United States Sports Academy
FINAL EXAM 1.9 FLIP SEGMENTATION CASE
FLIP SEGMENTATION CASE
Name: Rishabh Kulshrestha
Roll no: k066
1) Read the questionnaire in Appendix 2 carefully. Do you, like Anand, have any concerns?
Highlight any potential issues with the construction of the questions and/or the scales used
to collect the responses. Recommend changes, if you consi
...[Show More]
FLIP SEGMENTATION CASE
Name: Rishabh Kulshrestha
Roll no: k066
1) Read the questionnaire in Appendix 2 carefully. Do you, like Anand, have any concerns?
Highlight any potential issues with the construction of the questions and/or the scales used
to collect the responses. Recommend changes, if you consider them necessary, to the
questionnaire.
Ans: Like Anand I would have few concerns such as students appearing in the final year are more
interested in getting their placements rather than getting certifications, also they would not have
time to get certifications as they would be too busy with the campus process.
Instead of targeting the final year students they should target the first year students of Mba as
they would have the time as well be interested in improving their CV and getting more
certifications and knowledge.
Also not only they should target the finance students but also fields as well since students may
find it interesting and would be willing to apply for the certifications, operations and BIA students
could get certifications for research.
About the questionnaire
a) Since being a professional course the questionnaire should be more formal and
corporate level
b) Addition of question like age, name should be added and that too in the beginning.
c) The first question should be changed to what is the field you are planning for and not
only ask whether finance or not. It should be more generic.
d) The questionnaire should be divided in different segments starting from personal details
to personal opinions and end with the course questions.
e) Few questions based on rate yourself on a scale of 1- 10 should be added to view what
the applicant perceives him/herself as.
2) Run a cluster analysis (without discriminant analysis) on the encoded data. Identify the
appropriate number of clusters and name each cluster.
[Show Less]
-preview.png)
