Computech Technologies should launch CBA & CFM or CPBA & CFM first to maximize profit and minimize risk. These combinations require the fewest number of students (around 23) to break even based on their costs. CBA & CFM is preferred because it requires around 3 students per course, which is a safe assumption for the untested brand. A logistic regression model was created using 341 data points to predict enrollment in 3-month courses. The model found that only age was a significant predictor, with older customers less likely to enroll. The optimal model has a constant of 5.622 and an age coefficient of -0.133. This indicates the target profile for these short courses should be younger consumers.

1. PRATIMAAN CASE STUDY
VGSOM – IIT KHARAGPUR
TEAM: SOA
RAJIB LAYEK & ARINDAM ROUTH

2. Problem Statement 1:Which two products the Company should
launch first, and why?
Computech Technologies (P) ltd should launch CPA & CFM or CPBA & CFM as
of now to maximize their profit and minimize risk.
Reason: Irrespective of any program they are incurring fixed cost of 2.1048
Cr INR. So, the decision to launch which two products directly depends on
variable cost of those two programs.
Now if we assumethat 3 month course can be done repeatedly 4 times in a
year in the existing infrastructure then for CBA faculty cost will be 440,000
INR for 3 month (110 hr. * 1000 INR per hr.*4 times in a year. So, revenue
from CBA per year per person is480,000 INR (120,000 * 4 Times a year).
For CBA+CFM,TC: 21,048,000 (FC) + 440,000 (VC for CBA) + 396,000 (VC for
CFM) = 21,884,000.Revenue from these two product will be 480,000 (CBA
for 1 yr.) + 480,000 (CFM for 1 yr.) = 960,000.
Break-Even Analysis: So, min no of students required for Break-Even
21,884,000 /960,000 = 22.796 ≈ 23.
In similar way min no of students required for all the combination can be
calculated. And the least no of students are coming for CBA+CAPB or
CBA+CFM. For detailed calculation see Appendix A.
Risk Aspect:for CBA+CFM combo 23 students required for whole year which
means 23/ (4*2)≈3 students per course which is a safe assumption.
Qualitative Analysis:As the BRAND is not established yet people won’t like
to invest 6.2 Lakh for 2 year course at first. So, it’s better to start with 3
month course and then after reasonable brand awareness they can launch
other long term product.
1

3. APPENDIX 1: BREAK-EVEN ANALYSIS IN EXCEL
Table 1:
ABC College
Product Details
Product
Name
Course
Duration
Functional
Area
Product Unit
Price
Delivery
Hrs
Faculty
Cost per Hr.
Total Faculty
Cost per
course
Total
Faculty
Cost in one
year
PGDBA
12
Technology
₹ 360,000
460
₹ 1,000
₹ 460,000
₹ 460,000
PGDMA
24
Management
₹ 620,000
800
₹ 900
₹ 720,000
₹ 360,000
CBA
3
Technology
₹ 120,000
110
₹ 1,000
₹110,000
₹ 440,000
CPBA
3
Technology
₹ 120,000
110
₹ 1,000
₹ 110,000
₹ 440,000
CFM
3
Management
₹ 120,000
110
₹ 900
₹ 99,000
₹ 396,000
PGCBA
12
Management
₹ 360,000
460
₹ 900
₹ 414,000
₹ 414,000
Delivery
hour
Per M
38
33
37
37
37
38
Faculty
Cost Per
Month
₹ 38,333
₹ 30,000
₹ 36,667
₹ 36,667
₹ 33,000
₹ 34,500
Table 2:
Name
Code
Course 1: C1
Course 2: C2
Course 3: C3
Course 4: C4
Course 5: C5
Course 6: C6
PGDBA
PGDMA
CBA
CPBA
CFM
PGCBA
Course
Duration Cost
Revenue
12
460000 360000
24
360000 310000
3
440000 480000
3
440000 480000
3
396000 480000
12
414000 360000
21,048,000
FC
Table 3:
No of
Combination
Course
Combination
Variable
Cost
Total Cost
Total Price
No. of
Students in a
year
No. of Students
per course
Profit for min 50
students per year
1
C1+C2
820000 21,868,000
670000 32.64
11,632,000
2
C1+C3
900000 21,948,000
840000 26
20,052,000
3
C1+C4
900000 21,948,000
840000 26
20,052,000
4
C1+C5
856000 21,904,000
840000 26
20,096,000
5
C1+C6
874000 21,922,000
720000 30
14,078,000
6
C2+C3
800000 21,848,000
790000 28
17,652,000
7
C2+C4
800000 21,848,000
790000 28
17,652,000
8
C2+C5
9
C2+C6
756000 21,804,000
774000
790000 28
670000
17,696,000
11,678,000
2

4. 21,822,000
33
2.85
10
C3+C4
880000 21,928,000
960000 22.842
26,072,000
2.849
11
C3+C5
836000 21,884,000
960000 22.796
26,116,000
12
C3+C6
854000 21,902,000
840000 26
20,098,000
13
C4+C5
836000 21,884,000
960000 22.796
14
C4+C6
854000 21,902,000
840000 26
20,098,000
15
C5+C6
810000 21,858,000
840000 26
20,142,000
2.849
26,116,000
For calculation and formula see theattached excel file.
Problem statement 2:
Based on analysis of the research data provided by the Marketing research company, work out the profile
of the kind of Consumer, the Company must focus on.
Objective is to build a predictive logistic regression model which will help client to identify the customer profile.
For this as we have already seen that we are concerned about customer for only 3 month course
(CBA/CPBA.CFM) we have taken a sample of 341 data points which include all the data points who enrolled for
these three program also 74 randomly chosen data point who didn’t apply for any of the program.
On that sample points binary logistic regression is run and output is interpreted in the below section.
3

5. Here CBA is dependent variable with value 0 or 1 which represent customer will not Enroll (0) or Enroll (1) for
CBA/CPBA/CFM. All the other characteristics of the customer is taken as independent variable. Among these
apart from Salary, Work-Ex and Age all are categorical variable.
Forward LR method is taken as no prior research work has been done in this regard.
INTERPRETATION OF OUTPUT
Table1:341 sample points are taken. From the whole set of data for preparing logistic regression for
CBA/CPBA/CFM enrollment.
Case Processing Summary
Unweighted Cases
Selected Cases
a
N
Included in Analysis
Percent
341
100.0
0
.0
341
100.0
0
.0
341
100.0
Missing Cases
Total
Unselected Cases
Total
a. If weight is in effect, see classification table for the total number of
cases.
Table 2: Dependent variable CBA: Yes & No. (Whether people will enroll for CBA/CPBA/CFM or not.)
Dependent Variable Encoding
Original Value
Internal Value
N
0
Y
1
4

6. Table 3:Different label of specification, Functional area, level, Highest Qualification are coded.
5

7. Table 4: CBA/CPBA/CFM enrolled 266 person and not enrolled 75 person.
Classification Table
a,b
Predicted
CBA
Observed
Step 0
CBA
N
Y
Percentage Correct
N
0
75
.0
Y
0
266
100.0
Overall Percentage
78.0
a. Constant is included in the model.
b. The cut value is .500
Table 5:As we have done forward stepwise method for logistic regression, Initial model includes only
constant term (excluding all other predictors) and the log-likelihood of this base model is 359.301.
a,b,c
Iteration History
Coefficients
Iteration
-2 Log likelihood
Step 0
Constant
1
360.578
1.120
2
359.302
1.261
3
359.301
1.266
4
359.301
1.266
a. Constant is included in the model.
b. Initial -2 Log Likelihood: 359.301
c. Estimation terminated at iteration number 4 because parameter estimates changed by less than .001.
Table 6:78% time the model predicts correctly that the person will enroll for CBA/CPBA/CFM.
Classification Table
a,b
Predicted
CBA
Observed
Step 0
CBA
N
Y
Percentage Correct
N
0
75
.0
Y
0
266
100.0
Overall Percentage
78.0
6

8. a,b,c
Iteration History
Coefficients
Iteration
-2 Log likelihood
Step 0
Constant
1
360.578
1.120
2
359.302
1.261
3
359.301
1.266
4
359.301
1.266
a. Constant is included in the model.
b. The cut value is .500
Table 7:Value of the constant (b0) = 1.266
Variables in the Equation
B
Step 0
Constant
S.E.
1.266
Wald
.131
93.769
df
Sig.
1
Exp(B)
.000
3.547
Table 8:Residual Chi-square is calculated with <.001 level of significance which indicates co-efficient of
other variable are significant which means addition of one or more variables to the model will significantly
affect its predictive power.
7

9. Table 8:-2Log-Likelihood (-2LL) has decreased significantly to 106.994 from the initial -2LL value of 359.301.
Lower value of -2LL is indicating that model is predicting more accurately as 2LL indicates unexplained data.
Model Summary
Step
1
2
3
4
5
-2 Log likelihood
Cox & Snell R Square
Nagelkerke R Square
248.067
a
.278
.427
171.609
b
.423
.650
129.283
b
.491
.753
113.286
b
.514
.789
106.994
b
.523
.803
a. Estimation terminated at iteration number 5 because parameter estimates changed by less than .001.
b. Estimation terminated at iteration number 20 because maximum iterations has been reached. Final solution cannot be
found.
Table 9:At step 1 Age variable is included in the model. Wald statistic for Age is 65.499 with
significance label less than .001. Wald statistic follows a Chi square distribution and tells us bcoefficient of Age is significant. And co-efficient for Age (b1) = -0.113
Exp(B)= 0.893 which is less than 1 which indicates if Age increases probability for Enrollment
decreases. (If the value of Exp(B) would be greater than 1 then it would increase with increase in Age )
In Step 2, 3 and 4 Educational Qualification, Functional Area and Work-Ex is included but result is not
significant. So only step1 in considered. So, no other predictor is taken for the model.
8

10. Table 10:Change in -2LL for Age is 111.234 with significance label of <.001 which means it has a
significant effect on the predictive ability of the model.
Model if Term Removed
Change in -2 Log
Variable
Model Log Likelihood
Likelihood
df
Sig. of the Change
Step 1
Age
-179.650
111.234
1
.000
Step 2
Age
-118.584
65.558
1
.000
Edu
-124.033
76.458
17
.000
Age
-83.123
36.963
1
.000
Func_Area
-85.805
42.326
17
.001
Edu
-93.478
57.674
17
.000
Age
-82.133
50.980
1
.000
Func_Area
-84.296
55.306
17
.000
Work_Ex
-64.641
15.997
1
.000
Edu
-90.499
67.712
17
.000
Age
-79.534
52.073
1
.000
Func_Area
-82.120
57.245
17
.000
Work_Ex
-63.837
20.680
1
.000
Salary
-56.643
6.291
1
.012
Edu
-90.055
73.117
17
.000
Step 3
Step 4
Step 5
Table 11:Histogram of the predicted probabilities of the people enrolling in the CBA/CPBA/CFM program.
9

11. This graph tells us how well the predictive model is. As max clusters are at each end the model and no of
N in right hand side and no of Y in left hand side is very less the model is reliable.
So, apart from age no other characteristics can be predicted as a significant predictor for the
logistic regression which will help client to identify customer profile.
Co-efficient of constant and Age (B0) and (B1) for the model is 5.622and -0.133 respectively.
10