Dissertation - A study on consumer preference for bonus packs over price discounts in purchase of hair care products

A STUDY ON CONSUMER PREFERENCE FOR BONUS

PACKS OVER PRICE DISCOUNTS IN PURCHASE HAIR

CARE PRODUCTS

A dissertation submitted in partial fulfilment of the

requirements for the award of the degree of

MASTER OF BUSINESS ADMINISTRATION

By

ABHIMANYU SINGH

Register No 1121137

Under the guidance of

Prof REENA RAJ

Institute of Management

Christ University, Bangalore

March 2013

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DECLARATION

I, Abhimanyu Singh, do hereby declare that the dissertation entitled A study on

consumer preference for bonus packs over price discounts in purchase of hair care

products has been undertaken by me for the award of Master of Business Administration.

I have completed this study under the guidance of Prof Reena Raj, Professor of

Marketing, Christ University Institute of Management, Bangalore.

I also declare that this dissertation has not been submitted for the award of any Degree,

Diploma, Associate-ship or Fellowship or any other title in this University or any other

University.

Place: Bangalore Abhimanyu Singh

Date: Register No 1121137

3

CERTIFICATE

This is to certify that the dissertation submitted by Mr. Abhimanyu Singh on the title A

study on consumer preference for bonus packs over price discounts in purchase of

hair care products is a record of research work done by his during the academic year

2012 – 2013 under my guidance and supervision in partial fulfillment of Master of

Business Administration. This dissertation has not been submitted for the award of any

Degree, Diploma, Associate-ship or Fellowship or any other title in this University or any

other University.

Place: Bangalore (Signature of the Guide)

Date: Prof Reena Raj

ACKNOWLEDGEMENT

I am indebted to many people who helped me accomplish this dissertation successfully.

First, I thank the Vice Chancellor Dr Fr Thomas C Matthew of Christ University for

giving me the opportunity to do my research.

I thank Prof Ghadially Zoher, Associate Dean, Fr Thomas T V, Director, Prof C K T

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Chandrasekhara, Head-Administration and Prof Kshetragna C N, Head-Marketing of

Christ University Institute of Management for their kind support.

I thank Ms Reena Raj, Professor in marketing, for her support and guidance during the

course of my research. I remember her with much gratitude for her patience and

motivation, but for which I could not have submitted this work.

I thank my parents for their blessings and constant support, without which this

dissertation would not have seen the light of day.

Abhimanyu Singh

Register No: 1121137

5

ABSTRACT

Sales promotion tactics are activities which the marketers employ to attract customers and

persuade them to purchase the product. There is a plethora of products in a particular

category. Customers now have a wide variety to choose from than earlier when market

was dominated by few players. It has become all the tougher for marketers to devise

marketing plans and schemes to attract new customers and motivate them to buy. Even on

the customer`s side purchasing is not an easy task. There is a complex decision process

that is involved and runs in a customer`s mind while he decides to buy a product. A

customer apart from taking price as an index for purchase decision takes into account

various other factors that are equally important in decision making. The marketing

activities like sales promotion also becomes a part of the customer`s decision making.

Sales promotion activities like Bonus packs and Price discounts are the most frequently

employed by a marketer to attract customers. But customers do not just buy or opt for

whatever schemes are available. Their decision is based on calculation of the value

provided for bonus packs and price discounts. However it has been observed that most

customers tend to neglect base values which are associated with the percentage of bonus

packs and price discounts. They view offers as single outright offers and make decisions

on the basis of which provides greater benefit. Obviously, price discounts are lesser than

bonus packs, ex.( 5% off against 50% free), customers tend to go for bonus packs without

actually calculating the difference by taking into account the base value.

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TABLE OF CONTENTS

Declaration ii

Certificate iii

Acknowledgements iv

Abstract v

Table of Contents vi

List of Tables viii

List of Graphs ix

CHAPTER I

INTRODUCTION

1.1 BACKGROUND OF THE STUDY 1

1.2 GENESIS OF THE STUDY 2

1.3 NEED AND RATIONALE OF THE STUDY 2

1.4 OVERVIEW OF THE STUDY 2

CHAPTER II

REVIEW OF LITERATURE

2.1 INTRODUCTION 3

2.2 HOW REVIEW HAS BEEN CONDUCTED 3

2.3 STUDIES CONDUCTED ABROAD 3

2.4 STUDIES CONDUCTED IN INDIA 5

2.5 CONCLUSION 6

CHAPTER III

RESEARCH METHODOLOGY

3.1 STATEMENT OF THE PROBLEM 7

3.2 VARIABLES UNDER INVESTIGATION 7

3.3 OBJECTIVES OF THE STUDY 7

3.4 HYPOTHESIS 8

3.5 POPULATION AND SAMPLE OF THE STUDY 9

3.6 SAMPLING TECHNIQUE 10

7

3.7 DESCRIPTION OF THE TOOLS ADOPTED FOR STUDY 10

3.8 RELIABILITY OF THE INSTRUMENTS 10

3.9 CONCLUSION 11

CHAPTER IV

INDUSTRY OVERVIEW

4.1 FMCG MARKET IN INDIA 12

4.2 INDUSTRIAL ANALYSIS 13

4.3 SHAMPOO MARKET AND ITS GROWTH IN INDIA 14

4.4 SIGNIFICANCE OF THE STUDY 16

4.5 LIMITATIONS 17

CHAPTER V

DATA ANALYSIS AND INTERPRETATION

5.1 INTRODUCTION 18

5.2 RESPONDENT PROFILE 18

5.3 TESTING OF HYPOTHESES 21

5.4 FACTOR ANALYSIS 29

5.5 MULTIPLE REGRESSION 34

CHAPTER VI

FINDINGS, CONCLUSION ANDSUGGESTIONS

6.1 FINDINGS 49

6.2 CONCLUSION 41

6.3 SUGGESTIONS 42

6.4 SUGGESTIONS FOR FURTHER RESEARCH 44

BIBLIOGRAPHY 45

APPENDIX 46

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LIST OF CHARTS

S No Title Page No

4.3.1 Pie chart showing market share of shampoo companies. 15

4.3.2

Pie chart showing market share of top shampoo brands 16

5.2.1

Pie chart showing age grouping of respondents 18

5.2.2

Pie chart showing gender proportion among the

respondents.

19

5.2.3

Pie chart showing income level of respondents. 19

5.2.4

Pie chart showing base value neglect among

respondents

20

5.2.5

5.3.1

5.3.2

5.3.3

5.3.4

5.3.5

5.4.1

Pie chart showing computational complexities faced by

respondents.

Bar chart depicting base value neglect among

customers to support hypothesis 1

Bar chart depicting frequency of usage as a support for

hypothesis 2.

Bar chart depicting difference in percentage associated

with offer as a support for hypothesis 3

Bar chart depicting computational complexities as a

support for hypothesis 4

Bar chart depicting ability to pile stock for future use as

a support for hypothesis 5

Scree plot depicting variables with more than on eigen

value.

20

22

24

26

27

29

32

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1.1 BACKGROUND OF THE STUDY

Sales promotion tactics are used by marketers to attract customers and persuade them to

purchase the product. In the current market scenario where there is a plethora of products

to choose from, the task of a marketer gets all the tougher when devising a marketing

instrument such as a sales promotion scheme. Currently following are the promotional

tactics used in market:

Coupons

Price-off deals

Bonus packs

Contests/sweepstakes

Samples/trial offers

Product placement

Refunds

Rebates

Frequency programs

Out of these price discounts and bonus packs are most commonly used.

A bonus pack can be defined as an offer from the marketer, wherein the customer gets

more quantity of the product at the same or original price. A price discount simply refers

to selling a product at a certain percentage discount on the price.

Customers generally tend to be inclined towards bonus packs as they see additional value

being gained at the same price. Bonus packs are seen as pure gains by customers whereas

price discounts are seen as reduction in losses. Because gains are likely to be preferred to

reduction in losses due to the curvature of prospect theory’s value function (Kahneman

and Tversky 1979), bonus packs might be preferred to price discounts.

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1.2 GENESIS OF THE PROBLEM

Consumer buying behavior is a complex mechanism and decision making takes into

consideration a lot of factors. What goes into the mind of a customer when he is faced by

two different offers, a bonus pack and a price discount, in the same product category by

competing brands? Which one will the customer choose and what are the reasons of

factors that led him to choose a particular promotional offer. Do customers have a

preference for a particular promotional offer?

1.3 NEED AND RATIONALE OF THE STUDY

The project would help in knowing the factors that make a consumer decide on a

particular promotional offer, between bonus packs and price discounts. The project will

also help in knowing whether there is a preference for a particular promotional offer. The

study will help in understanding why a customer chooses a bonus pack over a price

discount, factors behind his purchase decision the basis on which the decision to select

bonus pack was made.

1.4 OVERVIEW OF THE STUDY

Bonus packs and price discounts are usually presented in percentages. Since consumers

generally ignore the base value of the product when exposed to these promotional offers

and percentages, they tend to incline towards bonus packs. The reason behind this is

because bonus packs carry higher percentage than price discounts, customers see them as

more valuable in terms of gains when base values are ignored.

This report is an extensive study of the various reasons for customer’s preference for

bonus packs. The report will also bring into picture other factors that play a significant

role in creating customer`s preference for bonus packs. The study will also give an idea

about framing bonus packs for maximum effect on customer`s preference and buying

behavior.

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2.1 INTRODUCTION

Before starting with a research or a project, a review of literature proves to be really

helpful in giving the researcher a clear idea about the topic and some areas which the

researcher needs to focus on. The review of literature provides knowledge about the

concerned area/domain on which the researcher is about to work, through researches that

are already conducted by other researchers in the same/ similar domain. It narrows down

the focus of the researcher and allows him to keep view of only activities and efforts that

are related to the topic of research.

2.2 HOW REVIEW WAS CONDUCTED

The review of literature was conducted keeping in mind the topic of this project. Articles

and research papers were collected from various sources like the internet, Journal of

Marketing, Journal of Consumer Marketing, university library. The idea was to collect

research papers for reviewing to get a better knowledge of the topic and a guided way to

conduct the research.

Articles were collected on studies that have been conducted both in India and abroad.

Since not much work was done in India and overall not many studies have been

conducted on such topic, I have reviewed some 10 articles out of which I am presenting

seven articles.

Since the main focus of the study relates to the Indian market and so I have reviewed 10

articles. The review gave me deeper insight into the promotional tactics being used in

market and customer`s response to it. It also gave me knowledge about how consumers

evaluate bonus packs and price discounts and what goes into choosing a bonus pack over

price discount or vice-versa.

2.3 STUDIES CONDUCTED ABROAD

In the article when more is less: the impact of base value neglect on consumer

preference for bonus packs over price discount tried the author tries to find out the

effect of base value neglect on customer`s preference for bonus packs. The paper tries to

explain customer`s preference for promotional offers and conducts a series of experiment,

both laboratory and real world, to know customer`s decision making in terms of

promotional offers. The result suggested that consumers have an inclination towards

bonus packs due to base value neglect, as they only see the percentages associated with

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the offer/product. It was also know that customers face computational difficulties in

measuring the offers among one another. Consumers see bonus packs as pure gains and

price discounts as reduction in loss. Therefore there is a slight inclination towards bonus

packs from the start itself. The article also suggests that preference for bonus packs or

price discounts may not stay same in case of inexpensive/expensive goods. (Haipeng,

2012)

In this research paper customers behavioural responses to sales promotion: the role of

fear of losing face the author tried to know whether there is a significant relationship

between (a) coupons (b) price discounts, (c) free sample, (d) bonus packs and (e) in-store

display, and product trial. The author also made an attempt to know if there is a direct

positive relationship between product trial and product repurchase. He further went on to

find whether product trial mediates in the relationship between the sales promotions

strategies namely, (a) coupon, (b) price discount, (c) free samples, (d) Bonus packs, (e)

in-store display and product repurchase. The author tried to justify that the impact of the

sales promotion strategies on the product trial will be weaker if the customers are afraid

of losing face.

The study resulted in the finding that in-store promotion played the most important role in

shaping customers product trial reactions. Price discounts also played a significant role in

customer`s product trial behaviour. Bonus packs also had a high likability. Bonus packs

are used to motivate consumers to try more of the product. Although the effect of bonus

pack to product trial was found lower when compared to other promotional tools.

The author concludes that:

Price discounts helps in customer repurchasing through product trial

Free samples lead to product trials which further leads to repurchase

Bonus packs culminates to repeat purchase by product trial

In-store display begets product trial, which precedes repurchase

(OlyNdubisi & Moi, 2005)

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The article consumer perceptions of bonus packs: an exploratory analysis tries to

investigate consumers' beliefs regarding bonus pack offers (quantity and price claimed),

their perception of the manufacturer and of the value of the deals, and their purchase

intentions. The author also examines the impact of types of user (e.g. light versus heavy)

and buyer (e.g. regular versus infrequent) on perceptions of bonus pack offers.

The results of the study were that consumers do not tend to give bonus pack too much

credence. Sometimes percentages associated with the offers are hard to believe like 80%

more, and that the offer would be more realistic at 20% more free. To prices, consumers

suspected that manufacturers raised prices for products in conjunction with bonus pack

offerings. One managerial implication was that retailers or manufacturers need to find out

ways to boost a bonus pack`s credibility. Retailers tend to move older products from the

shelves when bonus pack offers are introduced. Thus customers do not have anything to

compare the new offer with in terms of price and quantity. (Beng Soo Ong, 1997)

In this article the author tries to examine some strategic b enefits that free promotions

may offer over other types of promotions on the basis of customer`s differential

processing. In her study she found that differential processing of free promotions and

monetary discounts results in differences in consumer`s reaction to negative contextual

influence. (Sucharita Chandran, 2006)

2.4 STUDIES CONDUCTED IN INDIA

In this article analysis of hair care products with reference to shampoo market in

India the author tries to analyze different market players of shampoos available in India,

SWOT analysis of shampoo market and portfolio analysis for different shampoo brands

with the help of BCG matrix.

The findings of the study were that in India the current market share of hair care products

is 9% of the total FMCG sector which is continuously increasing from 6230.8 crores of

rupees to 8417.79 crores of rupees in the commercial years of 2008-09 to 2010-11. The

shampoo market is dominated by Hindustan Unilever Ltd. enjoying a market share of

46% followed by Procter % Gamble with 24%. Following are the top brands in the

country/: The top shampoo brands Sunsilk, Clinic Plus, Pantene and Head & Shoulders.

(Khawaja Mubeenur Rahman, 2011)

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In this article the influence of Price Discounts versus Bonus Packs on the preference

for virtue and vice foods the author tries to explore how price and quantity based sales

promotion can influence the consumption of unhealthy (vice) and healthy (virtue) food.

The findings of the study were that consumers prefer a bonus pack over a price discount

for a virtue food whereas consumers are inclined toward price discounts for a vice foods.

Earlier researches show that, all else being equal, consumers tend to incline toward bonus

packs. But through this research inclination towards a promotional offer is suggested to

be based on the type of product purchased. The authors makes an attempt to test that

preference towards a particular offer emerges because consumers find greater conflict

when they purchase a vice food, which ultimately leads them to look for a justification. A

price discount turns out to be a better justification than a bonus pack because consumers

then start to believe that they are saving money and at the same time consuming lesser

quantities of vice food. (Mishra, 2011)

2.5 CONCLUSION

In most studies it has been observed that customers in general have a liking for

bonus packs over price discounts. But this preference is not stable.

Customers also consider a lot of factors before selecting a bonus pack or price

discount. Price and added value is just one factor.

The studies conducted explored that consumer’s preference for bonus packs or

price discount is also based on the type of product they buy.

Consumer buying behaviour, attitude, personality, spending habit also play a

major role in preference for either a bonus pack or price discount.

On part of the retailer or manufacturers, sales promotion offers should be framed

in such a way that it is believable by the customers. Innovative ways to boost

bonus pack`s credibility must be used.

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3.1 STATEMENT OF THE PROBLEM

There are a lot of things that goes into a consumers mind while deciding to buy a product.

Moreover, marketers regularly come up with various types of schemes to attract

customers. The objective here is to understand why a customer would prefer a Bonus

Pack over Price Discount. Why consumers are directed towards bonus packs for certain

products and price discount for others.

3.2 VARIABLES UNDER INVESTIGATION

3.2.1. DEPENDENT VARIABLES

Customer’s preference for bonus packs over price discounts.

3.2.2. INDEPENDENT VARIABLES

Base Value Neglect

Consumption pattern

Brand switching/Loyalty

Difference in offer or percentage associated with offer

Computational complexity

Product familiarity

Frequency of shopping

Nature of product : Expensive or Inexpensive

Price-quality relationship

Customer`s stock keep capacity

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3.3 OBJECTIVES OF THE STUDY

To know why a customer prefers a bonus pack over price discount in purchasing

of shampoo.

To identify the variables that motivates consumer’s preference towards bonus

packs.

To find out whether base value neglect by consumers have any effect on

consumer`s preference for bonus packs

To find if consumption or usage pattern has any effect on consumer`s preference

for bonus packs

To find out if difference in percentage associated with offers have any significant

role in customer`s preference for bonus packs

To find out if computational complexities have a significant impact on customer`s

preference for bonus packs

To find out if stock piling ability of consumers have significant effect on

customer`s preference for bonus packs.

3.4 HYPOTHESIS

3.4.1 Hypothesis 1

H0: Base value neglect does not affects customer`s preference for bonus packs

over price discounts when both are expressed as percentages.

H1: Base value neglect affects customer`s preference for bonus packs over price

discounts when both are expressed as percentages.

3.4.2 Hypothesis 2

H0: There is no or less preference for bonus pack if consumption/usage is high.

H1: There is higher preference for bonus packs if consumption/usage of the

product is high.

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3.4.3 Hypothesis 3

H0: Difference in percentages associated with offers have no significant impact

on customer`s preference for bonus packs.

H1: Difference in percentages associated with offers have a significant impact on

customer`s preference for bonus packs.

3.4.4 Hypothesis 4

H0: computational complexity has no effect on consumer`s preference for bonus

packs over price discounts.

H1: computational complexity has significant effect on consumer`s preference for

bonus packs over price discounts.

3.4.5 Hypothesis 5

H0: Stock piling ability have no significant impact on customer`s preference for

Bonus packs.

H1: Stock piling ability have a significant impact on customer`s preference for

Bonus packs.

3.5 POPULATION AND SAMPLE OF THE STUDY

Type of research – the research is a descriptive research and analytical research.

Type of data used– Primary Data and Secondary data will be used for analysing the

research.

Sample units – Individuals purchasing FMCG products on a regular basis.

Sample size – 250

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3.5.1. DATA COLLECTION

3.5.1.1. Primary data

Questionnaires will be filled up by individuals who are normal day to day

customers of FMCG products.

3.5.1.2. Secondary data Journals, Websites, research papers and articles, books on

marketing and sales promotion.

3.6 SAMPLING TECHNIQUE

We are using the descriptive research as we know the problem and by using this type of

research we are able to get information regarding the attitude of consumers towards sales

promotion tactics. A research design is the arrangement of conditions for the collection

and analysis of data in a manner that aims to combine relevance to the research purpose

with economy in procedure. Research design can be classified into three broad classes,

exploratory, descriptive and casual. In this study descriptive research was used. This is

because descriptive research is a fact and finding approach related largely to the current

and abstracting generalizations by cross sectional study of situation in hand.

3.7 DISCRIPTION OF THE TOOLS ADOPTED FOR THE STUDY

Factor Analysis: It helps the researcher in finding the factors that affect the

customer`s preference for bonus packs over price discounts.

T-Test: To test the various hypothesis created in order to know the impact of

variables on customer`s preference for bonus pack.

Charts: Bar graph and pie – charts have been used in the study to get a pictorial

outlook of the responses, which will be later used as support to prove the

hypothesis.

Questionnaire: To assess elicit responses from the sample group in order to study

the factors for preferences.

Regression: a multiple regression model is used to know the relationship between

a dependent variable and other independent variable/s and their effect on each

other.

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3.8 RELIABILITY OF THE INSTRUMENTS

Table 3.8.1: Reliability Statistics

Cronbach's Alphaa

Cronbach's Alpha Based on

Standardized Itemsa N of Items

0.68 0.758 29

From the above table we can see that Cronbach's alpha is 0.68, which indicates a high

level of internal consistency for our scale with this specific sample. Since the Cronbach`s

alpha score is above 0.60, it depicts that the data collection method and the data itself is

reliable enough to further the study.

3.9 CONCLUSION

Research methodology provides us a guided pathway for further developing the report.

With the objectives undertaken being cleared, statistical tools and hypothesis identified a

reviewer can easily understand what the author is trying to prove from the research. The

findings at the end of the research are them compared with the objective to see if the

researcher has been able to accomplish the goals. The reports are compared with the

hypothesis and objectives to see if the research has been fruitful and researcher successful

or not.

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4.1 FMCG MARKET IN INDIA

Products which have a low turnover and are of comparatively low cost are known as Fast

Moving Consumer Goods (FMCG). FMCG products are those are consumed rapidly,

mostly on a daily basis. Examples of FMCG generally include a wide range of frequently

purchased consumer Products such as toiletries, soap, cosmetics, tooth cleaning products,

shaving products and detergents, as well as other non durables such as glassware, bulbs,

batteries, paper products, and plastic goods. Pharmaceuticals, consumer electronics,

packaged food products, etc are also included in fast moving consumer durables. Indian

FMCG sector is ranked as the fourth largest as compared to other firms in the world and

is said to create employment for around three million plus people in the downstream

activities that link the company to the consumers.

The industry is doing pretty well in the country and also around the world. Since the

industry is meeting the daily requirements of the consumers, its growth is inevitable.

Companies such as Marico Ltd and Nestle India Ltd, which is said to dominate in key

product categories, has significantly improved its market share and left its peers far

behind. A major source of help came out in the form of lack of competition in the

industry, as very few players are there in the Indian market. Product leaders such as

Colgate, Palmolive India and others have also seen strength in the respective products

categories, as a result of strong distribution infrastructure and continuous innovation.

Some strong players like Godrej in its consumer care products section has also presented

market share improvement by taking the most crucial step in markets: growth through the

semi-urban and rural markets.

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4.2 INDUSTRIAL ANALYSIS

Table 4.2.1: Category wise share of FMCG sector

Interpretation – Its is clearly visible from the above table that Food and Beverage sector

of the industry holds the maximum market share i.e. 43%, which is followed by Personal

care products at 22% and Fabric care at 13%. The market share for Hair care products

stand at a significant 9% of the total FMCG sector.

4.2.2 Hair Care Market Size In Terms Of Value

The current hair care category comprises shampoos, conditioners, herbal remedies, hair

dyes and hair oil. The market share for each of the product category is depicted in the

following table.

Table 4.2.2: Hair Care Market Size In Terms Of Value (Rs. In Crores)

Interpretation - It can be seen from the above table that the market size in the year 2008-

2009 stood at 6230.8 crores of rupees. Out of this combined market size, the market size

22

of hair care products was at 533.52%, which was followed by shampoo segment at

31.83%. In the year 2009-10 the total hair care market size incremented by 7283.8 crores

of rupees. However, a majority of the share belongs to the hair oil segment with an

astounding 54.13%, chased by shampoo segment at 31.49%. In the year 2010-11, the hair

care market size grew further to 8417.79 crores of rupees which included the hair oil

segment with a major share of 54.83%, followed by the shampoo segment with 31.28%.

4.2.3 Hair Care Market Size

Table 4.2.3: Hair Care Market Size In Terms of Volume

Interpretation – It can be seen from the table that even volume wise the market is

dominated by the hair oil segment at 62.45%, which is followed by the shampoo segment

at 31.76% for the year 2008-2009. Whereas in the year 2009-10 the hair oil segment grew

up to 62.94% followed by the shampoo segment which stood at a share of 31.21%. In the

year 2010-11, hair oil segment wins among its peers to hold the market share by 62.71%

as against the second ranker, the shampoo segment at 32.01%.

4.3 SHAMPOO MARKET AND ITS GROWTH IN INDIA

The current market value of hair care products is valued at $250 million in India. It

contribute to 8% of the total FMCG sector and has a recorded a growth of over 3.9% over

the previous year. The hair care market can be split into hair oils, shampoos, hair

colorants and conditioners, and hair gels. The current market size of the shampoo market

is 2700 Crores with maximum sales accounted from urban areas, around 80% and rest

20% in rural market. The market is expected to increase due to increased marketing by

prominent players, lower tax and availability of shampoo in smaller denominations.

Sachet constitutes 70% and anti-dandruff shampoo up to 20% of the total shampoo sale in

23

India. This is considered as a middle class product as more than 50% of the consumers

use toilet soaps to wash their hair. The penetration level is up to 30% in metros and the

major players are HUL and Procter and Gamble.

It has been researched that brand loyalty in shampoos are not as strong as seen in other

product categories. Consumers tend to shfit to other brands for change seeking for

fragrance in particular. Major expectations from the product are believed to be in

improving the texture it provides to hair, easy handling and at the same time giving

softness and bounce to the users hair. Southern market of the country is dominated by the

sachet market totalling for 75% of the sachet sales volume. In Contrast, shampoo bottles

are more preferred in the northern market and around 50% of the total sales in bottles

come from the northern market alone. The shampoo industry has immense scope and

possibilities of penetrating in the Indian market with current penetration levels at 53% in

urban markets and 47% in rural markets as of now.

4.3.1 Market Share of Shampoo Companies in India

From the chart below it can be observed that the top three companies of the shampoo

category in the country are Hindustan unilever Ltd., Procter & Gamble and Dabur. From

the pie chart, it is known that Hindustan Unilever Ltd. is leading the market with 46% of

market share chased by Procter and Gamble and Dabur standing at 24% and 11% of

market share. The other major players in the market are Indian Tobacco Company,

L’oreal and CavinKare with 6%, 3% and 2% of market share.

Chart 4.3.1: Market Share of Shampoo Companies in India

24

Chart 4.3.2: Top shampoo brands in India

Interpretation – The maximum selling brands in the country are Sunsilk and Clinic Plus

which are leading the market with 22% and 20% of market share of shampoo segment

respectively followed by Pantene and Head and Shoulders with 16% and 13%

respectively. Whereas, Dabur is seen to be dominating the herbal shampoos with 8% of

the total market share.

4.4 SIGNIFICANCE OF THE STUDY

The FMCG business in India has a history of playing with numbers. Every now and then

companies could be seen presenting promotional offers to customers to attract them.

Since FMCG goods are consumables with low shelf life, companies come up with

innovative marketing tactics to attract customers and at the same time get noticed by

customers in among the clutter of competitive products. Companies have been using sales

promotion tools of various kinds to keep the brand name up in the mind of the customers.

At the same time the companies also tries to motivate customers to buy the products

available on offers. This technique help markets to boost sales and clear up inventory

before new arrivals. A very effective way to boost sales for a short period of time is

25

through sales promotional activities, out of which bonus packs and price discounts are

most widely used.

The shampoo industry has been using it for a long time to attract customers. Earlier the

focus was largely on women, but now due to changing demographics, marketers have

started focusing equally on men.

This study would therefore be useful in portraying the different reasons and factor that

play an important role in motivating a customer to prefer bonus packs over price

discounts.

4.5 LIMITATIONS

As with other research, this study is not without limitations. However, some of the

limitation may be used as an area of research by other researchers. First, the findings of

this study may not apply to other types of bonus packs (for example buy three get one

free). However research on these types of bonus packs may be interesting.

The second limitation is that bonus pack was studied under two or three levels (50% off

or 25% off). However, if more quantities were used the study would have yielded a

different result. Future research should consider different levels for studying bonus packs.

It might be fruitful to study different types of bonus packs based on percentages to

provide more insights into the current situation. .

A third limitation was that to know usage patter among buyers, they were self-assigned.

This might have given us erroneous data as buyers might not keep track of their purchase

or might not remember their last purchase or use. Therefore the responses were kept

around 250 to get a clear idea.

A limitation of this research is its concentration on the shampoo usage setting. Further

studies can consider other product categories and the influence of price discount versus

bonus packs.

26

5.1 INTRODUCTION

After collection of all the responses and summarising the data it was time to analyse and

interpret the results. The data has been interpreted on SPSS17.0 software. This statistical

software has generated all the tables, charts and other statistical tools shown in this

chapter.

5.2 RESPONDENT PROFILE

Chart 5.2.1: Age Group

Interpretation - As it is seen in the table majority of the respondents i.e. 34% fall in the

age group of 21-25 years, whereas around 28% of the respondents fall between 26-30

years. Only 8.40% of the respondents are above 41 years of age.

As it is seen the respondents are a mixed crowd because all such people are consumers of

FMCG products and shampoo. Therefore, respondents were selected to represent different

age groups to get a wider view from the people about the topic.

27

Gender

Chart 5.2.2: Gender proportion

Interpretation - Out of the total respondents 54% were female and 46% were male.

Income

Chart 5.2.3: Income level

28

Interpretation - around 44.80% of people earn less than Rs. 20,000 a month, 27.20% earn

between Rs. 20001-30000, 14.80% of the respondents earn Rs. 30001-40000.

Base value neglect:

Chart 5.2.4: Base value neglect

On given a question to asses customers tendency to neglect base value while evaluating

promotional offers like bonus packs and price discounts, around 47.20% of the

respondents agreed to prefer bonus packs whereas 28.40% of the respondents strongly

agreed to prefer bonus packs even when base price of both the products in respective

offers were same.

Computational complexity

29

Chart5.2.5: Computational Complexity

Interpretation - On being presented with a question which secretly provided two bonus

packs one with 20% more + additional 25% more and another with straight 40% more

(base price and quantities of both the products kept same), 44.80% of the respondents

agreed to prefer 20% + 25% more offer to straight 40% more offer. This is because

consumers to not prefer to or find mathematical calculations complex and as a result of

this 20+25=45 is preferred which is more than 40.

5.3 TESTING OF HYPOTHESES

Hypothesis 1

H0: Base value neglect does not affects customer`s preference for bonus packs over price

discounts

H1: Base value neglect affects customer`s preference for bonus packs over price

discounts

Table 5.3.1: One-Sample Statistics

N Mean Std. Deviation Std. Error Mean

Base value neglect as

preference for BP

250 3.8680 1.05405 .06666

Table 5.3.2: One-Sample Test

Test Value = 0

T Df

Sig. (2-

tailed)

Mean

Difference

95% Confidence Interval of

the Difference

Lower Upper

Base value neglect as

preference for BP

58.022 249 .000 3.86800 3.7367 3.9993

30

Chart 5.3.1: Base value neglect

Analysis

Here, t (249) = 58.022, p<0.001

As we can see that the p-value (sig value) is less than 0.05 we reject null hypothesis or HO

Interpretation

As we have rejected null hypothesis, we would accept H1 and can conclude that Base

value neglect affects customer`s preference for bonus packs over price discounts when

both are expressed as percentages.

As seen in the bar graph, when customers were asked questions on preference for bonus

packs or price discounts with percentages associated with each offer, 47.2% of the

respondents agreed to prefer bonus packs even when both the offers were economically

equivalent.

Hypothesis 2

H0: There is no or less preference for bonus pack if consumption/usage is high.

H1: There is higher preference for bonus packs if consumption/usage of the product is

high.

31

Table 5.3.3: Correlations

How often do

you prefer

bonus packs

Frequency of

use

How often do you prefer

bonus packs

Pearson Correlation 1 .054

Sig. (2-tailed) .000

N 250 250

Frequency of use Pearson Correlation .054 1

Sig. (2-tailed) .000

N 250 250



Frequency of use 250 4.0520 .91028 .05757


Test Value = 0

T Df Sig. (2-tailed)

Mean

Difference

95% Confidence Interval of the

Difference

Lower Upper

Frequency of use 70.382 249 .000 4.05200 3.9386 4.1654

32

Chart 5.3.2: Frequency of use

Analysis

Here, t (249) = 70.382, p<0.001

As we can see that the p-value (sig value) is less than 0.05 we reject null hypothesis or H0

Interpretation

As we have rejected null hypothesis, we would accept H1 and can conclude that there is

higher preference for bonus packs if consumption/usage of the product is high.

As seen in the bar graph, around 52.8% of the respondents agreed to use shampoo every

alternate day and hence prefer bonus packs due to heavy consumption.

Hypothesis 3

H0: Difference in percentages associated have no significant impact on customer`s

preference for bonus packs.

H1: Difference in percentages associated have a significant impact on customer`s

preference for bonus packs.

33



Difference in percentage

associated with offers

250 1.7640 1.08119 .06838


Test Value = 0

T df

Sig. (2-

tailed)

Mean

Difference

95% Confidence Interval of

the Difference

Lower Upper

Difference in

percentage associated

with offers

25.797 249 .000 1.76400 1.6293 1.8987

Table 5.3.8: ANOVA

How often do you prefer bonus packs

Sum of Squares Df Mean Square F Sig.

Between Groups 8.159 3 2.720 2.432 .001

Within Groups 275.105 246 1.118

Total 283.264 249

34

Chart 5.3.3: Difference in percentage associated with offers

Analysis

Here, t (249) = 25.797, p<0.001


Interpretation

As we have rejected null hypothesis, we would accept H1 and can conclude difference in

percentages associated have a significant impact on customer`s preference for bonus

packs.

Also, the bar graph depicts that 60.8% of the respondents preferred 50% more for free

offer. The question was framed to know if difference in percentages offered had any

impact on preference or not.

Hypothesis 4

H0: computational complexity has no effect on consumer`s preference for bonus packs

over price discounts.

H1: computational complexity has significant effect on consumer`s preference for bonus


35



Computational complexity 250 3.8800 1.01870 .06443


Test Value = 0

T Df Sig. (2-tailed)

Mean

Difference

95% Confidence Interval of the

Difference

Lower Upper

Computational complexity 60.222 249 .000 3.88000 3.7531 4.0069

Chart 5.3.4: Computational Complexity

Analysis

Here, t (249) = 60.222, p<0.001


Interpretation

As we have rejected null hypothesis, we would accept H1 and can conclude that

computational complexity has significant effect on consumer`s preference for bonus


36

Also, from the bar graph is can be seen that 44.8% of the respondents agreed and 27.2%

strongly agreed to preferring a 20% more for free plus additional 25% more for free

against a single 40% discount offer, even when both the offers are economically

equivalent.

Hypothesis 5

H0: Stock piling ability have no significant impact on customer`s preference for Bonus

packs.

H1: Stock piling ability have a significant impact on customer`s preference for Bonus

packs.

Table 5.3.11: ANOVA

How often do you prefer bonus packs

Sum of Squares df Mean Square F Sig.

Between Groups 10.951 4 2.738 2.463 .046

Within Groups 272.313 245 1.111

Total 283.264 249



Relationship between bonus

pack and Stock piling

250 4.3360 .65177 .04122

Ability to pile stock 250 3.9040 .98929 .06257

37

Chart 5.3.5: Ability to pile stock

Analysis

Here, t (249) = 62.396, p<0.001


Interpretation

As we have rejected null hypothesis, we would accept H1 and can conclude that Stock

piling ability has a significant impact on customer`s preference for Bonus packs.

38

Also from the bar graph it can be seen that around 48% of the respondents agreed to and

28.4% of the respondents strongly agreed to preference for bonus packs for the purpose of

piling stocks for future use.

5.4 FACTOR ANALYSIS

Table 5.4.1: KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .696

Bartlett's Test of Sphericity Approx. Chi-Square 443.407

Df 171

Sig. .000

Interpretation - The KMO and Bartlett’s test table above depicts a sampling adequacy of

.696 which suggests that 69% of the sample is adequate.

Table 5.4.2 Communalities

Initial Extraction

Base value neglect as preference for BP 1.000 .828

Difference in percentage associated with offers 1.000 .516

Computational complexity 1.000 .624

Frequency of use 1.000 .777

Size of pack purchased 1.000 .822

Frequency of change among brands 1.000 .717

Value for money obtained 1.000 .795

Ability to pile stock 1.000 .875

Frequency of usage 1.000 .791

Frequency of shopping 1.000 .754

Quality perception when discounts allowed 1.000 .889

Base value neglect as preference for BP 1.000 .637

Kind of buyer 1.000 .555

Kind of user 1.000 .808

Frequency of purchase 1.000 .784

Type of customer segment 1.000 .810

Offer preference for familiar products 1.000 .747

Offer preference for unfamiliar products 1.000 .516

Customers offer preference for inexpensive products 1.000 .832

39

Extraction Method: Principal Component Analysis.

Table5.4.3: Total Variance Explained

Component

Initial Eigen values Rotation Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative %

1 6.536 34.402 34.402 5.772 30.378 30.378

2 3.204 16.862 51.263 3.006 15.819 46.197

3 1.725 9.077 60.341 2.303 12.120 58.317

4 1.402 7.378 67.719 1.513 7.963 66.279

5 1.210 6.369 74.088 1.484 7.808 74.088

6 .979 5.155 79.242

7 .758 3.990 83.232

8 .695 3.657 86.889

9 .585 3.078 89.967

10 .434 2.284 92.251

11 .348 1.832 94.084

12 .308 1.620 95.703

13 .239 1.256 96.959

14 .166 .873 97.832

15 .104 .549 99.174

16 .075 .396 99.570

17 .044 .234 99.804

18 .037 .196 100.000

Extraction Method: Principal Component Analysis

Interpretation - The table above with 5 Eigen value greater than 1 explains a total variation of

75% which makes enough ground for extraction of 5 factors.

40

Chart 5.4.1: Scree Plot for various variables

Interpretation - The Scree plot clearly shows that the scree begins from the 5th factor where the

Eigen value is 1. It further strengthens the grounds of extraction of 5 factors.

So the number of factors extracted = 5

41

Table 5.4.4: Component Matrix

1 2 3 4 5

Base value neglect as preference

for BP

.712

Computational complexity .617

Difference in percentage associated

with offers

.615 .444

Customers offer preference for

inexpensive products

.480 -.444

Frequency of change among brands .459

Ability to pile stock .454 .442 -.448

Frequency of shopping -.634 .436

How often do you prefer bonus

packs

-.563

Quality perception when Discounts

allowed

.470

Frequency of use .436

Offer preference for familiar

products

.671

Type of customer segment -.455 .551

Offer preference for unfamiliar

products

-.544

Frequency of usage -.590 .436

Frequency of purchase

Kind of buyer -.462

Quality perception when discount

allowed

-.467 .575

Relationship between bonus pack

and Stock piling

.457 .509

Size of pack purchased

Value for money obtained

Kind of user

42

Table 5.4.5: Rotated Component Matrix

1 2 3 4 5

Ability to pile stock .722

Quality perception when

discounts allowed

.708

How often do you prefer bonus

packs

.599

Kind of buyer -.585

Base value neglect .845

Frequency of shopping .741

Customers offer preference for

inexpensive products

.759

Type of customer segment .594

Computational complexity .569 .570

Frequency of usage .569

Difference in percentage

associated with offers

.835

Offer preference for familiar

products

.500

Size of pack purchased .873

Frequency of change among

brands

.489

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 15 iterations.

Analysis

The component matrix in table shows that 5 factors can be extracted. Since the scores

were not clear the matrix had to be rotated.

After 15 iterations or number of rotations using Varimax method, the rotated component

matrix in table was obtained.

From the above table 5 factors were identified:

a) Factor 1 – Ability to pile stock, likability for bonus packs, frequency of shopping.

b) Factor 2 – Quality perception when discount allowed, Offer preference for

familiar product.

43

c) Factor 3 - Base value neglect, Computational complexity, Difference in

percentage associated with offers.

d) Factor 4 - Type of customer segment

e) Factor 5 - Frequency of usage, Size of the pack purchased, Frequency of change

among brands.

Interpretation

Five factors for customer`s preference for bonus packs over price discounts are:

1) Consumption behaviour - Ability to pile stock, likability for bonus packs,

frequency of shopping.

2) Product familiarity - Quality perception when discount allowed, Offer preference

for familiar product.

3) Mental complexity - Base value neglect, Computational complexity, Difference in

percentage associated with offers.

4) Customer segment – Type of customer segment

5) Brand loyalty - Frequency of usage, Size of the pack purchased, Frequency of

change among brands.

5.5 MULTIPLE REGRESSION ANALYSIS

Regression is a statistical technique which uncovers the relationship between one

dependent variable and one or more independent variable. The relationships are between

a dependent variable and one or more dependent variable. It also helps us to understand

how the value of dependent variable changes or responds to a change in the independent

variable.

44

So for the analysis, the dependent variable here is preference for bonus packs and price

discounts. After running the regression analysis in SPSS, the output was obtained which

consisted of a model summary as shown below:

Table 5.5.1: Model Summary of Regression Analysis

In the model summary box, R, R2 and standard error of estimate (Sy,x) has been calculated

denoting the square root of the mean squared error or MSE.

Model Summary

Model R R Square Adjusted R Square Std. Error of the

Estimate

1 .648a .582 .579 .594

Source: Primary Data (SPSS output)

Table 5.5.2: Correlation

Preference

for bonus

pack

Value

for

money

obtaine

d

Value

for

money

obtaine

d

Frequ

ency

of

usage

Frequency

of shopping

Base

value

negle

ct

Computati

onal

complexit

y

Offer

preferenc

e for

familiar

products

Customers.offer

preference.for

inexpensive

products

Preference for

bonus pack

Pearso

n

Correla

tion

1.000 .254* .226* .099* .129* .018* -.129* -.061 .154*

Sig. (2-

tailed)

. .000 .000 .040 .021 .031 .021 .169 .007

N 250 250 250 250 250 250 250 250 250

Value for money

obtained

Pearson

Correlati

on

.254* 1.000 .174* -.142* .076 .098 .000 -.026 -.133*

Sig. (2-

tailed)

.000 . .003 .012 .116 .062 .499 .341 .018

N 250 250 250 250 250 250 250 250 250

Ability to pile

stock

Pearso

n

Correla

tion

.226* .174* 1.000 -.066 .230* .223* .335* .074 -.069

Sig. (2-

tailed)

.000 .003 . .149 .000 .000 .000 .122 .139

N 250 250 250 250 250 250 250 250 250

Frequency of

usage

Pearso

n

Correla

tion

.099* -.142* -.066 1.000 .232* -.076 .273* .151* .257*

Sig. (2-

tailed)

.040 .012 .149 . .000 .116 .000 .008 .000

N 250 250 250 250 250 250 250 250 250

Frequency of

shopping

Pearso

n

Correla

tion

.129* .076 .230* .232* 1.000 .533* -.134* .290* -.044

Sig. (2-

tailed)

.021 .116 .000 .000 . .000 .017 .000 .246

N 250 250 250 250 250 250 250 250 250

Base value

neglect

Pearso

n

Correla

tion

.018* .098 .223* -.076 .533* 1.000 -.019 .369* -.086

Sig. (2-

tailed)

.031 .062 .000 .116 .000 . .385 .000 .087

N 250 250 250 250 250 250 250 250 250

Computational

complexity

Pearso

n

Correla

tion

-.129* .000 .335* .273* -.134* -.019 1.000 .025 .262*

Sig. (2-

tailed)

.021 .499 .000 .000 .017 .385 . .346 .000

N 250 250 250 250 250 250 250 250 250

Offer preference

for familiar

products

Pearso

n

Correla

tion

-.061 -.026 .074 .151* .290* .369* .025 1.000 .105*

Sig. (2-

tailed)

.169 .341 .122 .008 .000 .000 .346 . .049

N 250 250 250 250 250 250 250 250 250

Customers

offer

preference

for

inexpensive

products

Pearson

Correlatio

n

Sig. (2

tailed)

N

.154*

.007

250

-.133*

.018

250

-.069

.139

250

.257*

.

000

250

-.044

.246

250

-.086

.087

250

.262*

.000

250

.105*

.049

250

1.000

.

250

xlvi

5.5.1: CORRELATION OF PREFERENCE FOR BONUS PACKS WITH OTHER

FACTORS

Preference for bonus packs in purchase of shampoo is significantly correlated with the

value for money, ability to pile stock, frequency of usage, frequency of shopping, base

value neglect, computational complexity and customer`s offer preference (bonus packs

vs. price discounts) for inexpensive products, as the significance value of these factors is

less than 0.05 indicating a significant relationship between these factors and the

preference of customer. The preference for bonus packs is positively correlated with other

variables except computational complexity and offer preference for familiar products as

computational complexity might not be consistent with every consumer and consumers

tend to be indifferent of promotional offers for familiar products.

5.5.1.1: Correlation of value for money obtained with other factors:

Value for money obtained was found to be significantly correlated with factors like

preference for bonus packs, ability to pile stock, frequency of use, offer preference for

inexpensive products as the significance value for these factors is less than 0.05 indicating

a significant relationship. The value for money was found to be positively correlated with

most of the variable except frequency of use and customer offer preference for

inexpensive products. This may be because usage pattern may hive slight influence on

preference as customers may also look for other options.

5.5.1.2: Correlation of ability to pile stock with other factors

Stock piling ability of customers was found to be significantly correlated with factors like

preference for bonus packs, value for money, frequency of shopping, base value neglect,

computational complexity as the significance value for these are below 0.05 which

represents a significant relationship. The ability to pile stock was found to be positively

correlated with most factors except frequency of use and offer preference for inexpensive

products. This is because frequency of use does not positively determine customer`s stock

piling ability/habit and customers would not want to stock inexpensive products because

of the low cost, low quantity feature.

5.5.1.3: Correlation of frequency of usage with other factors

Frequency of use by customers was found to be significantly correlated with factors like

preference for bonus packs, value for money obtained, frequency of shopping,

xlvii

computational complexity, offer preference for familiar products and offer preference for

inexpensive products as significance value for these were below 0.05 which show

significant relationship between these factors. Frequency of use was found to be

positively correlated with most of the factors except value for money obtained and stock

piling ability. Users may not like to pile stocks due to various factors even when they are

heavy consumers of the product under study.

5.5.1.4: Correlation of frequency of shopping with other factors

Frequency of shopping was found to be significantly correlated with factors like

preference for bonus packs, ability to stock pile, frequency of use, base value neglect,

computational complexity, offer preference for familiar product as significance level for

these factors were below 0.05 which represents a significant relationship. Frequency of

shopping was found to be positively correlated with most factors except offer preference

for inexpensive products and computational complexity. This may be because

computational complexity has little positive influence on customers shopping frequency

and similar is the case with inexpensive products (sachets of shampoo).

5.5.1.5: Correlation of base value neglect with other factors:

Customer`s tendency to neglect base value of the product was found to be significantly

related to factors like preference for bonus packs, ability to pile stock, frequency of

shopping and offer preference for familiar products as significance values for these

factors were below 0.05 which represents significant relationship. Base value neglect was

found to be positively correlated with many factors except frequency of use,

computational complexity and offer preference for inexpensive products. This may be

because frequency of use can have no effect on customer`s tendency to neglect base

values while purchasing and evaluating offers. Computational complexity cannot arise as

customer`s have neglected base values and inexpensive products are so relatively cheap

that base value are not worth considering.

5.5.1.6: Correlation of computational complexity with other factors

Computational complexity arises when consumers are unable to evaluate offers so as to

know where he is profiting. Computational complexity was found to be significantly

xlviii

related to factors like preference for bonus packs, ability to pile stock, frequency of use,

frequency of shopping and offer preference for inexpensive products as significance

values for these factors were below 0.05 which represent significant relationship.

Computational complexity was found to be positively related to most factors except

preference for bonus packs, frequency of shopping and base value neglect. This is

because not all consumers can be weak in mental accounting and frequency of shopping

seems to have no effect on mental processing of the consumers. Base value neglect has

negative correlation because if base values are neglected there is no scope for

computational complexity.

5.5.1.7: Correlation of offer preference for familiar products and other factors

Offer preference for familiar products was found to be significantly related to frequency

of use, frequency of shopping, base value neglect and computational complexity as

significance values for these factors were lower than 0.05 which shows a significant

relationship. Offer preference for familiar products was found to be positively correlated

with most factors except for preference for bonus packs and value for money obtained.

This may be because certain consumers tend to be indifferent about offers available on

familiar products. The prefer price discount for new products as an attempt to reduce loss.

5.5.1.7: Correlation of customer`s offer preference for inexpensive product

(shampoo sachet)

Customers offer preference for inexpensive product was found to be significantly related

to factors like preference for bonus packs, value for money obtained, frequency of use,

computational complexity and offer preference for familiar product as significance level

for these factors were below 0.05 which represents a significant relationship. Offer

preference for inexpensive products was found to be positively correlated with most

factors except value for money obtained ability to pile stock, frequency of use and

frequency of shopping. Customers would not prefer to stock pile of inexpensive products

and so there is a negative correlation, similarly value obtained cannot be easily measured

as shampoo sachets are offered for Rs. 1-2 and value obtained becomes difficult to

measure because of low quantity. Frequency of use and shopping would not necessarily

mean that customers would prefer purchasing bonus packs of shampoo sachets, rather

would prefer bonus packs on bottles.

xlix

6.1 FINDINGS

According to the study conducted, there are 5 factors identified that contributes to

customer`s preference for bonus packs over price discounts.

a) Consumption behaviour - Ability to pile stock, likability for bonus packs,

frequency of shopping.

b) Product familiarity - Quality perception when discount allowed, Offer

preference for familiar product.

c) Mental complexity - Base value neglect, Computational complexity,

Difference in percentage associated with offers.

d) Customer segment – Type of customer segment

e) Brand loyalty - Frequency of usage, Size of the pack purchased, Frequency of

change among brands.

It has also been observed that consumers have a tendency to ignore base value of

the product when evaluating bonus packs and price discounts. Since bonus packs

are higher than price discount in terms of percentage associated with them

consumers tend to incline towards preference for bonus packs. This has been

presented by a hypothesis testing backed by a bar graph depicting that consumers

tend to ignore or neglect base values of the product and hence prefer bonus packs.

A hypothesis testing also confirmed that higher the frequency of use of shampoo

higher will be the preference for bonus packs. Since consumers are using the

product on a regular basis, a bonus packs would be preferred to a price discount as

the product will last longer. The bar graph shows that 52.8% of the respondents

agreed to use shampoo every alternate day and hence prefer bonus packs due to

heavy consumption.

The third hypothesis depicts that consumers tend to prefer bonus packs against

price discounts in the same product category when difference in percentages are

associated with bonus packs and price discounts are high. The percentage

associated with bonus packs are usually higher than price discounts, but if the

l

difference in the two percentages is also high, consumers tend to prefer bonus

packs as they conclude that the bonus pack offer makes more sense as it has

higher value than price discount. Thus if a customer is faced with two offer for ex.

A 50% more free against a price discount of 10%, consumers will prefer bonus

pack because it give relatively more profitable to the customer. The hypothesis is

backed by a bar graph which shows that 60.8% of the respondents preferred 50%

more for free offer.

It was also found that consumers make computation errors when evaluating offers

which are presented in percentage. When aced by offers like 20% more free plus

additional 25% more against straight 40% off, consumers tend to prefer bonus

pack that gives them additional quantity. However, both the offers are

economically equivalent i.e. monetary value of 20%+25% = 40%, but still

consumer make errors in the accounting which leads to the preference for bonus

packs. The bar graph shows that 44.8% of the respondents agreed and 27.2% of

the respondents strongly agreed to prefer bonus packs because they evaluated that

20+25=45 which is obviously more than 40.

The study also helped in finding that customers who believe in piling stock for

future use prefer bonus packs to price discounts as bonus packs provide them with

offers like buy one get one free. The hypothesis is supported by a bar graph which

depicts that 48% of the respondents agreed to and 28.4% of the respondents

strongly agreed to preference for bonus packs for the purpose of piling stocks for

future use.

Around 34% of the respondents fall in the age group of 21-25 years, 28.80% fall

in 26-30 years, 10.80% are above 41 years of age and 8.40% of the respondents

were below 20 years of age. The idea was to survey a mixed group of respondents

who fall in different age groups so that preference could be generalized and not

limited to a particular age group since the product under consideration is a

shampoo which is used by all age groups.

It was also found that many consumer goods firm present both premium and

discounted products together. They also try to market both established and new

products to their existing customer base. The result suggested that consumer

li

goods manufacturing firm can use separate promotional tools for separate product,

like a price discount for new product category.

6.2 CONCLUSION

Percentage information is found everywhere in the communication of market information,

which ranges from price, quantity and quality metrics to a customer’s financial status.

Most of the sales promotion activates are base on percentages, especially promotional

tools like bonus packs and price discounts. Whether to choose a bonus pack or price

discount is not just a matter of deciding between the two. There is a lot that goes into the

mind of the customer when he is exposed to marketing tactics like sales promotion which

involves percentages. The major difficulty that customers face in choosing an offer is

evaluation the percentages associated with them in an attempt to purchase the one that

brings most profit.

Customers tend to analyze a lot of information before selecting a particular offer. There

are various factors, some on the part of the customers and others come along with the

promotional tools, which motivates the customers to prefer a certain promotional offer.

Various factors like neglecting base value, computational complexity, promotional

strategy, customer`s buying behavior, consumption behavior and many more are some

reasons which helps or influences a customer to incline towards either a bonus pack or

price discount. It is the job of the marketers to present their offers in such a way that it

has certain credibility among the customer and is also believable. On the customer`s side,

the customers must carefully evaluate competing offer, both in terms of value addition

and price to come up with the most profitable selection. However, in cases where

competing offers in same product category are economically equivalent, customers must

depend or rely on their consumption behavior and asses their needs before the purchase.

In the current marketing world, where products are beautifully packed in promotional

offers but have no credibility, it is the job of the customers to understand what the

marketers are actually offering, and carefully evaluate the offers.

Whether a consumer chooses a bonus pack or price discount depends on various factors.

Knowing the reasons behind selection of a particular promotional offer can help a

marketer in a devising promotional strategy to capture the market.

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6.3 SUGGESTIONS

a) Understanding the role of base value neglect can help marketers present a

more meaningful and attractive communication message to customers. Since

consumers tend to ignore base value of the product while evaluating

percentages and hence prefer bonus packs, marketers can mould their

communication message for promotional offers to highlight the bonus element

which customers generally seek. For ex. Instead of persuading a customer by

saying that a particular car has 33% decreased fuel consumption, the marketer

can effectively persuade the customer by saying that the car has 50% increased

mileage.

b) Marketers should understand the importance of computational errors that

customers tend to make while evaluating offers which are associated with

percentages. Providing a customer with a additional bonus packs for same

price is better than providing an equivalent price discount. It is the nature of

consumers to seek more value at no additional cost.

c) Studying consumer`s consumption pattern can also help the marketers in

devising new promotional offers. To target customers who are heavy users of

a particular product category, bonus packs can be introduced to boost sales of

the product. The product can also be bundled with other complementary

product to boost sales in other product categories. Consumers who tend to

purchase in advance to keep stocks for future use can be targeted through

bonus packs to motivate them to buy more of the product and at the same time

clear company’s inventories.

d) Studying the shopping behavior of the customers can also help the marketers

in attracting infrequent shoppers. Infrequent shoppers are one`s who shop at

long intervals. Thus introducing bonus packs for such customers, in specific

product categories that they would regularly want can help the firm increase

its sales revenue. Also, this would motivate the customer towards repurchase

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of the same product as it suits his needs and time constraints involved with

shopping.

e) Stating promotional offers is a crucial task for the marketers. Marketers must

present offers such as bonus packs or price discounts and associate

percentages with them which are believable by the customers. Too much price

discount can lead to lower perceived quality, and too high bonus pack offer

can lead to skepticism by the consumers.

f) Marketers must cater to separate segments of customers with different

marketing tactics and promotional offers. A certain type of customers

segments believe in shopping for products (FMCG, apparels etc) only during

offer seasons. In order to attract such customers both price discounts and

bonus packs can be used. However, using a bonus packs could be more

profitable as bundled products attract customers more effectively than

discounted products. Moreover, the firm can sell bundled products with slight

increase in price and still make profits and clear inventories at the same time.

g) Marketers must also use different promotional strategies for existing products

and introduction products. A introduction products will be new for customers

and hence trust on the product need to be build. Offering the product at

discounts will motivate customers to try the product. Similarly, for existing

products the idea should be to increase consumption and sale of units through

bonus packs.

h) Demographic study can also help marketers in planning promotional offers. In

case of shampoo, where it is assumed that female section of the society require

more of the product as their consumption is high, offering a bonus packs or

bundling the product with another related product can help boost sales.

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i) Studying past trends, in terms of consumer`s preference for bonus packs or

price discounts for a particular product category, both of the company and the

competitors can give marketers an idea about the what type of offers

customers prefer in a particular product category. Applying the customer

centered promotional tool to a particular product category that customers are

interested in can help marketers attract large customer segments and boost

sales.

6.4 SUGGESTIONS FOR FURTHER RESEARCH

A limitation of the study is that the research is concentrated on a particular product

category i.e. shampoo in the hair care segment. Further studies might be conducted by

other researchers on preference for bonus packs over price discounts in other product

categories. I understand that bonus pack might not always be preferred for shampoo: this

would purely depend on the percentage carried by the bonus pack offered.. Bonus packs

with higher percentages to offer at free might raise questions about stocking, which

includes only a small section of the society. It might be worthwhile to study bonus packs

and price discounts on different percentage level which would provide a deeper insight

into the topic. Moreover, further studies can be conducted in areas where preference for

bonus packs and price discounts can be related to customer`s personality and attitude or

surrounding environment of the store, customer`s purchase intentions.

Data & Analytics

Dissertation - A study on consumer preference for bonus packs over price discounts in purchase of hair care products