To date, defining "Influence" has been associated with the black art of "lies, damned lies, and statistics." 'Buzz metrics' have been a noble attempt to apply Google's PageRank (PR) algorithm to identifying Influence, but there's a small problem - it doesn't work, and it never did . . . at least not when we evaluate social interaction. Influence is contextual, specific, often short-lived, and lies manifests within a network. If we each have 5 RTs and @Mentions, we're not equally "Influential", although the Google PR may rank us as such. The paradigm shift is from PageRank to PeopleRank, and the latter is all about visualization. Seeing Influence is all about moving beyond mathematical rankings, and in this case, pictures are truly worth a thousand words. In this session, you'll learn how to visually map out a conversation network of social interaction, in addition to identifying where "Influence" truly lies. Being able to visualize the social structure and pattern of Influence will open your mind to a world of new possibilities with Social Media. What to do with this knowledge will depend on your goals and objectives, but one thing is for sure - you'll never see Influence in quite the same light again.

1. Seeing Influence and
Visualizing Buzz –
Recruiting Focus
Josh Letourneau
jl@knightbishop.com
www.KnightBishop.com
(404) 418-8152

2. “Social Networks”
 Far pre-date Facebook!
 “Can I stay in your cave tonight? Dinosaurs want to eat me.”

3. Social Behavior
 Humans live and self-organize into “Small Worlds”
(mathematically)
 We form lots of small ‘Clusters’ (4 to 6 groups of 10 or less
people each)
 Information travels through the cluster quickly (“short network
path”)
 It’s why humans are so resilient . . .
 It’s why we’ve evolved so quickly (i.e. talent/intelligence versus
network structure)

4. Social Behavior – Visual Example

5. From Network Organization to the
Individual
 Do we have lots of “Clusters” of Friends?
 Relatives | Associates | Common Interest/Hobby Friends |
Phases of our life

6. So do we really have one mass
bucket of ‘Friends’?

7. The Web Has Changed …
 Yesterday = We “Consumed Content”
 Today = We “Interact with Others”
 This is a significant distinction when we consider how
Candidates come in contact with our branding, our advertised
positions, etc.
 They trust each other more than they trust us.

8. To Anyone that Says Social Media
isn’t important . . .
 Simply explain yesterday’s ‘consumption’ versus today’s
‘interaction’
 Ask them how they make major purchases
 Changing a job is as big of a decision as buying a new car,
perhaps bigger
 Tell them Social Media manufacturers a product called “Word
of Mouth”

9. The “Social Web” is Improving
 Originally built to link to static documents (left pic)
 Along came Social Media (middle pic)
 Now our profiles and activity follow us (i.e. Facebook
Connect)

10. HR/Recruiting Originally Got It
Wrong
 “Build it and they will come” never worked (Field of Dreams)
 “Talent Communities”
 “Talent Portals”
Examples:
1. Facebook pages of 10k members with NO activity!
2. Dormant Ning Communities – they’re being left behind like
foreclosed homes in the real estate market
3. “Social Media Bubble?”

11. Key Factors are Converging for
HR/Recruiting
 Thousands of years of Social Behavior (and Science that
explains it – “Small Worlds”)
 An Improving “Social Web” (better design to incorporate
‘interaction’, not just consumption)
 People/Candidates have a more discerning eye
 They don’t need (and don’t want) to join another ‘community’
 They know garbage when they see it

12. The Key Question
 How can we, as Recruiters & Sourcers, tap into the small
portion of our Target Talent Pools that is overlapping in
hundreds of thousands of "Mass Friend Buckets" on the
web?

13. Before We Get There . . .
 Let’s talk “Influence”
 What is it?
 Can we identify it?
 Travel evenly?

14. Finding Influence through Buzz
Metrics
 “Buzz Metrics” count things:
 Facebook: # of Fans, Likes, Wall Posts, Comments, etc.
 Twitter: # of Followers, Tweets, Re-Tweets
 YouTube: # of Views, Comments
 Blogs: # of Subscribers, Comments, etc.
 Traackr is an example of a Buzz Metrics Engine
 Most “Influencer Lists” are composed through Buzz Metrics
Engines

15. Is There a Better Way?
 Welcome to Network Science
 Network Science is more concerned with how people
connect and interact than drop-down data (think Human
Capital versus Social Capital)
 SNA is a means of mapping relationships and flows in a
network.

16. SNA Trumps Buzz Metrics
 SNA doesn’t ‘count things’ haphazardly
 With SNA, Influence is a mathematical property that naturally
emerges from the pattern of connections we have
 Put better, the pattern of connections that surround us
effectively portrays our credibility and influence within our
social networks

17. Buzz Metrics versus SNA

18. SNA – Let’s take a look
 #HRFL10 (Sized by # of Tweets)
 Mike Vandervort (top spot) = 31k tweets!
 Channele Schneider (2nd spot) only mentions #HRFL10 once

19. #HRFL10 Sized by # of Followers
 Jessica Merrell = 15k+ Followers (RT’d 11x)
 Laurie Reuttiman = 11.7k Followers (@mentioned & RT’d
28x)

20. # of Tweets & # of Followers
 Is there a better, more insightful way to find & see the
Influence? Yes!
 “Bridge Score” (aka ‘Betweeness’)
 Measures how often you’re ‘between’ members in a
conversation . . .
 “Gatekeepers” can ‘broker’ or ‘bottleneck’, right?

21. Bridge Scores show Influence
 Those w/ high Bridge Scores reach otherwise disconnected
clusters – they move conversation along – they ‘Influence’.
 Jennifer McClure (top spot) = 40 @mentions and RTs’.
 As there are 335 edges in the map, she’s directly involved in
40 of them (12%).

22. Multi-Scale View
 Conversation from End-to-End
 Would contacting the top 5 ‘Between’ people (Bridge Scores)
be better than making random calls to the 301 people on the
map?

23. Other Things We Can Look At
Through SNA
 Hub Scoring – how many links are coming in versus going
out?
 Many links coming in shows authority & credibility
 Many links going out shows someone actively building a network
– “reaching”.
 Leads us to consider influence from the perspective of ‘Expertise’
versus ‘Network Building/Sharing’
 “Directionality” of connections

24. What Do High Hub Scores
Mean?
 High Hub Scores mean that you’re (usually) seeing an
‘Expert’ visually – someone that is seen as credible in the
eyes of their peers
 Could be your Candidate – put them on your radar, build
relationship with them
 Their messages are rcvd with consideration, respect, &
reverence

25. Network Topology Doesn’t Lie
 SNA allows us to see the 96% of data that is the iceburg
under the water.
 The network topology doesn’t lie . . . And the larger the
sample grows, the more valid it is (statistically).
 Network Science trumps Buzz Metrics . . . By a long shot.

26. It’s All About Leveraging
Conversation & “Social Interaction”
 We’re talking way bigger than Keyword (Boolean) Searching
 Patterns of Connection trump drop-down data. In Advertising
Speak, patterns of connections matter more than basic
demographics.

27. So How Can We Use All This to
Help Us Recruit?
 Follow & Build Relationships with the following people:
 High Bridge Scores (highly ‘Between’)
 Many incoming connections (In-Degree), as they have high
credibility and authority
 Many outgoing connections (Out-Degree), as they are “reaching”
wide areas of the network (network-building)
 Think “Moderately Connected Influencers”
 Map and find those connecting to unique & different clusters

28. Recruiting Insight
 Connecting and building relationships with these individuals
offers the most bang for your buck in Social Media
 These individuals are the most mathematically “close” to your
Target Talent Pools and ideal candidates
 People want to help others in their clusters of Friends (think
“Small Worlds”)
 “50% of my Advertising Budget is wasted, but I can’t identify
which 50%!”

29. Observation versus Creation
 Can we manipulate social behavior to our advantage?
 Can we create content (articles, videos, posts, etc.) and
entice key players (influencers) to share it?
 Can we encourage participation and engagement?
 Does it have to be Us? What about our Employees?

30. World Cup Example – Nike versus
Adidas
 2.6 Billion People following the games
 $1.5B to $1.7B USD in total merchandise revenue
opportunity
 Adidas was official sponsor
 Soccer ball is Adidas branded
 Refs wear Adidas patches
 All game-time commercials are Adidas, etc.

31. Nike Goes Digital
 “Write the Future” Campaign
 Featured top world players discussing plays that changed
their lives
 Consumers (Us) were given ability to edit 3 minutes of video
 Winning commercial aired around the world

32. Results of Consumer
Empowerment
 35% Buzz Penetration versus 14% (Adidas)
 Nike is now doing this with R&D as well
 Are there lessons here we can implement with our Recruiting
initiatives?

33. What Do I Do?

34. Questions & Comments?
 Call me!
 Josh Letourneau
 jl@knightbishop.com
 www.KnightBishop.com
 (404) 418-8152