Research Report on Social Networking in India and Revenue models

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Research Report on Social Networking in India and Revenue models

  1. 1. Research to enhance experience of IndianSocial Networking SiteTEAM NAME: Intel_InsideTEAM MEMBER: Vaibhav Sarangale Shishira HegdeCOLLEGE NAME: IES Management College and Research Center,Mumbai
  2. 2. EXECUTIVE SUMMARYSocial Networking sites are the fastest growing media for all the corporate as well as users tointeract with each other. The popularity of Social Networking sites in India spread withpopularity of Orkut. Recently Facebook emerged as the most popular networking site in Indiawith 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 mostpopular sites are all established global players. It would not be an overstatement if we say thatthe Indian counterparts have failed to make an impact comparatively.Across the globe, social networking sites operate under different revenue models. Most ofthem rely on advertising as their major source of revenue. Marketers have found social mediaan effective and cheap alternative to grab eyeballs. But the Indian users have differentpsychology 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 theprospective segments of users which can be targeted to gain more visibility. It is alsonecessary 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 modelsThe research design included both qualitative and quantitative studies. The quantitativeresponses were collected using online survey where as qualitative data was collected throughin-depth interviews. Analysis was done using SPSS and Microsoft Excel 2010. Randomsampling was done and response was collected form 89 respondents.
  3. 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. 4. INTRODUCTIONSocial networking site is used to describe any Web site that enables users to create publicprofiles within that Web site and form relationships with other users of the same Web sitewho access their profile. Social networking sites can be used to describe community-basedWeb sites, online discussions forums, chat-rooms and other social spaces online.[1]Experian Hitwise, the global information services company, has conducted an internationalstudy 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 thestudy. As per this study, India ranks 4th and has 14 per cent market share for social networksand forums. Facebook, YouTube and Orkut continue to be the top three social networkingwebsites in India. [2]India with its large population has millions of users accessing Facebook, there are 25 millionpeople using Facebook in India. This means 18% of the online population is from India. It isestimated that within a year India will have at least 27 million Facebook users.
  5. 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 popularportal in India launched its own version, Yaari, Minglebox, Hi5 and dozens of other sites areattracting 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, dueto the popularity of Bit torrent in India, most users prefer to download their music rather thanlisten to it online.Revenue models of social networking sites-Within all investigated social networking sites the following significant revenue models weredetermined: 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. 6. Direct Sales: Fewer social networks had included an e store in their environment togather revenue directly from sales of products.
  7. 7. RESEARCH METHODALOGYObjectives- 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. 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. 9. RESULTS OF THE QUALITATIVE STUDYName of Occupation Age (in Responserespondent 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. 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. 11. RESULTS AND ANALYSIS OF THE SURVEYOverall demographics Age Monthly Family Income 15001 to 30000 INR 25.816 to 25 years 76.4 30001 to 45000 INR 25.8 45001 to 60000 INR 16.926 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. 12. Top of Mind Awareness and the most preferred site as per the respondent Top Of Mind Awareness Sites most visited by the respondentMyspace 1.12 Orkut 1.12 Twitter 1.12Linkedin 1.12 linkedin 5.62 Orkut 3.37 FB 93.26Facebook 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. 13. The most DISLIKED parameter for the mentioned social networking sitesMost Dislike (%)Parameter Facebook Twitter Linkedin Orkut Bharatstudent Indyarock BigaddaEase of Navigation/ User 8.99 12.36 11.24 4.49 5.62 3.37 4.49FriendlySharing and Networking 3.37 3.37 2.25 4.49 7.87 6.74 10.11Privacy 29.21 12.36 15.73 26.97 12.36 11.24 11.24Speed 26.97 15.73 17.98 13.48 10.11 14.61 11.24Gaming 23.60 17.98 14.61 16.85 8.99 8.99 6.74No 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. 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 4.48friendlySpeed 4.2 3.63 0.57 4.77Privacy 4.42 3.7 0.72 5.14Networking and Chatting 4 3.97 0.03 4.03Sharing (e.g. Video, Music, 3.84 3.9 0 3.84Photo, Status etc)Applications 3.12 3.45 0 3.12Information visibility 3.64 3.54 0.1 3.74Earning in monetary terms 2.89 2.97 0 2.89Online shopping 2.58 2.92 0 2.58Gaming 2.6 3.12 0 2.6Downloading (e.g. Videos, 3.31 3.35 0 3.31music, photos, wallpapersetc)Ease of payment in suitable 3.08 3.12 0 3.08currency and paymentgateways 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. 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 sitesComments on Proposed Revenue Model: The acceptance for both the models is very high as in comparison with the currents model as seen above.
  16. 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. 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. 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. 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 modelH1: Occupation of the respondent is dependent of the current revenue modelAs Pearson Chi-Square value = 0.048 is less than α = 0.05 at 95% Confidence Interval, wereject H0 and accept H1Hence Occupation of the respondent is dependent of the current revenue model.
  20. 20. Cluster AnalysisExtract of Agglomeration Schedule by Hierarchical method of cluster analysis Coefficients Difference between consecutive coefficientsStage8 81 25.507 82 26.33 0.836 83 30.27 3.935 84 31.31 1.044 85 32.11 0.803 86 36.31 4.202 87 36.87 0.561 88 48.26 11.39Beyond first stage the maximum difference between the coefficients is observed at the 3 rdstage from bottom hence we can conclude that there are 3 clusters are emerging from thegiven lifestyle statements.From the table given bellow, the number of users per cluster is found out by K-meansmethod for cluster analysisNumber of Cases in each ClusterCluster 1 26 2 31 3 32Valid 89Missing .000
  21. 21. Testing significance of lifestyle statements using ANOVA Cluster Error F Sig. Mean df Mean df Square SquareI_use_credit_card 41.265 2 .683 86 60.450 .000I_like_online_game 9.443 2 .937 86 10.073 .000i_like_download_free 5.228 2 1.227 86 4.260 .017I_like_build_professional_network_online 6.153 2 .921 86 6.680 .002I_like_online_shopping 14.413 2 .730 86 19.747 .000i_like_video_chatting 6.454 2 .832 86 7.753 .001i_use_my_phone_4_professional 18.956 2 .731 86 25.918 .000I_download_paid_app 8.514 2 .826 86 10.314 .000I_visit_site_through_my_phone 21.905 2 .984 86 22.251 .000i_visit_sites_to_earn 12.180 2 .983 86 12.391 .000i_dnt_mind_pay_downloading 11.032 2 .886 86 12.453 .000The F tests should be used only for descriptive purposes because the clusters have been chosen to maximize thedifferences among cases in different clusters. The observed significance levels are not corrected for this and thuscannot 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 saythat all these parameters are relevant for this model
  22. 22. Cluster 1 characteristicsFinal Cluster Centers Cluster No.1 Remark (Mean)I_use_credit_card 2 Not so Influential factorI_like_online_game 3 Neutrali_like_download_free 3 NeutralI_like_build_professional_network_online 3 NeutralI_like_online_shopping 2 Not so Influential factori_like_video_chatting 3 Neutrali_use_my_phone_4_professional 2 Not so Influential factorI_download_paid_app 2 Not so Influential factorI_visit_site_through_my_phone 2 Not so Influential factori_visit_sites_to_earn 2 Not so Influential factori_dnt_mind_pay_downloading 2 Not so Influential factorAverage 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. 23. Cluster No.1- Monthly Family Income15001 to 30000 INR 19.230001 to 45000 INR 23.145001 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. 24. Cluster No.1- Responses for proposed model YES NOIn 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. 25. Cluster 2 characteristics-Final Cluster Centers Cluster Remark No.2 (Mean)I_use_credit_card 2 Not so Influential factorI_like_online_game 2 Not so Influential factori_like_download_free 4 Most Influential factor for respondent in cluster no.2I_like_build_professional_network 4 Most Influential factor for respondent in_online cluster no.2I_like_online_shopping 3 Neutrali_like_video_chatting 4 Most Influential factor for respondent in cluster no.2i_use_my_phone_4_professional 4 Most Influential factor for respondent in cluster no.2I_download_paid_app 3 NeutralI_visit_site_through_my_phone 4 Most Influential factor for respondent in cluster no.2i_visit_sites_to_earn 3 Neutrali_dnt_mind_pay_downloading 3 Neutral
  26. 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. 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. 28. Cluster 3 characteristicsFinal Cluster Centers Cluster Remark No.3I_use_credit_card 4 Most Influential factor for respondent in cluster no.3I_like_online_game 3 Neutrali_like_download_free 4 Most Influential factor for respondent in cluster no.3I_like_build_professional_network_o 4 Most Influential factor for respondent in clusternline no.3I_like_online_shopping 4 Most Influential factor for respondent in cluster no.3i_like_video_chatting 4 Most Influential factor for respondent in cluster no.3i_use_my_phone_4_professional 4 Most Influential factor for respondent in cluster no.3I_download_paid_app 3 NeutralI_visit_site_through_my_phone 4 Most Influential factor for respondent in cluster no.3i_visit_sites_to_earn 3 Neutrali_dnt_mind_pay_downloading 3 Neutral
  29. 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. 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 NOIn 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. 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. 32. Hence these models are would be highly effective if implemented in the revenue modelfor the social networking sites.From different cross tabulations, it was observed that the proposed models no.1 & 2 werereadily 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 theincome group of INR 60000 and above.From these observations we can propose that these models would be highly effective inthese segments.When Cluster analysis was conducted for the 89 respondents, it was found that threeclusters emerged out of which Cluster No.2 and Cluster No.3 comprise of the mostprospective users for the proposed revenue models.The proposed revenue models are designed in such a way that it would benefit all thestake 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. 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. 34. APPENDIXSurvey to enhance experience of Indian Social Networking SiteThe survey is conducted to understand the hidden opportunities in social media for Indian markets andto 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 yearsOccupation Student Service Self employed Professional Home maker UnemployedMonthly Family Income below 15000 INR 15001 to 30000 INR 30001 to 45000 INR 45001 to 60000 INR above 60000 INR
  35. 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 Bigadda3. 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 FriendlyFacebookTwitterLinkedinOrkutBharatstudent.comIndyarocksBigadda
  36. 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 FriendlyFacebookTwitterLinkedinOrkutBharatstudent.comIndyarocksBigadda5. How important are these parameters according to you? Least Unimportant Neutral Important Most Important ImportantEase ofnavigation/User friendlySpeedPrivacyNetworkingand ChattingSharing (e.g.Video, Music,Photo, Statusetc)ApplicationsInformation
  37. 37. visibilityEarning inmonetarytermsOnlineshoppingGamingDownloading(e.g. Videos,music, photos,wallpapersetc)Ease ofpayment insuitablecurrency andpaymentgateways likepaypal6. How satisfied you are from these parameters? Least satisfied Dissatisfied Neutral Satisfied Most satisfiedEase ofnavigation/User friendlySpeedPrivacyNetworkingand Chatting
  38. 38. Sharing (e.g.Video, Music,Photo, Statusetc)ApplicationsInformationvisibilityEarning inmonetarytermsOnlineshoppingGamingDownloading(e.g. Videos,music, photos,wallpapersetc)Ease ofpayment insuitablecurrency andpaymentgateways likepaypal
  39. 39. 7. Kindly tick the appropriate option for the following Strongly Disagree Neutral Agree Strongly agree disagreeI mostly usemy credit cardfor paymentI like onlinegamingI like todownloadmovie for freeI like to buildmyprofessionalnetwork onlineI like do onlineshoppingI like do videochattingI use myphone forprofessionalpurposeI like todownload paidapplications onmy phoneI visit socialnetworkingsites throughmy phone
  40. 40. I like to visitsocialnetworkingwebsite whichgivesopportunity toearnI dont mind topay fordownloading8. Kindly tick appropriate option for the following Yes NoI notice the advertisementwhich appears in socialnetworking siteI click on the advertisementwhich appears in the socialnetworking siteI pay for the value addedservices provided by the socialnetworking site (e.g. Linkedin,Bharatmatrimoniy.com)I pay for special in-game itemswhile gaming in socialnetworking sitesI design apps and sell on socialnetworking sites
  41. 41. 9. Kindly tick appropriate option for the following questions Yes NoI would visit the socialnetworking sites which areproviding earning optionsthrough paid surveys,application development etc.which can be redeemed inonline shopping (e.g. Livestreaming, Video downloading,mobile recharge etc)In association to OPTION NO.1would you like to use theseearnings for legally watchinglatest released movies (i.e. toinhibit piracy) on socialnetworking sites

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