Case study organized by IIM Rohtak in association with Phoenix Global

1. College: XIMB
Team Name: Art of War
Team Leader: Devark Chauhan
Email : UM19152@stu.ximb.ac.in
Mobile: 9466670234
Art of War
THE TEAM

2. 147
65
Placed Unplaced
Batch Statistics
₹ 298,853
₹ 270,385
₹ 288,783
₹ 250,000 ₹ 270,000 ₹ 290,000 ₹ 310,000
Marketing and Finance
Marketing and HR
Overall Average Salary
Average Placement
Highest Package
Rs. 9,40,000
35%
65%
Work Ex/Freshers
Work Experience Fresher
65%
35%
Gender Ratio
Male Female
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0%
50%
100%
Marketing &
Finance
Marketing & HR
Placed Not Placed
Status and Work Experience are two Dichotomous variables. The entire
dataset (removing the anomalies) have been visualized with respect to four
scores- MBA %, HSC %, SSC % and DEGREE %. By visualization, it is evident that
irrespective of whether a candidate has work-experience or not, HSC %, SSC %
and DEGREE % is higher in Placed candidates than Unplaced ones suggesting a
possible correlation between these variables and Placements. The difference is
almost negligible in MBA % but it is evident nevertheless
Null Hypothesis- The variables, MBA %, HSC %, SSC % and DEGREE % do not
significantly influence a candidate’s chance in getting placed
Alternate Hypothesis- The variables above have sufficient say in a candidate’s chance
of getting placed
Percentage and Work Experience drive Placements

3. Duplicate Data and Information Paradox
On analysing the data, there were a few inconsistencies-
Index 1032, 1054, 1093, 1108 had duplicate entries. This could skew the data
into giving incorrect insights. These were deleted before performing any models
 On further analysis, students bearing index number 1001, 1069 and 1100
have taken up Commerce during HSC but have their graduation in Science and
Technology. Since Science subjects are mandatory (during HSC) for doing a
graduation in Science and Technology, these entries are discarded
Index Gender ssc_% ssc_board hsc_% hsc_board hsc_stream degree_% degree_domain
1032 F 67 Central 53 Central Science 65 Science & Technology
1032 F 67 Central 53 Central Science 65 Science & Technology
1054 M 80 Others 70 Others Science 72 Science & Technology
1054 M 80 Others 70 Others Science 72 Science & Technology
1093 F 60.23 Central 69 Central Science 66 Commerce & Management
1093 F 60.23 Central 69 Central Science 66 Commerce & Management
1108 M 82 Others 90 Others Commerce 83 Commerce & Management
1108 M 82 Others 90 Others Commerce 83 Commerce & Management
1001 M 67 Others 91 Others Commerce 58 Science & Technology
1069 F 69.7 Central 47 Central Commerce 72.7 Science & Technology
1100 M 54 Central 82 Others Commerce 63 Science & Technology
Assumptions- a) None of the students who have taken up Commerce during
HSC and have done their graduation in “Medical and Others”, actually did their
graduation in Medical (as that would contradict the point 2 above)
b) SSC & HSC Boards are divided into two segments here- Central & Others.
“Others” might include different boards, so these variables are not taken into
account as they are incomplete themselves
c) Since SSC and HSC percentages are taken into account in the model, the Board
differentiation is assumed to have no effect on the overall data
Running the Binary Logistic Regression
The “Case Processing Summary” table shows a total of 212 data points and
there are No Missing Data. The data points are obviously not taking into
account the duplicates and the ones with anomaly
The “Dependent Variable Encoding” table shows the coded Dependent
Variable with “1” denoting the candidate is placed and “0” denoting the
candidate is unplaced
The “Categorical Variables Coding” table shows the division of data
according to the various categories of the independent variables. The
frequency shows the count of students under each Independent Variable
Category
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4. Testing the Model Strength for accurate INSIGHTS
The Classification Table for Block 0, an intercept only model,
i.e. when all independent variables are kicked out and only
the Intercept (Constant) is kept into consideration. It shows
that the model correctly predicted 147 people out of 212
will get placed. Thus, the null model (Block 0) assuming that
everyone got placed, the model gave a 69.33% accuracy
The “Variables in the Equation” (Block 0) tests the
Hypothesis whether the frequency of Not Placed (65)
and Placed (147) are statistically significantly different
from one another. The Wald Chi-square tests the null-
hypothesis that the constant equals “0”. Since p-value
is less than 0.05, the null hypothesis is rejected. In
simpler terms, the Wald statistic tests the null
hypothesis whether the probability of a student
getting placed or not placed are equal and that null
hypothesis is rejected. The Beta Value here is the
intercept
In “Omnibus Test table”, the Chi-square values
are statistically significant proving that the model
has some amount of predictive capacity
The “Model Summary” shows pseudo-R^2 values
of 0.537 and 0.757. They’re not calculated like
original R^2 (Ordinary Least Squares) but on the
basis of Maximum Likelihood Estimation. Values
are greater than 50% suggesting dependent
variable variance is explained significantly by
independent variables
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5. Work Ex is the most important criteria for getting Placed
The “Hosmer and Lemeshow Test” is an opposite test, meaning its p-value has
to be extremely statistically insignificant to prove that the model is a good fit
and the model has good predictive capabilities. Since the significance level is
0.0950 (which is much greater than 0.050), the model is a good fit
The “Classification Table” shows that the model correctly predicted 54
candidates out of 65 to be not placed (83.1% accuracy) and 138 out of 147 to
be placed (93.9% accuracy) and overall the model correctly predicted the status
of 192 candidates out of 212 which leads to an accuracy of 90.6%. Thus, the
overall accuracy of the model shot by 21.3 % points
The “Variables in the Equation” is perhaps the most important table of all. The
significant independent variables (with p-values less than 0.050) are-
a) SSC percentage b) HSC percentage c) Degree Percentage d) Work Experience
e) MBA Percentage
The Wald statistic acts like t-statistic in the Normal Regression Model and the
Wald Statistic value of all significant independent variables is more than the
other independent variables
The unstandardized Beta coefficients of all the significant variables affect the
change in status of placement from 0 (Not Placed) to 1 (Placed). Thus, SSC
Percentage affects 0.223 times the change in status. The variable that affects
the most is Work Experience (1.968) and the one which affects the least is MBA
Percentage (-0.208). Thus MBA Percentage is negatively affecting the change in
Status for students
The Exp(B) or Odds Ratios, function same as the Beta coefficients but they take
into account the other significant independent variables as well. Work
Experience leads here as well
Answer to Preet Kumar Mathur’s Conundrum
Work Experience with a Beta Value of 1.968 is the most
important factor in changing the Status of Placements from “0”
to “1”
While SSC, HSC and Degree Percentages do matter (Beta values of
0.223, 0.097, 0.190 respectively), MBA Percentage has a negative
Beta Value of -0.208 showing the need for change in book oriented
learning during the two years.
Choosing a particular MBA Specialization does not increase the
chance of getting placed (Significance level of 0.618). According to
the placement figures, Marketing and Finance command a higher
average salary than Marketing and HR. Out of students pursuing
Marketing and Finance 79.9% got placed. Out of students
pursuing Marketing and HR 55.9% got placed
HR can be made as a separate specialization with those candidates
sitting only for HR profiles. While Marketing and finance can
continue to be a combined specialization. We have generated three
innovative ideas to fit into our strategy to improve placements these
being: 1-Day Internships , Virtual Portfolio and Simulation
Thus, based on the SPSS output, we have sufficient statistical confidence to
reject the Null Hypothesis and accept the Alternate Hypothesis
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6. Vague CV shortlist
Applications open Group Discussion Second Shortlist Interview Selection
The Traditional Placement Scenario
Amidst the tens of thousands of management graduates churned out by the 5,500 B-schools in the country, only 7 per cent turn out to be employable, says a study conducted by ASSOCHAM
Maybe they are hired through a wrong recruitment process making them unfit for the Job profile offered!
“Everyone is a genius. But if you judge a fish by its ability to climb a tree, it will live its whole life believing that it is stupid. ”-Albert Einstein
Top Skills
recruiters
look for in
MBA Grads
Problem
Solving
Self
Awareness
Conflict
Resolution
Adaptability
Critical
Observation
Active
Listening
Curiosity
Leadership
Creativity
Teamwork
Lack of Knowledge
No Prior Experience
Nervousness
Lack of communication skills
No Shortlists
Problems faced by students during placements
We conducted a primary research for finding the
problems faced by students during their placements.
With the help of this research we concluded that the
major problem that students were facing was the less
number of opportunities in the form of less shortlists.
They were nervous during a Group Discussion and
interview which majorly hampered their performance.
They were not confident about their own abilities.
In a B-school, diversity plays a key role and one can find students with various talents and abilities. It will
not be correct to judge them on the basis of a traditional approach say only a GD and Pi. Therefore, we
developed a model that will cater to some of the problems faced by the students during the placement
season, meanwhile keeping in mind the top skills that recruiters look for in MBA graduates. We divided a
major chunk of our sample into various personas and made a model that will cater to their needs
Modern problems requires Modern Solutions
COVID 19 has had a huge impact on the education sector. Schools and colleges have been closed since
the past six months and it is being predicted that the placement season will be held online for the most
part. Students will have to accept this change and will have to overcome all the problems. It is
therefore important to come up with a solution that will cater to the problems caused due to the
pandemic as well as the general problems faced by the students
Source: insideiim
Primary Research
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7. One Day Internship Program
Meet
Shamiksha Jain
Pursuing 2nd Year MBA
Practical person
Can Think on feet
Samiksha believes in
practical learning, she has
never rote learned anything.
She enjoys working on
various data analytics tools
and is second to none when
it comes to software
packages.
Samiksha always feels a lot
uncomfortable if she is in a
big gathering, hence, she is
unable to talk properly in a
group discussion. This has
made her face problems go
past the GD round.
How can we help her ?
Big Idea
After Initial Round, shortlisted candidates get to be a part of the company for a 1 day internship!
Key Point and
Assumptions:
1) We will be able to convince
companies to allow students
to take 1 day internship as a
part of evaluation process
2) This will be a key stage to
check the cultural fit and
skillset actually required to
take up the role.
3) Companies are not liable to
give any certificate for this as
this is just an evaluation
criteria
4) The process is limited to a day
as to not lengthen the
recruitment process
Use Case: Internshala 1-Day
Internshala provides opportunities to students to intern at dream companies
like Viacom-18, Spotify, Uber eats, etc. This has proved to be a successful
campaign both for companies as well as students. This makes it an innovative
yet feasible component for our placement process.
Review
Students were able to understand what goes behind running the show of the
companies. They were able to understand the job roles on ground and how
things work real time.
How will this help the Students
 Will be able to understand the other side in a much better way.
 Will be able to decode how skills work in reality
How will this help the Recruiter
 Understand how they fit in real time rather than just one to one
 Feedback from the actual teams for which the recruitment process is
being carried out.
Teamwork and Collaboration
Curiosity and General Awareness
Active listening Cultural Fit
Adaptability
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8. Meet
Ritesh Nagpal
Ritesh is hardworking
and passionate to
learn about business
in depth. Ritesh is a
dreamer, he finds it
difficult to explain his
thought explicitly to
others.
Ritesh works really
hard on every subject
assignment but he is
worried how will he
explain the data
heavy projects orally !
Pursuing 2nd Year MBA
Ambivert
Team Player
Virtual Portfolio
How can we help him ?
Big Idea
Allowing students to maintain an online portfolio comprising of their projects undertaken as a part of course curriculum
Subject: Marketing
Analytics
Objective: Gap Analysis
for chocolate Industry
Carried out
multidimensional scaling
to identify relevant gaps
using SPSS.
Obtained Excellent rating
by faculty In charge
Recruiter would be able to access projects completed by students as a part of course curriculum via a college owned
portal. This portal will be maintain by the college IT team.
How will this help Recruiters
 Evaluate Students Holistically
 Evaluate College Curriculum
 Evaluate Domain Knowledge
How will this help the University
 Showcase the pedagogy
 Showcase quality teachers
 Track student progress
How will this help the Students
 Display their hard skills
 Showcase their thought process
 Add tangible elements to their profile
Technical Skills
Creativity
Conflict Resolution
Leadership
~The Journal of Higher Education Vol. 71, No. 1 (Jan. - Feb., 2000), pp. 60-83
Group project experiences enhanced students communication,
planning and technical skills , which help them in their career ahead.
Placed 62.6%
Unplaced 61.6%
Avg MBA Percentage
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9. Simulation
Meet
Maneet Singh Bhatia
Pursuing 2nd Year MBA
3 Years of Work Ex
Talks Numbers
Big Idea
Real time business simulation to evaluate the candidate
according to the required profile
Maneet worked in an
MNC, he tries to
connect everything he
learns to the business
scenarios. This has
enhanced his
understanding of
management
manifolds.
Maneet loves strategy
and strongly focusses
result oriented work.
Maneet believes in
Acta non Verba!
How can we help him ?
How will this help Recruiters
 Parameters and Evaluating criteria can be adjusted
according to the needs of the profile
 Candidates can be judged on a lot of parameters
 Decision making ability in a manager’s shoes is
evaluated
How will this help Students
 Students will be able to combine learning from
various domains and apply it synchronized manner
 Skewness towards a particular trait will be
removed
 Show their business acumen
~Sample Simulation
Simulation serve as an overall knowledge exam. Our Institute
will make simulations on Excel, which is fairly easy to make.
The parameters and scenarios can be changed according to
company requirement. This will serve as a great tool to
display managerial acumen. Simulations will quantify the
results leading to selection. This will generate competition
among the students. According to a psychology
paper(PMC4554955), in a competitive environment
participants had 4% better reaction time.
Critical Observation
Problem-solving Abilities
Quantitative Skills
Analytics
Typical Results Include:
•Up to 72% reduction in turnover
•Up to 66% reduction in training time
•Up to 286% increase in sales /
referrals
•Up to 700% return on investment
(~ employmenttechnologies.com)
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10. Revamping
Shortlist
CV shortlisting can be
replaced with an excel
based simulation. The top
performers in simulation
will be shortlisted. A
traditional CV shortlisting
can be followed if the
company insists on it.
Blog Portfolio Virtual Interview 1 day internship Selection
After the shortlist the
recruiter will go through
the projects and the CV of
the student that he has
uploaded in the blog. It is
upto the discretion of the
company to make it an
elimination round.
After the recruiter has
gone through the blogs,
he can conduct telephonic
interviews and ask him
questions from his CV and
projects. He will also
judge the communication
skills of the students here.
After the interview the
remaining students will
have to work with the
company for a day
wherein the recruiter will
judge if he is culturally fit
in the company and can
work efficiently.
After the student has
cleared all the rounds he
will get the job . The
recruiter can give an
overall feedback as to
what all can be improved
in both the recruitment
process as well as the
students.
Overhauling the process to suit the current scenario
No Bias
Students will do the
simulation on their
own and will know
where they lag.
The above method will have various advantages when compared to the traditional process. This will create a Win-Win situation for both the recruiters as well as the
students. The students will learn and use and their applied skills and even the recruiters will get those students who will directly have a positive impact on their
company.
Better Projects
Students will work
harder on their college
projects as they will be
valuable for placement
No GD
GD will now not be a
factor for elimination
of soft spoken but
skilled students
Culture Fit
Company will have the
option to hire only those
students who culturally
fit with them.
Pandemic Proof
This process has been
made keeping the
COVID 19 situation in
mind.
Feedback
Students can gain
feedback from the
company in which they
have worked
More Exposure Handy CV
Students will now have
their full CV, projects
and Portfolio on their
tips.
Students will get to
experience a variety of
companies and their
work culture
Skills Evaluated Virtual Portfolio 1 day Internship
Technical Skills
Creativity
Leadership
Conflict Resolution
Curiosity
Teamwork
Collaboration
Self awareness
Active listening
Problem-solving
Critical observation
Quantitative skills
Analytics
Problem-solving
Adaptability
Simulation
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THANK
YOU