ASSESSMENT
OF ATTRIBUTES
CONSUMERS
USE TO EVALUATE
BANKS
We’re
looking for a
professional consultant
who can help us
determine how
consumers evaluate
our services. How
can ARI help us? Please
give an example.
A
regional bank from
Albuquerque contacted
ARI because senior
management wanted
to know how consumers
in New Mexico evaluate
banking services. In
other words, what
banking attributes
are important to
consumers? The
insight from a
recent focus group
revealed several
underlying dimensions
but this was based
on a small sample
size and the bank
wanted a more conclusive
marketing analysis.
The
research objective
for this project
was centered around
the statistical
importance of various
bank attributes
so a survey was
selected as the
obvious method for
collecting this
information. An
online survey was
recommended because
it eliminated the
costs associated
with conventional
survey methods,
such as printing,
postage and data
entry. In
addition to these
advantages, online
surveys offer better
response rates
compared to questionnaires
sent through the
mail.
For
the next phase
of the project,
ARI developed a
questionnaire to
measure the importance
of specific banking
attributes. For
this abbreviated
example, survey
participants were
asked to rate
the importance
of eight banking
attributes on
a 5point scale
ranging from unimportant
to very important. The
banking attributes
are shown below:
V_{1} How
important to you
is online banking?
V_{2} How
important to you
is the interest
rate on a loan?
V_{3} How
important to you
is the obtainability
of a loan?
V_{4} How
important to you
are extended banking
hours?
V_{5} How
important to you
are auxiliary banking
services?
V_{6} How
important to you
are convenient ATM
locations?
V_{7} How
important to
you
is a bank’s
community involvement?
V_{8} How
important to
you
is the attractiveness
of a bank’s
exterior?
For
this project the
relevant population
was defined as homeowners
in six
counties served by
the bank between
the ages of 32 and
65 with an estimated
household income
equal to or greater
than $45,000. In
order to gain access
to the targeted
consumer group, ARI
purchased a list
from a trusted direct
mail vendor. This
list was used as
the sample frame
for the project.
For
the next phase of
the project, ARI
assisted the
marketing director
in drafting a survey
invitation on the
bank’s
letterhead. Participants
were offered an
incentive to participate
with the assurance
that their identity
would not be disclosed
to the bank. Survey
participation
for any project
is essential for
the marketing
research profession,
therefore a participant’s
anonymity and
privacy must be
protected. The
invitation directed
participants
to
the ARI website
which was used
as the survey
portal.
Sample
size has a significant
impact on the cost
of any project,
therefore ARI used
a proven statistical
formula to calculate
the correct sample
size based on estimated
variance, the bank’s
desired accuracy
and the level
of confidence needed
for an estimate
of the true population
value. Variance
and accuracy
are estimated
with a percentage
and the level
of confidence
is a value used
to estimate the
distribution
characteristics
of the sample. These
values enable
us to scientifically
determine the
correct sample
size for a survey. For
this project
the
correct net
sample
size was calculated
to be 474 total
participants
with
a precision
level
of ± 4.5%.
The
selection method
is a very important
component of
the research
project because
the selection
technique, not
the size of
the sample, determines
a sample’s
representativeness. In
other words,
everyone from
the targeted
population should
have an equal
probability of
being selected
into the sample,
therefore a random
sampling procedure
was used to draw
names for the
survey invitations. This
sampling technique
ensures an unbiased
estimate of the
true population
value. In
spite of the
best incentives,
many people simply
refuse to participate
in surveys. This
situation is
always an issue
so the initial
sample size
was proportionally
increased to
2,975 because
of anticipated
nonresponse.
An
average response
rate of 16% yielded
474 completed
surveys within
10 days. Data
analysis was
performed with
advanced statistical
software to obtain
the information
needed for the
project’s
research objective. In
other words,
how do consumers
from the targeted
population evaluate
a bank?
For
this project,
factor analysis
was used to identify
the underlying
dimensions
which consumers
use to evaluate
banks. Factor
analysis begins
with the construction
of a correlation
matrix because
we’re
interested in
the interdependent
relationships
among all the
variables. A
partial matrix
constructed
from ten surveys
is shown below. (the
actual matrix
has 474 rows) Cell
values represent
importance
ratings
for each
of
the eight
attributes
measured
on
a 5point
scale.
i.e.
1=Unimportant;
5=Very important
RESPONDENT
NUMBER

V1

V2

V3

V4

V5

V6

V7

V8

1

2

1

5

3

5

3

2

4

2

4

4

3

5

5

2

2

3

3

4

4

3

5

5

2

2

3

4

4

3

3

3

5

1

5

3

5

4

4

3

5

5

2

2

3

6

4

4

3

5

5

2

2

3

7

4

4

3

5

5

2

2

3

8

5

3

3

2

1

2

5

4

9

4

4

3

5

5

2

2

3

10

4

4

3

5

5

2

2

3

Factor
analysis uses
a statistic based
on a chisquare
transformation
to test the null
hypotheses
that the variables
are uncorrelated
in the population. The
chisquare value
for the actual
matrix was greater
than the critical
value of the
test statistic. This
means that the
variables within
the matrix are
correlated. If
correlations
between the
variables are
not significant
then conclusions
based on factor
analysis would
be inappropriate.
In
order to summarize
the data within
the matrix, a
smaller number
of factors must
be extracted. Several
statistical procedures
are utilized
to determine the
resulting number
of factors. Retention
of factors for
this project
was based on
the amount of
variance associated
with each factor. Factors
with a variance
less than 1
are
not retained
using this
procedure. This
is done because
ARI looks for meaningful dimensions
within the
matrix. The
variance
of
these factors
is shown
in
the following
table. Focus
your attention
on the Total
column.
Only
three factors
are greater
than 1,
thus
they were
extracted
for a closer
analysis.

Initial
Eigenvalues

Component

Total

%
of Variance

Cumulative
%

1

3.237

40.466

40.466

2

1.763

22.032

62.498

3

1.298

16.226

78.724

4

0.629

7.857

86.581

5

0.510

6.372

92.953

6

0.308

3.853

96.806

7

0.209

2.612

99.418

8

0.047

0.582

100.000

The
final interpretation
of factor analysis
is facilitated
by identifying
the variables
that have large
loadings on the
same factor. Three
factors were
extracted based
on the data from
the table shown
above. Which
banking attributes
are more closely
related to factor
1, 2 or 3? Variables
with large
loadings
on the same
factor
have something
in common. The
factor matrix
shown below
offers answers
to these questions. The
individual
values
shown in
the
numbered
columns
represent
factor
loadings
or
correlations
between three
factors and
eight variables; V_{1} through V_{8}.

Factor


1

2

3

How
important
to you
is
online banking?

0.171

0.880

0.316

How
important
to you
is
the
interest
rate on a
loan?

0.934

0.195

0.066

How
important
to you
is
the
obtainability
of a loan?

0.962

0.084

0.124

How
important
to you
are
extended
banking hours?

0.027

0.834

0.373

How
important
to you
is
auxiliary
banking services?

0.898

0.047

0.224

How
important
to you
are
convenient
ATM locations?

0.137

0.856

0.284

How
important
to you
is a bank's
community
involvement?

0.089

0.269

0.770

How
important
to you
is the
attractiveness
of a bank's
exterior?

0.226

0.081

0.714

Factor
1 has high coefficients
for auxiliary
banking services,
obtainability
of loans and
the interest
rate on loans. Therefore
this factor
was labeled traditional
banking services. Factor
2 has high coefficients
for online banking
services, extended
banking hours
and convenient
ATM locations. Therefore
this factor
was labeled convenience. Factor
3 has high coefficients
for the attractiveness
of a bank’s
exterior and
community involvement. Therefore
this factor
was labeled visibility.
All
of the factors
are significant
however more
attention is
focused on Factor
1 because it
explains 40%
of the total
variance. Variability
indicates how
similar or dissimilar
respondents are
compared to an
average value. i.e.
assessment of
bank attributes. Advantage
Research concluded
that consumers
evaluate banks
using three
basic factors;
traditional
banking services,
convenience and
visibility.
What
you have just
read is a brief
example of applied
marketing research. The
survey data shown
in this matrix
is fictitious. ARI
will never reveal
the identity
of a client
or the actual
results of a
project under
any circumstances
without a client’s
consent. This
example was
developed
to help potential
clients fathom
an application
of factor
analysis
which might
be useful
to their profession.