Research Report on Social Networking in India and Revenue models

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

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.

http://www.orkut.com/

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.

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.

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.

http://www.orkut.com/

Direct Sales: Fewer social networks had included an e store in their environment to

gather revenue directly from sales of products.

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.

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.

RESULTS OF THE QUALITATIVE STUDY

Name of

respondent

Occupation Age (in

Years)

Response

1. Vijesh

Hegde

Service

(Oracle)

29 Advertisements not catchy and noticeable.

Game becomes monotonous and boring after a certain

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

Hegde

Service

(Tesco)

29 Herd mentality among Indian users of using pirated

contents.

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

Bhatia

Student at

IES MCRC

22 Don’t click on advertisements nor pay for in-game

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

participate in online features and spend.

4. Prasad

Vesawkar

MS in NY

Univ

24 Used to play games and buy in-games item like Mafia

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

RESULTS AND ANALYSIS OF THE SURVEY

Overall demographics

above 60000 INR

45001 to 60000 INR

30001 to 45000 INR

15001 to 30000 INR

0 5 10 15 20 25 30 35

31.5

16.9

25.8

25.8

Monthly Family Income

9% 5%

20%

66%

Occupation

Professional Self employed Service Student

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.

26 to 35 years

16 to 25 years

0 10 20 30 40 50 60 70 80 90

23.6

76.4

Age

Top of Mind Awareness and the most preferred site as per the respondent

The most LIKED parameter for the mentioned social networking site

Most Like (%)

Parameter Facebook Twitter Linkedin Orkut Bharatstudent Indyaroc

k

Bigadda

Ease of Navigation/

User Friendly

46.07 16.85 7.87 29.21 15.73 17.98 14.61

Sharing and

Networking

43.82 31.46 41.57 21.35 14.61 13.48 11.24

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

FB

linkedin

Orkut

0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00

93.26

5.62

1.12

Sites most visited by the respondent

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

Facebook

Orkut

Linkedin

Twitter

Myspace

0 10 20 30 40 50 60 70 80 90 100

93.26

3.37

1.12

1.12

1.12

Top Of Mind Awareness

Most Dislike (%)

Parameter Facebook Twitter Linkedin Orkut Bharatstudent Indyaroc

k

Bigadda

Ease of Navigation/ User

Friendly

8.99 12.36 11.24 4.49 5.62 3.37 4.49

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

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

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%)

Opportunity Score Matrix

Importance =

i (Mean)

Satisfaction= s

(Mean)

i-s [If i-s is

negative

consider it as 0]

Opportunity

score.=i+(i-s)

Ease of navigation/ User

friendly

4.12 3.76 0.36 4.48

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,

Photo, Status etc)

3.84 3.9 0 3.84

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,

music, photos, wallpapers

etc)

3.31 3.35 0 3.31

Ease of payment in suitable

currency and payment

gateways like paypal

3.08 3.12 0 3.08

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

Responses received for Current and Proposed Model

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

67.42

24.72

13.48

6.74

8.99

32.58

75.28

86.52

93.26

91.01

Responses on Current Revenue Model

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

67.42

62.92

32.58

37.08

Responses on the proposed model

YES NO

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.

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%


% 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


% of Total 14.6% 9.0% 23.6%


% of Total 48.3% 28.1% 76.4%


% 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)

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%


% 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%


% of Total 15.7% 4.5% 20.2%


% of Total 41.6% 24.7% 66.3%


% 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)

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%


% 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%


% 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)

Verifying dependence between

OCCUPATION V/s CURRENT REVENUE MODEL

Crosstab

I_pay_for_special_in_game_items_while_gamin

g

Total

yes no

Occupatio

n

Professiona

l

Count 2 6 8

% of Total 2.2% 6.7% 9.0%

Self

employed

Count 1 3 4

% of Total 1.1% 3.4% 4.5%


% of Total .0% 20.2% 20.2%


% 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)

Pearson Chi-Square 7.922a 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.

Cluster Analysis

Extract of Agglomeration Schedule by Hierarchical method of cluster analysis

Stage

Coefficients Difference between consecutive

coefficients

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

Testing significance of lifestyle statements using ANOVA

Cluster Error F Sig.

Mean

Square

df Mean

Square

df

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_onlin

e

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

Cluster 1 characteristics

Final Cluster Centers Cluster

No.1

(Mean)

Remark

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_onlin

e

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

26 to 35 years

16 to 25 years

0 10 20 30 40 50 60 70 80 90

15.4

84.6

Cluster 1-Age

above 60000 INR

45001 to 60000 INR

30001 to 45000 INR

15001 to 30000 INR

0 5 10 15 20 25 30 35 40 45

38.5

19.2

23.1

19.2

Cluster No.1- Monthly Family Income

7.7 3.8

23.1

65.4

Cluster No.1-Occupation

Valid ProfessionalValid Self employedValid ServiceValid Student






73.10%

19.20%

15.40%

3.80%

7.70%

26.90%

80.80%

84.60%

96.20%

92.30%

Cluster No.1- Responses on current revenue model

YES NO



54%

50%

46%

50%

Cluster No.1- Responses for proposed model

YES NO

Cluster 2 characteristics-


No.2

(Mean)

Remark

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_networ

k_online

4 Most Influential factor for respondent in

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

Average age- 22.43 years Average income- INR 43790

16 to 25 years

26 to 35 years

0 10 20 30 40 50 60 70 80 90

80.6

19.4

Cluster No.2-Age

15001 to 30000 INR

30001 to 45000 INR

45001 to 60000 INR

above 60000 INR

0 5 10 15 20 25 30 35

29

29

12.9

29

Cluster No.2-Monthly Family Income

74.2

9.7

3.2 12.9


StudentServiceSelf employedProfessional






61%

29%

13%

7%

7%

39%

71%

87%

94%

94%

Cluster No.2-Responses on current revenue model

YES NO



71.00%

64.50%

21.00%

35.50%

Cluster No.2-Responses for proposed revenue model

YES NO

Cluster 3 characteristics


No.3

Remark

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_

online

4 Most Influential factor for respondent in

cluster 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

Average age- 23.93 years Average income- INR 44,511

16 to 25 years

26 to 35 years

0 10 20 30 40 50 60 70

65.6

34.4

Cluster No.3-Age

15001 to 30000 INR

30001 to 45000 INR

45001 to 60000 INR

above 60000 INR

0 5 10 15 20 25 30

Cluster No.3- Monthly Family Income

59.4

28.1

6.26.2


StudentServiceSelf employedProfessional






71.00%

25.80%

12.90%

9.40%

12.90%

29.00%

74.20%

87.10%

90.60%

87.10%

Cluster No.3-Responses on current revenue model

YES NO



74%

71%

26%

29%

Cluster No.3- Responses for the proposed revenue model

YES NO

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.

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.

REFERENCES

UsersIn-site advertisersSocial Networking SitesFlim Production HouseCorporate Clients (e.g. Data collection agency

WIN-WIN SITUATION

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

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

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

Navigation/

User

Friendly

Speed Privacy Gaming Sharing

and

Networking

Facebook

Twitter

Linkedin

Orkut

Bharatstudent.co

m

Indyarocks

Bigadda

4. Which of the following parameters you DISLIKE the most for the mentioned social networking

site?

Ease of

Navigation/

User

Friendly

Speed Privacy Gaming Sharing

and

Networking

Facebook

Twitter

Linkedin

Orkut

Bharatstudent.co

m

Indyarocks

Bigadda

5. How important are these parameters according to you?

Least

Important

Unimportant Neutral Important Most

Important

Ease of

navigation/

User friendly

Speed

Privacy

Networking

and Chatting

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

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

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

7. Kindly tick the appropriate option for the following

Strongly

disagree

Disagree Neutral Agree Strongly agree

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

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

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

Technology

Research Report on Social Networking in India and Revenue models