1. The Connection Between Social Media & Elections 2. How SocialMatica Correctly Predicted The Future Based On Social & Digital Data 3. How You Can Do The Same

1. SocialMatica’s Social Media Workshop Series
THE TRUTH ABOUT SOCIAL MEDIA
& THE GOP PRIMARY ELECTION

2. Today’s Agenda
What You’re Going To Learn
1. The Connection Between Social Media &
Elections
2. How SocialMatica Correctly Predicted The
Future Based On Social & Digital Data
3. How You Can Do The Same

3. Introduction
• Who is SocialMatica
– What We’re About
– Why We’re Doing This
– Free Social Performance Tool
http://agencysnap.socialmatica.com
• Hash Tag #smtca Questions
– This week or future workshop content

4. SocialMatica ToolKit

5. What People Want
• Social/Digital Media To Mean Something
• Predict The Future
• Get In The Mind Of The Consumer
• Track & Measure Results
• Tweak Accordingly

6. Why Most People Don’t Have That
• No Uniformity
• No Top-Down Measurement System
• Cool Versus Useful
• Systematic Guessing

7. Proper Methodology
• It’s Simple But Uncommon
• We Used It On The Elections

8. To Understand The Digital Landscape
• Topics
• Social Identities
• The Subject Matter
– Brands
– Candidates
– Categories
– Etc

9. What Matters & How To Tell

10. Measuring Who – A Digital Resume
• Performance
– # of Articles in well known websites with high traffic
– # of mentions in other articles
– # of comments
– # of Retweets, Followers, Mentions and Relevant
Topics
– Traffic
– Relationships To Others
– Volume Of Activity
– Volume Of Blog Posts
– Comments To Post Ratios

11. Where They Said It
• Traffic
• Links
• Articles
• Comments On Articles
• Social Media Performance
• Digital Audience Online

12. From This We Build
• Campaign Strategies
– Blogger Outreach
– Target Keywords
– Target Locations For Halo Effect
– Engagement
• Tactical
– Who, Where, How Important?
• Predict The Behavior?

13. Advantages As Marketers
• Build Knowledge
• Gather Proof
• Baseline & Demonstrate Progress
• With SM, You Save Time

14. The Project
• Track Elections
• Track Social Connections & Influence
• Determine If There Is A Relationship

15. The Wisconsin Recall Election

16. The Candidate Breakdown
Republican
Democrat
Republican
Democrat
Republican
Democrat

17. We Worked With A Campaign Advisor
• Websites
• Topical Lists
• Refined Duplicate Or Erroneous Data
• Here’s What We Were Left With

20. Top Observations
• There's a social world
• There's a real world
• Urban areas - connection to social
• Rural low income – no connection to social

21. The Right Way To Think About Social
• The Data Represents Intent
• That Data Represents Desire
• The Data Represents Purpose
• We Trust That We Can Act On It

22. Our GOP Performance
• Wisconsin Recall
– 3/3
• GOP Primaries
– 7-10 on Mitt Romney
– Ron Paul Is Interesting Because...
– Santorum Is Interesting Because...

23. Turns Out
• Ron Paul Motivates The Young & The Higher
45k-100k Wage Earners (Middle America)
• Santorum Motivates The Rural & Low
Income, Low Education
* Assumption Based On Social Network Demographic Breakdowns & State Voter Turnout Demographics

24. How We’ve Responded
• Incorporated Gallup poll data into our daily
polling
• Given it an appropriate weighting of the
overall score

25. Benefits
• Gives us a direct channel to non-social and
social alike
• Allows to read what real people say, think, and
respond
• Content oriented, so we can see trends and
start seeing agenda's
• Gives us a contrast for "sponsored" content
• So we can see the spin

26. Conclusions
• Verticalpoint gives us a way to connect the
digital world with the real world and compare
data points. We can see more than
mentions, we see rank, we see sources, we
see content and when you've observed long-
enough you can see trends and major shifts in
the market.

27. How You’re Able To Unleash This Data
• Know What’s Being Said
• Know The Importance Of The Author
• Know The Location Of The Content
• Sentiment
• Performance Trending

28. How You Can Do The Same
• VP License
• Build A Contextual Model Manually
• DEMO

29. SocialMatica ToolKit

30. Questions?
www.socialmatica.com
training@socialmatica.com
If you’d like a free account to AgencySnap, just
send us a note to the email above and we’ll
reply with login details.

31. Number of Articles and Number of
Tweets Mentioning the Candidate
Comparing the number of Articles that mention the candidate (7/12 – 8/9), and
number of Tweets that mention the candidate (8/3 – 8-9)
Candidate Num Articles Num Tweets
Race 1:
Darling 398 1855 (Winner)
Pasch 246 1837
Race 2:
King 231 1181 (Winner)
Hopper 296 291
Race 3:
Shilling 121 562 (Winner)
Kapanke 185 113

32. Number of Articles that Mention the
Candidate with SocialRank considered
Comparing the number of Articles that mention the candidate (7/12 – 8/9), broken
down by the SocialRank (SR) of the mentioner—3 groups: those with SocialRank
6.0 or higher, SocialRank 4.0-5.9, SocialRank 0-3.9.
Candidate Articles SR 6+ SR 4-6 SR 0-4
Race 1:
Darling 398 132 191 75 (Winner)
Pasch 246 97 98 51
Race 2:
King 231 73 120 38 (Winner)
Hopper 296 93 155 48
Race 3:
Shilling 121 55 46 20 (Winner)
Kapanke 185 72 69 44

33. Number of Tweets that Mention the
Candidate with SocialRank considered
Comparing the number of Tweets that mention the candidate (8/3 – 8/9), broken down by the
SocialRank (SR) of the mentioner—3 groups: those with SocialRank 6.0 or higher, SocialRank 4.0-
5.9, SocialRank 0-3.9.
Candidate Tweets (412)SR 6+ (626)SR 4-6 (3268)SR 0-4
Race 1:
Darling 1855 1018 552 285 (Winner)
Pasch 1837 1190 398 249
Race 2:
King 1181 652 448 81 (Winner)
Hopper 291 218 44 29
Race 3:
Shilling 562 321 211 30 (Winner)
Kapanke 113 58 46 9

34. The “Day After” Chatter
Number of Tweets Mentioning Candidate on day after election:
Candidate “Day After” Tweets
Race 1:
Darling 529 Winner (compared to 194 on election day)
Pasch 1516* (compared to 656 on election day)
Race 2:
King 1181 Winner (compared to 532 on election day)
Hopper 155 (compared to 114 on election day)
Race 3:
Shilling 894 Winner (compared to 338 on election day)
Kapanke 159 (compared to 12 on election day)

35. FaceBook Likes
Likes from Aug 16.
Candidate Num Facebook Likes
Race 1:
Darling 5381 76% more facebook likes Winner
Pasch 3062
Race 2:
King 3859 554% more facebook likes Winner
Hopper 590
Race 3:
Shilling 3791 37% more facebook likes Winner
Kapanke: 2763