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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
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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
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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
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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
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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
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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
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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
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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
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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.
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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
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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.
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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
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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
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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
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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.
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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.
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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)
Pearson Chi-Square 3.226 a 1 .022
Continuity Correction b 2.510 1 .113
Likelihood Ratio 3.209 1 .073
Fisher's Exact Test .091 .057
Linear-by-Linear Association 3.193 1 .074
N of Valid Cases b 100
Rcv msgs
TotalMsg Call
Gender Male36 28 64
Female 10 26 36
Total46 54 100
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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
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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
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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
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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
frequency * type_msg3 CrosstabulationCount
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
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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
occupation * type_msg4 CrosstabulationCount
type_msg4
Total1 2 3 4 5 6occupation Student 18 2 0 2 10 8
Private employee 4 10 0 3 0 3
Business 2 4 12 4 0 0
Home maker 0 0 0 2 2 0
Self employed 0 0 0 4 10 0Total 24 16 12 15 22 11
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occupation * type_msg4 CrosstabulationCount
type_msg4
Total1 2 3 4 5 6
occupation Student 18 2 0 2 10 8
Private employee 4 10 0 3 0 3
Business 2 4 12 4 0 0
Home maker 0 0 0 2 2 0
Self employed 0 0 0 4 10 0Total 24 16 12 15 22 11
frequency * type_msg4 CrosstabulationCount
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
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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
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occupation * language Crosstabulation
language
TotalHindi English Regional
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
Expected Count 5.3 11.2 5.5 22.0
Home maker Count 0 4 0 4
Expected Count 1.0 2.0 1.0 4.0
Self employed Count 0 12 2 14
Expected Count 3.4 7.1 3.5 14.0
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)
Pearson Chi-Square 37.100 a 8 .000Likelihood Ratio 48.007 8 .000Linear-by-Linear Association .417 1 .519N of Valid Cases 100
Crosstabs
age * language Crosstabulation
language
TotalHindi English Regional
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
Expected Count 7.7 16.3 8.0 32.0
40-50 Count 0 4 0 4
Expected Count 1.0 2.0 1.0 4.0
Above 50 Count 2 0 6 8
Expected Count 1.9 4.1 2.0 8.0
Total Count 24 51 25 100
Expected Count 24.0 51.0 25.0 100.0
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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
Expected Count 16.6 19.4 36.0
Total Count 46 54 100
Expected Count 46.0 54.0 100.0
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 Correction b 6.417 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 b 100
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
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Crosstab
rcv_msgs
Totalmsg call
age 20-30 Count 26 30 56
Expected Count 25.8 30.2 56.0
30-40 Count 18 14 32
Expected Count 14.7 17.3 32.0
40-50 Count 0 4 4
Expected Count 1.8 2.2 4.0
Above 50 Count 2 6 8
Expected Count 3.7 4.3 8.0
Total Count 46 54 100
Expected Count 46.0 54.0 100.0
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
Expected Count 9.2 10.8 20.0
Business Count 18 4 22
Expected Count 10.1 11.9 22.0
Home maker Count 0 4 4
Expected Count 1.8 2.2 4.0
Self employed Count 0 14 14
Expected Count 6.4 7.6 14.0
Total Count 46 54 100
Expected Count 46.0 54.0 100.0
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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
TotalComputerised Live
gender Male Count 36 28 64
Expected Count 33.9 30.1 64.0
Female Count 17 19 36
Expected Count 19.1 16.9 36.0
Total Count 53 47 100
Expected Count 53.0 47.0 100.0
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
Fisher's Exact Test .411 .255
Linear-by-Linear Association .746 1 .388
N of Valid Cases b 100
a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 16.92.
age * salespeople
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Crosstab
salespeople
TotalComputerised Live
age 20-30 Count 27 29 56Expected Count 29.7 26.3 56.0
30-40 Count 22 10 32
Expected Count 17.0 15.0 32.0
40-50 Count 2 2 4
Expected Count 2.1 1.9 4.0
Above 50 Count 2 6 8
Expected Count 4.2 3.8 8.0
Total Count 53 47 100
Expected Count 53.0 47.0 100.0
Chi-Square Tests
Value df
Asymp. Sig. (2-
sided)
Pearson Chi-Square 6.234 a 3 .101Likelihood Ratio 6.416 3 .093Linear-by-Linear Association .185 1 .667N of Valid Cases 100
occupation * salespeople
Crosstab
salespeople
TotalComputerised LIve
occupation Student Count 26 14 40
Expected Count 21.2 18.8 40.0
Private employee Count 13 7 20
Expected Count 10.6 9.4 20.0
Business Count 4 18 22
Expected Count 11.7 10.3 22.0
Home maker Count 0 4 4
Expected Count 2.1 1.9 4.0
Self employed Count 10 4 14
Expected Count 7.4 6.6 14.0
Total Count 53 47 100
Expected Count 53.0 47.0 100.0
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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
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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
Expected Count 21.2 14.8 36.0
Total Count 59 41 100
Expected Count 59.0 41.0 100.0
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)Exact Sig. (2-
sided)Exact Sig. (1-
sided)
Pearson Chi-Square 3.226 a 1 .072
Continuity Correction b 2.510 1 .113
Likelihood Ratio 3.209 1 .073
Fisher's Exact Test .091 .057
Linear-by-Linear Association 3.193 1 .074
N of Valid Cases b 100
a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 14.76.
occupation * confirmation
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Crosstab
confirmation
Total1 2
occupation 1 Count 32 8 40
Expected Count 23.6 16.4 40.0
2 Count 13 7 20
Expected Count 11.8 8.2 20.0
3 Count 2 20 22
Expected Count 13.0 9.0 22.0
4 Count 2 2 4
Expected Count 2.4 1.6 4.0
5 Count 10 4 14
Expected Count 8.3 5.7 14.0
Total Count 59 41 100
Expected Count 59.0 41.0 100.0
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.
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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
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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.
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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.
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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.
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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
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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
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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
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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
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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
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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
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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
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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)
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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
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__________________________________________________________________
_____________________________________________________________________
_____________________________________________________________________
___
THANK YOU