Business Analytics :- Session 3 | Group F
We were provided with the data of
Retail Stores in our third session of Business Analytics.
The data that was to be analysed gave
the first impression of a pool of information relating to customer satisfaction.
We were required to analyse the data provided and figure out possible variables
(factors) that would have an influence on the customer satisfaction.
The satisfaction scores available
to us were Price Satisfaction, Organization Satisfaction, Variety Satisfaction,
Item Quality Satisfaction and Overall Satisfaction.
Some of the variables that we
used in our analysis were Gender, Shopping Frequency, Method of Payment, etc.
We had to find a relation or a correlation if it existed between the variable
and the customer satisfaction. This would enable us understand and identify the
key factors influencing customer satisfaction.
Crosstab enables us to find the
relationship between the two selected variables. We can select any two
variables and measure the extent of influence one has over the other. For
example, Crosstab allows us to find if the gender has any link to the customer
satisfaction or if the method of payment has any influence on the customer
satisfaction score. Store managers would benefit if the key variables to
customer satisfaction are identified and the extent to which they influence the
same is measured. They would be able to use this analysis to their advantage
and improve upon the customer satisfaction levels. A successful analysis of the
same would facilitate them to channel their energies and help them focus on the
specific issue that if resolved or enhanced would lead to improved customer
satisfaction.
Example :- Our group did
an analysis to find out if the Shopping Frequency was related to Service Satisfaction.
Chi-Square Test
is also selected from the Statistics Section in the Crosstab Function.
The Chi-Square
is a test that determines if the variables (one in row and the other in column
) are related to each other or not. The null hypothesis is created that stated there
is no relation between the variables compared and is accepted if the test
result is more than 0.05. This indicates that the variables used are not in
relation to each other. A test result of less than 0.05 means that there is a
relation between the variables and hence the null hypothesis is rejected.
The use
of Data Selection was taught that helped in filtering out the irrelevant data
which was found out after the first analysis. This resulted in an in-depth
analysis and helped us streamline our findings.
Our group
used the above tests with many different combinations of variables and their
relationships were tried like price satisfaction and gender etc. The use of
these with different combinations helped us get familiarised with the software,
but more importantly it helped us improve our analytical skills.
By
Piyush Upmanyu
11020841093
Group F
No comments:
Post a Comment