Image via CrunchBaseEmpire Avenue states that Connections, Recommendations, and Activity are factored into the Linkedin Score. Connections and Recommendations are easy metrics to derive once the LinkedIn account is hooked up in Empire Avenue. Activity measurements can be a challenge given the nature of Linkedin. Activity that shows up on a status update timeline are: Status Updates, Discussions, Shares, Polls, Group Activity, Question and Answers. There are also secondary activity measures such as likes and responses made to your questions and discussions. I started looking at the LinkedIn Scores when I noticed that Stacy Zapar, the #1 connected woman on Linkedin had an EAv LinkedIn network score of 68 and she is one that engages. At the time, I was scored a 32 and wondered what the highest EAv LinkedIn scores profiles looked like. Empire Avenue provides the capability to evaluate scores across a variety of dimensions including networks.
Comparison Chart
The following shows the Empire Avenue LinkedIn network scores and LinkedIn metrics as of 6/2/2011.
EAv Score Connections Recommendations Activity
Steve Cassady 34 1,200 10 1 Day
Stacy Zapar 68 30,000 11 1 Day
Jason R. Martin 75 16,240 30 .1 Day
Viveka von Rosen 82 16,000 61 1 Day
Neal Schaffer 88 26,000* 55 1 Day
Lewis Howes 91 20,000* 147 .25 Day
Laura Levitan 95 11,000* 226 None
Shally Steckerl 100 30,000 173 .33 Day
Data Methodology
Linkedin connection counts were provided to me by the users. For those accounts* that I didn't have the actual connection count, I derived the connection counts from relative positioning of LION accounts that state connections counts in geographic searches sorted in connection order. Recommendations are listed in the Summary section of the profile. I used LinkedIn signal to determine LinkedIn network activity over a two week period to be consistent with Empire Avenues two week activity averaging methodology. I removed Twitter based status updates from the LinkedIn activity since the scoring systems should be removing those duplicitous activity measures.
Observations
Bottom Line, is that it appears that the highest Linkedin scores are those users that have recommendation accounts in the mid 100's and above. The highest is Shally Steckerl who is connection count capped out with 173 recommendations. The second highest score is Laura Levitan with over 220 recommendations, but with "only" approximately 11,000 connections. Neither of the two are "active" linkedin users during the past two weeks (being consistent with Empire Avenue's measurement period) as measured by Linkedin Status, Sharing, and Group activity found through LinkedIn Signal. Although their usage may be more like Stacy's for recruiting searches, those activities aren't as "measureable"
It looks like that every 5 recommendations gets you "1" change in the LinkedIn network score. From a broadness of LinkedIn network scoring, I think Stacy Zapar's engagement with her 30,000 base should carry more weight than Laura Levitan's smaller base and less activity even with higher recommendations.
I have a better appreciation of why it may be taking Klout so long to roll out their LinkedIn logic especially with the challenges of LinkedIn's API and variety of ways to use it. How do you factor in poll activity and responses, responses to questions, likes of your shares, comments on discussions you initiated, etc.
My current thinking is that there should be a minimum level of recommendations for a decent LinkedIn network score as a sign of connection validation, but there should be some level where recommendations have diminishing returns. Especially in the past there have been "Top Recommended Contests" on Linkedin and the range of recommendations from being "substantial involvement" to more "thanks for answering my questions, you must be smart". I wouldn't go out to get recommendations just to raise my #EAv stock price since that could dilute the personal branding and the quality of your profile on LinkedIn. You should focus on real authentic recommendations on LInkedIn that have real meaning to clients or prospective employers.
As @Dups, cofounder of #EAv states "We do say we talk about Network Value, and in fact at Empire Avenue, the scores are the algorithmic measurable layer that any API gives us which is fraught with human assumptions, algorithmic issues and technical nightmares, but they do add value, they do give an idea of what's happening in a network."
What do you think about the weight of recommendations in LinkedIn network Empire Avenue scoring?
