mr final over

8/8/2019 MR Final Over

1/46

Consumer preferences and perception towards

Mobile marketing

TABLE OF CONTENTS

Declaration.... 2

Acknowledgement ....... 3

Abstract 5

Chapter 1

Introduction....... 6

Objective 13

Chapter 2

Research methodology... 14

Chapter 3

Analysis............................. 15

Discriminant analysis. 53

Chapter 4

Conclusion . 58

Chapter 5

Recommendations .. 59

Chapter 6

References .. 60

Chapter 7

Annexure 61


2/46

CHAPTER 1: INTRODUCTION

Mobile marketing

Although there are various definitions for the concept of mobile marketing, no

commonly accepted definition exists. Mobile marketing is broadly defined as the use

of the mobile medium as a means of marketing communication or distribution of

any kind of promotional or advertising messages to customer through wireless

networks. More specific definition is the following: using interactive wireless

media to provide customers with time and location sensitive, personalized information

that promotes goods, services and ideas, thereby generating value for all

stakeholders".

In November 2009, the Mobile marketing association updated its definition of Mobile

Marketing:

Mobile Marketing is a set of practices that enables organizations to communicate

and engage with their audience in an interactive and relevant manner through any

mobile device or network.

Mobile marketing is commonly known as wireless marketing. However wireless is

not necessarily mobile. For instance, a consumers communications with a Web site

from a desktop computer at home, with signals carried over a wireless local area

network (WLAN) or over a satellite network, would qualify as wireless but not

mobile communications.

Mobile marketing via SMS

Marketing on a mobile phone has become increasingly popular ever since the rise of

SMS (Short Message Service) in the early 2000s in Europe and some parts of Asiawhen businesses started to collect mobile phone numbers and send off wanted (or

2


3/46

unwanted) content.

Over the past few years SMS has become a legitimate advertising channel in some

parts of the world. This is because unlike email over the public Internet, the carriers

who police their own networks have set guidelines and best practices for the mobile

media industry (including mobile advertising). The IAB (Interactive Advertising

Bureau) and the Mobile marketing association as well have established guidelines and

are evangelizing the use of the mobile channel for marketers. While this has been

fruitful in developed regions such as North America, Western Europe and some other

countries, mobile SPAM messages (SMS sent to mobile subscribers without a

legitimate and explicit opt-in by the subscriber) remain an issue in many other parts or

the world, partly due to the carriers selling their member databases to third parties.

Mobile marketing via SMS has expanded rapidly in Europe and Asia as a new

channel to reach the consumer. SMS initially received negative media coverage in

many parts of Europe for being a new form of spam as some advertisers purchased

lists and sent unsolicited content to consumer's phones; however, as guidelines are put

in place by the mobile operators, SMS has become the most popular branch of the

Mobile Marketing industry with several 100 million advertising SMS sent out every

month in Europe alone.

In North America the first cross-carrier SMS, Labatt Brewing Company ran short

code campaign in 2002. Over the past few years mobile short codes have been

increasingly popular as a new channel to communicate to the mobile consumer.

Brands have begun to treat the mobile short code as a mobile domain name allowing

the consumer to text message the brand at an event, in store and off any traditional

media.

SMS services typically run off a short code, but sending text messages to an email

address is another methodology. Short codes are 5 or 6 digit numbers that have been

assigned by all the mobile operators in a given country for the use of brand campaign

and other consumer services. Due to the high price of short codes of $500-$1000 a

month, many small businesses opt to share a short code in order to reduce monthly

costs. The mobile operators vet every short code application before provisioning and

monitor the service to make sure it does not diverge from its original service

description. Another alternative to sending messages by short code or email is to doso through one's own dedicated phone number. Besides short codes, inbound SMS is

3


4/46

very often based on long numbers (international number format, e.g. +44 7624

805000), which can be used in place of short codes or premium-rated short messages

for SMS reception in several applications, such as product promotions and campaigns.

Long numbers are internationally available, as well as enabling businesses to have

their own number, rather than short codes which are usually shared across a number

of brands. Additionally, long numbers are non-premium inbound numbers.

One key criterion for provisioning is that the consumer opts in to the service. The

mobile operators demand a double opt in from the consumer and the ability for the

consumer to opt out of the service at any time by sending the word STOP via SMS.

These guidelines are established in the MMA Consumer Best Practices Guidelines,

which are followed by all mobile marketers in the United States.

Mobile

Marketing via MMS

MMS mobile marketing can contain a timed slideshow of images, text, audio and

video. This mobile content is delivered via MMS (Multimedia Message Service).

Nearly all-new phones produced with a color screen are capable of sending and

receiving standard MMS message. Brands are able to both send (mobile terminated)

and receive (mobile originated) rich content through MMS A2P (application-to-

person) mobile networks to mobile subscribers. In some networks, brands are also

able to sponsor messages that are sent P2P (person-to-person).

A good example of MMS mobile originated Motorolas

http://en.wikipedia.org/wiki/Motorola ongoing campaigns at House of blues venues

where the brand allows the consumer to send their mobile photos to the LED board in

real-time as well as blog their images online.

In-game mobile marketing

There are essentially four major trends in mobile gaming right now: interactive real-

time 3D games, massive multi-player games and social networking games. This

means a trend towards more complex and more sophisticated, richer game play. On

the other side, there are the so-called casual games, i.e. games that are very simpleand very easy to play. Most mobile games today are such casual games and this will

4
http://en.wikipedia.org/wiki/Motorolahttp://en.wikipedia.org/wiki/Motorola


5/46

probably stay so for quite a while to come.

Brands are now delivering promotional messages within mobile games or sponsoring

entire games to drive consumer engagement. This is known as mobile advergaming or

Ad-funded mobile game.

Mobile web marketing

\Google and Yahoo! as displayed on mobile phones

Advertising on web pages specifically meant for access by mobile devices is also an

option. The Mobile marketing association provides a set of guidelines and standards

that give the recommended format of ads, presentation, and metrics used in reporting.

Google, Yahoo, and other major mobile content providers have been selling

advertising placement on their properties for years already as of the time of this

writing. Advertising networks focused on mobile properties and advertisers are also

available.

Mobile marketing via Bluetooth

The rise of Bluetooth started around 2003 and a few companies in Europe have

started establishing successful businesses. Most of these businesses offer Hotspot"

systems, which consist of some kind of content-management system with a Bluetooth

distribution function. This technology has the advantages that it is permission-based,

has higher transfer speeds and is also a radio-based technology and can therefore not

be billed (i.e. is free of charge). The likely earliest device built for mobile marketing

via Bluetooth was the context tag of the Ambie Sense project (2001-2004). More

recently Tata Motors conducted one of the biggest Bluetooth marketing campaigns in

India for its brand the Sumo Grande and more of such activities have happened for

brands like Walt Disney promoting their movie 'High School Musical'

Mobile marketing via Infrared

Infrared is the oldest and most limited form of mobile Marketing. Some European

companies have experimented with "shopping window marketing" via free Infrared

waves in the late 90s. However, Infrared has a very limited range (~ approx. 10 cm -1meter) and could never really establish itself as a leading Mobile Marketing

5


6/46

technology.

Location-based services

Location-based services (LBS) are offered by some cell phone networks as a way to

send custom advertising and other information to cell-phone subscribers based on

their current location. The cell-phone service provider gets the location from a GPS

chip built into the phone, or using radiolocation and trilateration based on the signal-

strength of the closest cell-phone towers (for phones without GPS features). In the

UK, networks do not use trilateration; LBS services use a single base station, with a

'radius' of inaccuracy, to determine a phone's location.

Meantime, LBS can be enabled without GPS tracking technique. Mobile WiMAX

technology is utilized to give a new dimension to mobile marketing. The new type of

mobile marketing is envisioned between a BS (Base Station) and a multitude of CPE

(Consumer Premise Equipment) mounted on vehicle dashtops. Whenever vehicles

come within the effective range of the BS, the dashtop CPE with LCD touchscreen

loads up a set of icons or banners of individually different shapes that can only be

activated by finger touches or voice tags. On the screen, a user has a frame of 5 to 7

icons or banners to choose from, and the frame rotates one after another. This mobile

WiMAX-compliant LBS is privacy-friendly and user-centric, when compared with

GPS-enabled LBS.

In July 2003 the first location-based services to go Live with all UK mobile network

operators were launched.

User-controlled media

Mobile marketing differs from most other forms of marketing communication in that

it is often user (consumer) initiated (mobile originated, or MO) message, and requires

the express consent of the consumer to receive future communications. A call

delivered from a server (business) to a user (consumer) is called a mobile terminated

(MT) message. This infrastructure points to a trend set by mobile marketing of

consumer controlled marketing communications. Due to the demands for more user

controlled media, mobile messaging infrastructure providers have responded by

developing architectures that offer applications to operators with more freedom for the users, as opposed to the network-controlled media. Along with these advances to

6


7/46

user-controlled Mobile Messaging 2.0, blog events throughout the world have been

implemented in order to launch popularity in the latest advances in mobile

technology. In June 2007, Airwide solution became the official sponsor for the

Mobile Messaging 2.0 blog that provides the opinions of many through the discussion

of mobility with freedom.

Privacy concerns in mobile marketing

Mobile advertising has become more and more popular. However, some mobile

advertising is sent without a required permission from the consumer causing privacy

violations. It should be understood that irrespective of how well advertising messages

are designed and how many additional possibilities they provide, if consumers do not

have confidence that their privacy will be protected, this will hinder their widespread

deployment.

The privacy issue became even more salient as it was before with the arrival of

mobile data networks. A number of important new concerns emerged mainly

stemming from the fact that mobile devices are intimately personal and are always

with the user, and four major concerns can be identified: mobile spam, personal

identification, location information and wireless security.

Proposed changes to the existing legislation

Because the current telecom regulations are outdated in the EU and in the United

States particularly concerning unsolicited commercial communications and the spam

issue new legislation should be imposed. New laws should be more clear (simple),

flexible and comprehensive but still address only those issues, which are strictly

necessary. This is important because laws should promote competition, encourage

investment, cut unnecessary costs, and remove obstacles to doing business. They

should be drafted in a technologically neutral way to avoid the need to adapt the legal

framework constantly to new developments and independent from the parties

involved. Consumers privacy must be protected and marketers have to be able easily

to understand and comply with the rules. Kaspersen Henrik W.K. has proposed that

directives with regard to unsolicited commercial communications should regulate not

only electronic communications but also paper distribution. Moreover legislator

should cooperate with technological and business experts to create a reasonable legalframework

7


8/46

Application of these rules must be done in a sensible manner thus courts should avoid

applying new rules with too much severity because there is a risk of retarding or

limiting the development of a very promising industry. But with too loose

interpretation of the rules, consumers may not feel protected which may also limit

the development. In other words if consumers concerns about privacy are not

addressed, the growth of mobile advertising may be endangered by the same lack of

consumer trust that has discouraged the growth of email marketing. The protection of

privacy shall be achieved in combination with a number of efforts including

legislation, social norms, business practices and technical means.

Most of our respondents say that they need confirmation before receiving calls are

messages for marketing live salesperson preferred over computerized. The messages

they receive are of different category depending on the income occupation more than

humbleness and of salesperson They want his support in his knowledge content in all

their preferences for mobile marketing differ with respects to factor we have studied

and tried to cover in our study. So marketers should take into account the various

different preferences of various classes to make a proper effective mobile marketing

strategy

8


9/46

OBJECTIVE

1) To study the consumer preference/Perceptions towards mobile marketing

2) To recommend various ways and methods through which Mobile Marketing

can be made more effective.

3) To know the extent of willingness of the consumers for Mobile Marketing.

9


10/46

CHAPTER 2: RESEARCH METHODOLOGY

Research Design:

Type Of Research: Descriptive Research because here we are trying to describe the

consumer preference and perception towards mobile marketing.

Sampling Design

Sampling Technique: Convenience Sampling because we filled in questionnaires on

basis of our convenience

Sampling Area: Ghaziabad and Noida were mainly considered for our research

study.

Sampling Unit: Mobile users were our sample units

Sampling Size: Our sample size was 100.

CHAPTER 3: ANALYSIS

Descriptive

10


11/46

Gender:

Frequency PercentValid

Male

Female

Total

64 64.036 36.0

100 100.0

Age:

Frequency Percent20-30 56 56.030-40 32 32.040-50 4 4.0

50 above 8 8.0Total 100 100.0

Occupation:

Frequency PercentStuden

t

40 40.0

Private

Employee

20 20.0

Busine

ss

22 22.0

Home

maker

4 4.0

Govt

emplo

yee

14 14.0

Total 100 100.0

Cross Tabulation

1. Among Occupation and Frequency

Out of 40 students, 14 get messages only once in a day.

Out of 20 private employee, 8 get message only once in a day.

Out of 22 business people, 14 get message only once in a day.

11


12/46

Out of 14 self-employed people, 10 get message on the regular basis.

frequencyTotalOnce in a day Once in a week Once in a month Regularly Never

occupation Student 14 2 0 20 4 40

Private employee 8 4 0 3 5 20

Business 14 0 8 0 0 22

Home maker 0 0 2 2 0 4

Self employed 4 0 0 10 0 14

Total 40 6 10 35 9 100

2. Among occupation and language of message

Out of 40 students, 28 get message in English.

Out of 20 private employee, 13 get message in English.

Out of 22 business people, 14 get message in Hindi.

Out of 4 home maker, 2 get message in Hindi.

Out of 14 self employed, 10 get message in English.

language

TotalHindi English Regional

occupation Student4 28 8 40

Private employee 2 13 5 20

Business 14 8 0 22

Home maker 2 1 1 4Self employed 2 10 2 14

Total24 60 16 100

12


13/46

3. Among gender and receive messages

Out of 64 male respondents, 36 prefer to receive messages through

message and 28 via call.

Out of 36 female respondents, 10 prefer to receive messages throughmessage and 26 via call.

Rcv msgs

Totalmsg call

gender Male36 28 64

Female 10 26 36

Total 46 54 100

4. Among gender and type of salespeople

Out of 64 male, 36 respondents prefer computerized salespeople over live

salespeople.

Out of 36 female, 19 respondents prefer live salespeople over

computerized salespeople.

salespeople

TotalComputerised Live

gender Male 36 28 64

Female 17 19 36

Total 53 47 100

5. Among Gender and confirmation

Out of 64 male, 42 respondents prefer confirmation message. Out of 36 female, 17 respondents prefer confirmation message.

confirmation Total

gender Male42 22 64

Female 17 19 36

13


14/46

confirmation Total

Total59 41 100

6. Among Occupation and confirmation message

Out of 40 students, 32 want to get confirmation message.

Out of 20 private employees, 13 want to get confirmation message.

Out of 22 business people, 20 want to get no confirmation message.

Out of 4 homemaker, 2 want to get confirmation message.

Out of 14 self employed, 10 wants to get confirmation message.

Confirmation

TotalYes No

Occupation Student32 8 40

Private Employee 13 7 20Business 2 20 22

14


15/46

Confirmation Total

Home maker 2 2 4

Self-employed 10 4 14

Total69 31 100

7. Among Occupation and timing of message

Out of 40 students, 20 prefer to get messages in the afternoon.

Out of 20 private employees, 13 prefer to get messages in the afternoon.

Out of 22 business people, 12 prefer to get messages in the evening.

Every homemaker prefers to get messages in the evening.

Out of 14 self employed, 12 prefer to get messages in the evening.

Timing

TotalMorning Afternoon Evening Late evening

Occupation Student 14 20 4 2 40

Private employee 0 13 7 0 20

Business 0 10 12 0 22

Home maker 0 0 4 0 4

Self employed 0 0 12 2 14

Total 14 43 39 4 100

8. Among Occupation and type of salespeople

Out of 40 students, 26 prefer computerized salespeople to live salespeople.

15


16/46

Out of 20 private employees, 13 prefer computerized salespeople to live

salespeople.

Out of 22 business people, 18 prefer live salespeople to computerized

salespeople. All the 4 homemaker prefer live salespeople.

Out of 14 self employed, 10 prefer computerized salespeople to live

salespeople.

Salespeople

TotalComputerized LiveOccupation Student

26 14 40

Private employee 13 7 20

Business 4 18 22

Home maker 0 4 4

Self employed 10 4 14

Total53 47 100

Chi Square

1. Among Gender and receive messages

Ho: There is no association among gender and message receiving i.e.

through message or call.

H1: There is association among gender and message receiving i.e.

through message or call.

As, 0.006< 0.05

Rejecting Null Hypothesis

Thus, there is association among gender and message receiving.

16


17/46

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)

Exact Sig. (2-

sided)

Exact Sig. (1-

sided)

Pearson Chi-Square 7.519 a 1 .006

Continuity Correction6.417 1 .011

Likelihood Ratio 7.728 1 .005

Fisher's Exact Test .007 .005

Linear-by-Linear Association 7.444 1 .006

N of Valid Cases 100

2. Among Gender and Confirmation of Message

Ho: There is no association among gender and message receiving i.e. through

message or call.

H1: There is association among gender and message receiving i.e. through

message or call.

As, 0.022< 0.05

Rejecting Null Hypothesis

Thus, there is association among gender and confirmation of message.

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)

Exact Sig. (2-

sided)

Exact Sig. (1-

sided)


Continuity Correction b 2.510 1 .113




N of Valid Cases b 100

Rcv msgs

TotalMsg Call

Gender Male36 28 64

Female 10 26 36

Total46 54 100

17


18/46

Cross tabulations

Gender * type_msg1 Cross tabulationCount

Type_msg1

Total1 2 3 4 5 6 7 8

Gender Male 4 18 10 8 4 10 6 4 64

Female 6 7 4 2 6 4 2 5 36

Total 10 25 14 10 10 14 8 9 100

Age * type_msg1 Cross tabulationCount

Type_msg1

Total1 2 3 4 5 6 7 8

Age 20-30 6 17 8 6 6 6 2 5 56

30-40 2 8 4 2 2 4 6 4 32

40-50 2 0 0 0 0 2 0 0 4

Above

500 0 2 2 2 2 0 0 8

Total 10 25 14 10 10 14 8 9 100

Occupation * type_msg1 Cross tabulationCount

Type_msg1

Total1 2 3 4 5 6 7 8

Occupation Student 2 14 10 4 4 4 2 0 40

Private

employee0 5 2 2 2 2 2 5 20

Business 2 4 2 0 4 4 4 2 22

Home

maker 0 2 0 2 0 0 0 0 4

Self

employed6 0 0 2 0 4 0 2 14

Total 10 25 14 10 10 14 8 9 100

18


19/46

frequency * type_msg1 Cross tabulationCount

Type_msg1

Total1 2 3 4 5 6 7 8

Frequency Once in a

day6 10 8 4 2 2 4 4 40

Once in a

week0 2 0 0 2 2 0 0 6

Once in a

month0 0 0 2 4 2 2 0 10

Regularly 4 8 4 2 2 8 2 5 35

Never 0 5 2 2 0 0 0 0 9Total 10 25 14 10 10 14 8 9 100

gender * type_msg2 Crosstabulation

Count

type_msg2

Total1 2 3 4 5 6 7 8gender Male 6 10 12 16 4 6 10 0 64

Female 2 2 4 9 8 0 7 4 36

Total 8 12 16 25 12 6 17 4 100

19


20/46

frequency * type_msg2 CrosstabulationCount

type_msg2

Total1 2 3 4 5 6 7 8

frequency Once in a day 4 6 2 10 4 6 6 2 40

Once in a week 0 4 2 0 0 0 0 0 6

Once in a

month2 2 0 2 4 0 0 0 10

Regularly 2 0 8 13 2 0 8 2 35

Never 0 0 4 0 2 0 3 0 9

Total 8 12 16 25 12 6 17 4 100

age * type_msg2 CrosstabulationCount

type_msg2

Total1 2 3 4 5 6 7 8

age 20-30 4 2 8 15 10 2 13 2 56

30-40 2 6 8 6 2 4 2 2 32

40-50 0 0 0 2 0 0 2 0 4

Above 50 2 4 0 2 0 0 0 0 8Total 8 12 16 25 12 6 17 4 100

occupation * type_msg2 CrosstabulationCount

type_msg2

Total1 2 3 4 5 6 7 8

occupation Student 4 2 8 10 6 2 6 2 40

Private

employee0 8 2 7 0 0 3 0 20

Business 4 2 2 0 6 4 2 2 22

Home maker 0 0 0 2 0 0 2 0 4

Self employed 0 0 4 6 0 0 4 0 14Total 8 12 16 25 12 6 17 4 100

20


21/46

gender * type_msg3 CrosstabulationCount

type_msg3

Total1 2 3 4 5 6 8

gender Male 6 8 10 16 12 10 2 64

Female 2 3 6 14 2 9 0 36Total 8 11 16 30 14 19 2 100

occupation * type_msg3 Crosstabulationount

t3

Total1 2 3 4 5 6 8

ccupation Student 6 2 12 6 8 4 2 40

Private employee 0 5 2 6 0 7 0 20

Business 2 2 0 10 2 6 0 22

Home maker 0 0 0 4 0 0 0 4

Self employed 0 2 2 4 4 2 0 14otal 8 11 16 30 14 19 2 100


t3

Total1 2 3 4 5 6 8

frequency Once in a day 0 4 8 10 8 10 0 40

Once in a week 2 0 0 4 0 0 0 6

Once in a month 2 2 0 6 0 0 0 10

Regularly 2 2 6 8 6 9 2 35

Never 2 3 2 2 0 0 0 9

Total 8 11 16 30 14 19 2 100

21


22/46

gender * type_msg4 CrosstabulationCount

type_msg4

Total1 2 3 4 5 6

gender Male 24 8 8 6 16 2 64

Female 0 8 4 9 6 9 36Total 24 16 12 15 22 11 100

age * type_msg4 CrosstabulationCount

type_msg4

Total1 2 3 4 5 6

age 20-30 16 4 8 11 8 9 56

30-40 2 12 4 4 10 0 3240-50 0 0 0 0 4 0 4

Above 50 6 0 0 0 0 2 8Total 24 16 12 15 22 11 100


type_msg4

Total1 2 3 4 5 6occupation Student 18 2 0 2 10 8


Business 2 4 12 4 0 0


Self employed 0 0 0 4 10 0Total 24 16 12 15 22 11

22


23/46


type_msg4

Total1 2 3 4 5 6

occupation Student 18 2 0 2 10 8


Business 2 4 12 4 0 0


Self employed 0 0 0 4 10 0Total 24 16 12 15 22 11


type_msg4

Total1 2 3 4 5 6

frequency Once in a day 20 8 8 2 2 0 40

Once in a week 2 4 0 0 0 0 6

Once in a month 0 0 4 6 0 0 10

Regularly 2 0 0 2 20 11 35

Never 0 4 0 5 0 0 9Total 24 16 12 15 22 11 100

23


24/46

occupation * frequency Crosstabulation

frequency

Total

Once in a

day

Once in a

week

Once in a

month Regularly Never

occupatio

n

Student Count 14 2 0 20 4 40

Expected

Count16.0 2.4 4.0 14.0 3.6 40.0

Private

employee

Count 8 4 0 3 5 20

Expected

Count8.0 1.2 2.0 7.0 1.8 20.0

Business Count 14 0 8 0 0 22

Expected

Count8.8 1.3 2.2 7.7 2.0 22.0

Home maker Count 0 0 2 2 0 4

Expected

Count 1.6 .2 .4 1.4 .4 4.0

Self employed Count 4 0 0 10 0 14

Expected

Count5.6 .8 1.4 4.9 1.3 14.0

Total Count 40 6 10 35 9 100

Expected

Count40.0 6.0 10.0 35.0 9.0 100.0

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)

Pearson Chi-Square 70.927 a 16 .000Likelihood Ratio 78.502 16 .000Linear-by-Linear Association .067 1 .796N of Valid Cases 100

24


25/46

occupation * language Crosstabulation

language


occupation Student Count 14 8 18 40

Expected Count 9.6 20.4 10.0 40.0

Private employee Count 2 13 5 20

Expected Count 4.8 10.2 5.0 20.0

Business Count 8 14 0 22


Home maker Count 0 4 0 4


Self employed Count 0 12 2 14


Total Count 24 51 25 100

Expected Count 24.0 51.0 25.0 100.0

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)


Crosstabs

age * language Crosstabulation

language


age 20-30 Count 18 27 11 56

Expected Count 13.4 28.6 14.0 56.0

30-40 Count 4 20 8 32


40-50 Count 0 4 0 4


Above 50 Count 2 0 6 8


Total Count 24 51 25 100

Expected Count 24.0 51.0 25.0 100.0

25


26/46

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)

Pearson Chi-Square 20.795 a 6 .002Likelihood Ratio 23.827 6 .001

Linear-by-Linear Association 6.032 1 .014N of Valid Cases 100

a. 6 cells (50.0%) have expected count less than 5. The minimum

expected

b. count is .96.

gender * rcv_msgs

Crosstab

rcv_msgs

Totalmsg call

gender Male Count 36 28 64

Expected Count 29.4 34.6 64.0

Female Count 10 26 36


Total Count 46 54 100


Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)

Exact Sig. (2-

sided)

Exact Sig. (1-

sided)







a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 16.56.b. Computed only for a 2x2 table

age * rcv_msgs

26


27/46

Crosstab

rcv_msgs

Totalmsg call

age 20-30 Count 26 30 56


30-40 Count 18 14 32


40-50 Count 0 4 4


Above 50 Count 2 6 8




Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)

Pearson Chi-Square 6.185 a 3 .103Likelihood Ratio 7.785 3 .051Linear-by-Linear Association 1.492 1 .222N of Valid Cases 100

a. 4 cells (50.0%) have expected count less than 5. The minimum expected

count is 1.84.

occupation * rcv_msgs

Crosstab

rcv_msgs

Totalmsg call

occupation Student Count 18 22 40

Expected Count 18.4 21.6 40.0Private employee Count 10 10 20


Business Count 18 4 22


Home maker Count 0 4 4


Self employed Count 0 14 14




27


28/46

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)

Pearson Chi-Square 26.841a

4 .000Likelihood Ratio 34.350 4 .000Linear-by-Linear Association 4.457 1 .035N of Valid Cases 100

a. 2 cells (20.0%) have expected count less than 5. The minimum expected

count is 1.84.

Crosstabsgender * salespeople

Crosstab

salespeople


gender Male Count 36 28 64


Female Count 17 19 36




Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)

Exact Sig. (2-

sided)

Exact Sig. (1-

sided)

Pearson Chi-Square .754 a 1 .385

Continuity Correction b .435 1 .510

Likelihood Ratio .754 1 .385


Linear-by-Linear Association .746 1 .388


a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 16.92.

age * salespeople

28


29/46

Crosstab

salespeople


age 20-30 Count 27 29 56Expected Count 29.7 26.3 56.0

30-40 Count 22 10 32


40-50 Count 2 2 4


Above 50 Count 2 6 8




Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)


occupation * salespeople

Crosstab

salespeople

TotalComputerised LIve

occupation Student Count 26 14 40


Private employee Count 13 7 20


Business Count 4 18 22


Home maker Count 0 4 4


Self employed Count 10 4 14




29


30/46

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)

Pearson Chi-Square 20.595a

4 .000Likelihood Ratio 22.962 4 .000Linear-by-Linear Association 1.647 1 .199N of Valid Cases 100

Crosstabs

occupation * timing Crosstabulation

timing

TotalMorning Afternoon Evening Late evening

occupation Student Count 14 20 4 2 40

Expected Count 5.6 17.2 15.6 1.6 40.0

Private employee Count 0 13 7 0 20

Expected Count 2.8 8.6 7.8 .8 20.0

Business Count 0 10 12 0 22

Expected Count 3.1 9.5 8.6 .9 22.0

Home maker Count 0 0 4 0 4

Expected Count .6 1.7 1.6 .2 4.0

Self employed Count 0 0 12 2 14

Expected Count 2.0 6.0 5.5 .6 14.0

Total Count 14 43 39 4 100

Expected Count 14.0 43.0 39.0 4.0 100.0

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)Pearson Chi-Square 58.842 a 12 .000Likelihood Ratio 71.607 12 .000Linear-by-Linear Association 35.387 1 .000N of Valid Cases 100

30


31/46

Case Processing Summary

CasesValid Missing Total

N Percent N Percent N Percent

gender * confirmation 100 100.0% 0 .0% 100 100.0%

occupation * confirmation 100 100.0% 0 .0% 100 100.0%

gender * confirmation

Crosstab

confirmation

Total1 2

gender 1 Count 42 22 64Expected Count 37.8 26.2 64.0

2 Count 17 19 36




Chi-Square Tests

Value df Asymp. Sig. (2-

sided)Exact Sig. (2-

sided)Exact Sig. (1-

sided)







a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 14.76.

occupation * confirmation

31


32/46

Crosstab

confirmation

Total1 2

occupation 1 Count 32 8 40


2 Count 13 7 20


3 Count 2 20 22


4 Count 2 2 4


5 Count 10 4 14




Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)

Pearson Chi-Square 31.272 a 4 .000Likelihood Ratio 33.741 4 .000Linear-by-Linear Association 4.677 1 .031N of Valid Cases 100

a. 2 cells (20.0%) have expected count less than 5. The minimum expectedcount is 1.64.

1.Factor Analysis

KMO and Bartlett's TestKaiser-Meyer-Olkin Measure of Sampling

Adequacy..578

Bartlett's Test of

Sphericity

Approx. Chi-Square 479.945df 55Sig. .000

Interpretation : - From the above KMO and Bartlett's Test table we can see the value

of Kaiser-Meyer-Olkin Measure of Sampling Adequacy is 0.578. That means data is

adequate and we can use it for further analysis.

32


33/46

Total Variance Explained

Com

pone

nt

Initial Eigenvalues

Extraction Sums of Squared

Loadings

Rotation Sums of Squared

Loadings

Total

% of

Variance

Cumulati

ve % Total

% of

Variance

Cumulati

ve % Total

% of

Variance

Cumulativ

e %1 3.264 29.670 29.670 3.264 29.670 29.670 2.886 26.236 26.2362 1.992 18.111 47.781 1.992 18.111 47.781 2.193 19.941 46.1763 1.760 16.002 63.783 1.760 16.002 63.783 1.937 17.607 63.7834 .993 9.027 72.8095 .761 6.918 79.7276 .656 5.963 85.6907 .597 5.432 91.1228 .409 3.720 94.8429 .335 3.042 97.88410 .128 1.160 99.04411 .105 .956 100.000

Interpretation:- From the above Total Variance Explained we can see that 63.783%

of variance is explained by 3 component

33


34/46

Rotated Component MatrixComponent1 2 3

Liking .843Meaningful .627Discounts .937Price .785Secure .534Convenient . 768Intention .546Entertaining .846Less Time .909Shopping .487Future aspects .787

Interpretation: -

Saving of resources Liking Shopping intentionDiscounts Meaningful IntentionPrice Liking Shopping

Less time Secure Future aspectsEntertainingConvenient

Discriminant Analysis.

34


35/46

Eigen valuesFuncti

on Eigen value

% of

Variance

Cumulative

%

Canonical

Correlation1 1.025 a 100.0 100.0 .583

Eigen value indicates the proportion of variance explained. (Between-groups sums of

squares divided by within-groups sums of squares). A large Eigen value is associated

with a strong function. The canonical relation is a correlation between the

discriminant scores and the levels of the dependent variable. A high correlation

indicates a function that discriminates well. The present correlation of 0.583 is not

extremely high (1.00 is perfect).

Wilks' Lambda

Test of Function(s) Wilks' Lambda Chi-square df Sig.1 .220 60.988 7 0.000

H0- All group means are equal

HA- All group means are not equal

Interpretation: - Wilks Lambda is the ratio of within-groups sums of squares to the

total sums of squares. This is the proportion of the total variance in the discriminant

scores not explained by differences among groups. A lambda of 1.00 occurs when

observed group means are equal (all the variance is explained by factors other than

difference between those means), while a small lambda occurs when within-groups

variability is small compared to the total variability. A small lambda indicates that

group means appear to differ. The associated significance value indicates whether thedifference is significant. Here, the Lambda of 0.220 has a significant value (Sig. =

0.000). Thus it is appropriate for discriminate analysis.

Here HA is accepted.

35


36/46

Functions at Group Centroids

Confirmation FunctionConfirm -1.131

Not Confirm 1.131

Interpretation: - there came out to be 2 functions or group centroids, confirm has a

discriminate value of -1.131 and not confirm 1.131. So the discriminant value coming

closer to -1.131 will fall in the confirm group category and vice versa.

Classification Statistics

ConfirmationPredicted Group M embership

TotalConfirm Not ConfirmConfirm 49 10 59

Not Confirm 6 35 41

Interpretation :- In group confirm 57 of the respondent actually said that they actually

want confirmation by them to be done before receiving M-Marketing, Messages &

call.

Canonical Discriminant Function

Coefficients

FunctionSales people .199Occupation -.291Gender .074Timing .942Frequency .408Rcv_msgs .340Language -.211Salespeople .199(Constant) .074

Interpretation:-

D= a + bx1

D= .074 + .199* salespeople + (-.291)*occupation + .074*gender + .942* timing + .

408* frequency + .340* rcv_msgs + (-.211)* language + .199* salespeople.

36


37/46

So, we can calculate the discriminant scores for different respondents and hence

can see in which group they fall in confirm group or non confirmation group.

1.Factors preferred more or less for call mobile marketing

Factors for Call Marketing

0

500

1000

1500

2000

2500

3000

3500

Weightage or scores given

Series1

37


38/46

So, here we categorized all the variables according to weightages or scores given

by our respondents.

2.Factors preferred more or less for call mobile marketing.

scores for Message Marketing

0

500

1000

1500

2000

2500

3000

3500

weightages or score given

Series1

38


39/46

So, here we categorized all the variables according to weightages or scores given

by our respondents.

CHAPTER 4: CONCLUSION

Respondents say that they need confirmation before receiving calls or

messages for marketing.

Live salespeople are preferred over computerized salespeople.

Type of messages received by the people differs according to their income and

occupation.

Humble salespeople are preferred, having good knowledge content.

Gender, age, income &occupation highly effect the consumer preferences for

mobile marketing

39


40/46

CHAPTER 5: RECOMMENSATIONS

Proper care should be given to timings at which the message should be

delivered to consumers.

Mobile marketing can also be used for 2G and 3G services.

There should be a free reply system to confirm that they are interested to

receive such kind of messages that a marketer wants to deliver. i.e. free

message reply service number.

Knowledge of sales people should be well updated

40


41/46

CHAPTER 6: REFERENCES

1. Karjaluoto Heikki and Leppniemi Matti, Factors influencing consumers

willingness to accept mobile advertising: a conceptual model, Int. J Mobile

Communications, Vol 3, No. 3, 2005, p. 198.

2. Leppniemi, Matti, Mobile marketing communications in consumer markets,

Faculty of Economics and Business Administration, Department of Marketing,

University of Oulu, 2008, p. 21.

3. MMA Updates Definition of Mobile Marketing Association. Nov 18, 2009.

Leppniemi, Matti, Mobile marketing communications in consumer markets,

Faculty of Economics and Business Administration, Department of Marketing,

University of Oulu, 2008, p. 50.

4. See also push-pull strategy and smart reply on the nature of mobile marketing in

practice by business.

5. Airwide Backs Messaging Blog Mobile Marketing Magazine. May 23, 20076. Cleff, Evelyne Beatrix, Privacy issues in mobile advertising British and Irish

41


42/46

Law, Education and Technology Association, 2007 Annual Conference Hertfordshire,

p. 3.

7. Camponovo Giovanni, Cerutti Davide, The Spam Issue in Mobile Business a

Comparative Regulatory Overview, Proceedings of the Third International

Conference on Mobile Business, M-Business, 2004.

8. Lodder, Arno R. and Kaspersen, Henrik W.K eDirectives: Guide to European

Union Law on E-Commerce, Kluwer Law International, 2001, p. 141-142.

CHAPTER 7: ANNEXURES

QUESTIONNAIRE

Dear respondents,

We are doing a brief survey at IMS, Ghaziabad to find out more about the customer

preference towards mobile marketing. Your cooperation is kindly solicited to provide

the relevant information. We assure that information will be kept confidential.

Name:- ____________________________________________________________

Address:-____________________________________________________________

1) Have you ever experienced mobile marketing

1 Yes 2 No

2) Please select your gender

1 Male 2 Female

3) Please select the age group you are in

42


43/46

1. 20 30 2. 30 40 3. 40 50 4. Above 50

4) Please specify your occupation

1. Student 2. Private employee

3. Business 4. home maker

5. Self-employed

5) How often do you receive commercial SMS on your mobile phone

1. Once in a day 2. Once in a week

3. Once in a month 4. Regularly

5. Never

6) Which type of messages you often receive

Related to Insurance

Related to Real estate

Related to NEWS

Related to sports

Related to various service alerts

Related to others

7) Rate the features of mobile marketing on following parameter-

(1- Strongly disagree, 2- disagree , 3- Neutral, 4- agree, 5- Strongly agree)

S.No. Attitude Statements 1 2 3 4 5

43


44/46

1 I like receiving advertisements via the mobile phone

2 I find SMS and MMS mobile advertising messages

meaningful

3 I like different offer discounts on mobile phones4 I prefer mobile shopping, if the prices and offers are

reasonable5 Messages are secure and reliable

6 I find mobile shopping convenient

7 My general intention to shop via mobile phone is very

high

8 I find mobile shopping more entertaining than traditionalshopping

9 I prefer mobile shopping when I have less time

10 I like shopping via mobile phone

11 I will shop via mobile phone in the future

Section II

1. Which type of language do you prefer?

Hindi English

Regional

2. You like receiving messages through

Messages Call

3. Which type do you prefer.

Computerised salespeople Live Salespeople

4. The time suitable for SMS / Call

Morning Afternoon

Evening Late Evening(after 7)

44


45/46

5. Mobile Marketing(via phone call)

5.1 I should receive calls only after a confirmation made by me through SMS or in a

kind.

Yes No

5.2 Out of 100 please give the specific weights to following factors.

S.No. Factors Scores1. Humble Salesperson2. Knowledgable Salesperson3. Supportive Salesperson4. Voice Clarity

5. Easy Transaction Service6. Prefered language usage7. Call should be on right time

6. Mobile Marketing(via Messages)

S.No. Factors Scores1. Specific Link Provided2. Prefered language usage3. Terms & Condition mentioned4. Phone contacts mentioned5. Messages as short as possible6. No Technical words used(simple words)7. Messages Should come to me only after subscription in a kind

7. Rank the top 3 preferences among the following which you would like to receive

via messages-

1.Insurance 2.Jobs

3.Matrimonial 4.Real Estate

5.Mobile services 6.Apparals

7.News 8.Consumer Durables

9.Educational Institutes 10.Others

8. Give your opinion towards mobile marketing

45


46/46

__________________________________________________________________

_____________________________________________________________________

_____________________________________________________________________

___

THANK YOU

mr final over

Documents