Social Analytics presentation delivered at www.suitsandspooks.com by PeopleBrowsr CEO Jodee Rich

1. PeopleBrowsr Spies and Suits 2011 Jodee Rich CEO PeopleBrowsr

3. 3 Social Media Strategy LISTEN ENGAGE INFLUENCE

4. 4 Social Media Stats 10,000 posts/second Over 10 Billion Conversations- 1% Gold 100% Real Millions of Small related Networks

5. 5 BIG PICTURE Human Socialization Swinging through the trees…

6. 6 BIG PICTURE Emerging from the jungle with Language…

7. 7 BIG PICTURE Thousands of years later we wrote it down…

8. 8 BIG PICTURE PCs, the internet, mobile phones, GPS have come together to enable a vast distributed data network of collective memory

9. 9 BIG PICTURE A collective stream of intelligence…

10. 10 Little Brother Connected Little Brothers will be a higher intelligence than Big Brother

11. 11 Little Brother

12. 12 An Inverted Orwellian Revolution Little Brother has access tovast amounts of data

13. 13 Human Connectedness Viral Streams will add light fiber power to the Collective Intelligence Small Networks close networks will be more powerful than individual Influencers

14. 14 Social Vectors CONNECTEDNESS BREAKING TRENDS SENTIMENT INFLUENCE RELEVANCE TRUST PERSONA

15. 15 Evolution of Influence 2009 number of Followers 2010 Followers and Engagement (RTs, @Replies) 2011 most number of Friends talking about the topic

16. 16 Huffington Post Influence

17. 17 Huffington Post Influence

18. 18 Cartoon Deck – Viral Influence http://bit.ly/hFltVp

19. 19 SOCIAL VECTORS Find Community Champions Architect | Blogger | Cat Lover | Celebrities | CEO | Coffee Lovers | Comcast | Comedy | Cool Brands | Dancers | Dating | Doctor | Dog Lovers | Engineer | Extreme Sports | Finance | Food | Lawyer | Marketing | Mommy Bloggers | Musician | News | Photography | Politics | Religion | Reporter | Social Media | Sport | Travel | VIP | Wall St | Wine Lovers People are 300% more likely to engage when properly targeted

20. 20 Case Studies

21. 21 CASE STUDIES TV Analytics Social TV Analytics will eventually replace Nielsen as the primary data used by Media Buyers…. Here’s Why…

22. 22 CASE STUDIES Objectives Replace Nielsen rating system with Social Media Data Identify TV Show preferences of the Social Audience Implement traditional ratings with Social Data to achieve more accurate results

23. 23 CASE STUDIES The Test Case Filter Social mentions of 900 major TV Shows in the United States Communities Composed of Social Media Users related by their Affinities

24. 24 CASE STUDIES The Solution TV Show Identification Search beyond exact Show Titles AKAs Typos Characters Names Actors Names House OR Gregory House OR GregoryHouse OR Doctor House OR DoctorHouse OR DrHouse OR Dr House OR Doctor Cuddy OR DoctorCuddy OR DrCuddy OR Lisa Cuddy OR Hugh Laurie OR ….

25. 25 CASE STUDIES The Solution TV Show Identification Filter out noise and irrelevant results Contextual Proximity Exclusions NOT the house OR my house OR your house OR *s house OR this house OR that house OR cleaning OR for sale OR buying OR sold OR bought OR dog house OR our house OR full house OR fire OR leave OR party OR white OR …

26. 26 CASE STUDIES The Solution Communities Identify demographics through Declared Age Marital Status Profession Followers of account Under18 = (student OR freshman OR junior OR senior) AND (list of 18K high schools) OR in high school OR I’m 6-17 years old OR I’m a teenager OR student of (high schools) OR studying for the ACTs OR learning to drive OR I want a fake ID OR …

27. 27 CASE STUDIES Data Size Total number of TV Show mentions since January 2011 30 Million

28. 28 Data Size Number of people in each Community Under 18 – 1,615,107 Age 19-24 – 412,479 Age 25-35 – 1,636,156 Moms – 370,762 Heavy Searchers U. 18 – 132,231 Heavy Searchers 19-24 – 40,980 Heavy Searchers 25-35 – 201,238 100K – 346,537 Allergy – 134,585 Tech – 5,111,413 Adventure + Tech – 1,673,600 Active Investors – 5,127 Adventurers/Outdoors – 139,121

29. 29 Data Size Number of people in each Community

30. 30 CASE STUDIES TV Shows Analytics TV Show: 60 Minutes

31. 31 CASE STUDIES TV Shows Analytics Communities: Under 18

32. 32 Data Flow - Communities Firehose RabbitMQ Xapian Search Indexer Search Engine Communitiser Text File

33. 33 CASE STUDIES Ad Measurement Industry: Media Entertainment Background: Creation of a Twitter report for studio executives/ impact of promo scheduling Length of Engagement: 8 months Goals:Evaluate the impact of traditional media on the social media sphere PeopleBrowsr Solution: 180 days historical reporting with overlay of traditional ad schedule Performance and Results: 75,353 # of Tweets extracted for 180 days 12:30p & 7:00p Peak times of engagement 60/40 M/F demographic breakdown of tweets

34. 34 CASE STUDIES Champions Campaign Performance and Results: 50% Percentage of total registrations from Twitter 5,000 # of new followers 36% # of CTR Industry: Computer Software Background: Large software company aiming to promote itself on social media channels Engagement: 12 months Goals: Maximize participation to online seminars and increase awareness PeopleBrowsr Solution: Extract all users aligned with SAP target audience; most influential selected for engagement

35. 35 CASE STUDIES 2011 Super Bowl YTD 387,162 vs 99,124 Total Tweets 2011 Total Tweets 2010 From last year, total volume of Tweets mentioning Super Bowl brands increased 271%. Doritos had the highest number of mentions in 2010 and was the 3rd top mentioned brand this year, with an 89% increase in volume in 2011. In 2011, most social activity of all ads was in the Auto industry, represented by Volkswagon, Chrysler and Chevrolet.

36. 36 CASE STUDIES Trend Analytics Viral Analytics… RT Acceleration

37. 37 CASE STUDIES

38. 38 CASE STUDIES Trust Triangulation There is a young girl trapped in the basement Location Influence Kredentials

39. 39 CASE STUDIES Bot Detection Metrics Sent Post Count to @Name Mention Ratio Sent Post Count to Key word frequency Velocity

40. 40 CASE STUDIES Human Border Trafficking in the Middle East Classified

41. 41 Kred Influence and Outreach Transparent Activity Statement Community Based Group Kred Outreach Meter Fresh Content Advisory Function Detailed Analysis

42. 42 What is Kred? Kred is measurable Influence Kred offers separate metrics for Influence and Outreach. Influence measures a user’s relative ability to inspire action from others like retweeting, replies or new follows. Outreach measures generosity and rewards actions like interaction with others and willingness to spread the message.

43. 43 KredInfluence Influence is the measure of what others do for you It is reported to on a normalized 1,000 point scale. Influence is measured by Retweets @replies New follows List following Follow/following ratio Influence is outbound – how you inspire others to take action.

44. 44 KredOutreach Outreach is the measure of generosity Outreach points are based in levels and will increase infinitely as users interact and spread messages from others. Outreach is measured by Retweets @replies New follows List following Outreach represents how others inspire you to interact and engage.

45. 45 Kredentials for every @name

46. 46 Swinging through the trees…Language evolved Little Brother will carry the next level of Human Evolution – Influencers and Authorities independent of Institutions @WingDudeJodeeRich@PeopleBrowsr.com

47. @WingDude jodeerich@peoplebrowsr.com http://slidesha.re/PBSnS