LAL BAHADUR SHASTRI INSTITUTE OF MANAGEMENT, DELHI

TWO YEAR FULL-TIME PGDM - GENERAL

(2020-22) - TRIMESTER - III
END TERM EXAMINATION, APRIL 2021

Subject: Marketing Research Paper Code: MKT104

Time: 2 Hours Max. Marks: 40

Instructions:

1. All questions are compulsory.

2. Answers must be handwritten on white sheets, with page numbers on the top right corner.
3. Scan the answer sheets, convert into one PDF document, and upload it on Moodle.
4. File name must be given as ‘Roll number) Name’ e.g. 306) Aman Kumar.
5. Do explain the concepts, use suitable model/framework(s) for your answers, but be brief and to the
point.
Section A

Thirty years ago, Eureka Forbes Eurochamp made its entry in modern Indian families in form of Aquaguard
purifier. In developed countries, the main impurities were the harmful chemicals only. But Eureka Forbes
observed that in India there were three-fold impurities in water such as chemical, sediments, and harmful
biological components such as bacteria and viruses. Eureka Forbes was ready with its product which can
filter the water and remove all these impurities. But still it was not able to tap the water purifier market in
India. This pushed Eureka Forbes to initiate a research to search the reason. The objective of this initiative
was to determine why Indian market is not accepting electronic purifiers despite of their capability to
remove impurities.
Q.1. What kind of exploratory research would you propose to conduct in this context? (3 Marks)
Q.2. Prepare a focus group discussion guide. What will be the respondent’s profile if you are to conduct
the focus group discussion for the company? (5 Marks)
Q.3. What kind of projective techniques you think will be suitable to understand user’s motivation and
insight about using high-end water purifiers? (4 Marks)
 Skin Care Market in India

The skin care market has been one of the most promising segments of the baby care market in India.
According to an estimate the total value of this market was Rs. 375 Crore in 2019 which rose to Rs. 394
Crore in 2020. Some of the companies engaged in the manufacture and sale of baby skin care market in
India are Dabur India Ltd., Johnson’s Johnson, Wipro and Oriflame. The market share of Dabur Lal Tel
was 34% while that of three products of Johnson’s Johnson were as follows:

Baby Lotion 21.9%

Baby Oil 17.9%

Baby Cream 11.8%

These figures relate to the year 2018. Although the total share of these three products comes to 51.6% which
is far greater than Dabur’s for one product 34%, Johnson’s Johnson feels there is good scope for its
products. As such it is very keen to increase its market share in respect of each of these products. However,
there are some challenges before the company, one of which is that in semi-urban and rural areas its
products are not so well received. The company feels that some effective research in the prevailing market
conditions is called for.

Q.4. Indicate an appropriate marketing research problem. As a marketing researcher, what type of study
would you undertake? (5 Marks)

Q.5. Assuming that a decision in favor of survey has been taken, develop a sampling plan for the same?
Which survey method would be appropriate? How would you handle non-response? (5 Marks)
Section B

Q.6.Private domestic airline operator raised their airfare in December 2012. Indian Airlines, the
government-owned carrier, was the only domestic airline which did not follow suit. Information
available (Source- Economic Times) showed that people still prefer to fly Jet Airways. The reasons
for the above preference had to be ascertained, as according to Indian Airlines officials’ domestic
flyers are price-conscious customers. This study consisted of 20 respondents who had recently
flown with Jet Airways. They were asked to indicate on a seven-point scale (1=completely agree,
7= completely disagree), their agreement or disagreement with a set of 10 statements relating to
their perceptions and attributes of the airline.
The 10 statements were as follows:
1. They (Jet Airways) are always on time.
2. The seats are very comfortable.
3. I love the food they provide.
4. Their airhostesses are very beautiful.
5. My boss/friend flies with the same airline.
6. The airlines have younger aircrafts.
7. I get the advantage of a frequent flyer program.
8. It (the flight timing) suits my schedule.
9. My mom feels safe when I Fly Jet.
10. Flying Jet compliments my lifestyle and social standing in the society.
The data obtained is given in Table 1. The SPSS output file is from Table 2 to Table 8.
Res. On Air Air Freq Life
Comfort Food Boss Timing Mom
No. time host young Fly style
1 1 2 2 3 1 1 2 2 1 2
2 2 1 2 2 5 2 2 1 2 2
3 1 3 1 4 6 2 3 2 5 3
4 3 4 2 2 4 3 2 4 1 3
5 4 2 4 3 2 4 3 2 3 3
6 5 3 4 2 6 5 2 2 1 2
7 2 1 2 1 5 2 1 1 4 1
8 1 2 1 4 2 2 4 2 2 4
9 1 1 1 2 3 1 2 2 5 2
10 2 5 1 3 1 1 2 5 3 2
11 2 4 2 6 3 2 5 5 2 5
12 2 1 2 4 5 2 4 1 1 4
13 3 2 3 7 6 3 6 2 2 6
14 2 2 2 5 2 1 5 2 3 5
15 2 1 1 2 2 2 2 1 1 2
16 1 1 1 3 4 1 3 1 2 3
17 4 1 4 2 6 4 2 1 5 2
18 1 2 2 2 2 2 2 1 7 2
19 2 3 2 1 2 2 2 3 7 3
20 2 1 2 3 1 2 3 1 5 3

The SPSS output of factor analysis is mentioned from Table 2 to Table 8.

Table 2: Descriptive Statistics
Mean Std. Deviation Analysis N
Ontime Flight 2.1500 1.13671 20
Comfortable 2.1000 1.20961 20
Quality of Food 2.0500 .99868 20
Beautiful Airhostess 3.0500 1.57196 20
Boss and Friend 3.4000 1.84676 20
Younger Aircraft 2.2000 1.10501 20
Frequent Flyer 2.8500 1.30888 20
Flight Timing 2.0500 1.27630 20
Mom Feel Safe 3.1000 1.97084 20
Lifestyle & Social 2.9500 1.27630 20

Table 3: KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling .518

Adequacy.
Approx. Chi-Square 194.483
Bartlett's Test of
Df 45
Sphericity
Sig. .000

Table 4: Communalities

Initial Extraction
Ontime Flight 1.000 .933
Comfortable 1.000 .934
Quality of Food 1.000 .835
Beautiful Airhostess 1.000 .943
Boss and Friend 1.000 .462
Younger Aircraft 1.000 .922
Frequent Flyer 1.000 .971
Flight Timing 1.000 .955
Mom Feel Safe 1.000 .193
Lifestyle & Social 1.000 .923
 Table 5: Total Variance Explained

Compo Initial Eigenvalues Extraction Sums of Squared Rotation Sums of Squared Loadings
nent Loadings
Total % of Cumulati Total % of Cumulative Total % of Cumulative %
Variance ve % Variance % Variance
1 3.177 31.775 31.775 3.177 31.775 31.775 3.041 30.408 30.408
2 3.050 30.499 62.274 3.050 30.499 62.274 3.030 30.296 60.703
3 1.845 18.447 80.720 1.845 18.447 80.720 2.002 20.017 80.720
4 .907 9.073 89.793
5 .666 6.662 96.455
6 .135 1.348 97.803
7 .112 1.118 98.921
8 .056 .562 99.483
9 .047 .470 99.953
10 .005 .047 100.000
Extraction Method: Principal Component Analysis.
Extraction Method:
Principal Component
Analysis.
 Table 6: Component Matrixa
Component
1 2 3
Ontime Flight .058 .937 .228
Comfortable .406 -.044 .876
Quality of Food .028 .913 .019
Beautiful Airhostess .941 -.052 -.236
Boss and Friend .039 .607 -.305
Younger Aircraft -.020 .953 .116
Frequent Flyer .927 -.010 -.336
Flight Timing .484 -.155 .834
Mom Feel Safe -.397 -.183 .033
Lifstyle & Social .933 -.007 -.228
Extraction Method: Principal Component Analysis.
a. 3 components extracted.
Table 7: Rotated Component Matrixa

Component
1 2 3
Ontime Flight .954 -.004 .153
Comfortable .037 .090 .962
Quality of Food .912 .037 -.052
Beautiful Airhostess -.062 .965 .096
Boss and Friend .578 .149 -.325
Younger Aircraft .959 -.040 .021
Frequent Flyer -.028 .985 -.005
Flight Timing -.077 .175 .958
Mom Feel Safe -.184 -.389 -.086
Lifstyle & Social -.016 .956 .097
Extraction Method: Principal
Component Analysis. Rotation
Method: Varimax with Kaiser
Normalization.
a. Rotation converged in 4 iterations.
 Table 8: Component Transformation Matrix
Component 1 2 3
1 .011 .943 .333
2 .996 .018 -.086
3 .087 -.333 .939
Extraction Method:
Principal Component
Analysis.
Rotation Method:
Varimax with Kaiser
Normalization.
Questions:
a) Explain how factor analysis is useful to a marketing manager. Are there any independent and
dependent variables in factor analysis? (2 Marks)
b) Is the sample size adequate? Explain your answer by quoting any relevant statistics. (2 Marks)
c) How many factors should be retained? Explain your answer by quoting any relevant statistics.
How to test the hypothesis that the variables are uncorrelated in the population. Explain your
answer by quoting any relevant statistics. (2 Marks)
d) Which items load onto which factors? Do these factors make psychological sense? (i.e. can
you name them based on the items that load onto them). (2 Marks)

Q.7. A credit card bank has been in the business for the last 14 years, during the last 2 years their repayment
default has shot up considerably. Even though the bank charges a penalty interest on all late payments, this
high default rate is putting a lot of pressure on the banks recovery mechanism and has now begun to impact
its profitability in this activity. The problem appears to be the credit appraisal mechanism used by the bank
to evaluate credit card applicants at the time of credit card allotment. Hence the bank desires to revamp its
appraisal system using its past experience. To determine this, they have conducted a suitable research.
Initially the variables that have an impact on consumers credit worthiness were identified, these variables
were:
A. Consumer’s age.
B. Monthly household income.
C. Relationship in Years.
 Historical data was collected from the banks own record and consumers were classified in to two
groups as follows:
A. High risk (code = 1)
B. Low Risk (code = 2)

This was done based on the banks experience with the customers during the last two years. Answer
the following questions based on the output of discriminant analysis.
a) Determine the classification accuracy of this discriminant model. (2.5 Marks)
b) State the statistical significance of the discriminant function. (2.5 Marks)
c) Which one of the three causative variables is the best discriminator for credit worthiness? (2.5 Marks)
d) Identify a discriminant criterion that would enable the firm to classify future applicants into high risk
and low risk categories using the discriminant function. (2.5 Marks)
2
The data obtained is given in Table 2. The SPSS output file is from Table 3 to Table 7.

Table 2
Risk Age Income Relationship in Years
2 35 40000 8
2 33 45000 6
2 29 36000 5
1 22 32000 0
1 26 30000 1
2 28 35000 6
1 30 31000 7
1 23 27000 2
2 22 48000 6
1 24 12000 4
1 26 15000 3
2 38 25000 7
2 40 20000 5
1 32 18000 4
2 36 24000 3
1 31 17000 5
1 28 14000 3
2 33 18000 6
 TABLE 3: Summary of Canonical Discriminant Functions

Eigenvalues

Function
Eigenvalue % of Variance Cumulative % Canonical
Correlatio
n
di 1 1.811a 100.0 100.0 .803
m
en
si
on
0
a. First 1 canonical discriminant functions were used in the analysis.

Wilks' Lambda
Test of Function(s) Wilks' Lambda Chi-square Df Sig.
dimension0 1 .356 14.985 3 .002

Table 4: Standardized Canonical Discriminant Function Coefficients

Function
1
Age .907
Income .924
Relationship in Years .285

Table 5: Canonical Discriminant Function Coefficients

Function
1
Age .194
Income .000
Relationship in Years .160
(Constant) -9.076
Unstandardized coefficients
 Table 6: Functions at Group Centroids

Risk Function
1
1 -1.269
2 1.269
Unstandardized canonical discriminant functions evaluated at group means

Table 7: Classification Resultsa

Loyalty Predicted Group Membership

High Risk Low Risk Total
Original Count 1 9 0 9
2 1 8 9
% 1 100 0 100.0
2 11.1 88.9 100.0