Description
The major objective behind this Research is to weigh the advantages and the disadvantages of a New Product entering an unexplored segment. The Product to be developed is a Chyawanprash which helps in improving skin, making it fairer, cleaner, clearer and healthier.
MARKET
RESEARCH
For
Dabur Yuva
Chyawanprash
\\
CONTENTS
1) Executive Summary
2) Objectives
3) Exploratory Research
4) Secondary Research
5) Research Methodology
i) Questionnaire Design
ii) Sampling Plan
6) Major Findings
7) Conclusion
8) Limitations
9) Annexure
EXECUTIVE SUMMARY
The major objective behind this Research was to weigh the advantages and the
disadvantages of a New Product entering an unexplored segment.
Our Research was conducted at the SIC campus in Hinjewadi, Pune, to
determine the Purchase Behaviour of young adults, that is, between the age
group of 20 to 30. We conducted the research on a Sample Size of 100
respondents.
The Product to be developed is a Chyawanprash which helps in improving
skin, making it fairer, cleaner, clearer and healthier. India is a country where
the youth considers fairness of skin as a very important factor, but there isn’t
any health food which enhances fairness and at the same time immunizes the
body against Common Cold, Cough and Infections. Hence, our product,
“Dabur Yuva Chyawanprash”, caters to the youth in bringing out fair and
clear skin and at the same time increases body immunity.
To help us in the research, we designed a Questionnaire based on Focus Group
Study which was administrated directly. To reach a logical conclusion we have
used Analytical Techniques such as:
ANOVA
Chi Square with Cross- Tabs
Regression
Factor Analysis
Cluster Analysis
We have also identified certain limitations which we encountered during this
project.
We are thankful to our Professor, Mr. Prantosh Banerjee, for providing us an
excellent opportunity to conduct a Market Research.
RESEARCH METHODOLOGY
Questionnaire Design
Our questionnaire was designed in a way to get honest and direct responses
from the respondents. We had not included any open ended questions to avoid
data ambiguity. Different scales were used in our questionnaire like the Likert
Scale and Rank Order Rating Scale. Multiple choice questions were the base
for most of our questions as it makes it easier for the respondent. These
questions were formatted based on the Exploratory Research.
We have tried to maintain a pattern in the sequence of our questions.
Sampling
The Sampling Technique used by us was Random Sampling with which we
selected a sample of 100 respondents around the SIC across all the three
institutes.
Target population Our target population was people in age group 20-30
Sampling frame/
Sampling unit
We decided to take responses from SIC students
Sample size
determination
By random sampling selected 100 students to
administer the questionnaire
Selection of actual
respondents
Visited students in hostel and academic block and also
in mess
MAJOR FINDINGS
Analysis Method
The data was analyzed using Chi Square with Cross Tabs, ANOVA (Analysis
Of Variance), Regression, Factor Analysis, Cluster Analysis. Also we have
depicted some relations through Bar and Pie Charts.
1. ANOVA
This method helps in bringing about a relationship between a dependent
variable and an independent variable.
We initially developed a null hypothesis which was as follows:
H
0
: There is no significant difference between the Monthly Expenditure of
Males and Females on Skin Care Products.
When we ran a One- Way ANOVA test, we got the following output:
From the ANOVA table, we get the significance as 0.000366, which is less
than 0.1.
Hence, we reject the Null Hypothesis and conclude that there is a significant
difference between Monthly Expenditure of Males and Females on Skin Care
Products.
2. Regression
The main objective of Regression is to explain the variation in one variable
based on the variation in one or more other variables.
The following was the Regression Equation:
Amount willing to spend on health food= 0.003(Monthly Expenditure) +
0.088(Monthly Expenses on Skin Care Products) +15.129
The R Square value is 16%. This implies that the above Regression equation is
able to explain 16% variance of the data.
3. Chi Square with Cross- Tabs
Chi Square test can be used to test if the two variables are statistically
associated with each other significantly.
Based on the questionnaire we were able to form 3 chi squares
The null hypothesis and result is in the following paragraph and the printed
results are attached as an annexure:
(A) For importance of fairness and gender
H
0:
Gender does not have a significant impact on importance of fairness for a
person at 95% confidence level
According to our test we cannot reject the null hypothesis as the Chi Square
value is 0.542 which is more than the p-critical at 95% confidence level.
(B) For choice of flavour and gender
H
0:
Gender does not have a significant impact on the choice of flavour at 95%
confidence level
According to our test we reject the null hypothesis as the Chi Square value is
0.000 which is less than the p-critical at 95% confidence level.
4. Factor Analysis
The output of factor analysis is obtained by requesting a principal components
analysis and specifying a rotation. We can see from the Cumulative % column
that the five factors extracted account for 79.3% of the total variance,
(information contained in the original thirteen variables).
This essentially means that we are able to economize on the number of
variables (from thirteen to five underlying factors); while we lose only
approximately 20% of the information content (80% is explained by the five
factors we have retained).
This is followed with interpreting what these five factors represent.
For that we shall take a look at the Rotated and Unrotated Component
Matrices.
From this we can say that variables (dead skin removal, clearer skin and price
have loadings of 0.830, 0.914, 0.885 respectively on factor one.)
So this suggests that factor one is a combination of these three variables).
A look at the Rotated Component Matrix also gives us a similar picture. The
Rotated Component Matrix suggests that factor one is a linear combination of
variables like dead skin removal, clearer skin and price (Loadings closer to one).
This factor can thus ideally be called as the Value for Money as they expect
results for the money that they spend on a particular product.
Factor two has variables like Blood Purification and Immunity with loading
closer to one (0.837 and 0.939 respectively).
So we can conclude that factor is a combination of these two variables. The
Components Matrix also shows a similar grouping. Thus this factor is called
“Protection”.
Factor three shows variables like Packaging and Taste to be heavily loaded on
the former, with values 0.769 and 0.707 respectively.
This suggests that factor three could be called “Aesthetics”.
Factor four reflects a high loading of just one variable which is Acne Removal
with a value of 0.754. We think it appropriate to call this factor as
“Medicinal Properties”.
Factor five is once again highly loaded with just one variable which is the
essence of our research and that being “Fairness” with a value of 0.906.
The variable by itself manages to spell out what the factor is.
5. CLUSTER ANALYSIS
Cluster Analysis is done to segment applications in Marketing. When similar
objects are grouped together, the result is a cluster and while segmenting our
end objective is to identify groups of customers with similar characteristics.
Hierarchical Clustering and Non- Hierarchical Clustering is used.
From our Agglomeration Schedule, we have identified 4 Clusters present in the
data. To identify the number of clusters, we used the difference between rows
in a measure called Coefficient.
Dendrogram is used to find information as to which cases link up in what
sequence to form clusters. It provides a distance measure between the various
cluster combines at various stages.
Final cluster Centre indicates the outputs of K Means Clustering for a four
cluster solution. They describe the mean value of each variable for each of the 4
clusters. For example, Cluster 1 is described by the mean values of variable 1
which is equal to 2.39, variable 2= 2.57, and so on.
We will now interpret the Clusters in terms of 12 original variables. For
example, Cluster 2 consists of Fashionable People who are willing to pay extra
for quality.
Now, we will briefly describe the 4 Clusters as follows:
Cluster 1: High Society
They are more fashionable and keep themselves updated about fashion trends
by reading fashion magazines. They are willing to pay more for better quality
and they believe in effectiveness of Herbal products. They are health conscious.
Cluster 2: Modern Aspirers
They are also fashionable and are willing to pay extra for quality, but they do
not believe in the effectiveness of Herbal products.
Cluster 3: Conservative
They are not very fashionable and do not spend a lot on quality goods. But
they are health conscious and believe in herbal products as compared to
chemical products.
Cluster 4: Orthodox
They are not very health conscious, but they would pay more for quality goods.
They do not read fashion magazines and they prefer herbal products to
synthetic products.
Bar / Pie Charts
Adding the ratings given to the attributes and multiplying them with
respective weights we have made a pie chart to depict the percentage
importance of the 13 attributes.
Fairness is the most important attribute this also falls in line with our
exploratory research and the data collected during secondary research stage.
We have considered the choice of flavours given by various respondents and
have formed a pie chart depicting respondents preference in terms of
percentage. Fruit is the most preferred flavour.
Fairness
14%
Blood Purification
8%
Propertie
s To
Remove
Acne
13%
Removal Of Dead
Skin
4% Clearer Skin
8%
Effectiveness
12%
Quality
11%
Price
6%
Availability
4%
Packaging
1%
Taste
8%
Immunity/Protect
ion
9%
Convenien
ce To
Consume
2%
Imporance Of Attributes
The pie chart below shows the percentage division of respondents on the basis
of average monthly expenditure. 41% of the respondents spend between 3000-
5000.
The bar chart below depicts the gender wise choice of flavours. It is quite
interesting to see that wine is the most preferred flavour of men while fruit is
the choice of over 3o% people.
Wine
16%
Honey
27%
Dry Fruits
24%
Fruits
33%
Flavours
1000-3000
38%
3000-5000
41%
>5000
21%
Expenditure
Gender wise division of average monthly expenditure is shown in the bar chart
below.
0
5
10
15
20
25
30
35
Wine Honey Dry Fruits Fruits
Female
Male
0
5
10
15
20
25
30
35
40
1000-3000 3000-5000 >5000
Female
Male
CONCLUSION
We were successfully able to find that there is a segment which is willing to
buy a Herbal Product which is to be consumed.
Based on the Questionnaire responses, we understand that a lot of the people
are very health conscious and are willing to spend on quality products. Even in
this Modern age, we see that still a lot of people prefer Herbal Products to
Chemical ones. Hence, we can definitely say that the introduction of such a
product will prove profitable for both the producer as well as the consumers.
We can also see that Gender plays a very important part, as it is the women
who spend more on Skin Care Products as compared to men. But with the
introductions of products like Fair and Handsome, there is a huge population
of men who consider fairness and healthy skin very important.
Also, interacting with our respondents told us that, people are slowly shifting
back to herbal or Ayurvedic products as such products help in the long run as
compared to the chemical products. Our respondents also mentioned that
Chyawanprash is perceived to be a tonic for growing kids and not so much
targeted at the youth. Therefore by introducing such a product a whole new
target group is tapped.
Hence, we can conclude by saying that such a product will definitely find a
market as people are willing to buy a health food that helps in enhancing
fairness and improving skin.
Limitations
? The regression model was not able to explain 84% of variance the
factors considered only affects 16% variance.
? Respondents reply could have been influenced by responses that are
perceived to be socially acceptable.
? There is a further scope of getting a clearer picture by doing a conjoint
analysis.
Annexure1
ANOVA
ANOVA
MonthlyExpOnSkinCarePdts
Sum of Squares Df Mean Square F Sig.
Between Groups 37529.055 1 37529.055 13.626 .000
Within Groups 269906.945 98 2754.153
Total 307436.000 99
Annexure2
Regression
Model Summary
Model R
R
Square
Adjusted R
Square
Std. Error of the
Estimate
1 .401(a) .160 .143 48.47224
a Predictors: (Constant), MonthlyExpOnSkinCarePdts, MonthlyExp
ANOVA(b)
Model
Sum of
Squares
df
Mean
Square
F Sig.
1
Regression 43542.853 2 21771.427 9.266 .000(a)
Residual 227907.147 97 2349.558
Total 271450.000 99
a Predictors: (Constant), MonthlyExpOnSkinCarePdts, MonthlyExp
b Dependent Variable: AmtWillingToSpendOnHealthFood
Coefficients(a)
Mod
el
Unstandardize
d Coefficients
Standardize
d
Coefficients
t Sig.
B
Std.
Error
Beta B
Std.
Erro
r
1
(Constant)
56.99
2
15.12
9
3.76
7
.000
MonthlyExp .008 .003 .253
2.70
5
.008
MonthlyExpOnSkinCareP
dts
.273 .088 .290
3.10
9
.002
a Dependent Variable: AmtWillingToSpendOnHealthFood
Annexure3
Chi Square with Cross Tabs
(A) For importance of fairness and gender
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
Gender * Fair 100 36.6% 173 63.4% 273 100.0%
Gender * Fair Crosstabulation
Fair Total
Important Unimportant Important
Gender
Male
Count 19 22 41
% within Fair 38.0% 44.0% 41.0%
Female
Count 31 28 59
% within Fair 62.0% 56.0% 59.0%
Total
Count 50 50 100
% within Fair 100.0% 100.0% 100.0%
Chi-Square Tests
Value df
Asymp. Sig. (2-
sided)
Exact Sig. (2-
sided)
Exact Sig. (1-
sided)
Pearson Chi-Square .372(b) 1 .542
Continuity Correction(a) .165 1 .684
Likelihood Ratio .372 1 .542
Fisher's Exact Test .685 .342
Linear-by-Linear
Association
.368 1 .544
N of Valid Cases 100
a Computed only for a 2x2 table
b 0 cells (.0%) have expected count less than 5. The minimum expected count is 20.50
(B) For choice of flavour and gender
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
Gender * Flavour 100 36.6% 173 63.4% 273 100.0%
Gender
Female Male
Count
40
30
20
10
0
Bar Chart
Unimportant
Important
Fair
Gender * Flavour Crosstabulation
Flavour Total
Wine Honey DryFruit Fruit Wine
Gender
Male
Count 15 8 6 12 41
% within Flavour 93.8% 29.6% 25.0% 36.4% 41.0%
Female
Count 1 19 18 21 59
% within Flavour 6.3% 70.4% 75.0% 63.6% 59.0%
Total
Count 16 27 24 33 100
% within Flavour 100.0% 100.0% 100.0% 100.0% 100.0%
Chi-Square Tests
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 22.681(a) 3 .000
Likelihood Ratio 24.821 3 .000
Linear-by-Linear Association 8.214 1 .004
N of Valid Cases 100
a 0 cells (.0%) have expected count less than 5. The minimum expected count is 6.56.
Gender
Female Male
Count
25
20
15
10
5
0
Bar Chart
Fruit
DryFruit
Honey
Wine
Flavour
Annexure4
Factor Analysis
Communalities
Initial Extraction
Fairness 1.000 .844
BloodPurification 1.000 .889
AcneRemoval 1.000 .884
DeadSkinRemoval 1.000 .821
ClearerSkin 1.000 .866
Effectiveness 1.000 .609
Quality 1.000 .927
Price 1.000 .862
Availability 1.000 .620
Packaging 1.000 .680
Taste 1.000 .717
ImmunityProtection 1.000 .941
ConvienceToConcume 1.000 .654
Extraction Method: Principal Component Analysis.
Total Variance Explained
Compone
nt
Initial Eigenvalues
Extraction Sums of Squared
Loadings
Rotation Sums of Squared
Loadings
Total
% of
Varianc
e
Cumulativ
e %
Total
% of
Varianc
e
Cumulativ
e %
Total
% of
Varianc
e
Cumulativ
e %
1
3.84
7
29.595 29.595
3.84
7
29.595 29.595
3.73
6
28.736 28.736
2
2.12
3
16.329 45.924
2.12
3
16.329 45.924
1.95
9
15.068 43.804
3
1.91
9
14.761 60.685
1.91
9
14.761 60.685
1.85
8
14.290 58.093
4
1.40
0
10.768 71.454
1.40
0
10.768 71.454
1.67
5
12.882 70.976
5
1.02
4
7.878 79.332
1.02
4
7.878 79.332
1.08
6
8.356 79.332
6 .969 7.453 86.785
7 .711 5.471 92.256
8 .388 2.988 95.244
9 .294 2.258 97.502
10 .179 1.374 98.876
11 .106 .816 99.692
12 .037 .282 99.974
13 .003 .026 100.000
Extraction Method: Principal Component Analysis.
Component Matrix(a)
Component
1 2 3 4 5
Fairness .218 -.011 .154 -.123 .870
BloodPurification .455 -.715 .341 -.220 -.080
AcneRemoval .399 -.279 -.233 .715 .284
DeadSkinRemoval .830 .092 .051 .284 -.204
ClearerSkin .914 .093 .107 .042 -.097
Effectiveness .383 .537 -.089 -.407 .006
Quality .640 .688 -.080 -.176 -.084
Price -.885 .200 .162 .109 .007
Availability -.040 .438 .641 -.114 .045
Packaging .085 .524 .464 .362 .226
Taste .071 .013 .627 .505 -.253
ImmunityProtection .167 -.458 .762 -.351 .020
ConvienceToConcume -.727 .197 .266 .110 -.064
Extraction Method: Principal Component Analysis.
a 5 components extracted.
Rotated Component Matrix(a)
Component
1 2 3 4 5
Fairness .078 .108 .075 -.003 .906
BloodPurification .356 .837 -.168 -.185 .012
AcneRemoval .469 -.217 .038 -.754 .215
DeadSkinRemoval .864 .005 .237 -.069 -.122
ClearerSkin .896 .138 .174 .115 .030
Effectiveness .353 -.183 .014 .660 .124
Quality .645 -.318 .205 .605 .043
Price -.867 -.202 .236 -.016 -.117
Availability -.132 .163 .636 .403 .093
Packaging .055 -.190 .769 .053 .216
Taste .074 .226 .707 -.285 -.281
ImmunityProtection -.002 .939 .202 .075 .113
ConvienceToConcume -.718 -.104 .318 .012 -.161
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a Rotation converged in 13 iterations.
Component Transformation Matrix
Component 1 2 3 4 5
1 .976 .151 .034 .075 .136
2 .033 -.640 .473 .605 .025
3 -.138 .637 .752 .091 .044
4 .124 -.393 .458 -.775 -.140
5 -.113 -.089 .015 -.141 .979
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization
Annexure5
Cluster Analysis
Agglomeration Schedule
Stage Cluster Combined Coefficients
Stage Cluster First
Appears
Next
Stage
Cluster
1
Cluster
2
Cluster 1 Cluster 2 Cluster 1 Cluster 2
1 92 100 .000 0 0 9
2 88 99 .000 0 0 13
3 96 98 .000 0 0 5
4 95 97 .000 0 0 6
5 1 96 .000 0 3 16
6 83 95 .000 0 4 17
7 91 94 .000 0 0 10
8 90 93 .000 0 0 11
9 57 92 .000 0 1 12
10 59 91 .000 0 7 59
11 58 90 .000 0 8 72
12 57 89 .000 9 0 31
13 2 88 .000 0 2 15
14 85 87 .000 0 0 16
15 2 86 .000 13 0 86
16 1 85 .000 5 14 33
17 83 84 .000 6 0 74
18 74 75 .000 0 0 19
19 73 74 .000 0 18 71
20 65 66 .000 0 0 36
21 42 43 .000 0 0 34
22 18 19 .000 0 0 69
23 11 12 .000 0 0 80
24 68 72 4.000 0 0 54
25 13 49 4.000 0 0 44
26 40 44 4.000 0 0 37
27 31 41 4.000 0 0 28
28 20 31 4.000 0 27 44
29 8 15 4.000 0 0 56
30 76 79 6.000 0 0 39
31 39 57 6.000 0 12 51
32 45 56 6.000 0 0 49
33 1 48 6.000 16 0 46
34 16 42 6.000 0 21 53
35 32 35 6.000 0 0 82
36 3 65 7.000 0 20 47
37 40 63 7.000 26 0 57
38 24 50 7.000 0 0 66
39 23 76 8.000 0 30 62
40 29 67 8.000 0 0 71
41 47 62 8.000 0 0 73
42 36 54 8.000 0 0 56
43 51 52 8.000 0 0 60
44 13 20 8.000 25 28 50
45 60 77 9.000 0 0 61
46 1 4 9.333 33 0 54
47 3 27 9.667 36 0 58
48 30 46 10.000 0 0 67
49 14 45 10.000 0 32 62
50 13 70 10.200 44 0 55
51 39 81 10.800 31 0 64
52 26 69 11.000 0 0 70
53 7 16 11.333 0 34 77
54 1 68 11.429 46 24 61
55 13 78 11.500 50 0 65
56 8 36 11.500 29 42 63
57 21 40 11.667 0 37 64
58 3 82 11.750 47 0 68
59 22 59 12.000 0 10 81
60 33 51 12.000 0 43 69
61 1 60 12.611 54 45 63
62 14 23 13.111 49 39 76
63 1 8 13.136 61 56 68
64 21 39 13.167 57 51 74
65 10 13 14.000 0 55 70
66 24 64 14.500 38 0 87
67 30 55 15.000 48 0 84
68 1 3 15.000 63 58 75
69 18 33 15.667 22 60 78
70 10 26 15.750 65 52 75
71 29 73 16.000 40 19 77
72 58 61 17.000 11 0 81
73 28 47 17.000 0 41 89
74 21 83 17.500 64 17 79
75 1 10 17.980 68 70 76
76 1 14 18.433 75 62 79
77 7 29 18.700 53 71 83
78 9 18 19.200 0 69 80
79 1 21 19.540 76 74 85
80 9 11 21.333 78 23 91
81 22 58 21.500 59 72 98
82 32 80 22.000 35 0 90
83 7 25 22.111 77 0 84
84 7 30 22.367 83 67 85
85 1 7 22.977 79 84 87
86 2 71 25.000 15 0 93
87 1 24 25.688 85 66 89
88 34 53 26.000 0 0 92
89 1 28 26.520 87 73 90
90 1 32 28.048 89 82 91
91 1 9 28.694 90 80 94
92 5 34 30.000 0 88 97
93 2 17 31.400 86 0 95
94 1 6 32.550 91 0 95
95 1 2 33.805 94 93 96
96 1 37 38.402 95 0 97
97 1 5 41.633 96 92 98
98 1 22 45.419 97 81 99
99 1 38 58.172 98 0 0
DENDOGRAM
Dendrogram using Average Linkage (Between Groups)
Rescaled Distance Cluster Combine
C A S E 0 5 10 15 20 25
Label Num +---------+---------+---------+---------+---------+
92 -^
100 --
57 -;---^
89 -u ·---^
39 -----u ·-^
81 ---------u ¯
40 ---×---^ ·---^
44 ---u ·---- ¯
63 -------u ¯ ¯
21 -----------u ·-^
95 -^ ¯ ¯
97 -- ¯ ¯
83 -;-------------u ¯
84 -u ¯
76 -----×-^ ¯
79 -----u ·---^ ¯
23 -------u ·---^ ¯
45 -----×---^ ¯ ¯ ·-^
56 -----u ·-u ¯ ¯ ¯
14 ---------u ¯ ¯ ¯
65 -×-----^ ¯ ¯ ¯
66 -u ·-^ ¯ ¯ ¯
3 -------u ·-^ ¯ ¯ ¯
27 ---------u ·-^ ¯ ¯ ¯
82 -----------u ¯ ¯ ¯ ¯
8 ---×-----^ ·-;-u ¯
15 ---u ·-^ ¯ ¯ ¯
36 -------×-u ¯ ¯ ¯ ¯
54 -------u ·-u ¯ ¯
60 -------×---- ¯ ¯
77 -------u ¯ ¯ ¯
68 ---×-----^ ¯ ¯ ¯
72 ---u ¯ ¯ ¯ ¯
96 -^ ¯ ¯ ¯ ¯
98 -- ·-u ¯ ¯
1 -;---^ ¯ ¯ ¯
85 -- ·---- ¯ ·---^
87 -u ¯ ¯ ¯ ¯ ¯
48 -----u ¯ ¯ ¯ ¯
4 ---------u ¯ ¯ ¯
26 ---------×---^ ¯ ¯ ¯
69 ---------u ¯ ¯ ¯ ¯
`
* * * * * * H I E R A R C H I C A L C L U S T E R A N A L Y S I S
* * * * * *
C A S E 0 5 10 15 20 25
Label Num +---------+---------+---------+---------+---------+
13 ---×---^ ¯ ¯ ¯ ¯
49 ---u ·-^ ·-u ¯ ¯
31 ---^ ¯ ¯ ¯ ¯ ¯
41 ---;---u ¯ ¯ ¯ ¯
20 ---u ·---- ¯ ¯
70 ---------- ¯ ¯ ¯
78 ---------u ¯ ¯ ¯
10 -------------u ¯ ¯
30 ---------×---^ ¯ ¯
46 ---------u ·------ ¯
55 -------------u ¯ ¯
42 -×---^ ¯ ¯
43 -u ·---^ ¯ ¯
16 -----u ·-------^ ¯ ¯
7 ---------u ¯ ¯ ¯
74 -^ ·-- ¯
75 -;-----------^ ¯ ¯ ·-^
73 -u ·---u ¯ ¯ ¯
29 -------×-----u ¯ ¯ ¯
67 -------u ¯ ¯ ¯
25 -------------------u ¯ ¯
24 -------×-----^ ¯ ¯
50 -------u ·---------- ¯
64 -------------u ¯ ¯
47 -------×-------^ ¯ ¯
62 -------u ·-------u ¯
28 ---------------u ¯
32 -----×-------------^ ·-^
35 -----u ·------ ¯
80 -------------------u ¯ ¯
11 -×-----------------^ ¯ ¯
12 -u ¯ ¯ ¯
18 -×-----------^ ·-----u ·-^
19 -u ·---^ ¯ ¯ ¯
51 -------×---^ ¯ ¯ ¯ ¯ ¯
52 -------u ·-u ·-u ¯ ¯
33 -----------u ¯ ¯ ¯
9 -----------------u ¯ ·---^
6 ---------------------------u ¯ ¯
88 -^ ¯ ¯
99 -- ¯ ¯
2 -;-------------------^ ¯ ·-^
86 -u ·-----^ ¯ ¯ ¯
71 ---------------------u ·-u ¯ ¯
17 ---------------------------u ¯ ·---^
`
* * * * * * H I E R A R C H I C A L C L U S T E R A N A L Y S I S *
* * * * *
C A S E 0 5 10 15 20 25
Label Num +---------+---------+---------+---------+---------+
37 ---------------------------------u ¯ ¯
34 -----------------------×-^ ¯ ¯
53 -----------------------u ·---------u ¯
5 -------------------------u
·---------^
91 -^ ¯ ¯
94 -;---------^ ¯
¯
59 -u ·-------^ ¯
¯
22 -----------u ¯ ¯
¯
90 -^ ·-------------------u
¯
93 -;-------------^ ¯
¯
58 -u ·---u ¯
61 ---------------u
¯
38
-------------------------------------------------u
Final Cluster Centres
Cluster
1 2 3 4
PayingForQuality 2.39 4.75 3.60 1.71
RoleOfTVAds 2.57 1.25 3.34 3.00
BrandedPdts 2.39 1.13 2.49 2.62
SpendingForGoodLooks 2.48 1.00 2.91 2.82
CelebrityEndorsements 3.48 1.13 4.00 3.79
WorryBeforeTryingNewSkinCarePdt 3.17 1.75 3.23 2.35
HerbalToChemicalPdts 2.74 2.63 2.06 2.06
FashionMags 2.00 3.00 3.69 3.88
StylishPckg 2.04 1.75 3.31 3.74
NonEffectivenessOfHerbalPdts 3.91 2.13 3.51 3.56
Taste 2.22 2.38 2.09 2.38
HealthConscious 1.83 2.75 1.97 2.76
Annexure6
Questionnaire
We are conducting a research kindly spare a few minutes
Name:
Age:
Sex:
Institute:
1. Is being fair skinned important to you?
a. Yes
b. No
2. Do you look for ingredients / properties that enhance fairness while
purchasing skin care products?
a. Yes
b. No
3. What type of skin care products would you prefer?
a. Herbal
b. Synthetic
c. A combination of both
4. What is your average monthly expenditure?
a. 1000-3000
b. 3000-5000
c. 5000+
5. Out of your monthly expenses how much do you spend on skin care
products?
a. 0 – 75
b. 75-150
c. 150+
6. While buying a skin care product do you think of long term effects?
a. Yes
b. No
c. Don’t care
7. What are the current setbacks you face with your current skin care product if
any?
a. Price
b. Effectiveness
c. Threat of long term ill effects
d. Availability
e. Skin reactions
f. No setbacks
8. Would you approve of a “health food” that helps in bringing out fairer,
cleaner and healthier skin?
a. Yes
b. No
9. How much are you willing to spend on a health food with the above
mentioned characteristics?
a. 50-80
b. 80-115
c. 115-150
10. If you were to buy the health food what would your consumption pattern
be?
a. Daily
b. 2 or 3 times a week
c. Weekly
d. Fortnightly
11. Would you prefer your health food to be available in flavors of your
choice?
a. yes
b. no
12. If yes, tick your preference from below
a. wine
b. honey
c. dry fruits
d. fruit flavored
11. Rate the important of these factors for a health food on a scale of 1-5 with
1=Most Important and 5=Least Important
S. No. Attribute 1 2 3 4 5
1 Fairness
2 Blood Purification
3 Properties To Remove Acne
4 Removal Of dead Skin
5 Clearer Skin
6 Effectiveness
7 Quality
8 Price
9 Availability
10 Packaging
11 Taste
12 Immunity/Protection
13 Convenience To Use
16. Please rate the following where:
SA=Strongly Agree
A=Agree
NAND=Neither Agree Nor Disagree
D=Disagree
SD=Strongly Disagree
S.No. SA A NAND D SD
1 I don’t mind paying for quality
2
I think TV ads play an important role in
my purchase decisions
3 I prefer branded products
4
I don’t mind spending more for good
looks
5
Celebrity endorsements affect my buying
decisions
6
I always get worried before trying a new
skin care product
7
I prefer herbal products to chemical skin
care products
8 I read fashion magazines
9
Stylish packaging affects my buying
behaviour
10
I don’t believe in the effectiveness of
herbal products
11
If consuming health food, taste is
important to me
12 I am health conscious
15. Are you aware of any other health food that enhances fairer healthier skin?
a. Yes
b. No
16. If yes mention them.
Thank you
Return to Janani
Sharanya
Ashima
Objectives
PRO:
To determine the purchase behaviour for health food.
SRO:
? To determine the importance of a healthy skin among young adults.
? To determine the need for health food that enhances immunity.
? To determine the need for health food that gives you a radiant, acne free
clear skin.
? To determine the need for health food that helps in purifying blood.
These objectives will help us determine the need and importance of a health
food that will help young adults to fight against diseases by enhancing
their immunity provide cleaner, clearer and healthier looking skin to
analyse the standing of Dabur Chyawanprash in consumers mind with
respect to other food products.
Exploratory Research
We considered a sample size of 20 to conduct exploratory research out of
which we have 1% non response error. The questionnaire for our research is
given below.
We received about 30 attributes to start with which was quite an exhaustive
list. There after we conducted a more focused group discussion among a group
of 3 students to decide the most occurant and relevant attribute and assign
weightage to be given to each attribute. Most of the respondents were
unanimous in writing fairness as a quality and also the group decided upon
giving it the highest weightage. Based on the outcome of Focus group
discussion, we have decided to give the following weightage to each of the
attribute-
Attribute Weights
Fairness 18
Blood Purification 15
Properties To Remove Acne 20
Removal Of dead Skin 5
Clearer Skin 10
Effectiveness 10
Exploratory Research Questionnaire
Name:
College:
Roll No:
According to you name the top 5 attributes you look for in
a health food?
Do you think a health food such as Chyawanprash with
added ingredients can be used in place of skin creams?
Quality 10
Price 7
Availability 4
Packaging 2
Taste 7
Immunity/Protection 12
Convenience To Use 5
Total 100
Secondary Research
Our secondary data comes primarily from various sites.
Firstly, we have tried to understand patterns in buying behaviour with regard
to skin care products.
People across different realms of life exhibit different characteristics when it
comes to purchasing. A variety of variables play vital and significant roles in
affecting their decisions.
Here we have first tried to identify what triggers buying of skin care products
and there after respondent reactions to the concept of a health food which
essentially caters to skin requirements such as acne removal, clearer and
healthier skin.
Despite the saying that “beauty is not skin deep” mind sets and back grounds
and culture still have fair skin as a priority. Why else would matrimonial
columns be filled with “fair skinned brides” wanted!
The Indian obsession with fair skin is century’s old and to date advertisements
play their part in luring men and women alike to clean healthy glowing skin
seen as per their glossy ads.
The point to be noted is that chemicals in the long run will have its toll on
skin.
So the concept of a Chyawanprash with all its prior benefits of immunity and
blood purification now comes in a bottle that promises to do much more.
In a not so widely explored market we try to distinguish a segment that is
health conscious and prefer herbal products to synthetic.
A UK based men’s magazine says that wine is a much favoured flavour and
having kept in mind that red wine boosts or rather boasts of giving you that
glow have brought out our product in wine flavour too. Along with fruit and
dry fruits and honey all with their own patrons.
If the saying “if health is lost all is lost” is to be gone by then our product will
definitely be their trademark!
GFK’s survey says that for grocery products price is more important than
quality where as for health products it’s vice versa.
Chyawanprash, Ayurveda and Dabur together have this link that more or less
spells out youth vigour and vitality. Dabur is a well known brand and
Chyawanprash has been priced as per below.
Available in:
1 kg (Rs. 180.00)
500 Gms (Rs.103.00)
250 gms (Rs. 57.00)
With time as the biggest constraint, we aim to provide you with that one stop
solution that promises to take care of most of your health related issues.
doc_343579192.docx
The major objective behind this Research is to weigh the advantages and the disadvantages of a New Product entering an unexplored segment. The Product to be developed is a Chyawanprash which helps in improving skin, making it fairer, cleaner, clearer and healthier.
MARKET
RESEARCH
For
Dabur Yuva
Chyawanprash
\\
CONTENTS
1) Executive Summary
2) Objectives
3) Exploratory Research
4) Secondary Research
5) Research Methodology
i) Questionnaire Design
ii) Sampling Plan
6) Major Findings
7) Conclusion
8) Limitations
9) Annexure
EXECUTIVE SUMMARY
The major objective behind this Research was to weigh the advantages and the
disadvantages of a New Product entering an unexplored segment.
Our Research was conducted at the SIC campus in Hinjewadi, Pune, to
determine the Purchase Behaviour of young adults, that is, between the age
group of 20 to 30. We conducted the research on a Sample Size of 100
respondents.
The Product to be developed is a Chyawanprash which helps in improving
skin, making it fairer, cleaner, clearer and healthier. India is a country where
the youth considers fairness of skin as a very important factor, but there isn’t
any health food which enhances fairness and at the same time immunizes the
body against Common Cold, Cough and Infections. Hence, our product,
“Dabur Yuva Chyawanprash”, caters to the youth in bringing out fair and
clear skin and at the same time increases body immunity.
To help us in the research, we designed a Questionnaire based on Focus Group
Study which was administrated directly. To reach a logical conclusion we have
used Analytical Techniques such as:
ANOVA
Chi Square with Cross- Tabs
Regression
Factor Analysis
Cluster Analysis
We have also identified certain limitations which we encountered during this
project.
We are thankful to our Professor, Mr. Prantosh Banerjee, for providing us an
excellent opportunity to conduct a Market Research.
RESEARCH METHODOLOGY
Questionnaire Design
Our questionnaire was designed in a way to get honest and direct responses
from the respondents. We had not included any open ended questions to avoid
data ambiguity. Different scales were used in our questionnaire like the Likert
Scale and Rank Order Rating Scale. Multiple choice questions were the base
for most of our questions as it makes it easier for the respondent. These
questions were formatted based on the Exploratory Research.
We have tried to maintain a pattern in the sequence of our questions.
Sampling
The Sampling Technique used by us was Random Sampling with which we
selected a sample of 100 respondents around the SIC across all the three
institutes.
Target population Our target population was people in age group 20-30
Sampling frame/
Sampling unit
We decided to take responses from SIC students
Sample size
determination
By random sampling selected 100 students to
administer the questionnaire
Selection of actual
respondents
Visited students in hostel and academic block and also
in mess
MAJOR FINDINGS
Analysis Method
The data was analyzed using Chi Square with Cross Tabs, ANOVA (Analysis
Of Variance), Regression, Factor Analysis, Cluster Analysis. Also we have
depicted some relations through Bar and Pie Charts.
1. ANOVA
This method helps in bringing about a relationship between a dependent
variable and an independent variable.
We initially developed a null hypothesis which was as follows:
H
0
: There is no significant difference between the Monthly Expenditure of
Males and Females on Skin Care Products.
When we ran a One- Way ANOVA test, we got the following output:
From the ANOVA table, we get the significance as 0.000366, which is less
than 0.1.
Hence, we reject the Null Hypothesis and conclude that there is a significant
difference between Monthly Expenditure of Males and Females on Skin Care
Products.
2. Regression
The main objective of Regression is to explain the variation in one variable
based on the variation in one or more other variables.
The following was the Regression Equation:
Amount willing to spend on health food= 0.003(Monthly Expenditure) +
0.088(Monthly Expenses on Skin Care Products) +15.129
The R Square value is 16%. This implies that the above Regression equation is
able to explain 16% variance of the data.
3. Chi Square with Cross- Tabs
Chi Square test can be used to test if the two variables are statistically
associated with each other significantly.
Based on the questionnaire we were able to form 3 chi squares
The null hypothesis and result is in the following paragraph and the printed
results are attached as an annexure:
(A) For importance of fairness and gender
H
0:
Gender does not have a significant impact on importance of fairness for a
person at 95% confidence level
According to our test we cannot reject the null hypothesis as the Chi Square
value is 0.542 which is more than the p-critical at 95% confidence level.
(B) For choice of flavour and gender
H
0:
Gender does not have a significant impact on the choice of flavour at 95%
confidence level
According to our test we reject the null hypothesis as the Chi Square value is
0.000 which is less than the p-critical at 95% confidence level.
4. Factor Analysis
The output of factor analysis is obtained by requesting a principal components
analysis and specifying a rotation. We can see from the Cumulative % column
that the five factors extracted account for 79.3% of the total variance,
(information contained in the original thirteen variables).
This essentially means that we are able to economize on the number of
variables (from thirteen to five underlying factors); while we lose only
approximately 20% of the information content (80% is explained by the five
factors we have retained).
This is followed with interpreting what these five factors represent.
For that we shall take a look at the Rotated and Unrotated Component
Matrices.
From this we can say that variables (dead skin removal, clearer skin and price
have loadings of 0.830, 0.914, 0.885 respectively on factor one.)
So this suggests that factor one is a combination of these three variables).
A look at the Rotated Component Matrix also gives us a similar picture. The
Rotated Component Matrix suggests that factor one is a linear combination of
variables like dead skin removal, clearer skin and price (Loadings closer to one).
This factor can thus ideally be called as the Value for Money as they expect
results for the money that they spend on a particular product.
Factor two has variables like Blood Purification and Immunity with loading
closer to one (0.837 and 0.939 respectively).
So we can conclude that factor is a combination of these two variables. The
Components Matrix also shows a similar grouping. Thus this factor is called
“Protection”.
Factor three shows variables like Packaging and Taste to be heavily loaded on
the former, with values 0.769 and 0.707 respectively.
This suggests that factor three could be called “Aesthetics”.
Factor four reflects a high loading of just one variable which is Acne Removal
with a value of 0.754. We think it appropriate to call this factor as
“Medicinal Properties”.
Factor five is once again highly loaded with just one variable which is the
essence of our research and that being “Fairness” with a value of 0.906.
The variable by itself manages to spell out what the factor is.
5. CLUSTER ANALYSIS
Cluster Analysis is done to segment applications in Marketing. When similar
objects are grouped together, the result is a cluster and while segmenting our
end objective is to identify groups of customers with similar characteristics.
Hierarchical Clustering and Non- Hierarchical Clustering is used.
From our Agglomeration Schedule, we have identified 4 Clusters present in the
data. To identify the number of clusters, we used the difference between rows
in a measure called Coefficient.
Dendrogram is used to find information as to which cases link up in what
sequence to form clusters. It provides a distance measure between the various
cluster combines at various stages.
Final cluster Centre indicates the outputs of K Means Clustering for a four
cluster solution. They describe the mean value of each variable for each of the 4
clusters. For example, Cluster 1 is described by the mean values of variable 1
which is equal to 2.39, variable 2= 2.57, and so on.
We will now interpret the Clusters in terms of 12 original variables. For
example, Cluster 2 consists of Fashionable People who are willing to pay extra
for quality.
Now, we will briefly describe the 4 Clusters as follows:
Cluster 1: High Society
They are more fashionable and keep themselves updated about fashion trends
by reading fashion magazines. They are willing to pay more for better quality
and they believe in effectiveness of Herbal products. They are health conscious.
Cluster 2: Modern Aspirers
They are also fashionable and are willing to pay extra for quality, but they do
not believe in the effectiveness of Herbal products.
Cluster 3: Conservative
They are not very fashionable and do not spend a lot on quality goods. But
they are health conscious and believe in herbal products as compared to
chemical products.
Cluster 4: Orthodox
They are not very health conscious, but they would pay more for quality goods.
They do not read fashion magazines and they prefer herbal products to
synthetic products.
Bar / Pie Charts
Adding the ratings given to the attributes and multiplying them with
respective weights we have made a pie chart to depict the percentage
importance of the 13 attributes.
Fairness is the most important attribute this also falls in line with our
exploratory research and the data collected during secondary research stage.
We have considered the choice of flavours given by various respondents and
have formed a pie chart depicting respondents preference in terms of
percentage. Fruit is the most preferred flavour.
Fairness
14%
Blood Purification
8%
Propertie
s To
Remove
Acne
13%
Removal Of Dead
Skin
4% Clearer Skin
8%
Effectiveness
12%
Quality
11%
Price
6%
Availability
4%
Packaging
1%
Taste
8%
Immunity/Protect
ion
9%
Convenien
ce To
Consume
2%
Imporance Of Attributes
The pie chart below shows the percentage division of respondents on the basis
of average monthly expenditure. 41% of the respondents spend between 3000-
5000.
The bar chart below depicts the gender wise choice of flavours. It is quite
interesting to see that wine is the most preferred flavour of men while fruit is
the choice of over 3o% people.
Wine
16%
Honey
27%
Dry Fruits
24%
Fruits
33%
Flavours
1000-3000
38%
3000-5000
41%
>5000
21%
Expenditure
Gender wise division of average monthly expenditure is shown in the bar chart
below.
0
5
10
15
20
25
30
35
Wine Honey Dry Fruits Fruits
Female
Male
0
5
10
15
20
25
30
35
40
1000-3000 3000-5000 >5000
Female
Male
CONCLUSION
We were successfully able to find that there is a segment which is willing to
buy a Herbal Product which is to be consumed.
Based on the Questionnaire responses, we understand that a lot of the people
are very health conscious and are willing to spend on quality products. Even in
this Modern age, we see that still a lot of people prefer Herbal Products to
Chemical ones. Hence, we can definitely say that the introduction of such a
product will prove profitable for both the producer as well as the consumers.
We can also see that Gender plays a very important part, as it is the women
who spend more on Skin Care Products as compared to men. But with the
introductions of products like Fair and Handsome, there is a huge population
of men who consider fairness and healthy skin very important.
Also, interacting with our respondents told us that, people are slowly shifting
back to herbal or Ayurvedic products as such products help in the long run as
compared to the chemical products. Our respondents also mentioned that
Chyawanprash is perceived to be a tonic for growing kids and not so much
targeted at the youth. Therefore by introducing such a product a whole new
target group is tapped.
Hence, we can conclude by saying that such a product will definitely find a
market as people are willing to buy a health food that helps in enhancing
fairness and improving skin.
Limitations
? The regression model was not able to explain 84% of variance the
factors considered only affects 16% variance.
? Respondents reply could have been influenced by responses that are
perceived to be socially acceptable.
? There is a further scope of getting a clearer picture by doing a conjoint
analysis.
Annexure1
ANOVA
ANOVA
MonthlyExpOnSkinCarePdts
Sum of Squares Df Mean Square F Sig.
Between Groups 37529.055 1 37529.055 13.626 .000
Within Groups 269906.945 98 2754.153
Total 307436.000 99
Annexure2
Regression
Model Summary
Model R
R
Square
Adjusted R
Square
Std. Error of the
Estimate
1 .401(a) .160 .143 48.47224
a Predictors: (Constant), MonthlyExpOnSkinCarePdts, MonthlyExp
ANOVA(b)
Model
Sum of
Squares
df
Mean
Square
F Sig.
1
Regression 43542.853 2 21771.427 9.266 .000(a)
Residual 227907.147 97 2349.558
Total 271450.000 99
a Predictors: (Constant), MonthlyExpOnSkinCarePdts, MonthlyExp
b Dependent Variable: AmtWillingToSpendOnHealthFood
Coefficients(a)
Mod
el
Unstandardize
d Coefficients
Standardize
d
Coefficients
t Sig.
B
Std.
Error
Beta B
Std.
Erro
r
1
(Constant)
56.99
2
15.12
9
3.76
7
.000
MonthlyExp .008 .003 .253
2.70
5
.008
MonthlyExpOnSkinCareP
dts
.273 .088 .290
3.10
9
.002
a Dependent Variable: AmtWillingToSpendOnHealthFood
Annexure3
Chi Square with Cross Tabs
(A) For importance of fairness and gender
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
Gender * Fair 100 36.6% 173 63.4% 273 100.0%
Gender * Fair Crosstabulation
Fair Total
Important Unimportant Important
Gender
Male
Count 19 22 41
% within Fair 38.0% 44.0% 41.0%
Female
Count 31 28 59
% within Fair 62.0% 56.0% 59.0%
Total
Count 50 50 100
% within Fair 100.0% 100.0% 100.0%
Chi-Square Tests
Value df
Asymp. Sig. (2-
sided)
Exact Sig. (2-
sided)
Exact Sig. (1-
sided)
Pearson Chi-Square .372(b) 1 .542
Continuity Correction(a) .165 1 .684
Likelihood Ratio .372 1 .542
Fisher's Exact Test .685 .342
Linear-by-Linear
Association
.368 1 .544
N of Valid Cases 100
a Computed only for a 2x2 table
b 0 cells (.0%) have expected count less than 5. The minimum expected count is 20.50
(B) For choice of flavour and gender
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
Gender * Flavour 100 36.6% 173 63.4% 273 100.0%
Gender
Female Male
Count
40
30
20
10
0
Bar Chart
Unimportant
Important
Fair
Gender * Flavour Crosstabulation
Flavour Total
Wine Honey DryFruit Fruit Wine
Gender
Male
Count 15 8 6 12 41
% within Flavour 93.8% 29.6% 25.0% 36.4% 41.0%
Female
Count 1 19 18 21 59
% within Flavour 6.3% 70.4% 75.0% 63.6% 59.0%
Total
Count 16 27 24 33 100
% within Flavour 100.0% 100.0% 100.0% 100.0% 100.0%
Chi-Square Tests
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 22.681(a) 3 .000
Likelihood Ratio 24.821 3 .000
Linear-by-Linear Association 8.214 1 .004
N of Valid Cases 100
a 0 cells (.0%) have expected count less than 5. The minimum expected count is 6.56.
Gender
Female Male
Count
25
20
15
10
5
0
Bar Chart
Fruit
DryFruit
Honey
Wine
Flavour
Annexure4
Factor Analysis
Communalities
Initial Extraction
Fairness 1.000 .844
BloodPurification 1.000 .889
AcneRemoval 1.000 .884
DeadSkinRemoval 1.000 .821
ClearerSkin 1.000 .866
Effectiveness 1.000 .609
Quality 1.000 .927
Price 1.000 .862
Availability 1.000 .620
Packaging 1.000 .680
Taste 1.000 .717
ImmunityProtection 1.000 .941
ConvienceToConcume 1.000 .654
Extraction Method: Principal Component Analysis.
Total Variance Explained
Compone
nt
Initial Eigenvalues
Extraction Sums of Squared
Loadings
Rotation Sums of Squared
Loadings
Total
% of
Varianc
e
Cumulativ
e %
Total
% of
Varianc
e
Cumulativ
e %
Total
% of
Varianc
e
Cumulativ
e %
1
3.84
7
29.595 29.595
3.84
7
29.595 29.595
3.73
6
28.736 28.736
2
2.12
3
16.329 45.924
2.12
3
16.329 45.924
1.95
9
15.068 43.804
3
1.91
9
14.761 60.685
1.91
9
14.761 60.685
1.85
8
14.290 58.093
4
1.40
0
10.768 71.454
1.40
0
10.768 71.454
1.67
5
12.882 70.976
5
1.02
4
7.878 79.332
1.02
4
7.878 79.332
1.08
6
8.356 79.332
6 .969 7.453 86.785
7 .711 5.471 92.256
8 .388 2.988 95.244
9 .294 2.258 97.502
10 .179 1.374 98.876
11 .106 .816 99.692
12 .037 .282 99.974
13 .003 .026 100.000
Extraction Method: Principal Component Analysis.
Component Matrix(a)
Component
1 2 3 4 5
Fairness .218 -.011 .154 -.123 .870
BloodPurification .455 -.715 .341 -.220 -.080
AcneRemoval .399 -.279 -.233 .715 .284
DeadSkinRemoval .830 .092 .051 .284 -.204
ClearerSkin .914 .093 .107 .042 -.097
Effectiveness .383 .537 -.089 -.407 .006
Quality .640 .688 -.080 -.176 -.084
Price -.885 .200 .162 .109 .007
Availability -.040 .438 .641 -.114 .045
Packaging .085 .524 .464 .362 .226
Taste .071 .013 .627 .505 -.253
ImmunityProtection .167 -.458 .762 -.351 .020
ConvienceToConcume -.727 .197 .266 .110 -.064
Extraction Method: Principal Component Analysis.
a 5 components extracted.
Rotated Component Matrix(a)
Component
1 2 3 4 5
Fairness .078 .108 .075 -.003 .906
BloodPurification .356 .837 -.168 -.185 .012
AcneRemoval .469 -.217 .038 -.754 .215
DeadSkinRemoval .864 .005 .237 -.069 -.122
ClearerSkin .896 .138 .174 .115 .030
Effectiveness .353 -.183 .014 .660 .124
Quality .645 -.318 .205 .605 .043
Price -.867 -.202 .236 -.016 -.117
Availability -.132 .163 .636 .403 .093
Packaging .055 -.190 .769 .053 .216
Taste .074 .226 .707 -.285 -.281
ImmunityProtection -.002 .939 .202 .075 .113
ConvienceToConcume -.718 -.104 .318 .012 -.161
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a Rotation converged in 13 iterations.
Component Transformation Matrix
Component 1 2 3 4 5
1 .976 .151 .034 .075 .136
2 .033 -.640 .473 .605 .025
3 -.138 .637 .752 .091 .044
4 .124 -.393 .458 -.775 -.140
5 -.113 -.089 .015 -.141 .979
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization
Annexure5
Cluster Analysis
Agglomeration Schedule
Stage Cluster Combined Coefficients
Stage Cluster First
Appears
Next
Stage
Cluster
1
Cluster
2
Cluster 1 Cluster 2 Cluster 1 Cluster 2
1 92 100 .000 0 0 9
2 88 99 .000 0 0 13
3 96 98 .000 0 0 5
4 95 97 .000 0 0 6
5 1 96 .000 0 3 16
6 83 95 .000 0 4 17
7 91 94 .000 0 0 10
8 90 93 .000 0 0 11
9 57 92 .000 0 1 12
10 59 91 .000 0 7 59
11 58 90 .000 0 8 72
12 57 89 .000 9 0 31
13 2 88 .000 0 2 15
14 85 87 .000 0 0 16
15 2 86 .000 13 0 86
16 1 85 .000 5 14 33
17 83 84 .000 6 0 74
18 74 75 .000 0 0 19
19 73 74 .000 0 18 71
20 65 66 .000 0 0 36
21 42 43 .000 0 0 34
22 18 19 .000 0 0 69
23 11 12 .000 0 0 80
24 68 72 4.000 0 0 54
25 13 49 4.000 0 0 44
26 40 44 4.000 0 0 37
27 31 41 4.000 0 0 28
28 20 31 4.000 0 27 44
29 8 15 4.000 0 0 56
30 76 79 6.000 0 0 39
31 39 57 6.000 0 12 51
32 45 56 6.000 0 0 49
33 1 48 6.000 16 0 46
34 16 42 6.000 0 21 53
35 32 35 6.000 0 0 82
36 3 65 7.000 0 20 47
37 40 63 7.000 26 0 57
38 24 50 7.000 0 0 66
39 23 76 8.000 0 30 62
40 29 67 8.000 0 0 71
41 47 62 8.000 0 0 73
42 36 54 8.000 0 0 56
43 51 52 8.000 0 0 60
44 13 20 8.000 25 28 50
45 60 77 9.000 0 0 61
46 1 4 9.333 33 0 54
47 3 27 9.667 36 0 58
48 30 46 10.000 0 0 67
49 14 45 10.000 0 32 62
50 13 70 10.200 44 0 55
51 39 81 10.800 31 0 64
52 26 69 11.000 0 0 70
53 7 16 11.333 0 34 77
54 1 68 11.429 46 24 61
55 13 78 11.500 50 0 65
56 8 36 11.500 29 42 63
57 21 40 11.667 0 37 64
58 3 82 11.750 47 0 68
59 22 59 12.000 0 10 81
60 33 51 12.000 0 43 69
61 1 60 12.611 54 45 63
62 14 23 13.111 49 39 76
63 1 8 13.136 61 56 68
64 21 39 13.167 57 51 74
65 10 13 14.000 0 55 70
66 24 64 14.500 38 0 87
67 30 55 15.000 48 0 84
68 1 3 15.000 63 58 75
69 18 33 15.667 22 60 78
70 10 26 15.750 65 52 75
71 29 73 16.000 40 19 77
72 58 61 17.000 11 0 81
73 28 47 17.000 0 41 89
74 21 83 17.500 64 17 79
75 1 10 17.980 68 70 76
76 1 14 18.433 75 62 79
77 7 29 18.700 53 71 83
78 9 18 19.200 0 69 80
79 1 21 19.540 76 74 85
80 9 11 21.333 78 23 91
81 22 58 21.500 59 72 98
82 32 80 22.000 35 0 90
83 7 25 22.111 77 0 84
84 7 30 22.367 83 67 85
85 1 7 22.977 79 84 87
86 2 71 25.000 15 0 93
87 1 24 25.688 85 66 89
88 34 53 26.000 0 0 92
89 1 28 26.520 87 73 90
90 1 32 28.048 89 82 91
91 1 9 28.694 90 80 94
92 5 34 30.000 0 88 97
93 2 17 31.400 86 0 95
94 1 6 32.550 91 0 95
95 1 2 33.805 94 93 96
96 1 37 38.402 95 0 97
97 1 5 41.633 96 92 98
98 1 22 45.419 97 81 99
99 1 38 58.172 98 0 0
DENDOGRAM
Dendrogram using Average Linkage (Between Groups)
Rescaled Distance Cluster Combine
C A S E 0 5 10 15 20 25
Label Num +---------+---------+---------+---------+---------+
92 -^
100 --
57 -;---^
89 -u ·---^
39 -----u ·-^
81 ---------u ¯
40 ---×---^ ·---^
44 ---u ·---- ¯
63 -------u ¯ ¯
21 -----------u ·-^
95 -^ ¯ ¯
97 -- ¯ ¯
83 -;-------------u ¯
84 -u ¯
76 -----×-^ ¯
79 -----u ·---^ ¯
23 -------u ·---^ ¯
45 -----×---^ ¯ ¯ ·-^
56 -----u ·-u ¯ ¯ ¯
14 ---------u ¯ ¯ ¯
65 -×-----^ ¯ ¯ ¯
66 -u ·-^ ¯ ¯ ¯
3 -------u ·-^ ¯ ¯ ¯
27 ---------u ·-^ ¯ ¯ ¯
82 -----------u ¯ ¯ ¯ ¯
8 ---×-----^ ·-;-u ¯
15 ---u ·-^ ¯ ¯ ¯
36 -------×-u ¯ ¯ ¯ ¯
54 -------u ·-u ¯ ¯
60 -------×---- ¯ ¯
77 -------u ¯ ¯ ¯
68 ---×-----^ ¯ ¯ ¯
72 ---u ¯ ¯ ¯ ¯
96 -^ ¯ ¯ ¯ ¯
98 -- ·-u ¯ ¯
1 -;---^ ¯ ¯ ¯
85 -- ·---- ¯ ·---^
87 -u ¯ ¯ ¯ ¯ ¯
48 -----u ¯ ¯ ¯ ¯
4 ---------u ¯ ¯ ¯
26 ---------×---^ ¯ ¯ ¯
69 ---------u ¯ ¯ ¯ ¯
`
* * * * * * H I E R A R C H I C A L C L U S T E R A N A L Y S I S
* * * * * *
C A S E 0 5 10 15 20 25
Label Num +---------+---------+---------+---------+---------+
13 ---×---^ ¯ ¯ ¯ ¯
49 ---u ·-^ ·-u ¯ ¯
31 ---^ ¯ ¯ ¯ ¯ ¯
41 ---;---u ¯ ¯ ¯ ¯
20 ---u ·---- ¯ ¯
70 ---------- ¯ ¯ ¯
78 ---------u ¯ ¯ ¯
10 -------------u ¯ ¯
30 ---------×---^ ¯ ¯
46 ---------u ·------ ¯
55 -------------u ¯ ¯
42 -×---^ ¯ ¯
43 -u ·---^ ¯ ¯
16 -----u ·-------^ ¯ ¯
7 ---------u ¯ ¯ ¯
74 -^ ·-- ¯
75 -;-----------^ ¯ ¯ ·-^
73 -u ·---u ¯ ¯ ¯
29 -------×-----u ¯ ¯ ¯
67 -------u ¯ ¯ ¯
25 -------------------u ¯ ¯
24 -------×-----^ ¯ ¯
50 -------u ·---------- ¯
64 -------------u ¯ ¯
47 -------×-------^ ¯ ¯
62 -------u ·-------u ¯
28 ---------------u ¯
32 -----×-------------^ ·-^
35 -----u ·------ ¯
80 -------------------u ¯ ¯
11 -×-----------------^ ¯ ¯
12 -u ¯ ¯ ¯
18 -×-----------^ ·-----u ·-^
19 -u ·---^ ¯ ¯ ¯
51 -------×---^ ¯ ¯ ¯ ¯ ¯
52 -------u ·-u ·-u ¯ ¯
33 -----------u ¯ ¯ ¯
9 -----------------u ¯ ·---^
6 ---------------------------u ¯ ¯
88 -^ ¯ ¯
99 -- ¯ ¯
2 -;-------------------^ ¯ ·-^
86 -u ·-----^ ¯ ¯ ¯
71 ---------------------u ·-u ¯ ¯
17 ---------------------------u ¯ ·---^
`
* * * * * * H I E R A R C H I C A L C L U S T E R A N A L Y S I S *
* * * * *
C A S E 0 5 10 15 20 25
Label Num +---------+---------+---------+---------+---------+
37 ---------------------------------u ¯ ¯
34 -----------------------×-^ ¯ ¯
53 -----------------------u ·---------u ¯
5 -------------------------u
·---------^
91 -^ ¯ ¯
94 -;---------^ ¯
¯
59 -u ·-------^ ¯
¯
22 -----------u ¯ ¯
¯
90 -^ ·-------------------u
¯
93 -;-------------^ ¯
¯
58 -u ·---u ¯
61 ---------------u
¯
38
-------------------------------------------------u
Final Cluster Centres
Cluster
1 2 3 4
PayingForQuality 2.39 4.75 3.60 1.71
RoleOfTVAds 2.57 1.25 3.34 3.00
BrandedPdts 2.39 1.13 2.49 2.62
SpendingForGoodLooks 2.48 1.00 2.91 2.82
CelebrityEndorsements 3.48 1.13 4.00 3.79
WorryBeforeTryingNewSkinCarePdt 3.17 1.75 3.23 2.35
HerbalToChemicalPdts 2.74 2.63 2.06 2.06
FashionMags 2.00 3.00 3.69 3.88
StylishPckg 2.04 1.75 3.31 3.74
NonEffectivenessOfHerbalPdts 3.91 2.13 3.51 3.56
Taste 2.22 2.38 2.09 2.38
HealthConscious 1.83 2.75 1.97 2.76
Annexure6
Questionnaire
We are conducting a research kindly spare a few minutes
Name:
Age:
Sex:
Institute:
1. Is being fair skinned important to you?
a. Yes
b. No
2. Do you look for ingredients / properties that enhance fairness while
purchasing skin care products?
a. Yes
b. No
3. What type of skin care products would you prefer?
a. Herbal
b. Synthetic
c. A combination of both
4. What is your average monthly expenditure?
a. 1000-3000
b. 3000-5000
c. 5000+
5. Out of your monthly expenses how much do you spend on skin care
products?
a. 0 – 75
b. 75-150
c. 150+
6. While buying a skin care product do you think of long term effects?
a. Yes
b. No
c. Don’t care
7. What are the current setbacks you face with your current skin care product if
any?
a. Price
b. Effectiveness
c. Threat of long term ill effects
d. Availability
e. Skin reactions
f. No setbacks
8. Would you approve of a “health food” that helps in bringing out fairer,
cleaner and healthier skin?
a. Yes
b. No
9. How much are you willing to spend on a health food with the above
mentioned characteristics?
a. 50-80
b. 80-115
c. 115-150
10. If you were to buy the health food what would your consumption pattern
be?
a. Daily
b. 2 or 3 times a week
c. Weekly
d. Fortnightly
11. Would you prefer your health food to be available in flavors of your
choice?
a. yes
b. no
12. If yes, tick your preference from below
a. wine
b. honey
c. dry fruits
d. fruit flavored
11. Rate the important of these factors for a health food on a scale of 1-5 with
1=Most Important and 5=Least Important
S. No. Attribute 1 2 3 4 5
1 Fairness
2 Blood Purification
3 Properties To Remove Acne
4 Removal Of dead Skin
5 Clearer Skin
6 Effectiveness
7 Quality
8 Price
9 Availability
10 Packaging
11 Taste
12 Immunity/Protection
13 Convenience To Use
16. Please rate the following where:
SA=Strongly Agree
A=Agree
NAND=Neither Agree Nor Disagree
D=Disagree
SD=Strongly Disagree
S.No. SA A NAND D SD
1 I don’t mind paying for quality
2
I think TV ads play an important role in
my purchase decisions
3 I prefer branded products
4
I don’t mind spending more for good
looks
5
Celebrity endorsements affect my buying
decisions
6
I always get worried before trying a new
skin care product
7
I prefer herbal products to chemical skin
care products
8 I read fashion magazines
9
Stylish packaging affects my buying
behaviour
10
I don’t believe in the effectiveness of
herbal products
11
If consuming health food, taste is
important to me
12 I am health conscious
15. Are you aware of any other health food that enhances fairer healthier skin?
a. Yes
b. No
16. If yes mention them.
Thank you
Return to Janani
Sharanya
Ashima
Objectives
PRO:
To determine the purchase behaviour for health food.
SRO:
? To determine the importance of a healthy skin among young adults.
? To determine the need for health food that enhances immunity.
? To determine the need for health food that gives you a radiant, acne free
clear skin.
? To determine the need for health food that helps in purifying blood.
These objectives will help us determine the need and importance of a health
food that will help young adults to fight against diseases by enhancing
their immunity provide cleaner, clearer and healthier looking skin to
analyse the standing of Dabur Chyawanprash in consumers mind with
respect to other food products.
Exploratory Research
We considered a sample size of 20 to conduct exploratory research out of
which we have 1% non response error. The questionnaire for our research is
given below.
We received about 30 attributes to start with which was quite an exhaustive
list. There after we conducted a more focused group discussion among a group
of 3 students to decide the most occurant and relevant attribute and assign
weightage to be given to each attribute. Most of the respondents were
unanimous in writing fairness as a quality and also the group decided upon
giving it the highest weightage. Based on the outcome of Focus group
discussion, we have decided to give the following weightage to each of the
attribute-
Attribute Weights
Fairness 18
Blood Purification 15
Properties To Remove Acne 20
Removal Of dead Skin 5
Clearer Skin 10
Effectiveness 10
Exploratory Research Questionnaire
Name:
College:
Roll No:
According to you name the top 5 attributes you look for in
a health food?
Do you think a health food such as Chyawanprash with
added ingredients can be used in place of skin creams?
Quality 10
Price 7
Availability 4
Packaging 2
Taste 7
Immunity/Protection 12
Convenience To Use 5
Total 100
Secondary Research
Our secondary data comes primarily from various sites.
Firstly, we have tried to understand patterns in buying behaviour with regard
to skin care products.
People across different realms of life exhibit different characteristics when it
comes to purchasing. A variety of variables play vital and significant roles in
affecting their decisions.
Here we have first tried to identify what triggers buying of skin care products
and there after respondent reactions to the concept of a health food which
essentially caters to skin requirements such as acne removal, clearer and
healthier skin.
Despite the saying that “beauty is not skin deep” mind sets and back grounds
and culture still have fair skin as a priority. Why else would matrimonial
columns be filled with “fair skinned brides” wanted!
The Indian obsession with fair skin is century’s old and to date advertisements
play their part in luring men and women alike to clean healthy glowing skin
seen as per their glossy ads.
The point to be noted is that chemicals in the long run will have its toll on
skin.
So the concept of a Chyawanprash with all its prior benefits of immunity and
blood purification now comes in a bottle that promises to do much more.
In a not so widely explored market we try to distinguish a segment that is
health conscious and prefer herbal products to synthetic.
A UK based men’s magazine says that wine is a much favoured flavour and
having kept in mind that red wine boosts or rather boasts of giving you that
glow have brought out our product in wine flavour too. Along with fruit and
dry fruits and honey all with their own patrons.
If the saying “if health is lost all is lost” is to be gone by then our product will
definitely be their trademark!
GFK’s survey says that for grocery products price is more important than
quality where as for health products it’s vice versa.
Chyawanprash, Ayurveda and Dabur together have this link that more or less
spells out youth vigour and vitality. Dabur is a well known brand and
Chyawanprash has been priced as per below.
Available in:
1 kg (Rs. 180.00)
500 Gms (Rs.103.00)
250 gms (Rs. 57.00)
With time as the biggest constraint, we aim to provide you with that one stop
solution that promises to take care of most of your health related issues.
doc_343579192.docx