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105
CHAPTER – V
CONSUMER PREFERENTIAL BUYING PATTERNS OF
COSMETICS - AN ANALYSIS
Purchase Place of Cosmetics
The data collected through a separate questionnaire was administered on a sample
of 1200 consumers at the northern parts in order to ascertain the purchase place of
cosmetics.
Table 1
Purchase Place of Cosmetics by Consumers
Source No. of
Respondents Percentage
Departmental Store 381 31.75
Big Retail Shop 243 20.25
Any Shop 325 27.08
Door to door seller 251 20.92
Total 1200 100.0
Source: Primary data.
Table 1 gives the purchase place of cosmetics from different stores. It is clear
from the table that 31.75% respondents purchase from departmental stores, 20.25%
respondents purchase from big retail shop, 27.08% respondents purchase from any shop
and 20.92%, respondents purchase from a door to door seller.
�
�
106
�
31.75
20.25
27.08
20.92
0
5
10
15
20
25
30
35
Pe
rce
nta
ge
Departmental Store Big Retail Shop Any Shop Door to door seller
Place of Purchase
Chart 1
Purchase Place of Cosmetics by Consumers
�
Religion and Gender
A study on consumer is an important factor and warrants an enquiry into the
gender and religion of the users of cosmetics. Table 2 shows the religion and gender of
sample consumers.
107
Table 2
Religion and Gender of Sample Consumers
Religion
Male Female Group Total
No. of
Respondents %
No. of
Respondents %
No. of
Respondents %
Hindu 205 17.08 285 23.75 490 40.83
Christian 110 9.17 130 10.83 240 20.00
Muslim 65 5.42 40 3.33 105 8.75
Jainism 200 16.67 165 13.75 365 30.42
Total 580 48.33 620 51.67 1200 100
Source: Primary data.
Calculated Chi-square
Value Degrees of Freedom Level of Significance
22.73 3 0.01 (Significant)
�
Hy: There is no association between the religion and gender sample consumers.
A perusal of Table 2 reveals that out of 1200 respondents, 580 (48.33%) are males
and 620 (51.67%) are females. Further the number of respondents of Hindu, Christian,
Muslim and Jainism are 490, 240, 105 and 365 respectively. It is also known that the
sample population consists of 40.83% of Hindus and 23.75% of females that dominate
the other divisions. So females of Hindus are using more cosmetics than those of other
communities.
The calculated Chi-square value (22.73), is significant at 0.01 level. So, the stated
hypothesis is rejected. Hence, it is concluded that there is an association between religion
and gender sample consumers.
108
17.08
23.75
9.17
10.83
5.42
3.33
16.67
13.75
0
5
10
15
20
25
Perc
en
tag
e
Hindu Christian Muslim Jainism
Religion
Chart 2
Religion and Gender Sample Consumers
Male Female
�
Educational Qualification
Educated customers may have greater knowledge of market condition, new
brands, various products, selling places etc., Table 3 shows the educational qualification
of sample consumers.
109
Table 3
Educational Qualification of Consumers
Educational
Qualification
No. of
Respondents Percentage
Illiterate 88 7.33
Less than X Std 52 4.33
X Std 64 5.33
XII Std 124 10.33
Graduate 510 42.5
Post Graduate 326 27.17
Ph.D
36 3.00
Total 1200 100
Source: Primary data.
It is noted from the Table 3 that the number of respondents who are Illiterate,
qualified below X Std, X
Std, XII Std, Graduates, Post Graduates and PhDs are 88, 52,
64, 124, 510, 326 and 36 respectively. As usual females’ are more than the male
members. So there is a chance of using more cosmetics. Another important point to note
is that the more qualified consumers using cosmetics is: Graduates 510 and Postgraduates
326. So the educational qualification influences in using the cosmetics.
110
7.33
4.335.33
10.33
42.5
27.17
3
0
5
10
15
20
25
30
35
40
45
Pe
rce
nta
ge
Illiterate Less than
X Std
X Std XII Std Graduate Post
Graduate
Ph.D
Educational Qualification
Chart 3
Educational Qualifications of Consumer Sample
�
Income
Income is one of the most important factors for the consumers’ decision whether
to spend or not to spend on cosmetic. Normally it is expected that the consumers of high
income would spend more on cosmetics. Table 4 shows the income-wise analysis of
sample consumers.
111
Table 4
Income-wise Analysis of Sample Consumers
Income (in Rupees) Number of
Respondents Percentage
2500 and less 198 16.5
2501 – 5000 312 26.0
5001 – 10000 324 27.0
10001 – 15000 258 21.5
15001 and above 108 9.0
Total 1200 100.0
Source: Primary data.
It is understood from the Table 4 that out of 1200 consumers, 198 (16.5%)
persons are having an income of Rs.2500 and less, 312 (26.0%) are having an income Rs.
2501-5000, 324 (27.0%) persons are having an income of 5001-10,000, 258 (21.5%)
persons earn an income of 10001-15000 and 108 (9.0%) persons get an income of 15001
and above. It is imperative to know that only middle class people are use more of the
cosmetics.
112
Chart 4
Income-wise Analysis of Sample Consumers
16.5
26 27
21.5
9
0
5
10
15
20
25
30
2500 and less 2501- 5000 5001 – 10000 10001 –
15000
15001 and
above
Income (in Rupees)
Pe
rce
nta
ge
113
Age Group
Age-group also determines the preference pattern of cosmetics. The frequency
distributions are constructed from the responses of consumers regarding age groups who
buy cosmetics more from them.
Table 5
Frequency Distribution of Age Groups
Age Number of
Respondents Percentage
15-20 years 146 12.17
21-30 324 27.0
31-40 550 45.83
41 above 180 15.0
Total 1200 100.0
Source: Primary data.
Table 5 indicates the frequency distribution of age groups and this leads to the
conclusion that the age group of 146 (12.17%) persons are between 15-20 years, 324
(27.0%) are in between 21-30 years, 550 (45.83%) persons are within 31-40 years and
180 (15.0%) persons are 41 and above. It is imperative to know that the majority of
adolescents use more of the cosmetics.
�
�
�
�
�
114
�
Chart 5
Frequency Distribution of Age Groups
12.17
27
15
45.83
0
10
20
30
40
50
60
70
80
90
100
15-20 years 21-30 31-40 41 above
Age
Pe
rce
nta
ge
�
115
Gender Group
Gender-group also determines the preference pattern of cosmetics. The frequency
distribution is constructed from the responses of consumers regarding gender groups who
buy more cosmetics.
Table 6
Frequency Distribution of Gender Groups
Gender Number of
Respondents Percentage
Male 580 48.33
Female 620 51.67
Total 1200 100.0
Source: Primary data.
Table 6 indicates that 580 (48.33%) persons are male and 620 (51.67%) persons
are female. It is imperative to know that the majority of female use more of the
cosmetics.
116
48.3351.67
0
10
20
30
40
50
60
Pe
rce
nta
ge
Male Female
Gender
Chart 6
Frequency Distribution of Gender Groups
�
117
Monthly Expenditure on Cosmetics
Every family sets aside some amount in its family budget for the purchase of
cosmetics. The monthly expenditure on cosmetics are constructed from the consumers’
based on the sample buy cosmetics.
Table 7
Frequency Distribution of Monthly Expenditure on Cosmetics
Expenditure
(in Rupees)
Number of
Respondents Percentage
200 and less 179 14.92
201 – 500 372 31.00
501 – 1000 324 27.00
1001 – 2000 208 17.33
2001 and above 117 9.75
Total 1200 100.0
Source: Primary data.
Table 7 Shows the Frequency distribution of monthly expenditure on cosmetics. It
is clear from the above frequency distribution of monthly expenditure on cosmetics, 179
(14.92%) persons spend Rs.200 and less, 372 (31.0%) Rs. 201-500, 324 (27.0%) persons
501-1000, 208 (17.33%) persons with an income of 1001-2000 and 117 (9.75%) persons
with an income of 2001 and above. So there is more number of respondents found in Rs.
201- 500’ monthly expenditure group.
118
14.92
31
27
17.33
9.75
0
5
10
15
20
25
30
35
Pe
rce
nta
ge
200 and less 201 – 500 501 – 1000 1001 – 2000 2001 and
above
Expenditure (in Rupees)
Chart 7
Frequency Distribution of Monthly Expenditure on Cosmetics
�
119
Occupation Group
Occupation-group also determines the preference pattern of cosmetics. The
frequency distributions are constructed from the responses of consumers regarding
occupation groups who buy more cosmetics.
Table 8
Occupation of Sample Consumers
Occupation Number of
respondents Percentage
Agriculturist 700 58.33
Non-agriculturist 500 41.67
Total 1200 100
Source: Primary data.
Out of the 1200 respondents contacted, the agriculturists are found to be 700
(58.33%) and non-agriculturists are found to be 500 (41.67%). So, the majority of the
respondents are agriculturist.
Chart 8
Occupation of Sample Consumers
Non-agriculturist
41.67
Agriculturist
58.33
120
Pattern of Family
It is necessary to remark that the number of users of cosmetics of any place could
depend on the family type such as joint family or nuclear family. It is investigated that
the percentage of respondents of cosmetic users are from Joint/Nuclear families.
Table 9
Family-wise Analysis of Sample Consumers
Patterns No of
Respondents Percentage
Joint family 717 59.75
Nuclear family 483 40.25
Total 1200 100.0
Source: Primary data.
It is clear from Table 9 that 717 (59.75%) are using cosmetics in the case of joint
family and 483 (40.25) persons in the case of nuclear family. So, the majority of the
respondents are in joint family.
Chart 9
Family-wise Analysis of Sample Consumers
Nuclear family
40.25
Joint family
59.75
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121
Consumer Preferences
Consumers’ preference in buying and using cosmetics, and use of the cosmetics
differs –cosmetics used by females when compared with the cosmetics used by males. Of
course cosmetics like eyeliner, henna etc., are used by both females and males. But the
number of users vary accordingly.
Table 10
Number and Percentage of Sample Consumers using cosmetics
Name of cosmetics N Percentage
Eyebrow 121 10.08
Marudani 112 9.33
Lipsticks 119 9.92
Snow 120 10.00
Nail Polish 128 10.67
Powder 240 20.00
Body spray 100 8.33
Deodorants 114 9.5
Shampoo 146 12.17
Total 1200 100.0
Source: Primary data.
Table 10 shows the number and percentage of sample consumers using cosmetics.
It is clear from that though all the cosmetics are used by both men and women, the
number and percentage of women using the above cosmetics are more than those of men
uniformly. Members are using powder and shampoos differ to a considerable extent.
�
�
�
122
10.089.33
9.92 10 10.67
20
8.339.5
12.17
0
2
4
6
8
10
12
14
16
18
20
Perc
en
tag
e
Eyebro
w
Maru
dani
Lip
stic
ks
Snow
Nail
Polis
h
Pow
der
Body s
pra
y
Deodora
nts
Sham
poo
Name of Cosmetics
Chart 10
Number and Percentage of Sample Consumers using Cosmetics
�
Branding
In olden days most of the products went unbranded and sellers sold the products
without the suppliers’ identification. In the present day world almost all products are
branded and packaged beautifully. Now brand is an important attribute of a product. The
study of marketing is incomplete, if we do not take into account the study of branding
and packaging. The basic purpose of branding is to identify the producer of a given
product. In India branding was started for agricultural, industrial and other products. A
brand is a name, term, symbol or design to identify the goods or services and to
differentiate it from those of the competitors.
In other words the use of a name, term symbol or design or some combination of
these to identify the product of a certain seller from those of competitors. A brand
123
identifies the product for a buyer. A seller can earn the goodwill and will have the
patronage repeated.
Thus branding is the practice of giving a specified name to a product or group of
products of a seller. Branding is the process of finding and fixing the means of
identification. In other words, naming a product, like naming a baby, is known as
branding. Parents have children and manufacturers too have children i.e., products. As
parents, the manufacturers also are eager to know the character and capacity of their
products on their birth. Thus branding is a management process by which a product is
named i.e., branded.
The features of a good brand are given below:
1. A brand should suggest something about the product, benefits, product’s name,
purpose, and performance of action.
2. The brand name should be short, easy to pronounce, to spell and remember and
easy to identify and explain. It should be very easy for advertisement.
3. It should be capable of being registered and protected legally under the
legislations.
4. It should have stable life and be unaffected by time. It should not depend upon
fashions and styles as they have a short life.
5. It should create pleasant associations.
6. It should not be used as general or common name for all products.
7. It should be unique, attractive and distinctive.
�
�
124
The various types of brands are given below
1. Individual Brand Name: Each product has a special and unique brand name
such as Ranipal, Surf Aspro etc. The manufacturers have to promote each
individual brand in the market separately.
2. Family brand Name: Family name is limited to one line of a products i.e.,
products, which complete the sales cycle for cosmetics like “Ponds”. However if
the consumers reject one number of the family brand, the prestige of all other
products under the family brand may be adversely affected. Family brand name
enables creation of strong shelf-display. It kelps to secure quick popularity. It is
preferable to separate brand for each product.
3. We may have for all products the name of the company or the manufacturer. All
products such as soaps, chemicals, textiles engineering goods etc., manufactured
by the Tata concern will have “Tata as one umbrella brand”. Such a device will
also entail low promotion cost and minimize marketing effort.
4. Combination device: - Products have individual names and company brands to
indicate the firm producing them. For instance, Tata Tej.
5. Private or Middleman’s brand: Such brands are owned and controlled by
middlemen rather than by manufacturers. A manufacturer introduces his products
under a distribution’s brand name.
In Cosmetic Industry, consumer’s inclination (or) tendency to price as a good
guide to quality has been fully exploited by the manufacturers of cosmetics. Sellers of
cosmetics spend lavishly in advertising their brands. There is large margin between the
cost of manufacture and retail price. For example, one face cream costs only Rs.15 to
make. It sells upto Rs.30/- in the retail market. It has been pointed out through
comparative testing that expensive cosmetics often work better or quality being well and
125
are safer than expensive, complicated and sophisticated cosmetics. Perfumes with
identical fragrance are sold at different prices viz Rs.200/- per ounce of national brand
while it is Rs.80/- per ounce in dealers’ brand.
Preference of Brands
Every consumer prefers to buy a particular brand of cosmetics. So it is necessary
to study their preference in buying cosmetics. Some consumers are particular in buying a
particular brand of cosmetics whereas some consumers are not brand conscious.
Table 11
Preference of Brand by Sample Consumers
�
Category N Percentage
Purchasing the particular brand 677 56.42
Not purchasing the particular brand 523 43.58
Total 1200 100.0
Source: Primary data.
It is clear from the Table that 677 (56.42%) are purchasing particular brand and
523 (43.58) are not purchasing particular brand.
Brand Loyalty:
There are different approaches to the definition and measurement of brand
loyalty. Brand is a topic of much concern of all marketers. Every company seeks to have
a sturdy group of unwavering customers for its products or service, because an increase
in market share is related to improved brand loyalty. Thus brands that seek to improve
126
their market positions have to be successful both in getting brand users and in increasing
their loyalty.
Chart 11
Preference of Brand by Sample Consumers
43.58
56.42
0
10
20
30
40
50
60
Purchasing the particular brand Not purchasing particular brand
Category
Perc
en
tag
e
One definition of brand loyalty indicates that it is not simply repeated purchasing
behaviour but should be defined in terms of six necessary and collectively sufficient
conditions. According to this definition brand loyalty is
i. Biased (non-random)
ii. Behavioural response (purchase)
iii. Expressed over time.
iv. By some decisions making unit.
v. With respect to one or more alternative brands out of such brands.
vi. Is the function of physiological (decision making evaluative_ processes.
127
This definition suggests that consumers can be loyal towards more than one brand
i.e., multi-brand loyal. Brand loyalty not only selects same brands but also rejects certain
brands from a set of alternatives. Brand name may be more important for some products
than for other. For users’ product, it is usually necessary for carrying out marketing
research to measure loyalty, while for industrial products such can often be directly
observed. Brand loyalty is one of the most heavily researched areas of consumers’
behaviour. But very little is positively known about it.
George H. Brown, in one of his earliest studies of repeat purchasing behaviour,
identified following four loyalty patterns:
1. Undivided Loyalty:
A panel member buys only one brand in a product category. This is the classic
instance of “We have our own customers and our competitors have therein”.
2. Divided Loyalty:
A panel member divides the purchase between two (or) sometimes three or four
brands in a product category.
3. Unstable Loyalty:
A panel member purchase brands A and B in the following order ABAABBBB.
This is an indicator that the consumer has switched over to undivided loyalty from A to B.
4. No Loyalty:
The brands in a product category are purchased in a completely random order.
128
Consumers are not always brand loyal. They often switch over to other brands
expecting only more satisfaction.
Brand Switching:
Since man is a developing animal, learning animal and social animal it would be
observed to assume that the preferences of any member of any household remain
unchanged overtime and unaffected by their environment. There are three outstanding
preferences viz:
a. Advertising
b. Choice of consumers and
�� Prices and Preferences.�
It has been observed that advertising is more concerned with persuading people to
switch from one brand of commodity to another. If one interprets different brands of a
commodity (e.g. Talcum Powder) as goods, which supply the same characteristics in
different proportions, a good part of brand advertising may be integrated as our attempt to
uniform people of the characteristics of a given brand. It may result in brand switch over.
It is obvious that preferences of consumers are affected by what others consume and
prices of different brands.
Some consumers engage in brand switching because of their dissatisfaction or
boredom with a product. Others are more concerned with price than with brand name.
The phenomenon of consumer brand shifting is a central element underlying the dynamic
129
of the place. Subsequent purchase data can provide some insight into consumer brand
switching.
It is not possible to conclude that all consumers are brand loyal or disloyal. But
most of the consumers are very careful in decision making before purchasing a product of
a particular brand.
Table 12
Brand Loyalty for Face Powder
Face Powders N Percentage
Emami 144 12.0
Gokul 228 19.0
Lavender 96 8.0
Ponds 540 45.0
Ztalc 84 7.0
Others 108 9.0
Total 1200 100.0
Source: Primary data.
Table: 12 shows the brand loyalty of consumers for face powder. It is observed
from the Table that only 108 (9%) respondents are not buying the popular brands of
powder such as Emami, Gokul, Lavender, Ponds and Ztalc, Further 540 (45%) of the
respondents prefer Ponds powder. It is their opinion that this brand is of high quality and
the company maintains the same goodwill and reputation. Next, 228 (19%), 144 (12%),
96 (8%) and 84 (7%) of the respondents buy Gokul, Emami, Lavender and Ztalc
respectively. Thus Ponds powder tops the list with regard to brand loyalty.
130
12
19
8
45
79
0
5
10
15
20
25
30
35
40
45
Pe
rce
nta
ge
Emami Gokul Lavender Ponds Ztalc Others
Face Powders
Chart 12
Brand Loyalty for Face Powder
�
131
Table 13
Brand Loyalty for Scent
�
Scent N Percentage
Charlie 192 16.00
Rexona 156 13.00
Some Indian 204 17.00
Some Foreign 294 24.5
Charlie 6 0.5
Jasmine 6 0.5
Magnent 6 0.5
Majuma 6 0.5
Raymonds 6 0.5
Tomy girl 6 0.5
Not Using 318 26.5
Total 1200 100.0
Source: Primary data.
�
Table 13 it is seen that 192 (16.0%) of consumers use Charlie, 156 (13.0%) of
them use Rexona, 204 (17.00%) of them use Some Indian, 294 (24.5%) of them Some
Foreign products, 318 (26.5%) of them do not use any brand of scent at all. Out of the
remaining users, 36 (3.0%) other brands of scent. This scent is in favour of the
hypothesis, that is “Foreign cosmetics are popular in the Indian Market” as it holds well
at the Northern area Tamilnadu also.
132
Chart 13
Brand Loyalty for Scent
16
13
17
24.5
0.5 0.5 0.5 0.5 0.5 0.5
26.5
0
5
10
15
20
25
30
Ch
arl
ie
Re
xo
na
So
me
In
dia
n
So
me
Fo
reig
n
Ch
arl
ie
Ja
sm
ine
Ma
gn
en
t
Ma
jum
a
Re
ym
on
ds
To
my
gir
l
No
t U
sin
g
Scent
Pe
rce
nta
ge
133
Table 14
Brand Loyalty for Shampoo
Shampoo Number of
Respondents Percentage
Clinic 444 37.0
Lux 90 7.5
Meera 126 10.5
Sunsilk 270 22.5
Velvet 96 8.0
Others 174 14.5
Total 1200 100.0
Source: Primary data.
It is observed from Table 14 that out of 1200 respondents in the Northern area
Tamilnadu 174 (14.5%) of the users are not using popular brands of shampoo. On the
other hand, 444 (37%) of the users are using “Clinic” brand which stands the first. The
second, third and fourth places go to “Sunsilk”, “Meera”, “Velvet” and “Lux” brands
respectively.
�
134
37
7.5
10.5
22.5
8
14.5
0
5
10
15
20
25
30
35
40
Pe
rce
nta
ge
Clinic Lux Meera Sunsilk Velvet Others
Shampoo
Chart 14
Brand Loyalty for Shampoo
�
135
Table 15
Brand Loyalty for Snow
Snow No. of
Respondents Percentage
Fair & Lovely 276 23.0
Fairever 204 17.0
Ponds 204 17.0
Not Using 192 16.0
Nivea 138 11.5
Others 186 15.5
Total 1200 100.0
Source: Primary data.
The data on Snow brands in Table 15 shows that out of 1200 respondents in
Northern area Tamilnadu, 192 (16.0%) respondents are not using any one of the brands of
snow. From the remaining group, 276 (23%) respondents are using “Fair & Lovely”
which leads to the first rank. Other categories “Fairever”, “Ponds” and “Others” of snow
brands occupy more or less the same second position. The third rank goes to “Nivea”
brand.
136
23
17 1716
11.5
15.5
0
5
10
15
20
25P
erc
en
tag
e
Fair &
Lovely
Fairever Ponds Not Using Nivea Others
Snow
Chart 15
Brand Loyalty for Snow
�
137
Table 16
Brand Loyalty for Deodorant
Deodorants Number of
Respondents Percentage
Not Using 330 27.5
Spinz 150 12.5
Rexona 288 24.0
Ponds 144 12.0
Nivea 114 9.5
Impulse 6 0.5
Others 168 14.0
Total 1200 100.0
Source: Primary data.
From the data contained in Table 16 it is understood that out of 1200 respondents,
330 (27.5%) users do not use any brand of deodorants. The remaining 168 (14%) of users
do not purchase any standard brand of deodorants. But first place goes to Rexona Brand
of deodorants as 288 (24%) of consumers buy it. The second, third and fourth places go
to Spinz, Ponds, Nivea and Impulse brands respectively.
138
27.5
12.5
24
12
9.5
0.5
14
0
5
10
15
20
25
30
Pe
rce
nta
ge
Not Using Spinz Rexona Ponds Nivea Impulse Others
Deodorants
Chart 16
Brand Loyalty for Deodorant
�
139
40Table 17
Consumers using Indian Foreign Cosmetics
Category Number of
Retailers Percentage
Foreign 490 40.83
Indian 710 59.17
Total 1200 100
Source: Primary data.
�
The viewpoint indicates that larger number of consumers 710 (59.17%) prefer
Indian cosmetics and the remaining prefer Foreign cosmetics respectively.
Chart 17
Consumers using Indian Foreign Cosmetics
Indian 59.17
Foreign 40.83
�
140
Table 18
Brand Loyalty of Consumers View for Powder and Soap
Brand loyalty Frequency Percentage
Yes 990 82.5
No 210 17.5
Total 1200 100.0
Source: Primary data.
From Table 18 it is understood that out of 1200 respondents, 990 (82.5%) have
agreed that they have strong brand loyalty for powder and soap while 210 (17.5%) are
against brand loyalty. So brand loyalty plays a vital role in the sale of cosmetics in
general and on the sale of powder and soap in particular in the study areas.
Chart 18
Brand Loyalty of Consumers View for Powder and Soap
No 17.5
Yes 82.5
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141
Table 19
Consumers’ View on Advertisement in T.V. and F.M. Radio
Advertisement Frequency Percentage
Yes 1080 90.0
No 120 10.0
Total 1200 100.0
Source: Primary data.
Table 19 contains the opinions of respondents relating to the influence of
advertisement towards the purchase of cosmetics. It is observed from the Table that out
of the 1200 respondents, 1080 (90%) are of the opinion that T.V and F.M. Radio
advertisements necessarily influence the purchase of cosmetics. Though this may appear
as an ordinary conclusion, this should be viewed against the rural and urban setting where
T.V. exposure is only of recent years and listening of radio is in vogue for quite some
years.
Chart 19
Consumers’ View on Advertisement by T.V. and Radio
Yes, 90
No, 10
142
Apart from the advertisements through T.V. and radio, advertisements in
newspapers also play a significant role in the sale of cosmetics. A villager’s first and
foremost duty is to go to tea shop where he reads popular daily newspapers. Necessarily
he reads out some advertisements in those newspapers which will induce him to buy
some cosmetics. Moreover government has installed a T.V. in every village Panchayat
and villagers have started seeing the T.V. in the night after their day’s work. So they are
compelled to listen to some advertisements which will certainly persuade them to go for
cosmetics. Recent introduction of FM radios which broadcast the events almost around
the clock are having more advertisements in between good music or cinema songs. So
such advertisements also make villagers cosmetic conscious. Thus the influence of
advertisement through the above mass media is more in rural areas also. The result is the
sale of cosmetics will increase in the days to come.
Enquiry with people about marketing with reference to cosmetics leads to certain
revealing conclusions. They are
1. Populations are aware of cosmetics and constitute a good exposure of cosmetics
market.
2. They have a strong brand loyalty and preference for certain cosmetics.
3. Advertisements can be a powerful potent instrument in cosmetics marketing.
4. There is a good segment basis on price and quality and
5. Consumers are quality conscious.
Dr. Prahalad, a management expert and an outstanding Professor of USA in
International Business has been reiterating the need for tapping the rich Indian rural
143
markets for consumer marketing. He has further cautioned that if the Indian marketers
fail in this respect, Multi National Companies are ready to exploit the same. He has
quoted several successful stories of Indian marketers in successful rural marketing, for
example the AMUL (a co-operative undertaking of India) which has made a tremendous
success.
Thus cosmetics has in it full of fragrance, beauty and love and the emerging rural
India can be a market of paradise if manufacturers and marketers of cosmetics make hay
while the sun shines. Then only the demand pattern of cosmetics in rural areas will be an
attractive one. More and more rural people will become cosmetics conscious and use
more cosmetics and its demand would be multiplied in the years to come.
144
Table 20
Brand Preference of Powder
Powder Number of
Respondents Percentage
Ponds 450 37.5
Cuticura 150 12.5
Sandal 288 24.0
Santhoor 144 12.0
Others 168 14.0
Total 1200 100.0
Source: Primary data.
From the data contained in Table 20 it is understood that out of 1200 respondents,
the brand preference of powder is also observed. The first preference goes to Ponds.
Further the second, third and fourth places go to ‘Cuticura’, ‘Sandal’ and ‘Santhoor’
respectively.
Chart 20
Brand Preference of Powder
37.5
12.5
24
1214
0
5
10
15
20
25
30
35
40
Ponds Cuticura Sandal Santhoor Others
Powder
Perc
en
tag
e
145
Table 21
Brand Preference of Tooth Powder
Tooth powder Number of
Respondents Percentage
Colgate brand 422 35.17
Pepsodent 272 22.67
Anchor 184 15.33
Aim 168 14.00
Closeup 154 12.83
Total 1200 100.0
Source: Primary data.
From the data contained in Table 21 it is understood that out of 1200 respondents,
the brand preference of tooth powder is also observed. It is found that the Colgate brand
422 (35.17%) of tooth paste occupies the first place while Pepsodent 272 (22.67%)
occupies the second place, Anchor 184 (15.33%) occupies the third place, Aim 168
(14.00%) occupies the fourth place and Closeup (12.83%) occupies the fifth place.
Chart 21
Brand Preference of Tooth Powder
35.17
22.67
15.3314 12.83
0
5
10
15
20
25
30
35
40
Colgate brand Pepsodent Anchor Aim Close up
Tooth Pow der
Perc
en
tag
e
146
Table 22
Brand Preference of Soaps
Soaps Number of
Respondents Percentage
Hamam 382 31.83
Lifebuoy 272 22.67
Medimix 184 15.33
Margo 168 14.00
Mysore
Sandal 124 10.33
Rexona 70 5.83
Total 1200 100.0
Source: Primary data.
From the data contained in Table 22 it is understood that out of 1200 respondents,
the brand preference of soaps is also observed. With regard to soaps it is observed that
out of five brands available for the use of consumer, the first place of popularity goes to
‘Hamam soap’. The second place goes to ‘Lifebuoy’. The third place goes to ‘Medimix’.
The fourth place goes to ‘Margo’. The fifth place is shared by two brands viz., Mysore
Sandal and Rexona soap. The reason for the preference of Hamam soap by more people
is its economical price and quality.
�
�
�
�
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147
Chart 22
Brand Preference of Soaps
31.83
22.67
15.3314
10.33
5.83
0
5
10
15
20
25
30
35
Hamam Lifebuoy Medimix Margo Mysore Sandal Rexona
Soaps
Pe
rce
nta
ge
��
148
Table 23
Preference of Channel of Distribution
Channels Number of
Respondents Percentage
Manufacturer → Wholesaler →
Retailer → Consumer 534 44.5
Manufacturer → Wholesaler →
Consumer 302 25.17
Manufacturer → Consumer 186 15.5
Manufacturer → Retailer →
Consumer 178 14.83
Total 1200 100.0
Source: Primary data.
From the data contained in Table 23 it is understood that, out of 1200
respondents, the channel distribution is also observed. 44.5% of them prefer
Manufacturer → Wholesaler → Retailer → Consumer, 25.17% of them prefer
Manufacturer → Wholesaler → Consumer, 15.5% of them prefer Manufacturer →
Consumer and 14.83% of them prefer Manufacturer → Retailer → Consumer. So
majority of the customer prefer Manufacturer → Wholesaler → Retailer → Consumer
channels.
149
Table 24
Testing the Difference in Average Expenditures of
Male and Female users of Cosmetics
Gender N Mean Std.
Deviation t-value
Level of
Significance
Male 580 264.51 175.90 2.093
0.05
(Significant) Female 620 211.01 169.07
Source: Primary data.
Hy: There is a significant difference between testing the difference in average
expenditures of male and female users of cosmetics.
As the observed “Sig. (2-tailed)” t-value is 2.093, which is less than 0.05, it is
concluded that the difference in mean expenditure of male and female groups is
significant. Since, the average expenditure of male group is Rs. 264.51 while the average
expenditure of female group is Rs.211.01, it is further concluded that the average
expenditure of male group is larger than that of the female group. Hence, the stated
hypothesis is accepted.
150
Table 25
Testing the Equality of Mean Expenditure of different
Shop Groups using one way ANOVA
Shops N Mean Std.
Deviation F-value
Level of
Significance
Departmental Store 381 222.34 159.09
6.489 0.01
(Significant)
Big Retail Shop 243 192.85 114.33
Any Shop 325 246.25 154.28
Door to door seller 251 496.42 381.45
Total 1200 230.27 173.05
Source: primary data.
Hy: There is a significant difference between testing the difference in average
expenditure of different shops.
As the observed calculated F-value is 6.489 (P<0.01) in the above ANOVA table,
it is concluded that the hypothesis of equal of average expenditures of different shop
groups is accepted as 1% level of significance.
151
Table 26
Testing the Equality of Mean Expenditure of different Educational Qualification
Groups using one way ANOVA
Educational
Qualification N Mean
Std.
Deviation
F-
value
Level of
Significance
Illiterate 88 212.24 142.12
7.621 0.01
(Significant)
Less than X Std 52 182.42 121.42
X Std 64 243.12 151.42
XII Std 124 488.21 372.24
Graduate 510 341.66 112.66
Post Graduate 326 312.21 241.33
Ph.D
36 154.21 182.12
Total 1200 230.27 173.05
Source : Primary data
Hy: There is a significant difference between testing the difference in the average
expenditure of different educational qualification.
As the observed calculated F-value is 7.621 (P<0.01) in the above ANOVA table,
it is concluded that the hypothesis of equal of average expenditure of different
educational qualification groups is accepted at 1% level of significance.
�
152
Table 27
Testing the Equality of Mean Expenditure of different
Income Groups using one way ANOVA
Income N Mean Std.
Deviation F-value
Level of
Significance
2500 and less 198 224.24 124.42
6.755 0.01
(Significant)
2501 – 5000 312 174.11 110.12
5001 – 10000 324 223.04 121.42
10001 – 15000 258 478.42 312.12
15001 and
above 108 321.12 114.42
Total 1200 230.27 173.05
Source : Primary data
Hy: There is a significant difference between testing the difference in the average
expenditure of different income.
As the observed calculated F-value is 6.755 (P<0.01) in the above ANOVA table,
it is concluded that the hypothesis of equal of average expenditure of different income
groups is accepted as 1% level of significance.
153
Table 28
Testing the Equality of Mean Expenditure of different
Age Groups using one way ANOVA
Age N Mean Std.
Deviation F-value
Level of
Significance
15-20 years 146 212.10 102.12
5.211 0.01
(Significant)
21-30 324 162.42 121.42
31-40 550 225.42 145.11
41 above 180 468.11 242.45
Total 1200 230.27 173.05
Source : Primary data.
Hy: There is a significant difference between testing the difference in average
expenditure of different income.
As the observed calculated F-value is 5.211 (P<0.01) in the above ANOVA table,
it is concluded that the hypothesis of equal of average expenditures of different age
groups is accepted at 1% level of significance.
154
Table 29
Testing the Equality of Mean Expenditure in ‘Joint Family’ and ‘Nuclear Family’
Family Type N Mean Std.
Deviation t-value
Level of
Significance
Joint Family 717 239.48 184.26
2.34 0.01
(Significant) Nuclear
Family 483 217.28 156.04
Source : Primary data.
Hy: There is a significant difference between testing the difference in average
expenditure of family type.
As the observed t-value is 2.34 (P<0.01) in the above t-table, it is concluded that
the hypothesis of equality of average expenditure of ‘Joint family’ and ‘Nuclear family’
groups is accepted at 1% level of significance.
155
Table 30
Testing the Equality of Mean Expenditure of ‘Male’ and ‘Female’
Gender N Mean Std.
Deviation t-value
Level of
Significance
Male 580 221.21 175.42 2.82
0.01
(Significant) Female 620 208.45 145.32
Source : Primary data.
Hy: There is a significant difference between testing the difference in average
expenditure of gender.
As the observed t-value is 2.82 (P<0.01) in the t-table, it is concluded that the
hypothesis of equality of average expenditure of ‘male’ and ‘female’ groups is accepted
at 1% level of significance.
156
Table 31
Testing the Equality of Mean Expenditure of
‘Agriculturist’ and ‘Non-Agriculturist’
Occupation N Mean Std.
Deviation t-value
Level of
Significance
Agriculturist 700 231.54 144.12 2.94
0.01
(Significant) Non-agriculturist 500 217.12 134.11
Source : Primary data.
Hy: There is a significant difference between testing the difference in average
expenditure of occupation.
As the observed t-value is 2.94 (P<0.01) in the t-table 30, it is concluded that the
hypothesis of equality of average expenditure of ‘Agriculturist’ and ‘Non-agriculturist’
groups is accepted at 1% level of significance.
Regression and Correlation Analyses
Regression and correlation analyses are often used in problems in Business and
Trade, Management Science, Econometrics, Social Sciences and so on. Regression
analysis is a widely used tool in evaluating the relationship among a number of variables
and then using this relationship to predict the value of a variable or to forecast the value
that will be taken by a variable at a particular time.
Firstly the study relates to, multiple linear relationships of monthly expenditure,
age, religion, income and gender.
�
�
157
Table 32
Multiple Regression Analysis on Age, Religion, Income and
Gender on the basis of Monthly Expenditure
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimated
1 0.288a 0.083 0.064 167.4220
a. Predictors : (Constant) : Age, Religion, Income, Gender
�
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 493483.2 4 123370.8 4.401 0.002a
Residual 5465877 1195 28030.137
Total 5959360 1199
a. Predictors : (Constant), Age, Religion, Income, Gender
b. Dependent Variable: Monthly expenditure on cosmetics
�
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
B Std. Error Beta t Sig
(Constant) 73.581 56.578 1.301 0.195
Income 19.425 10.352 0.136 1.877 0.062
Religion 44.281 16.182 0.190 2.736 0.007
Gender 41.177 26.506 0.115 1.554 0.122
Age 1.180 2.182 0.042 0.541 0.589
a. Dependent Variable : Monthly expenditure on cosmetics
158
As the R-square value is 0.083 it is concluded that 8.3% of the variation of
monthly expenditure is due to the influence of age, religion, income level and gender of
those cosmetic users.
As the observed probability value of “F” statistics i.e., Sig value in the ANOVA
table is 0.002 which is less than 0.005, it is concluded that the multiple linear regression
fitting is a good fit to the observed data on monthly expenditure of cosmetics.
As the Sig value of “Religion” alone is 0.007, which is less than 0.05 from the
above coefficients table, it is concluded that the observed regression coefficient of
monthly expenditure on religion is significant. It implies that among the independent
variables such as age, religion, income level and gender, religion alone influences the
dependent variable of monthly expenditure much.
Application of Correlation Analysis
As long as we use a simple linear model (i.e., a model with two variables where
we have one predictor variable and one independent variable), R-square, the coefficient
of determination can also be written as r2, the square of the correlation co-efficient. The
correlation co-efficient measures the degree of linear association between the two
variables under consideration. A positive correlation co-efficient indicates that as one
variable increases in magnitude, the other variable also tends to go up in value.
Conversely, a negative correlation co-efficient indicates that as one variable goes up in
magnitude, the other variable tends to go down in value.
159
As the monthly income and the monthly expenditure of all 1200 users of
cosmetics are collected from the study areas of the current survey, correlation analysis is
carried out for the case of rural users and is reported in the Table 33.
Table 33
Correlation between Monthly Expenditure and Income
Monthly Income
Monthly Expenditure 0.514**
** Correlation is significant at 0.01 level
The co-efficient of correlation between monthly income and expenditure is found
from the Table as 0.514. This is significant at 5% level of significance since the value of
Sig (2-tailed) is 0.000, which is less than 0.01. As expected, this correlation is found to be
positive which indicates that as the income increases the expenditure also increases.
The various conclusions that emerge from the statistical analysis are given below:
(i) Among the independent variables age, religion, income level and sex, age
factor influence the dependent variable namely the monthly expenditure.
(ii) In very big metropolis, men also spend more on cosmetics.
(iii) People of all religions use cosmetics.
Though no statistical projection is made, in the present economic and
demographic conditions, marketing is bright with continued economic planning. There is
marginal increase in income both in urban and rural areas. There is a lot of awareness for
beauty and fragrance. The teenage boys and girls highly exposed to T.V and Internet are
160
bound to make greater demands for cosmetics in the years to come. It is here that the
marketer should be innovative to find newer products for men and women of different
ages. Thus the statistical analysis helps not only to prove the hypotheses framed but also
to arrive at some important findings and conclusions.
Table 34
Showing Stepwise Regression Analysis for Predicting Quality of Cosmetic Product
�
Sl.No Step/Source Cumulative
R2
∆∆∆∆R2 Step t P
1. Educational
qualification 0.082 0.078
* 3.212 0.01
2. Type of shops 0.092 0.086* -4.421 0.01
� � � � � � � � � �����������
Constant value = 21.428
The results of regression analysis such as cumulative R2, ∆R
2, step t and P value
have been given in Table 34.
An attempt is made to find out whether the variables in the respondents'
educational qualification and type of shops would be possible predictors of quality of
cosmetic products. The results indicate that the two variables are very significant in
predicting the quality of cosmetic product. The respondents' educational qualification is
poised to predict the quality of cosmetic product to an extent of 0.082 which is found to
be statistically significant at 0.01 level.
161
The second variable respondents in the type of shops, is able to predict the quality
of cosmetic product to a higher level of 0.092. (significant at 0.01 level).
Table 35
Showing Stepwise Regression Analysis Availability of the Product
�
Sl.No Step/Source Cumulative R2 ∆∆∆∆R
2 Step t P
1. Gender 0.048 0.042* 3.089 0.01
2. Educational Qualification 0.059 0.046* 2.745 0.01
3. Income 0.082 0.059* 2.465 0.01
4. Age 0.075 0.068* 2.546 0.01
5. Family Type 0.114 0.079* 2.412 0.01
� * P < 0.01
Constant value = 19.248
Five variables viz gender, educational qualification, income, age and family type
have significantly contributed for predicting the availability of the product. The variable
gender predictive value of buying bike seems to be 0.048, when paired with the variable
educational qualification which is 0.059, with income 0.082, with age 0.075 and with
family type 0.114. The predictive value of these variables separately is 0.01.
Table 36
Showing Stepwise Regression Analysis Predicting the Source of Information
Sl.No Step/Source Cumulative
R2
∆∆∆∆R2 Step t P
1. Educational
Qualification 0.034 0.024
* -2.422 0.01
*����������
Constant value = 27.621
162
Educational Qualification is the only variable that has contributed significantly for
predicting the source of information. The R2 value is 0.034. This R
2 value is statistically
significant.
Table 37
Showing Stepwise Regression Analysis Predicting the Media of Advertisement
�
Sl.No Step/Source Cumulative R2 ∆∆∆∆R
2 Step t P
1. Gender 0.032 0.024* 3.124 0.01
2. Monthly Income 0.039 0.046* 2.140 0.01
� � � � � � � � * P < 0.01
Constant value = 20.421
The variables namely gender and monthly income have contributed significantly
in predicting the media of advertisement. The R2 value for gender is 0.032, which is
statistically significant. The second variable monthly income when added to monthly
income increases the R2 value to the extent of 0.039. The t-ratio for the monthly income
increases in R2 which is statistically significant.
Table 38
Showing Stepwise Regression Analysis Predicting
Over-all Opinion about the Product
Sl.No Step/Source Cumulative
R2
∆∆∆∆R2 Step t P
1. Age 0.032 0.048* 4.621 0.01
2. Educational
Qualification 0.066 0.063
* 3.546 0.01
� � � � � � � * P < 0.01
Constant value = 23.211
163
The results of regression analysis such as cumulative R2, ∆R
2, step t and P value
have been given in Table 38.
An attempt has been made to find out whether the variables respondents' age and
educational qualification would be possible predictors of over all opinion about the
product. The results indicate that the two variables are very significant in predicting the
over-all opinion about the product. The respondents' age is poised to predict an over-all
opinion of the product to an extent of 0.032 which is found to be statistically significant
at 0.01 level.
The second variable, educational qualification jointly with age, is able to predict
an over-all opinion about the product to a higher level of 0.066. (significant at 0.01 level).
Table 39
Showing Stepwise Regression Analysis Predicting the Smell of Cosmetic Products
Sl.No Step/Source Cumulative R2 ∆∆∆∆R
2 Step t P
1. Age 0.042 0.056* 3.045 0.01
2. Educational Qualification 0.053 0.052* 2.242 0.01
3. Monthly Income 0.058 0.045* 2.342 0.01
4. Age 0.068 0.058* 2.421 0.01
5. Family type 0.124 0.072* 2.545 0.01
� � � � � � * P < 0.01
Constant value = 17.421
Five variables viz age, educational qualification, monthly income, age and family
type have significantly contributed for predicting the smell of cosmetic products. The
variable age predictive value of smell of cosmetic products seems to be 0.042, when
164
paired with the variable educational qualification which is 0.053, with monthly income
0.058, with the age 0.068 and with family type 0.124. The predictive value of these
variables separately is 0.01.
Table 40
Showing Stepwise Regression Analysis Predicting the
Feeling about Skin Safety of Product
�
Sl.No Step/Source Cumulative
R2
∆∆∆∆R2 Step t P
1. Monthly income 0.046 0.032* 3.429 0.01
� � � � � � * P < 0.01
Constant value = 29.462
Subject Specialization is the only variable that has contributed significantly for
predicting the feeling about the skin safety of the product. The R2 value is 0.046. This R
2
value is statistically significant.
Table 41
Showing Stepwise Regression Analysis Predicting the Flavour of the Product
Sl.No Step/Source Cumulative
R2
∆∆∆∆R2 Step t P
1. Educational
qualification 0.034 0.032
* 2.812 0.01
2. Monthly Income 0.049 0.038* 2.624 0.01
� � � � � � * P < 0.01
Constant value = 18.424
The variables namely educational qualification and monthly income have
contributed significantly in predicting the flavour of the product. The R2 value for
165
educational qualification is 0.034, which is statistically significant. The second variable
monthly income when added to monthly income increases the R2 value to the extent of
0.049. The t-ratio increase in R2 is statistically significant.
Table 42
Showing Correlation between the Quality of Cosmetic
Product and Demographic Variables
� �
Demographic Variables Quality of cosmetic product
Gender 0.152**
Shops 0.238**
Educational Qualification 0.245**
Income -0.421**
Age -0.542**
Family Type -0.052
Occupation -0.492**
* Significant at 0.01 level
** Significant at 0.05 level
The Quality of cosmetic product is positively and significantly related to Gender
(0.152), Shops (0.238), Educational qualification (0.245), Income (0.421), Age (0.542)
and occupation (0.492). It shows a weak negative relationship with family type.
166
Table 43
Showing Correlation between the Opinion about Availability
of the Product and Demographic Variables
� Demographic Variables Opinion about availability
of the product
Gender 0.136**
Shops -0.138**
Educational Qualification 0.586**
Income -0.472**
Age -0.022
Family Type -0.024
Occupation -0.042
* Significant at 0.01 level
** Significant at 0.05 level
Opinion about the availability of the product is positively and significantly related
to gender (0.136), shops (0.138), and educational qualification (0.586). It shows a weak
positive relationship to age, family type and occupation.
Table 44
Showing Correlation between the Source of
Information and Demographic Variables
Demographic Variables Source of information
Gender 0.149**
Shops 0.146**
Educational Qualification 0.212**
Income 0.118*
Age 0.123*
Family Type 0.375**
Occupation 0.158**
* Significant at 0.01 level
** Significant at 0.05 level
167
The source of information is positively and significantly related to gender (0.149),
shops (0.146), educational qualification (0.212), income (0.118), age (0.123), family type
(0.375) and occupation (0.158).
Table 45
Showing Correlation between the Media of
Advertisement and Demographic Variables
Demographic Variables Media of
advertisement
Gender 0.121*
Shops 0.142*
Educational Qualification -0.082
Income 0.145
Age 0.112
Family Type -0.076
Occupation 0.011
* Significant at 0.01 level
** Significant at 0.05 level
The media of advertisement is positively and significantly related to gender
(0.121) and shops (0.142). It shows a weak positive relationship with educational
qualification, income, age, family type and occupation.
168
Table 46
Showing Correlation between the Over-all Opinion
about the Product and Demographic Variables
Demographic Variables Over-all opinion about
the product
Gender 0.158**
Shops 0.143**
Educational Qualification 0.262**
Income -0.132*
Age 0.375**
Family Type 0.299**
Occupation 0.232**
* Significant at 0.01 level
** Significant at 0.05 level
The over-all opinion about the product is positively and significantly related to
gender (0.158), shops (0.143), educational qualification (0.262), income (0.132), age
(0.375), family type (0.299) and occupation (0.232).
Table 47
Showing Correlation between the Smell of Cosmetic
Products and Demographic Variables
Demographic Variables Smell of cosmetic
products
Gender -0.172**
Shops 0.152**
Educational Qualification 0.192**
Income 0.155*
Age -0.247**
Family Type 0.332*
Occupation 0.229**
* Significant at 0.01 level
** Significant at 0.05 level
169
The Smell of cosmetic products is positively and significantly related to gender
(0.172), shops (0.152), educational qualification (0.192), income (0.155), age (0.247),
family type (0.332) and occupation (0.229).
Table 48
Showing Correlation between the Skin Safety of
Product and Demographic Variables
Demographic Variables Skin safety of product
Gender 0.382**
Shops -0.152*
Educational Qualification 0.121
Income 0.152*
Age -0.099
Family Type 0.134
Occupation 0.112
* Significant at 0.01 level
** Significant at 0.05 level
The Skin safety of product is positively and significantly related to gender
(0.382), shops (0.152) and income (0.152). It shows a weak positive relationship with
educational qualification, age, family type and occupation.
170
Table 49
Showing Correlation between the Flavour of Product and Demographic Variables
Demographic Variables Flavour of product
Gender 0.183**
Shops -0.192**
Educational Qualification 0.182**
Income 0.132*
Age 0.342*
Family Type 0.282**
Occupation 0.262**
* Significant at 0.01 level
** Significant at 0.05 level
The Flavour of product is positively and significantly related to gender (0.183),
shops (0.192), educational qualification (0.182), income (0.132), age (0.342), family type
(0.282) and occupation (0.262).
171
Table 50
Showing Factor Loading, Communality, Eigen value
and Percentage of Variance of the Emerging Factors
Sl. Factors Significant variables Factor
loading Communality
Eigen
Value
% of
variance
1. Socio-economic
factors
a) Age 0.619 0.542
9.482 9.268 b) Educational qualification 0.794 0.659
c) Income 0.854 0.646
d) Family type 0.868 0.510
2. Brand factors
a) Brand Loyalty 0.464 0.436
3.482 2.924 b) Brand Preference 0.532 0.594
c) Band Image 0.835 0.812
3. Quality
a) Quality of cosmetic product 0.345 0.380
3.404 3.736 b) Smell of cosmetic products 0.776 0.549
c) Over-all opinion 0.834 0.642
d) Flavour of product 0.508 0.594
From Table 49 Factor analysis has been done with the main objective, to find out
the underlying common factors among 19 variables included in this study. The principal
component factoring method with variance rotation is used for factor extraction. A three
factor solution was derived using a score test.
Table 49 shows the results of the factor analysis. The name of all the 19 variables
and their respective loadings in all the three factors are given in the Table. An arbitrary
value of 0.3 and above is considered significant loading. A positive loading indicates that
greater the value of the variable greater is the contribution to the factor. On the other
hand, a negative loading implies that greater the value, lesser its contribution to the factor
or vice versa. Keeping these in mind, a study of the loadings indicates the presence of
172
some significant pattern. Effort is made to fix the size of correlation that is meaningful,
club together the variables with loadings in excess of the criteria and search for a concept
that unifies them, with greater attention to variables having higher loadings. Variables
have been ordered and grouped by the size of loadings to facilitate interpretation as
shown in Table 51.
Table 51 signifies that factor analysis has been done among 19 variables used in
the study. The principal component analysis with varimax rotation was used to find out
the percentage of variance of each factor, which can be grouped together from the total
pool of 19 variables considered in the study. The results are given in Table 50 and
column 1 shows the serial number, ‘2’ shows the name given for each factor, ‘3’ shows
variables loaded in each factor, ‘4’ gives the loadings, ‘5’ gives the communality for each
variable, ‘6’ gives the Eigen value for each factor and ‘7’ gives the percentage of
variance found out through the analysis. The factor, variance percentage for each factor is
9.268, 2.924 and 3.736, respectively.
The factors are arranged based on the Eigen value viz
F1 (Eigen value 9.482)
F2 (Eigen value 3.482)
F3 (Eigen value 3.404)
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These three factors are described as “Brand Preference”. This model has a strong
statistical support and the Kaiser-Maya-Olkin (KMO) test of sampling adequacy concurs
that the sample taken to process the factor analysis is statistically sufficient.
Table 51
Showing related Factor Matrix loadings between nineteen variables and three
factors identified through Factor analysis
Sl.No. Variable Factor-I Factor-II Factor-III
1. Gender 0.092 -0.010 0.058
2. Age 0.153 -0.0002 0.106
3. Shops 0.121 -0.001 0.290
4. Educational qualification -0.164 0.076 -0.071
5. Income -0.040 0.060 -0.149
6. Family Type 0.003 0.107 0.034
7. Occupation -0.110 -0.120 0.100
8. Brand Preference -0.131 0.093 0.086
9. Brand Loyalty -0.129 0.069 0.132
10. Media of advertisement 0.061 -0.037 0.115
11. Brand Preference 0.197 -0.023 -0.009
12. Expenditure -0.086 -0.016 0.129
13. Quality of cosmetic -0.040 0.075 0.150
14. Availability of the product 0.268 -0.097 -0.013
15. Source of information 0.629 -0.115 -0.174
16. Over-all opinion about product 0.703 -0.050 -0.114
17. Smell of cosmetic products 0.568 0.066 -0.146
18. Skin safety of product 0.750 0.017 0.141
19. Flavour of product -0.204 0.068 0.121
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