving organisational goals that emphasise product profitability.
Therefore, while developing a service product it is important that the package of
benefits in the service offer must have a customer's perspective. Hence, in the same
direction our first hypothesis states that:
H1. Product has a positive and significant effect on customer.
3.2.2. Price
Price could be considered as an attribute that must be scarified to obtain certain kinds of
products or services. In banking industry, price includes fees, bank charges, and interest
rates. If prices are not charged with fairness and competitiveness, it triggers customer
switching immediately in banking and other financial services. It means that perceptions
of price have a direct impact on customer satisfaction and customer loyalty.
Pricing is yet another strong variable of the marketing mix (Shanker, 2002). The
service pricing should be such as to provide value addition and quality indication to
the customers. Customers see price as a key part of the costs they must incur to obtain
wanted benefits. To calculate whether a particular service is worth it, they may go
beyond just money and also assess the outlays of their time and effort. Otherwise
customers have a lot of alternatives to choose in the market and can easily switch over
11. 6
from one service provider to other who promises to offer better goods/services at lower
prices. Customers are becoming more price-sensitive and less loyal. Customer attrition
has become a real and pressing concern. There- fore, service marketers must not only
set prices that target customers are willing and able to pay as a low cost, but also
convey the message that they are getting more in using that particular product or
service. Many marketing researchers investigated that pricing is an important key
driver for different customer related variables such as, attraction, satisfaction, retention
and loyalty. Therefore, it is expected that:
H2. Price has a positive and significant effect on customer.
3.2.3.Place
A flurry of research has considered that services differ from products in terms of
characteristics such as, intangibility, inse- parability, perishability, and interactivity.
Hence, traditional distribution channels available for product marketing cannot be
used in services marketing (Gronroos, 1983). Services cannot be separated from
selling; it must be created and sold at the same time. The field of logistics has not
been recognised as an area of consideration for effective distribution of services
whether it is the question of locating a site for a new branch of a bank, location of
educational institutions, hotels, etc. In India, these logistical problems are always
overshadowed by govern- ment policy or interventions. There are guidelines
suggesting that to open a single branch in any urban area, a nationalised bank has to
first open a fixed number of branches in rural areas.
Over the last three decades, the proliferation of new informa- tion and communication
technologies in the banking sector has changed the way banks service their customers.
The increased availability of self-service technologies has enabled banks to pursue an
electro- nically mediated multi-channel strategy. Automated teller machines (ATMs)
have been considered as one of the most well-known and classic examples of self-
service technology application in the banking sector since 1960s. Now the banks are
able to deploy more and more ATMs and replace costly counter tellers in order to
improve cost efficiency. To enhance their customer service, attract new customers and
remain competitive in banking industry, all domestic as well as foreign banks in India
are establishing technology-driven delivery channels based branches near to customer.
In banking sector, customers choose different service delivery channels in a com-
plementary way such as, the bank's physical location, the opening hours, distance to
reach a bank, parking places, and ATM availability also argued that the large number of
branches and ATMs at various locations make the bank more approachable to the
customers. Consequently, the study states the following hypothesis:
H3. Place has a positive and significant effect on customer.
12. 7
3.2.4.Promotion
It represents the communications that marketers use in mar- ketplace including
advertising, public relations, personal selling and sales promotion (McCarthy, 1960). In
certain service industries it is not possible to use the conventional promotion tools with
success. For example, a bank may face difficulty to afford heavy promotional budgets
due to its small size of the operations. Therefore, promotional activities like community
relations, event management, media blitz, and corporate identity pro- grammes have
relevance and they should be used innovatively and effectively. The impact of
marketing communication on customer behavioural intentions such as, satisfaction,
loyalty, retention and among others. All the techniques and strategies of promotional
mix are used so that ultimately they induce the people to do business with a particular
firm. Indian Market Research Bureau, one of the largest market research consultancy
organisations, has conducted market research studies in the field of banking and
evaluated the bank's advertising and publicity and its image among the people. It reflects
a customer's overall perception about that firm. Hence, we put forward the following
hypothesis:
H4. Promotion has a positive and significant effect on customer.
3.2.5. People
Judd (1987) came out with another ‘P’, People. He even went further by recommending
that people power should be formalised, institutionalised and managed like the other
4Ps as a distinctive component of the market mix. Judd's argument was that it is the
employees of an organisation who represent the organisation to the customers. If these
employees are not given training in how to go about face-to-face customer contact, the
entire marketing effort may not prove to be effective. A service is a performance and it
is usually difficult to separate the performance from the people. The way service is
delivered by the people can be an important source of differentiation as well as
competitive advantage. These are the reasons why the ‘People’ element forms such an
important part of the 7Ps of services marketing mix.
In the case of banking, the service employee is often the primary contact point for the
customer whenever the customer interacts with the employee. Customers' perceptions
of the performance of service employees play an important role in custo- mers'
evaluations of service quality. Therefore, the bankers' attention should be focused on
employee service quality and to develop of their services skills consistently. Many
consider personal interaction is a key driver among the dimensions of service quality
and merged together some of the SERVQUAL's items related to responsiveness,
assurance, and empathy. More specifically, it includes attitude, behaviour, expertise,
confidence, courtesy, and willingness to help of the employees toward customers. In
13. 8
addition, customer-oriented service employees with a focus on showing personal
attention, interpersonal care, willing to help, politeness, and promptness behaviour are
likely to contribute significantly toward the strength of customer–employee
relationship. Thus, we propose the following hypothesis:
H5. People have a positive and significant effect on customer.
3.2.6.Physical evidence
Services are often intangible, and customers cannot assess their quality well. So
customers use the service environment as an important proxy for quality (Shanker,
2002). Service environ- ments, also called servicescape or physical evidence, relate to
the style and appearance of the physical surroundings and other experiential elements
encountered by customers at service delivery sites. Service firms need to manage
physical evidence carefully, because it can have a profound impact on customers'
impressions. The appearance of build- ings, landscaping, interior furnishing, equipment,
staff members' uniforms, signs, communication materials, and other visible cues all
provide tangible evidence of a firm's service quality.
The physical evidence is also important for banks because it conveys to the customers
an external image of the service package. If a bank wants to have user friendly, hi-tech
and efficient image, the branch infrastructure will have a comfortable seating, pleasant
lighting and temperature, computer systems with advanced technology and network
connectivity. The modern infrastructure with latest technology influences customers'
perceptions of the service provider and customers' behavioural intentions. Many
technological and structural changes have taken place within the global banking
environment to attract and retain the customers. In the post-liberalised economy, Indian
public and private sector banks have reformed their workplace layout to give a
comfortable, efficient and user-friendly image. Therefore, we hypothesise:
H6. Physical evidence has a positive and significant effect on customer.
3.2.7.Process
Processes are the architecture of services. Process describes the method and sequence in
services and creates the value proposition that has been promised to customers. In high-
contact services, customers themselves are an integral part of the opera- tion and the
process becomes their experience. Badly designed processes are likely to annoy
customers because they often result in slow, frustrating, bureaucratic and poor-quality
service delivery. The well designed process assures service availability, consistent
quality, total ease and convenience to the customers. As service cannot be inventoried, it
is essential to designed sound process management system which can balance service
demand with service supply in peak hours.
14. 9
For service industries, such as banking, process is an important way of creating better
value-in-use (Zeithaml, 2008). The availability of advanced self-service technologies within
the financial industry has changed the way banks service their customers. The financial
service sector has used remote distribution channels such as the telephone or Internet to
reach more customers, cut out intermediaries, bring down overheads and increase
profitability Banking custo- mers today can access a variety of services from their home,
office or elsewhere. But the processes involved in the banking services should be easy and
smooth, fast and accurate, and customer friendly. Businesses have moved from off-line to
on-line through electronic channels. This approach is commonly called ‘e-banking’ in terms
of banking services. Many authors argued that the accessibility of e-banking from any
location, at any time of the day, is an important factor for customers. In banking services,
customer satisfaction mainly depends on the process of service delivery. Therefore, we
hypothesis (Fig. 1):
H7. Process has a positive and significant effect on customer.
Fig. 1. The conceptual framework of the study.
4. Methodology
4.1.Measurement instrument
The survey instrument was developed based on an extensive review of the literature
and studied definitions. The constructs and their observable items are presented in
Table 1. The final set of 20 items was examined by an academic experienced in
questionnaire design. The final questionnaire consisted of three sections. In the first
section, questions were related to banking services in terms of 7P's of service
marketing toward customer. The second section contained questions regard- ing
demographic characteristics of the respondents such as gender, age, education,
15. 10
profession and gross income per month. In the last, respondents were asked about
their bank name. All the items were put on a five-point Likert scale where a value of
1 expresses strongly disagree and a value of 5 expresses strongly agree.
4.2.Sampling design and data collection
Testing the suggested research hypotheses was accomplished through an online
convenience sample survey of bank customers of Gwalior city in India. There was a
note enclosed with the questionnaire that the customers have to share one of the
banking services experiences which is being operating by them frequently.72
respondents filled up the questionnaire online within the months of May–June,
2018. Total of 73 questionnaires were received out of which 72 were found to be
completely and accurately filled, the rest 1 were discarded due to incomplete
information. Respondents were the customers of different 19 banks.
These banks namely, State Bank of India (SBI), Central Bank of India (CBI), Vijay
Bank, Bank of India (BOI), Punjab National Bank (PNB), Canara Bank, Allahabad
Bank, Bank of Baroda (BOB), Union Bank of India (UBI), and United Commercial
Bank. Housing Development Finance Corporation (HDFC) Bank, Industrial Credit and
Investment Corporation of India (ICICI) Bank, Industrial Development Bank of India
(IDBI) Bank, Axis Bank, and Citi Bank, Yes Bank, Syndicate Bank, Canara Bank
etc. All 19 banks have the largest network of branches in India.
5. Data analysis and findings
All constructs in the research model are measured using multi-item scales. Scale items in the
questionnaire are measured with a 5-point Likert scale 1=strongly disagree and 5=strongly
agree presents all measures and reliabilities of scores. The regression analysis was
conducted to reveal how different factors affect the customer. A multi-correlation problem
was identified and minimized using the statistical choice methods (Nummenmaa et al.,
1996). As the conceptual model is relatively complex, the procedure using the IBM
SPSS statistics software 23.0 version.
5.1.Scale validity and reliability
Table 1
Variables and their observable indicators.
Variables Obserable variables
Product PRO1: innovative products/services.
PRO2: value added products/services.
16. 11
Price PRI1: low cost.
PRI2: getting more.
Place PLA1: branch location convenience.
PLA2: easy availability of ATM.
Promotion PROM1: bank advertisement.
PROM2: social and cultural events.
PROM3: promotional strategies impact.
People PEO1: personal attention.
PEO2: politeness.
PEO3: willing to help.
PEO4: quick response.
Physical PHY1: modern infrastructure.
Evidence PHY2: advanced technology.
Process PROC1: easy and smooth.
PROC2: fast online services.
PROC3: services at your convenience.
Customer CUS1: overall products/services quality.
CUS2: safe and reliable
Table 2
Demographic breakdown of participants.
Category n Percentage(%)
Gender
Male 53 73.6
Female 19 26.3
Age
< 21 22 30.6
21–30 42 58.3
31–40 8 11.1
41–50 0 0.00
> 50 0 0.00
Education
Under graduate 17 23.6
Graduate 31 43.1
Post-graduate 22 30.6
Doctorate 2 2.8
17. 12
Occupation
Service 11 15.3
Businessman 14 19.4
Professional 9 12.5
Self-employed 10 13.9
Student 28 38.9
Monthly
Income
<&10000 29 40.3
&11000 -
&20000
22 30.6
&21000 -
&30000
21 29.2
&31000 -
&40000
0 0.00
>&41000 0 0.00
Composite reliability (CR) and should be exceed the recommended threshold criterion of
0.70.
5.2.Structural model analysis
5.2.1.Model assessment
A comprehensive statistical technique for examining relations between observed and latent
variables. In the present study, the calculated values as multiple regression can affect
the results, the study examined the tolerance for multiple regression assessment. To
assess multiple regression issues of the study model, the latent variable scores
(calculated by SPSS) can be used as input for multiple regressions in IBM SPSS
software to get the tolerance.
Reliability
Scale: ALL VARIABLES
Combined Reliability Analysis
Reliability Statistics
Cronbach's
Alpha
Cronbach's Alpha
Based on Standardized
Items N of Items
.761 .757 8
Composite reliability (CR) and should be exceed the recommended threshold criterion of
0.70, the Reliability Statistics Table which provides the value for Cronbach alpha which in this
18. 13
case is .761 and reflects high reliability of the measuring instrument. Furthermore, it indicates
high level of internal consistency with respect to the specific sample.
Regression
1. ProductCustomer
Model Summary
b
Model R
R
Square
Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
Durbin-
Watson
R Square
Change
F
Change df1 df2
Sig. F
Change
1 .222
a
.049 .036 .77193 .049 3.631 1 70 .061 1.399
a. Predictors: (Constant), TOTALPRO
b. Dependent Variable: TOTALCUS
This table provides the R and R2
values. The R value represents the simple correlation and is
.222 (the "R" Column), which indicates a high degree of correlation. The R2
value (the "R
Square" column) indicates how much of the total variation in the dependent variable Customer
can be explained by the independent variable Product . In this case, 4.9% can be explained,
The Durbin-Watson statistic is 1.399 which is between 1.5 and 2.5.
ANOVA
a
Model Sum of Squares df Mean Square F Sig.
1 Regression 2.163 1 2.163 3.631 .061
b
Residual 41.712 70 .596
Total 43.875 71
a. Dependent Variable: TOTALCUS
b. Predictors: (Constant), TOTALPRO
This table indicates that the regression model predicts the dependent variable significantly well.
Look at the "Regression" row and the "Sig." column. This indicates the statistical significance
of the regression model that was run. Here, p < 0.0005, which is less than 0.05, and indicates
that, overall, the regression model statistically significantly predicts the outcome variable (i.e., it
is a good fit for the data), Singinificant values is .061 whihic is >P Value, no significant effect
on Customer.
Coefficients
a
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
95.0% Confidence
Interval for B
B
Std.
Error Beta
Lower
Bound
Upper
Bound
1 (Constant) 6.814 1.391 4.900 .000 4.040 9.588
TOTALPRO .288 .151 .222 1.905 .061 -.013 .590
a. Dependent Variable: TOTALCUS
19. 14
The Coefficients table provides us with the necessary information to predict satisfaction from
independent variables, as well as determine whether independent variables contribute
statistically significantly to the model (by looking at the "Sig." column). A multiple regression
analysis was conducted to verify this and explore the relationship between Prodcut and
Customer, the variability in different dimensions. Such analysis is appropriate in the case that
there is one predictor variables (product) and one response variable (customer). The regression
results shown in Table indicate that the independent variables have a significant.
2. PriceCustomer
Model Summary
b
Model R
R
Square
Adjusted
R Square
Std. Error of
the Estimate
Change Statistics
Durbin-
Watson
R Square
Change
F
Change df1 df2
Sig. F
Change
1 .263
a
.069 .056 .76379 .069 5.209 1 70 .026 1.371
a. Predictors: (Constant), TOTALPRI
b. Dependent Variable: TOTALCUS
This table provides the R and R2
values. The R value represents the simple correlation and is
.263 (the "R" Column), which indicates a high degree of correlation. The R2
value (the "R
Square" column) indicates how much of the total variation in the dependent variable, Customer,
can be explained by the independent variable Price. In this case, 6.9% can be explained.
The Durbin-Watson statistic is 1.371 which is between 1.5 and 2.5.
ANOVA
a
Model Sum of Squares df Mean Square F Sig.
1 Regression 3.039 1 3.039 5.209 .026
b
Residual 40.836 70 .583
Total 43.875 71
a. Dependent Variable: TOTALCUS
b. Predictors: (Constant), TOTALPRI
This table indicates that the regression model predicts the dependent variable significantly well.
Look at the "Regression" row and the "Sig." column. This indicates the statistical significance
of the regression model that was run. Here, p < 0.0005, which is less than 0.05, and indicates
that, overall, the regression model statistically significantly predicts the outcome variable (i.e., it
is a good fit for the data), Singinificant values is .026 whihic is >P, Value no significant effect
on Customer.
Coefficients
a
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
95.0% Confidence Interval for B
B Std. Error Beta Lower Bound Upper Bound
1 (Constant) 7.254 .970 7.477 .000 5.319 9.189
20. 15
TOTALPRI .238 .104 .263 2.282 .026 .030 .447
a. Dependent Variable: TOTALCUS
The Coefficients table provides us with the necessary information to predict satisfaction from
independent variables, as well as determine whether independent variables contribute
statistically significantly to the model (by looking at the "Sig." column). A multiple regression
analysis was conducted to verify this and explore the relationship between Pric and Customer,
the variability in different dimensions. Such analysis is appropriate in the case that there is one
predictor variables (price) and one response variable (customer). The regression results shown
in Table indicate that the independent variables have a significant.
3. PlaceCustomer
Model Summary
b
Model R
R
Square
Adjusted R
Square
Std. Error of
the Estimate
Change Statistics
Durbin-
Watson
R Square
Change F Change df1 df2
Sig. F
Change
1 .162
a
.026 .012 .78122 .026 1.890 1 70 .174 1.352
a. Predictors: (Constant), TOTALPLA
b. Dependent Variable: TOTALCUS
This table provides the R and R2
values. The R value represents the simple correlation and is
.162 (the "R" Column), which indicates a high degree of correlation. The R2
value (the "R
Square" column) indicates how much of the total variation in the dependent variable, Customer,
can be explained by the independent variable Product . In this case, 2.6% can be explained.
The Durbin-Watson statistic is 1.352 which is between 1.5 and 2.5, Singinificant values is .174
whihic is >P Value no significant effect on Customer.
ANOVA
a
Model Sum of Squares df Mean Square F Sig.
1 Regression 1.153 1 1.153 1.890 .174
b
Residual 42.722 70 .610
Total 43.875 71
a. Dependent Variable: TOTALCUS
b. Predictors: (Constant), TOTALPLA
This table indicates that the regression model predicts the dependent variable significantly well.
Look at the "Regression" row and the "Sig." column. This indicates the statistical significance
of the regression model that was run. Here, p < 0.0005, which is less than 0.05, and indicates
that, overall, the regression model statistically significantly predicts the outcome variable (i.e., it
is a good fit for the data).
Coefficients
a
21. 16
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
95.0% Confidence Interval
for B
B Std. Error Beta Lower Bound Upper Bound
1 (Constant) 7.878 1.153 6.830 .000 5.577 10.178
TOTALPL
A
.171 .124 .162 1.375 .174 -.077 .419
a. Dependent Variable: TOTALCUS
The Coefficients table provides us with the necessary information to predict satisfaction from
independent variables, as well as determine whether independent variables contribute
statistically significantly to the model (by looking at the "Sig." column). A multiple regression
analysis was conducted to verify this and explore the relationship between Place and Customer,
the variability in different dimensions. Such analysis is appropriate in the case that there is one
predictor variables (place) and one response variable (customer). The regression results shown
in Table indicate that the independent variables have a significant.
4. PromotionCustomer
Model Summary
b
Model R
R
Square
Adjusted R
Square
Std. Error of
the Estimate
Change Statistics
Durbin-
Watson
R Square
Change
F
Change df1 df2
Sig. F
Change
1 .296
a
.087 .074 .75630 .087 6.705 1 70 .012 1.378
a. Predictors: (Constant), TOTALPROM
b. Dependent Variable: TOTALCUS
This table provides the R and R2
values. The R value represents the simple correlation and is
.296 (the "R" Column), which indicates a high degree of correlation. The R2
value (the "R
Square" column) indicates how much of the total variation in the dependent variable, Customer,
can be explained by the independent variable Product . In this case, 8.7% can be explained.
The Durbin-Watson statistic is 1.378 which is between 1.5 and 2.5, Singinificant values is .012
whihic is <P Value, positive and significant effect on Customer.
ANOVA
a
Model
Sum of
Squares df
Mean
Square F Sig.
1 Regression 3.835 1 3.835 6.705 .012
b
Residual 40.040 70 .572
Total 43.875 71
a. Dependent Variable: TOTALCUS
b. Predictors: (Constant), TOTALPROM
This table indicates that the regression model predicts the dependent variable significantly well.
Look at the "Regression" row and the "Sig." column. This indicates the statistical significance
22. 17
of the regression model that was run. Here, p < 0.0005, which is less than 0.05, and indicates
that, overall, the regression model statistically significantly predicts the outcome variable (i.e., it
is a good fit for the data).
Coefficients
a
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
95.0% Confidence Interval for B
B Std. Error Beta Lower Bound Upper Bound
1 (Constant) 6.435 1.171 5.495 .000 4.099 8.770
TOTALPROM .218 .084 .296 2.589 .012 .050 .386
a. Dependent Variable: TOTALCUS
The Coefficients table provides us with the necessary information to predict satisfaction from
independent variables, as well as determine whether independent variables contribute
statistically significantly to the model (by looking at the "Sig." column). A multiple regression
analysis was conducted to verify this and explore the relationship between Promotion and
Customer, the variability in different dimensions. Such analysis is appropriate in the case that
there is one predictor variables (promotion) and one response variable (customer). The
regression results shown in Table indicate that the independent variables have a significant.
5. PeopleCustomer
Model Summary
b
Model R
R
Square
Adjusted
R Square
Std. Error of
the Estimate
Change Statistics
Durbin-
Watson
R Square
Change
F
Change df1 df2
Sig. F
Change
1 .499
a
.249 .238 .68618 .249 23.183 1 70 .000 1.676
a. Predictors: (Constant), TOTALPEO
b. Dependent Variable: TOTALCUS
This table provides the R and R2
values. The R value represents the simple correlation and is
.499 (the "R" Column), which indicates a high degree of correlation. The R2
value (the "R
Square" column) indicates how much of the total variation in the dependent variable, Customer,
can be explained by the independent variable Product . In this case, 24.9% can be explained.
The Durbin-Watson statistic is 1.676 which is between 1.5 and 2.5, Singinificant values is .000
whihic is <P Value, positive and significant effect on Customer.
ANOVA
a
Model
Sum of
Squares df
Mean
Square F Sig.
1 Regression 10.916 1 10.916 23.183 .000
b
Residual 32.959 70 .471
Total 43.875 71
23. 18
a. Dependent Variable: TOTALCUS
b. Predictors: (Constant), TOTALPEO
This table indicates that the regression model predicts the dependent variable significantly well.
Look at the "Regression" row and the "Sig." column. This indicates the statistical significance
of the regression model that was run. Here, p < 0.0005, which is less than 0.05, and indicates
that, overall, the regression model statistically significantly predicts the outcome variable (i.e., it
is a good fit for the data).
Coefficients
a
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
95.0% Confidence Interval for B
B Std. Error Beta Lower Bound Upper Bound
1 (Constant) 3.803 1.177 3.230 .002 1.455 6.151
TOTALPE
O
.303 .063 .499 4.815 .000 .177 .428
a. Dependent Variable: TOTALCUS
The Coefficients table provides us with the necessary information to predict satisfaction from
independent variables, as well as determine whether independent variables contribute
statistically significantly to the model (by looking at the "Sig." column). A multiple regression
analysis was conducted to verify this and explore the relationship between People and
Customer, the variability in different dimensions. Such analysis is appropriate in the case that
there is one predictor variables (people) and one response variable (customer). The regression
results shown in Table indicate that the independent variables have a significant.
6. Physical evidenceCustomer
Model Summary
b
Model R
R
Square
Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
Durbin-
Watson
R Square
Change F Change df1 df2
Sig. F
Change
1 .381
a
.145 .133 .73199 .145 11.885 1 70 .001 1.544
a. Predictors: (Constant), TOTALPHY
b. Dependent Variable: TOTALCUS
This table provides the R and R2
values. The R value represents the simple correlation and is
.381 (the "R" Column), which indicates a high degree of correlation. The R2
value (the "R
Square" column) indicates how much of the total variation in the dependent variable, Customer,
can be explained by the independent variable Product . In this case, 14.5.% can be explained.
The Durbin-Watson statistic is 1.544 which is between 1.5 and 2.5, Singinificant values is .001
whihic is <P Value, positive and significant effect on Customer.
ANOVA
a
Model
Sum of
Squares df
Mean
Square F Sig.
24. 19
1 Regression 6.368 1 6.368 11.885 .001
b
Residual 37.507 70 .536
Total 43.875 71
a. Dependent Variable: TOTALCUS
b. Predictors: (Constant), TOTALPHY
This table indicates that the regression model predicts the dependent variable significantly well.
Look at the "Regression" row and the "Sig." column. This indicates the statistical significance
of the regression model that was run. Here, p < 0.0005, which is less than 0.05, and indicates
that, overall, the regression model statistically significantly predicts the outcome variable (i.e., it
is a good fit for the data).
Coefficients
a
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
95.0% Confidence Interval for B
B Std. Error Beta Lower Bound Upper Bound
1 (Constant) 6.065 .988 6.138 .000 4.094 8.036
TOTALPH
Y
.363 .105 .381 3.447 .001 .153 .573
a. Dependent Variable: TOTALCUS
The Coefficients table provides us with the necessary information to predict satisfaction from
independent variables, as well as determine whether independent variables contribute
statistically significantly to the model (by looking at the "Sig." column). A multiple regression
analysis was conducted to verify this and explore the relationship between Physical evidence
and Customer, the variability in different dimensions. Such analysis is appropriate in the case
that there is one predictor variables (physical evidence) and one response variable (customer).
The regression results shown in Table indicate that the independent variables have a significant.
7. ProcessCustomer
Model Summary
b
Model R
R
Square
Adjusted
R Square
Std. Error of
the Estimate
Change Statistics
Durbin-
Watson
R Square
Change
F
Change df1 df2
Sig. F
Change
1 .551
a
.303 .293 .66091 .303 30.446 1 70 .000 1.440
a. Predictors: (Constant), TOTALPROC
b. Dependent Variable: TOTALCUS
25. 20
This table provides the R and R2
values. The R value represents the simple correlation and is
.551 (the "R" Column), which indicates a high degree of correlation. The R2
value (the "R
Square" column) indicates how much of the total variation in the dependent variable, Customer,
can be explained by the independent variable Product . In this case, 30.3% can be explained.
The Durbin-Watson statistic is 1.440 which is between 1.5 and 2.5, Singinificant values is .000
whihic is <P Value, positive and significant effect on Customer.
ANOVA
a
Model
Sum of
Squares df
Mean
Square F Sig.
1 Regression 13.299 1 13.299 30.446 .000
b
Residual 30.576 70 .437
Total 43.875 71
a. Dependent Variable: TOTALCUS
b. Predictors: (Constant), TOTALPROC
This table indicates that the regression model predicts the dependent variable significantly well.
Look at the "Regression" row and the "Sig." column. This indicates the statistical significance
of the regression model that was run. Here, p < 0.0005, which is less than 0.05, and indicates
that, overall, the regression model statistically significantly predicts the outcome variable (i.e., it
is a good fit for the data).
Coefficients
a
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
95.0% Confidence Interval for B
B Std. Error Beta Lower Bound Upper Bound
1 (Constant) 4.215 .954 4.420 .000 2.313 6.116
TOTALPROC .374 .068 .551 5.518 .000 .239 .509
a. Dependent Variable: TOTALCUS
The Coefficients table provides us with the necessary information to predict satisfaction from
independent variables, as well as determine whether independent variables contribute
statistically significantly to the model (by looking at the "Sig." column). A multiple regression
analysis was conducted to verify this and explore the relationship between Pric and Customer,
the variability in different dimensions. Such analysis is appropriate in the case that there is one
predictor variables (price) and one response variable (customer). The regression results shown
in Table indicate that the independent variables have a significant.
Correlations
Correlations
26. 21
TOTAL
CUS
TOTAL
PRO
TOTAL
PRI
TOTAL
PLA
TOTAL
PROM
TOTAL
PEO
TOTAL
PHY
TOTALP
ROC
TOTALCUS Pearson
Correlation
1 .222 .263
*
.162 .296
*
.499
**
.381
**
.551
**
Sig. (2-tailed) .061 .026 .174 .012 .000 .001 .000
N 72 72 72 72 72 72 72 72
TOTALPRO Pearson
Correlation
.222 1 .161 .218 .080 .281
*
.221 .278
*
Sig. (2-tailed) .061 .177 .065 .504 .017 .062 .018
N 72 72 72 72 72 72 72 72
TOTALPRI Pearson
Correlation
.263
*
.161 1 .076 .008 .207 .290
*
.207
Sig. (2-tailed) .026 .177 .525 .949 .082 .013 .081
N 72 72 72 72 72 72 72 72
TOTALPLA Pearson
Correlation
.162 .218 .076 1 .222 .182 .200 .192
Sig. (2-tailed) .174 .065 .525 .061 .126 .092 .106
N 72 72 72 72 72 72 72 72
TOTALPROM Pearson
Correlation
.296
*
.080 .008 .222 1 .407
**
.408
**
.219
Sig. (2-tailed) .012 .504 .949 .061 .000 .000 .065
N 72 72 72 72 72 72 72 72
TOTALPEO Pearson
Correlation
.499
**
.281
*
.207 .182 .407
**
1 .535
**
.585
**
Sig. (2-tailed) .000 .017 .082 .126 .000 .000 .000
N 72 72 72 72 72 72 72 72
TOTALPHY Pearson
Correlation
.381
**
.221 .290
*
.200 .408
**
.535
**
1 .497
**
Sig. (2-tailed) .001 .062 .013 .092 .000 .000 .000
N 72 72 72 72 72 72 72 72
TOTALPROC Pearson
Correlation
.551
**
.278
*
.207 .192 .219 .585
**
.497
**
1
Sig. (2-tailed) .000 .018 .081 .106 .065 .000 .000
N 72 72 72 72 72 72 72 72
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).
Now let's take a close look at our results: the strongest correlation is between PEO and between PROC :
r = 0.585. It's based on N = 72 .and its 2-tailed significance, p = 0.000. This means there's a 0.000 probability
of finding this sample correlation -or a larger one- if the actual population correlation is zero.
27. 22
¼
5.2.2.Main effects and path coefficients
The results indicated thatPromotion, People, Physical evidence had positive and
significant effect on customer. Thus H4, H5, H6 and H7 were accepted. However,
Product, Price, Place showed no significant effect on customer. Therefore, H1, H2
and H3 were rejected. And strongest correlation between People and process.
6. Discussion and conclusion
The purpose of the study was to demonstrate the most important elements of services
marketing mix that influence Indian customer and to determine the right services
marketing mix in the context of banking sector. The research emphasises the important
role of services marketing mix on banking industry. In bank marketing, little prior
research focuses on the relationship among the ‘7Ps’ of service marketing mix efforts
toward customer. The present study examined a model to explain the mentioned
relationship in the context of Indian banking customer. In other words, the effect of
individual ‘P’ of services marketing mix on customer was determined. Regressioon
analysis produce dimensions of product, price, place, promotion, people, physical
evidence, process, and customer.
7. Managerial implications
Indian banking industry has gone through the pre-independence, post-independence, pre-
nationalisation, nationalisation and post- liberalisation stages. Marketing was always
consid- ered not to be a banker's cup of tea. But today, it is considered to be an integral
management function in the banking sector. And if a bank is functioning based on
marketing tools and techniques, it simply means that a bank's decisions are made through
the eyes of the customers of the bank. As banks do not provide tangible products, their
managers need to put a lot of emphasis on services marketing mix to acquire and retain
the customers. The study suggested that physical evidence, process, place and people are
the main services marketing mix elements in the context of bank services. Importantly,
right combination of services marketing elements can be used to create stronger customer-
firm relationships, as shown in the present study. All these are important for banks
because it helps customers to develop an image of the bank. This can be achieved by
attaching more importance to the indicators of these ‘Ps’. Moreover, there is a dire need to
improve banking services with modern infrastructure and advanced technology, followed
by easy and smooth banking process, fast online and other services at customer's
convenience. A user-friendly image of the bank can be built by its interior design with
a comfortable seating arrangement, pleasant lighting, temperature and cleanliness,
compu- ter systems with advanced technology and network connectivity, and
28. 23
convenient and easy accessible counters. Further, branch location and easy availability
of ATM machines should be considered in the view point of customer convenience by
the bankers. The convenience of the location of branches of the bank and its ATMs are
the dominant criterion both for subsequent satisfaction and selection of bank. A large
number of branches and ATMs at various locations make the bank more approachable
to the customers (Kranias and Bourlessa, 2013). Banks should also encourage
employees to develop friendship and long-term relationship with cusomers. It can be
achieved by listening to what customer has to say, pay personal attention to him.
Especially, in the case of Indian customers, they look for a personal attention in all
their transaction. To have a close relation with customers, the bank management has to
ensure that core service is delivered on the time with quick response. Because quick and
timely response is important for banking in order to create customer satisfaction and
loyalty. It can also be helpful to handle the possible conflicts between staff and
customer. The banks must undertake strategies, such as employees training to make
them courteous, caring and responsive. The speed in service delivery, courtesy and
helpfulness of bank staff are the most critical attributes that influence customers. In
general, customers look for an environment, where the employees listen to their
problems and show willingness to help them, and are polite to them. Customers feel
more satisfied when they get quick response to their problems. Ultimately, the findings
of the study indicated that the proper implementation of right services marketing mix
elements may be helpful for banks to attract new customers and retain old customers
which results in higher sales, market share, and profits. Because overall the banks are
delivering the identical products, charges are fixed and driven by marketplace. Thus,
banker tends to differen- tiate its firm from competitors through right services
marketing mix dimensions.
8.Limitations and future research directions
The study gauges the effect of ‘7Ps’ of services marketing mix on Indian customer in the
context of bank marketing. The research, however, is subject to some limitations. The
study results obtained by the convenience sampling method were difficult to generalise
to the population because it was a type of non- probability sampling. A more
representative sampling technique needs to be considered in future research to generalise
the findings of the study. The current study was primarily a cross- sectional due to time
and cost constraints, although a longitudinal study is recommended to monitor the
evolution of customer behaviour over time. It is important to note that the study is
limited to a sample size of 72 Indian customers. The larger sample sizes with foreign
customers residing and having bank accounts in India can be considered by future
researchers. The scope of the study is also limited to the number of banks (19) in the
research. The application of the services marketing mix elements identified in the study
cannot be generalised as we have taken only one industry (banking). To confirm its
applicability in other financial services like insurance, loans, share trading etc., the same
study should carry out in various other financial service based firms. There is obviously
29. 24
opportunity for a similar study in different geographic locations. Finally, the future
research is recommended to measure the effect of identified services market- ing mix
elements on bank performance.
9. References
Gronroos, C. (1983). The internal marketing function. marketing science, 83-104.
Gronroos, C. (2004). The relationship marketing process: communication, interac- tion, dialogue, value. J. Bus. Ind. Mark,
99-113.
Judd, V. C. (1987). Differentiate with the 5th P: People. Mark Manag, 241-247.
Kotler, P. (2000). Marketing Management. Millenium Edition Prentice-Hall of India.
Kranias, A., & Bourlessa, M. (2013). Investigating the relationship between service quality and loyalty in Greek banking
sector. Procedia Econ. Financ, 453-458.
Krasnikov, A., Jayachandran, S., & Kumar, V. (2009). The impact of customer relation- ship management implementation
on cost and profit efficiencies: evidence from the U.S. commercial banking industry. J. Mark., 61-76.
Kushwaha, G. S., & Agrawal, S. R. (2014). An Indian customer surrounding 7 P's of service marketing. Science dierect, 85-
96.
McCarthy, E. J. (1960). Basic Marketing: A Managerial Approach. Homewood.
Shanker, R. (2002). Services Marketing, The Indian Perspective. Excel Books.
Wallis, S. (1997). The financial system inquiry final report. AGPS.
Zeithaml, V. A., Bitner, M. J., & Pandit, D. D. (2008). Services Marketing, Integrating Customer Focus across the Firm. Tata
McGraw-Hill.
31. 25
10. Procedure of Reliability and Regression analysis
Step 1: Select a base paper and create questionnaire on Google
Form.
Step 2: Create a Link of questionnaire and send it to different
customer of the Banking.
https://goo.gl/forms/POiI73Aa9afr7JoZ2
32. 26
Step 3: Generate an Excel Sheet of the Responses, there is 72
respondent.
Step 4: Paste our responses in SPSS, in Date View section.
33. 27
Step 5: Coding in of the data in Variable View.
Step 6: Change the Measures, Label and Value.
34. 28
Step 7: Compute Variable, TransformCompute Variable
option available.
Step 8: All Compute Variable given in below table as
TOTALPRO….etc.
35. 29
Step 9: Reliability Test ,AnalyzeScaleReliability Analysis
Step 10: Dialogue Box Open
36. 30
Composite reliability (CR) and should be exceed the recommended threshold
criterion of 0.70, the Reliability Statistics Table which provides the value for
Cronbach alpha .761.
Step 11: Regression Analysis, AnalyzeRegressionLinear
37. 31
Step 12: Dependent Customer and Independent 7P’s (Product,
Price, Place, Promotion, People, Physical evidence and Process)
Step 13: Dialogue Box Open
38. 32
Step 14: Regression Analysis Output
This table provides the R and R2
values. The R value represents the simple
correlation and is .619 (the "R" Column), which indicates a high degree of
correlation. The R2
value (the "R Square" column) indicates how much of the total
variation in the dependent variable, Customer , can be explained by the
independent variable, 7P’s . In this case, 38.3% can be explained.
This table indicates that the regression model predicts the dependent variable
significantly well. How do we know this? Look at the "Regression" row and go to
the "Sig." column. This indicates the statistical significance of the regression
model that was run. Here, p < 0.0005, which is less than 0.05, and indicates that,
overall, the regression model statistically significantly predicts the outcome
variable (i.e., it is a good fit for the data).
The Coefficients table provides us with the necessary information to predict price
from income, as well as determine whether income contributes statistically
significantly to the model (by looking at the "Sig." column). Furthermore, we can
use the values in the "B" column under the "Unstandardized Coefficients"
column, as shown below:
39. 33
The Coefficients part of the output gives us the values that we need in order to
write the regression equation. The regression equation will take the form:
Predicted variable (dependent variable) = slope * independent variable + intercept
11. Questionnaires
(Attached)
41. Questionnaire
* Required
An Indian customer surrounding 7P's of service marketing in
banking sectors.
1. Gender *
Mark only one oval.
Male
Female
2. Age *
Mark only one oval.
< 21
21 - 30
31 - 40
41 - 50
< 50
3. Education *
Mark only one oval.
Under graduate
Graduate
Post-graduate
Doctorate
4. Occupation *
Mark only one oval.
Service
Businessman
Professional
Self-employed
Student
5. Monthly Income *
Mark only one oval.
< 10000
11000 - 20000
21000 - 30000
31000 - 40000
> 41000
42. 6. Which bank do you prefer? *
Instructions
1. Strongly disagree
2. Disagree
3. Neutral/Neither agree nor disagree
4. Agree
5. Strongly agree
7. 1. Innovative products/services. *
Mark only one oval.
1 2 3 4 5
Strongly disagree Strongly agree
8. 2. Value added products/services. *
Mark only one oval.
1 2 3 4 5
Strongly disagree Strongly agree
9. 3. Low cost. *
Mark only one oval.
1 2 3 4 5
Strongly disagree Strongly agree
10. 4. Getting more. *
Mark only one oval.
1 2 3 4 5
Strongly disagree Strongly agree
11. 5. Branch location convenience. *
Mark only one oval.
1 2 3 4 5
Strongly disagree Strongly agree
12. 6. Easy availability of ATM. *
Mark only one oval.
1 2 3 4 5
Strongly disagree Strongly agree
43. 13. 7. Bank advertisement. *
Mark only one oval.
1 2 3 4 5
Strongly disagree Strongly agree
14. 8. Social and cultural events. *
Mark only one oval.
1 2 3 4 5
Strongly disagree Strongly agree
15. 9. Promotional strategies impact. *
Mark only one oval.
1 2 3 4 5
Strongly disagree Strongly agree
16. 10. Personal attention. *
Mark only one oval.
1 2 3 4 5
Strongly disagree Strongly agree
17. 11. Politeness. *
Mark only one oval.
1 2 3 4 5
Strongly disagree Strongly agree
18. 12. Willing to help. *
Mark only one oval.
1 2 3 4 5
Strongly disagree Strongly agree
19. 13. Quick response. *
Mark only one oval.
1 2 3 4 5
Strongly disagree Strongly agree
44. 20. 14. Modern infrastructure. *
Mark only one oval.
1 2 3 4 5
Strongly disagree Strongly agree
21. 15. Advanced technology. *
Mark only one oval.
1 2 3 4 5
Strongly disagree Strongly agree
22. 16. Easy and smooth *
Mark only one oval.
1 2 3 4 5
Strongly disagree Strongly agree
23. 17. Fast online services. *
Mark only one oval.
1 2 3 4 5
Strongly disagree Strongly agree
24. 18. Services at your convenience. *
Mark only one oval.
1 2 3 4 5
Strongly disagree Strongly agree
25. 19. Overall products/services quality. *
Mark only one oval.
1 2 3 4 5
Strongly disagree Strongly agree
26. 20. Safe and reliable. *
Mark only one oval.
1 2 3 4 5
Strongly disagree Strongly agree
THANK YOU