This study examines customer switching behavior for bank services. It identifies four dimensions of customer satisfaction: personal, financial, environmental, and convenience factors. The study develops and tests hypotheses about the relationship between customer satisfaction and switching likelihood. It finds that satisfaction is negatively related to switching, and that importance of a service mediates this relationship. A survey was administered and factor analysis was conducted. The results show the effect of importance varies across service categories, and that competition levels also influence switching decisions. The findings suggest banks should focus more on personal, atmospheric, and convenience factors to reduce switching.

1. Banking Analytics Case Study
Modeling in Operation Management

2. A field study of customers’
switching behavior for bank
services
Author: Lalita A. Manrai, Ajay K. Manrai College of Business and
Economics, University of Delaware, Newark, DE 19716, USA
doi:10.1016/j.jretconser.2006.09.005
Reference

3. Customer
Switching
behaviour for
Bank services
Objective:

4. ● A customer is the most important
visitor on our premises, he is not
dependent on us. We are dependent
on him.
● He is not an interruption in our work.
He is the purpose of it. He is not an
outsider in our business. He part of it.
● We are not doing him a favour by
serving him. He is doing us a favour
by giving us an opportunity to do so.
Father of Nation

5. Problem Statement
In This Presentation we makes three important
contributions with managerial implications concerning the
issue of bank service switching behaviour.

6. Three contribution
First
It identifies four overall
dimensions of customer
satisfaction with bank
services.These are -
1. Personal related
consideration
2. Financial
3. Environment
4. Convenience
Second
Here we develop and test
some hypothesis
pertaining to the
relationship between
customer satisfaction
and bank service
switching behaviour.
Third
Based on the empirical
results of a large scale
field study, we provide
several theoretical and
managerial implications.

7. Introduction
The research on this subject suggests that dissatisfaction with various aspects
related to service quality provided by a particular bank is the major reason why
customers switch banks. The contribution of our research is to expand the
understanding of relationship between satisfaction/dissatisfaction and bank
switching behavior. Three research questions that will be addressed in our
research are:-

8. Three research questions that will be addressed in our
research are:-
Question 1
What are the
dimensions of bank
service along which
customer
satisfaction/dissatisfacti
on may be measured?
Question 2
Does the relationship
between satisfaction
/dissatisfaction and
bank switching behavior
vary across the different
service dimensions?
Question 3
How does the nature of
competition for a service
dimension modify the
relationship between
satisfaction/
dissatisfaction and bank
switching behavior
studied in question 2?

9. Research Hypotheses

10. 1. Satisfaction with the banking services
A recent research study suggests that bank customers are more likely to change
banks due to high interest rates on credit cards, low interest rates on savings
account and denial of loan applications. A customer may think of changing banks
due to any of these considerations, i.e., financial considerations, service
considerations, convenience considerations, etc.; if s/he is not satisfied with the
offerings of the bank.

11. Research
Hypotheses
Satisfaction with the banking
services
H1: Satisfaction with a particular
banking service would be negatively
related to the likelihood of
switching to another bank for that
particular service.

12. 2. Importance of the banking services
Two questions in this context need to be addressed in order to understand the
effect of importance of a banking service on the relationship between satisfaction
and customers’ bank switching behavior.
1. Does a given level of dissatisfaction from a particular service lead to the
same response from different customers? And,
2. Does a given level of dissatisfaction from different services lead to the
same response from a particular customer?
An intuitive answer to both these questions is NO.

13. Research
Hypotheses
Importance of the banking
services
H2: Importance of a service
dimension would mediate the
relationship between satisfaction
with a banking service and the
likelihood of switching to another
bank for that service.

14. Research Study

15. Research Study
● Here we create a small survey related to switching behaviour with the
help of my colleagues
● Survey instrument was a questionnaire based on online survey
● Satisfaction with bank services are conceptualized as a multidimensional
construct in our research.
● Importance of bank services, The respondents were asked this time to
indicate the importance of each of these 23 service attributes on an
importance scale ranging from 1 (Not Important at All) to 5 (Extremely
Important).

16. Research Study
● Likelihood of switching for a service, The respondents were asked to
consider one service attribute at a time and indicate their likelihood of
switching to another bank for that particular service attribute.
● Other measures, The other questions included in the survey were
demographics such as age, gender, marital status, educational background,
work status, occupation, income, etc.

17. Attached table provides
a list of 23 service
attributes along which
satisfaction was
measured in this
research.

18. Results

19. What is Factor Analysis??
Factor analysis examines the interrelations among a large number of variables
and, then, attempts to explain them in terms of their common underlying
dimension.
● Common underlying dimensions are referred to as factors.
● Interdependence technique :
-- No I.V.s or D.V.s
-- All variables are considered simultaneously.
★ It is Simply Data Summarization and Reduction technique.
★ Factor analysis mainly done on continuous data only.

20. Understanding Factor Analysis
● Regardless of purpose, factor analysis is used in:
● The determination of a small number of factors based on a particular no. of
inter-related quantitative variables.
● To conduct a factor analysis sample size should be around 100 at least.
● There are different types of methods used to extract the factor from the data
set:

21. 1. Principal component analysis: This is the most common method used by researchers. PCA starts
extracting the maximum variance and puts them into the first factor. After that, it removes that variance
explained by the first factors and then starts extracting maximum variance for the second factor. This
process goes to the last factor.
2. Common factor analysis: The second most preferred method by researchers, it extracts the common
variance and puts them into factors. This method does not include the unique variance of all variables.
This method is used in SEM.
3. Image factoring: This method is based on correlation matrix. OLS Regression method is used to
predict the factor in image factoring.
4. Maximum likelihood method: This method also works on correlation metric but it uses maximum
likelihood method to factor.
5. Other methods of factor analysis: Alfa factoring outweighs least squares. Weight square is another
regression based method which is used for factoring.

23. Some components
Factor loading:(Rotated Loading)
Factor loading is basically the correlation coefficient for the variable and factor.
Eigenvalues: Eigenvalues is also called characteristic roots. Eigenvalues shows
variance explained by that particular factor out of the total variance.
Factor score: The factor score is also called the component score. This score is
of all row and columns, which can be used as an index of all variables and can be
used for further analysis.
Rotation method: Rotation method makes it more reliable to understand the
output. Eigenvalues do not affect the rotation method, but the rotation method
affects the Eigenvalues or percentage of variance extracted.

24. Factor Label (Cronbach alpha)
Cronbach’s alpha is a measure how closely related a set of items are as a group.
score.alpha(keys, items, labels = NULL, totals=TRUE,digits = 2) #deprecated
Keys - A matrix or dataframe of -1, 0, or 1 weights for each item on each scale
Items -Data frame or matrix of raw item scores
Labels -column names for the resulting scales
Totals -Find sum scores (default) or average score
Digits -Number of digits for answer (default =2)
-Here N is equal to the number of items,
-c-bar is the average inter-item covariance
among the items and
-v-bar equals the average variance.

25. Table 2
Simple Correlation range is (+0.02 to -0.18)
Partial Correlation range is (-0.11 to -0.21)
These results suggest that effect of importance varies across different
service categories.

26. Table 3

27. ANOVA

28. Variables

29. The Finding of this Research
● Bank marketers need to pay much more attention in promoting factors like
personnel, atmospherics, and convenience than what was done in the past.
● The findings suggest that the nature of competition for different aspects of a
bank’s service plays an important role in a customer’s decision to stay or
switch. Even if a customer was dissatisfied with a service and the service
was extremely important, the customer may not switch to another bank if the
competitive offering was the same.

30. Thanking You !!