consumer preferential buying patterns of...

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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.

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31.75

20.25

27.08

20.92

0

5

10

15

20

25

30

35

Pe

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Departmental Store Big Retail Shop Any Shop Door to door seller

Place of Purchase

Chart 1

Purchase Place of Cosmetics by Consumers

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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.

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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


Calculated Chi-square

Value Degrees of Freedom Level of Significance

22.73 3 0.01 (Significant)

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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.

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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

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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.

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Table 3

Educational Qualification of Consumers

Educational

Qualification

No. of


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


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.

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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

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Illiterate Less than

X Std

X Std XII Std Graduate Post

Graduate

Ph.D

Educational Qualification

Chart 3

Educational Qualifications of Consumer Sample

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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.

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Table 4

Income-wise Analysis of Sample Consumers

Income (in Rupees) Number of


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


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.

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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

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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


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


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.

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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

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nta

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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


Male 580 48.33

Female 620 51.67

Total 1200 100.0


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.

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48.3351.67

0

10

20

30

40

50

60

Pe

rce

nta

ge

Male Female

Gender

Chart 6

Frequency Distribution of Gender Groups

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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


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


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.

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14.92

31

27

17.33

9.75

0

5

10

15

20

25

30

35

Pe

rce

nta

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200 and less 201 – 500 501 – 1000 1001 – 2000 2001 and

above

Expenditure (in Rupees)

Chart 7

Frequency Distribution of Monthly Expenditure on Cosmetics

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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


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

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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


Joint family 717 59.75

Nuclear family 483 40.25

Total 1200 100.0


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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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


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.

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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

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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

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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.

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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

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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

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Category N Percentage

Purchasing the particular brand 677 56.42

Not purchasing the particular brand 523 43.58

Total 1200 100.0


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

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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.

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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.

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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

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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


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.

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12

19

8

45

79

0

5

10

15

20

25

30

35

40

45

Pe

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Emami Gokul Lavender Ponds Ztalc Others

Face Powders

Chart 12

Brand Loyalty for Face Powder

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Table 13

Brand Loyalty for Scent

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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


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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.

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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

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Table 14

Brand Loyalty for Shampoo

Shampoo Number of


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


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.

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37

7.5

10.5

22.5

8

14.5

0

5

10

15

20

25

30

35

40

Pe

rce

nta

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Clinic Lux Meera Sunsilk Velvet Others

Shampoo

Chart 14

Brand Loyalty for Shampoo

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Table 15

Brand Loyalty for Snow

Snow No. of


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


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.

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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

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Table 16

Brand Loyalty for Deodorant

Deodorants Number of


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


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.

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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

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40Table 17

Consumers using Indian Foreign Cosmetics

Category Number of

Retailers Percentage

Foreign 490 40.83

Indian 710 59.17

Total 1200 100


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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

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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


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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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


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

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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


Ponds 450 37.5

Cuticura 150 12.5

Sandal 288 24.0

Santhoor 144 12.0

Others 168 14.0

Total 1200 100.0



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


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



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


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



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


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


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


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.


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.


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


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.


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


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.


expenditure of different income.


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



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’


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



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



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’


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


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


R2


1. Educational

Qualification 0.034 0.024

* -2.422 0.01

*��


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

�


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


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


R2


1. Age 0.032 0.048* 4.621 0.01

2. Educational

Qualification 0.066 0.063

* 3.546 0.01

� � � � � � � * P < 0.01


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


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


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

�


R2


1. Monthly income 0.046 0.032* 3.429 0.01

� � � � � � * P < 0.01


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


R2


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


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**


Income -0.472**

Age -0.022

Family Type -0.024

Occupation -0.042



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**


Income 0.118*

Age 0.123*

Family Type 0.375**

Occupation 0.158**



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



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**


Income -0.132*

Age 0.375**

Family Type 0.299**

Occupation 0.232**



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**


Income 0.155*

Age -0.247**

Family Type 0.332*

Occupation 0.229**



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



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**


Income 0.132*

Age 0.342*

Family Type 0.282**

Occupation 0.262**



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)



�

173

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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consumer preferential buying patterns of...

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