1. Research to enhance experience of Indian
Social Networking Site
TEAM NAME: Intel_Inside
TEAM MEMBER:
Vaibhav Sarangale
Shishira Hegde
COLLEGE NAME: IES Management College and Research Center,
Mumbai

2. EXECUTIVE SUMMARY
Social Networking sites are the fastest growing media for all the corporate as well as users to
interact with each other. The popularity of Social Networking sites in India spread with
popularity of Orkut. Recently Facebook emerged as the most popular networking site in India
with 25 million users. There are also Indian social networking sites like Bharatstudent,
Fropper, Ibibo etc. A look at the Indian social networking space clearly shows that the most
popular sites are all established global players. It would not be an overstatement if we say that
the Indian counterparts have failed to make an impact comparatively.
Across the globe, social networking sites operate under different revenue models. Most of
them rely on advertising as their major source of revenue. Marketers have found social media
an effective and cheap alternative to grab eyeballs. But the Indian users have different
psychology which makes it difficult for social networking sites to earn added revenues.
Hence it is necessary to identify the gaps in the current social networking sites and the
prospective segments of users which can be targeted to gain more visibility. It is also
necessary to identify the effectiveness of current models and scope for new revenue models.
Following are the objectives of the study:
To understand the awareness about the social networking sites and their usage.
To identify the gaps in the current social networking sites available to exploit.
To understand the most liked and disliked factors of the social networking sites.
To identify the key positioning parameters in current scenario.
To identify potential market segments and target groups for a social networking sites.
To understand the efficiency of the current revenue models and proposed revenue
models
The research design included both qualitative and quantitative studies. The quantitative
responses were collected using online survey where as qualitative data was collected through
in-depth interviews. Analysis was done using SPSS and Microsoft Excel 2010. Random
sampling was done and response was collected form 89 respondents.

3. Key findings of the research are as follows:
Privacy is having the highest opportunity score followed by Speed and Ease of navigation
respectively.
It is observed that Indian users are noticing the in-site advertisements but are not
motivated to click it, the other models like Value Added Services, special paid In-Games
items and features, to design applications and sell based on shared revenue basis on social
networking sites are also not effective
Proposed revenue models were highly accepted. Hence these models are would be highly
effective if implemented in the revenue model for the social networking sites.
When Cluster analysis was conducted for the 89 respondents, it was found that three
clusters emerged out of which Cluster No.2 and Cluster No.3 comprise of the most
prospective users for the proposed revenue models.

4. INTRODUCTION
Social networking site is used to describe any Web site that enables users to create public
profiles within that Web site and form relationships with other users of the same Web site
who access their profile. Social networking sites can be used to describe community-based
Web sites, online discussions forums, chat-rooms and other social spaces online.[1]
Experian Hitwise, the global information services company, has conducted an international
study on just how much time people living in different countries spend on social networks.
Brazil, Singapore, USA, India, New Zealand, France, Australia and the UK were a part of the
study. As per this study, India ranks 4th and has 14 per cent market share for social networks
and forums. Facebook, YouTube and Orkut continue to be the top three social networking
websites in India. [2]
India with its large population has millions of users accessing Facebook, there are 25 million
people using Facebook in India. This means 18% of the online population is from India. It is
estimated that within a year India will have at least 27 million Facebook users.

5. Social networking in India-
The popularity of Social Networking sites spread with popularity of Orkut. Facebook, Twitter,
Orkut, LinkedIn are few of the biggest social networking sites in India. Rediff.com, a popular
portal in India launched its own version, Yaari, Minglebox, Hi5 and dozens of other sites are
attracting their own fan base. Online video and music sites are also doing reasonably well.
However, one of the major competitors of SNS is the Indian Television and Cinema industry,
which still has a grasp on a big share of the user attention. With respect to online music, due
to the popularity of Bit torrent in India, most users prefer to download their music rather than
listen to it online.
Revenue models of social networking sites-
Within all investigated social networking sites the following significant revenue models were
determined:
Onsite Advertising: Advertising is a very popular form of revenue generation. Most
common forms were contextual advertising, usually Google AdSense, and banner
advertising.
Application development- Many of the social networking sites have a special feature
which enables its users to develop their own applications for the social networking
website. The developer gets revenues by sharing revenues generated through
application downloads and/or application usage.
Affiliate Programs: Affiliate programs are revenue sharing arrangements set up by
companies selling products and services. Owners of social networking sites are
rewarded for sending customers to a specific third-party company.
Special in-game features- Some social networking sites provides the feature ofbuying
in-game special items to enhance their gaming experience. There are also some sites
which provide paid games participation.
Membership Fees: Only a few of the analyzed social networks had a membership
revenue model which is normally based on special features for a premium account or
in some cases like a club fee.

6. Direct Sales: Fewer social networks had included an e store in their environment to
gather revenue directly from sales of products.

7. RESEARCH METHODALOGY
Objectives-
The main objective of the study was to understand the market scenario of the social
networking sites in India.
Sub-objectives-
To understand the awareness about the social networking sites and their usage.
To identify the gaps in the current social networking sites available to exploit.
To understand the most liked and disliked factors of the social networking sites.
To identify the key positioning parameters in current scenario.
To identify potential market segments and target groups for a social networking sites.
To understand the efficiency of the current revenue models and proposed revenue
models.
Methodology
The entire research was a combination of qualitative and quantitative research.
The data collected was based on both exploratory and descriptive designs.
Qualitative data was collected through in-depth interviews.
Quantitative data was collected during online research through customer assisted
questionnaire based feedbacks. Google survey was used to prepare the questionnaire.
The research was initiated with a pilot questionnaire, which helped to draft the final
questionnaire.
The entire data analysis was done using SPSS and Microsoft Excel 2010.

8. Sampling design-
The sample consists of current and prospective users of social networking sites.
The quantitative research was conducted in the sample of 89 respondents.
While the qualitative data collection was done using in-depth interviews of 5
respondents
Sampling design was simple random sampling.
Limitations-
Following are some of the limitations of the study
As the quantitative research was conducted using online surveys, there was minimal
control over the composition of the respondents in total sample.
As many of the homemakers and senior citizens have not responded to the survey, the
results of the research will not be applicable to them.
Respondent Bias was one of the major limitations of research, which we tried to
overcome through different tools of research.

9. RESULTS OF THE QUALITATIVE STUDY
Name of Occupation Age (in Response
respondent Years)
1. Vijesh Service 29 Advertisements not catchy and noticeable.
Hegde Game becomes monotonous and boring after a certain
(Oracle) level and user feels it’s a waste of time.
As per Indian psychology, user only takes interest when
he sees some benefit or value addition for them. Hence
do not pay attention to Ads.
Feature of application development is not famous in
India due to lack of user friendly nature of developing
tools.
2. Rajprasad Service 29 Herd mentality among Indian users of using pirated
Hegde contents.
(Tesco) Credit card penetration in India is very low hence usage
is also low
Least knowledge for application development in India,
hence good support software is required.
Indian youth follow the trend of global youth and are
more influenced by the buzz created.
Indian youth follows their friend circle, hence they
switch along with their friends.
Social networking sites like facebook got recognition
due to its exclusive student user base at first. Hence
Indian networking sites should also follow the same path
to get recognition.
3. Roshnee Student at 22 Don’t click on advertisements nor pay for in-game
Bhatia IES MCRC features as the basic purpose of visiting is networking &
past-time for free.
But would consider spending if one can earn revenue on
the social networking site.
Do not know how to develop applications as tools are
not user friendly.
If benefited through shared revenues then would

10. participate in online features and spend.
4. Prasad MS in NY 24 Used to play games and buy in-games item like Mafia
Vesawkar Univ Wars, but later got bored.
Is aware about the feature of app development but not
used it much due to lack of experience.
Has noticed ads but found them irrelevant to his profile
hence don’t click on it except for LIKNEDIN which
relevant ads according to the group joined

11. RESULTS AND ANALYSIS OF THE SURVEY
Overall demographics
Age
Monthly Family Income
15001 to 30000 INR 25.8
16 to 25 years 76.4
30001 to 45000 INR 25.8
45001 to 60000 INR 16.9
26 to 35 years 23.6
above 60000 INR 31.5
0 50 100
0 10 20 30 40
Occupation
Professional Self employed Service Student
5%
9%
20%
66%
Here we can observe that the average age of the 89 respondent is 22.89 years and the
average monthly family income is INR 43061
Students formed the major percentage of the respondent, followed by the service
category.

12. Top of Mind Awareness and the most preferred site as per the respondent
Top Of Mind Awareness Sites most visited by the respondent
Myspace 1.12
Orkut 1.12
Twitter 1.12
Linkedin 1.12 linkedin 5.62
Orkut 3.37
FB 93.26
Facebook 93.26
0 20 40 60 80 100 0.00 20.00 40.00 60.00 80.00 100.00
The most LIKED parameter for the mentioned social networking site
Most Like (%)
Parameter Facebook Twitter Linkedin Orkut Bharatstudent Indyarock Bigadda
Ease of Navigation/ 46.07 16.85 7.87 29.21 15.73 17.98 14.61
User Friendly
Sharing and 43.82 31.46 41.57 21.35 14.61 13.48 11.24
Networking
Privacy 6.74 7.87 13.48 10.11 8.99 2.25 5.62
Speed 1.12 19.10 5.62 5.62 1.12 4.49 4.49
Gaming 1.12 1.12 2.25 6.74 6.74 10.11 14.61
No response 1.12 23.60 29.21 26.97 52.81 51.69 49.44
The Top of Mind Recall for FACEBOOK is highest with 93.26% followed by meager
percentage of 3.3 for ORKUT.
Among the seven social networking sites listed, FACEBOOK is the most visited site with
93.26% followed by LIKNEDIN with 5.62%
The most liked parameter for the following sites are as follows:
o FACEBOOK: Ease of navigation/User friendly (46.07%)
o TWITTER: Sharing and Networking (31.46%)
o LINKEDIN: Sharing and Networking (41.57%)
o ORKUT: Ease of navigation/User friendly (29.21%)
o BHARATSTUDENT.COM, INDYAROCKS & BIGADDA : majority of the
respondents couldn’t respond for these sites

13. The most DISLIKED parameter for the mentioned social networking sites
Most Dislike (%)
Parameter Facebook Twitter Linkedin Orkut Bharatstudent Indyarock Bigadda
Ease of Navigation/ User 8.99 12.36 11.24 4.49 5.62 3.37 4.49
Friendly
Sharing and Networking 3.37 3.37 2.25 4.49 7.87 6.74 10.11
Privacy 29.21 12.36 15.73 26.97 12.36 11.24 11.24
Speed 26.97 15.73 17.98 13.48 10.11 14.61 11.24
Gaming 23.60 17.98 14.61 16.85 8.99 8.99 6.74
No response 7.87 38.20 38.20 33.71 55.06 55.06 56.18
The most disliked parameter for the mentioned social networking sites are:
o FACEBOOK: Privacy (29.21%)
o TWITTER: Gaming (17.98%)
o LINKEDIN: Speed (17.98%)
o ORKUT: Privacy (26.97)
o BHARATSTUDENT.COM: Privacy (12.36%)
o INDYAROCKS: Speed (14.61%)
o BIGADDA: Privacy and Speed share the same percentage (11.24%)

14. Opportunity Score Matrix
Importance = Satisfaction= s i-s [If i-s is Opportunity
i (Mean) (Mean) negative score.=i+(i-s)
consider it as 0]
Ease of navigation/ User 4.12 3.76 0.36 4.48
friendly
Speed 4.2 3.63 0.57 4.77
Privacy 4.42 3.7 0.72 5.14
Networking and Chatting 4 3.97 0.03 4.03
Sharing (e.g. Video, Music, 3.84 3.9 0 3.84
Photo, Status etc)
Applications 3.12 3.45 0 3.12
Information visibility 3.64 3.54 0.1 3.74
Earning in monetary terms 2.89 2.97 0 2.89
Online shopping 2.58 2.92 0 2.58
Gaming 2.6 3.12 0 2.6
Downloading (e.g. Videos, 3.31 3.35 0 3.31
music, photos, wallpapers
etc)
Ease of payment in suitable 3.08 3.12 0 3.08
currency and payment
gateways like paypal
In the Opportunity Score Matrix amongst all the other parameter, PRIVACY scored
the highest with the score of 5.14. This is due to the higher IMPORTANCE given with
lower SATISFACTION level which shows the gap between the expectation and actual
experience of the user
These parameters were followed by:
o SPEED : Opportunity score 4.77
o EASE OF NAVIGATION/USER FRIENDLY: Opportunity score 4.48

15. Responses received for Current and Proposed Model
Responses on Current Revenue Model
YES NO
I design apps and sell on social networking sites 8.99 91.01
I pay for special in-game items while gaming in… 6.74 93.26
I pay for the value added services provided by the … 13.48 86.52
I click on the advertisement which appears in the… 24.72 75.28
I notice the advertisement which appears in social… 67.42 32.58
Responses on the proposed model
YES NO
In association to OPTION NO.1 would you like to use
these earnings for legally watching latest released 62.92 37.08
movies (i.e. to inhibit piracy) on social networking …
I would visit the social networking sites which are
providing earning options through paid 67.42 32.58
surveys, application development etc. which can …
Comments on Current Revenue Models:
The efficiency of revenue generation through in-site advertisements is very low as many of the
users are noticing the advertisements but are not motivated to click the advertisements.
The current revenue models of the social networking sites are not very strong such as:
o Value Added Services
o Special paid In-Games items and features
o To design applications and sell based on shared revenue basis on social networking sites
Comments on Proposed Revenue Model:
The acceptance for both the models is very high as in comparison with the currents model as
seen above.

16. Verifying relation between
AGE V/s PROPOSED REVENUE MODELS
Age Proposed Model No.1 Total
yes no
26 to 35 years Count 14 7 21
% of Total 15.7% 7.9% 23.6%
16 to 25 years Count 46 22 68
% of Total 51.7% 24.7% 76.4%
Total Count 60 29 89
% of Total 67.4% 32.6% 100.0%
Age Proposed Model No.2 Total
yes no
26 to 35 years Count 13 8 21
% of Total 14.6% 9.0% 23.6%
16 to 25 years Count 43 25 68
% of Total 48.3% 28.1% 76.4%
Total Count 56 33 89
% of Total 62.9% 37.1% 100.0%
Comments:
From the first table it can be seen that 51.7% of the total respondent are belonging to age group
of 16 to 25 years and are in favor of proposed model 1 (which provides scope to earn revenue
and spent them in online shopping)
Similarly it can be observed from the second table that 48.3% of the total respondent are
belonging to the age group of 16 to 25 years are in favor of proposed model 2 (which enables
the user to use the earnings from model 1 for legally watching latest released movies i.e. to
inhibit piracy on social networking sites)

17. OCCUPATION V/s PROPOSED REVENUE MODEL
Occupation Proposed Model No.1 Total
yes no
Professional Count 5 3 8
% of Total 5.6% 3.4% 9.0%
Self employed Count 3 1 4
% of Total 3.4% 1.1% 4.5%
Service Count 12 6 18
% of Total 13.5% 6.7% 20.2%
Student Count 40 19 59
% of Total 44.9% 21.3% 66.3%
Total Count 60 29 89
% of Total 67.4% 32.6% 100.0%
Occupation Proposed Model No.2 Total
yes no
Professional Count 3 5 8
% of Total 3.4% 5.6% 9.0%
Self employed Count 2 2 4
% of Total 2.2% 2.2% 4.5%
Service Count 14 4 18
% of Total 15.7% 4.5% 20.2%
Student Count 37 22 59
% of Total 41.6% 24.7% 66.3%
Total Count 56 33 89
% of Total 62.9% 37.1% 100.0%
Comments:
From the first table it can be seen that 44.9% of the total respondent are Student and are in
favor of proposed model 1 followed by Service accounting for 13.5% (Model 1:which
provides scope to earn revenue and spent them in online shopping)
Similarly it can be observed from the second table that 41.6% of the total respondent are
Students and are in favor of proposed model 2 (which enables the user to use the earnings
from model 1 for legally watching latest released movies i.e. to inhibit piracy on social
networking sites)

18. INCOME V/s PROPOSED REVENUE MODEL
Monthly_income Proposed Model No.1 Total
yes no
above 60000 INR Count 19 9 28
% of Total 21.3% 10.1% 31.5%
45001 to 60000 INR Count 13 2 15
% of Total 14.6% 2.2% 16.9%
30001 to 45000 INR Count 13 10 23
% of Total 14.6% 11.2% 25.8%
15001 to 30000 INR Count 15 8 23
% of Total 16.9% 9.0% 25.8%
Total Count 60 29 89
% of Total 67.4% 32.6% 100.0%
Monthly_income Proposed Model No.2 Total
yes no
above 60000 INR Count 16 12 28
% of Total 18.0% 13.5% 31.5%
45001 to 60000 INR Count 13 2 15
% of Total 14.6% 2.2% 16.9%
30001 to 45000 INR Count 14 9 23
% of Total 15.7% 10.1% 25.8%
15001 to 30000 INR Count 13 10 23
% of Total 14.6% 11.2% 25.8%
Total Count 56 33 89
% of Total 62.9% 37.1% 100.0%
Comments:
21.3% of the total respondent belong to the monthly income group of above 60000 INR who
are in favor of the model 1 (which provides scope to earn revenue and spent them in online
shopping) followed by the monthly income group of 150001 to 30000 INR who account for
16.9%
From the second table 18.0% of the total respondent belong to the monthly income group of
above 60000 INR followed by 15.7% belonging to the monthly income group of 30001 to
45000 INR in favor of model 2 (which enables the user to use the earnings from model 1 for
legally watching latest released movies i.e. to inhibit piracy on social networking sites)

19. Verifying dependence between
OCCUPATION V/s CURRENT REVENUE MODEL
Crosstab
I_pay_for_special_in_game_items_while_gaming Total
yes no
Occupation Professional Count 2 6 8
% of Total 2.2% 6.7% 9.0%
Self Count 1 3 4
employed % of Total 1.1% 3.4% 4.5%
Service Count 0 18 18
% of Total .0% 20.2% 20.2%
Student Count 3 56 59
% of Total 3.4% 62.9% 66.3%
Total Count 6 83 89
% of Total 6.7% 93.3% 100.0%
Chi-Square Tests
Value df Asymp. Sig. (2-sided)
a
Pearson Chi-Square 7.922 3 .048
Likelihood Ratio 6.734 3 .081
Linear-by-Linear Association 4.326 1 .038
N of Valid Cases 89
a. 5 cells (62.5%) have expected count less than 5. The minimum expected count is .27.
H0: Occupation of the respondent is independent of the current revenue model
H1: Occupation of the respondent is dependent of the current revenue model
As Pearson Chi-Square value = 0.048 is less than α = 0.05 at 95% Confidence Interval, we
reject H0 and accept H1
Hence Occupation of the respondent is dependent of the current revenue model.

20. Cluster Analysis
Extract of Agglomeration Schedule by Hierarchical method of cluster analysis
Coefficients Difference between consecutive coefficients
Stage
8 81 25.50
7 82 26.33 0.83
6 83 30.27 3.93
5 84 31.31 1.04
4 85 32.11 0.80
3 86 36.31 4.20
2 87 36.87 0.56
1 88 48.26 11.39
Beyond first stage the maximum difference between the coefficients is observed at the 3 rd
stage from bottom hence we can conclude that there are 3 clusters are emerging from the
given lifestyle statements.
From the table given bellow, the number of users per cluster is found out by K-means
method for cluster analysis
Number of Cases in each Cluster
Cluster 1 26
2 31
3 32
Valid 89
Missing .000

21. Testing significance of lifestyle statements using ANOVA
Cluster Error F Sig.
Mean df Mean df
Square Square
I_use_credit_card 41.265 2 .683 86 60.450 .000
I_like_online_game 9.443 2 .937 86 10.073 .000
i_like_download_free 5.228 2 1.227 86 4.260 .017
I_like_build_professional_network_online 6.153 2 .921 86 6.680 .002
I_like_online_shopping 14.413 2 .730 86 19.747 .000
i_like_video_chatting 6.454 2 .832 86 7.753 .001
i_use_my_phone_4_professional 18.956 2 .731 86 25.918 .000
I_download_paid_app 8.514 2 .826 86 10.314 .000
I_visit_site_through_my_phone 21.905 2 .984 86 22.251 .000
i_visit_sites_to_earn 12.180 2 .983 86 12.391 .000
i_dnt_mind_pay_downloading 11.032 2 .886 86 12.453 .000
The F tests should be used only for descriptive purposes because the clusters have been chosen to maximize the
differences among cases in different clusters. The observed significance levels are not corrected for this and thus
cannot be interpreted as tests of the hypothesis that the cluster means are equal.
As the significance values of the all the life style statements are below α = 0.05, we can say
that all these parameters are relevant for this model

22. Cluster 1 characteristics
Final Cluster Centers Cluster No.1 Remark
(Mean)
I_use_credit_card 2 Not so Influential factor
I_like_online_game 3 Neutral
i_like_download_free 3 Neutral
I_like_build_professional_network_online 3 Neutral
I_like_online_shopping 2 Not so Influential factor
i_like_video_chatting 3 Neutral
i_use_my_phone_4_professional 2 Not so Influential factor
I_download_paid_app 2 Not so Influential factor
I_visit_site_through_my_phone 2 Not so Influential factor
i_visit_sites_to_earn 2 Not so Influential factor
i_dnt_mind_pay_downloading 2 Not so Influential factor
Average age-22.03 year Average income- INR 49038
Cluster 1-Age
16 to 25 years 84.6
26 to 35 years 15.4
0 10 20 30 40 50 60 70 80 90

23. Cluster No.1- Monthly Family Income
15001 to 30000 INR 19.2
30001 to 45000 INR 23.1
45001 to 60000 INR 19.2
above 60000 INR 38.5
0 5 10 15 20 25 30 35 40 45
Cluster No.1-Occupation
7.7 3.8
Valid Professional
23.1
Valid Self employed
65.4
Valid Service
Valid Student
Cluster No.1- Responses on current revenue model
YES NO
I design apps and sell on social networking sites 7.70% 92.30%
3.80%
I pay for special in-game items while gaming in… 96.20%
I pay for the value added services provided by the… 15.40% 84.60%
I click on the advertisement which appears in the… 19.20% 80.80%
I notice the advertisement which appears in … 73.10% 26.90%

24. Cluster No.1- Responses for proposed model
YES NO
In association to OPTION NO.1 would you like to use
these earnings for legally watching latest released 50% 50%
movies (i.e. to inhibit piracy) on social networking
sites
I would visit the social networking sites which are
providing earning options through paid 53.80% 46.20%
surveys, application development etc. which can be
redeemed in online shopping (e.g. Live…

25. Cluster 2 characteristics-
Final Cluster Centers Cluster Remark
No.2
(Mean)
I_use_credit_card 2 Not so Influential factor
I_like_online_game 2 Not so Influential factor
i_like_download_free 4 Most Influential factor for respondent in
cluster no.2
I_like_build_professional_network 4 Most Influential factor for respondent in
_online cluster no.2
I_like_online_shopping 3 Neutral
i_like_video_chatting 4 Most Influential factor for respondent in
cluster no.2
i_use_my_phone_4_professional 4 Most Influential factor for respondent in
cluster no.2
I_download_paid_app 3 Neutral
I_visit_site_through_my_phone 4 Most Influential factor for respondent in
cluster no.2
i_visit_sites_to_earn 3 Neutral
i_dnt_mind_pay_downloading 3 Neutral

26. Average age- 22.43 years Average income- INR 43790
Cluster No.2-Age
26 to 35 years 19.4
16 to 25 years 80.6
0 10 20 30 40 50 60 70 80 90
Cluster No.2-Monthly Family Income
above 60000 INR 29
45001 to 60000 INR 12.9
30001 to 45000 INR 29
15001 to 30000 INR 29
0 5 10 15 20 25 30 35
Cluster No.2-Occupation
12.9
3.2
9.7 Student
Service
74.2 Self employed
Professional

27. Cluster No.2-Responses on current revenue model
YES NO
I design apps and sell on social networking sites 6.50% 93.50%
I pay for special in-game items while gaming in…6.50% 93.50%
I pay for the value added services provided by the… 12.90% 87.10%
I click on the advertisement which appears in the… 29% 71%
I notice the advertisement which appears in … 61.30% 38.70%
Cluster No.2-Responses for proposed revenue model
YES NO
In association to OPTION NO.1 would you like to
use these earnings for legally watching latest 64.50% 35.50%
released movies (i.e. to inhibit piracy) on social …
I would visit the social networking sites which are
providing earning options through paid 71% 21%
surveys, application development etc. which …

28. Cluster 3 characteristics
Final Cluster Centers Cluster Remark
No.3
I_use_credit_card 4 Most Influential factor for respondent in cluster
no.3
I_like_online_game 3 Neutral
i_like_download_free 4 Most Influential factor for respondent in cluster
no.3
I_like_build_professional_network_o 4 Most Influential factor for respondent in cluster
nline no.3
I_like_online_shopping 4 Most Influential factor for respondent in cluster
no.3
i_like_video_chatting 4 Most Influential factor for respondent in cluster
no.3
i_use_my_phone_4_professional 4 Most Influential factor for respondent in cluster
no.3
I_download_paid_app 3 Neutral
I_visit_site_through_my_phone 4 Most Influential factor for respondent in cluster
no.3
i_visit_sites_to_earn 3 Neutral
i_dnt_mind_pay_downloading 3 Neutral

29. Average age- 23.93 years Average income- INR 44,511
Cluster No.3-Age
26 to 35 years 34.4
16 to 25 years 65.6
0 10 20 30 40 50 60 70
Cluster No.3- Monthly Family Income
above 60000 INR
45001 to 60000 INR
30001 to 45000 INR
15001 to 30000 INR
0 5 10 15 20 25 30
Cluster No.3-Occupation
6.2 6.2
Student
28.1 Service
59.4
Self employed
Professional

30. Cluster No.3-Responses on current revenue model
YES NO
I design apps and sell on social networking sites 12.90% 87.10%
I pay for special in-game items while gaming in… 9.40% 90.60%
I pay for the value added services provided by the… 12.90% 87.10%
I click on the advertisement which appears in the… 25.80% 74.20%
I notice the advertisement which appears in… 71% 29%
Cluster No.3- Responses for the proposed revenue model
YES NO
In association to OPTION NO.1 would you like to
use these earnings for legally watching latest 71% 29%
released movies (i.e. to inhibit piracy) on social …
I would visit the social networking sites which
are providing earning options through paid 74.20% 25.80%
surveys, application development etc. which …

31. CONCLUSION AND RECOMMENDATIONS
From the most LIKED parameters where the Global networking sites score on Ease of
navigation/User friendly and Sharing and networking, Indian social networking sites need
to gear up on these fronts as they score very less in comparison to their Global
counterparts
On the other hand where Global social networking sites are lagging behind on parameters
like Privacy and Speed, Indian counterparts can build their strong positioning statements
and infrastructure on these parameters.
In the Opportunity Score Matrix on all the other parameter, Privacy is having the highest
opportunity score followed by Speed and Ease of navigation respectively. Hence ant new
social networking site can position themselves on the above mentioned parameters.
It is observed that Indian users are noticing the in-site advertisements but are not
motivated to click on it which is big road block according to the current revenue model.
The current revenue models of the social networking sites are not very strong such as:
o Value Added Services because very few people don’t like to spend money in the
social networking sites when its form their own pocket
o Special paid In-Games items and features because as games become monotonous
after certain period of time and users feels it’s not worth to spend time and money
on it
o To design applications and sell based on shared revenue basis on social
networking sites because as many of the users are unaware about the tools and are
lacking the skills to develop applications on their own
It was observed that there is a high acceptance for the proposed model no.1 that a user
would visit the social networking sites which are providing earning options through paid
surveys, application development etc. which can be redeemed in online shopping (e.g.
Live streaming, Video downloading, mobile recharge etc)
Also a high acceptance for the proposed model no.2 that a user would you like to use
these earnings for legally watching latest released movies (i.e. to inhibit piracy) on social
networking sites.

32. Hence these models are would be highly effective if implemented in the revenue model
for the social networking sites.
From different cross tabulations, it was observed that the proposed models no.1 & 2 were
readily accepted by the age group of 16 to 25 years and also by the Students.
It was observed that the proposed model no.1 & 2 are having higher acceptance in the
income group of INR 60000 and above.
From these observations we can propose that these models would be highly effective in
these segments.
When Cluster analysis was conducted for the 89 respondents, it was found that three
clusters emerged out of which Cluster No.2 and Cluster No.3 comprise of the most
prospective users for the proposed revenue models.
The proposed revenue models are designed in such a way that it would benefit all the
stake holders of Social Networking Media.
o Users: Mode of earning
o In-site advertisers: Enabling users to click on the in-site advertisements and
motivating them to buy using the earnings
o Film house production: Reducing piracy and increasing the viewership which will
increase the revenues
o Corporate clients: Applications could be build from crowd sourcing, data can be
collected etc
o Social Networking Sites: Adding to the revenue through the above mentioned
statements.

33. REFERENCES
1. www.webopedia.com/TERM/S/social_networking_site.html
2. http://www.afaqs.com/news/story.html?sid=31771
3. http://techcrunch.com/2009/10/20/web-2-0-summit-a-conversation-with-twitters-ev-
williams/
4. http://facebookrevenue.net/
5. http://www.iadis.net/dl/final_uploads/200810C024.pdf
6. http://www.quickonlinetips.com/archives/2009/02/top-social-networking-sites-india/
7. http://anand-illuminateddarkness.blogspot.com/2010/11/evolution-of-social-
networking-in-india.html

34. APPENDIX
Survey to enhance experience of Indian Social Networking Site
The survey is conducted to understand the hidden opportunities in social media for Indian markets and
to understand the scope for developing a new revenue models in social networking sites.
Name: ___________________
Contact number: ____________________
Email Id: ___________________
Age:
Upto 15 years
16 to 25 years
26 to 35 years
36 to 45 years
above 46 years
Occupation
Student
Service
Self employed
Professional
Home maker
Unemployed
Monthly Family Income
below 15000 INR
15001 to 30000 INR
30001 to 45000 INR
45001 to 60000 INR
above 60000 INR

35. 1. Enlist names of 5 social networking websites that you can recollect immediately.
______________
______________
______________
_______________
_______________
2. Which of the following social networking sites do you visit the most?
Facebook
Twitter
Orkut
Bharatstudent.com
Linkedin
Indyarocks
Bigadda
3. Which of the following parameters you LIKE the most for the mentioned social networking site?
Ease of Speed Privacy Gaming Sharing
Navigation/ and
User Networking
Friendly
Facebook
Twitter
Linkedin
Orkut
Bharatstudent.com
Indyarocks
Bigadda

36. 4. Which of the following parameters you DISLIKE the most for the mentioned social networking
site?
Ease of Speed Privacy Gaming Sharing
Navigation/ and
User Networking
Friendly
Facebook
Twitter
Linkedin
Orkut
Bharatstudent.com
Indyarocks
Bigadda
5. How important are these parameters according to you?
Least Unimportant Neutral Important Most
Important Important
Ease of
navigation/
User friendly
Speed
Privacy
Networking
and Chatting
Sharing (e.g.
Video, Music,
Photo, Status
etc)
Applications
Information

37. visibility
Earning in
monetary
terms
Online
shopping
Gaming
Downloading
(e.g. Videos,
music, photos,
wallpapers
etc)
Ease of
payment in
suitable
currency and
payment
gateways like
paypal
6. How satisfied you are from these parameters?
Least satisfied Dissatisfied Neutral Satisfied Most satisfied
Ease of
navigation/
User friendly
Speed
Privacy
Networking
and Chatting

38. Sharing (e.g.
Video, Music,
Photo, Status
etc)
Applications
Information
visibility
Earning in
monetary
terms
Online
shopping
Gaming
Downloading
(e.g. Videos,
music, photos,
wallpapers
etc)
Ease of
payment in
suitable
currency and
payment
gateways like
paypal

39. 7. Kindly tick the appropriate option for the following
Strongly Disagree Neutral Agree Strongly agree
disagree
I mostly use
my credit card
for payment
I like online
gaming
I like to
download
movie for free
I like to build
my
professional
network online
I like do online
shopping
I like do video
chatting
I use my
phone for
professional
purpose
I like to
download paid
applications on
my phone
I visit social
networking
sites through
my phone

40. I like to visit
social
networking
website which
gives
opportunity to
earn
I don't mind to
pay for
downloading
8. Kindly tick appropriate option for the following
Yes No
I notice the advertisement
which appears in social
networking site
I click on the advertisement
which appears in the social
networking site
I pay for the value added
services provided by the social
networking site (e.g. Linkedin,
Bharatmatrimoniy.com)
I pay for special in-game items
while gaming in social
networking sites
I design apps and sell on social
networking sites

41. 9. Kindly tick appropriate option for the following questions
Yes No
I would visit the social
networking sites which are
providing earning options
through paid surveys,
application development etc.
which can be redeemed in
online shopping (e.g. Live
streaming, Video downloading,
mobile recharge etc)
In association to OPTION NO.1
would you like to use these
earnings for legally watching
latest released movies (i.e. to
inhibit piracy) on social
networking sites