“Betweenishly” Social Recruiting Through #HRFL10 (HR Florida)

A couple months ago, I wrote about how Social Recruiting 3.0 would be all about leveraging interaction patterns and social behavior itself. There is no denying the “Why” – look at any company’s referral programs, or the fact that most HiPos’ come from employee referral as the source of hire, and it makes utter sense that we should try to become better in this arena.  Unless you’re a status-quo thinker (which you’re not since you’re reading FOT in the first place), you’ve likely considered how to improve some of your own internal programs in this regard.

The challenge we all have is that the Social Web, in its current state, is broken… however we’re moving in the right direction.  Hey, the Web wasn’t first built to enable “Social”, it was designed for the Department of Defense as an extension of the Cold War through DARPA.

Given that concern, we’re not completely dead in the water.  There are emerging tools that allow us to make sense of the data and coinciding patterns of interaction all around us.  They are going to change not only the way we measure “Social” (it’s bigger than media, Folks!), but the way we look at the concept entirely. One of the tools I’ve been using in my practice is NodeXL, for example, in my visuals below (Hat Tip to my friend, Marc Smith [former Head of Microsoft's Internet Services Research Center] for not only helping develop the tool, but really extending what is possible through it.)

When I wrote my original post on Social Recruiting 3.0 (both Recruiting and Sourcing-related), my thinking about this topic was evolving.  I could see the links like a ship coming through the fog, but couldn’t entirely make out how to create Recruiting and Sourcing value through social interaction patterns.  Today, my goal is to take a simple question and show you how you can approach it in a manner that accelerates your path and trajectory to someone who you believe would fit greatly into your organization given your goals.  That question is the following:

“Being a Florida-based company, we’re looking to begin building a Recruiting Team that really gets it.  We think that these people are most likely going to be at, or at least tuning into the conference, HR Florida.  We can’t personally make the event ourselves, so what should we do?”

1. Given that your approach is central to the HR Florida event, start following the hashtag #HRFL10.
2. Take all the data you have (could be +/- 5 days before and after the event if you want to evaluate the event in entirety; could be a few days leading up to the event; could be during the event itself, etc.) and import it into NodeXL.
3. Generate the Maps.
4. Contact those with the most privileged network locations, and be confident you’re going to staff your team with the right talent in a fraction of the time it would otherwise take.

For the following, I looked to analyze ‘during-the-event’ interaction (specifically from 8/31/10 at 3pm through today, 9/2/10).  You’re probably wondering what that would yield you and/or what visuals you might produce, not to mention what insights you might pull from this process.  So here we go . . .

The first map you can look at is just an overall visual of Tweets mentioning #HRFL10.  We’ve applied no algorithms to make this visual ‘prettier’ and/or more conducive to visual insights – it’s just a raw picture of tweets.  Notice the node colored in red on the bottom?  I colored it that way because it stood out as a person of interest.  We’ll get to that shortly.

At this point, you can recreate the map by darkening the ‘edges’ (i.e. mentions or retweets) depending on how frequently 2 people interact between one another (called a dyadic exchange, but we’ll leave the scientific lingo alone!)  In addition, you can also decide to associate twitter images with each node, thereby sizing them according to how many tweets they have (The image to the right is how that looks.)  If you look closely, Mike VanderVort blows everybody away.  He has over 31000 tweets! The second largest node (based on tweet volume) is Chanelle Schneider, founder of #GenYChat at over 28000 tweets.  However, Chanelle only mentions the term #HRFL10 once, so this illustrates how you have to look deeper than solely number of tweets as an insightful metric.

What if we recreated the map in terms of linking node size to number of followers?  Would that help?  The most followed person on this map is Jessica Merrell at 15,552.  She doesn’t mention #HRFL10 to anyone,  yet has it mentioned (or she is RT’d) 11 times.  She presented at the event also – not too shabby!  In second place is Laurie Ruettimann with 11,702 followers.  She only mentions #HRFL10, yet has it mentioned (or she is RT’d) 28 times.  That’s big.  The challenge with identifying influencers through number of followers is the lack of context - nobody can be influential within all domains.  In this particular case, it just so happens that Jessica and Laurie have followers and are also conversation catalysts in regards to #HRFL10.  That isn’t always the case.

So far, we’ve looked at two lenses: ‘Number of Tweets’ and ‘Number of Followers’.  Yes, they each offer their own perspective, but my personal favorite is the following: Betweeness. When looking inside of organizations, individuals with a high level of Betweeness have great influence over what flows, or doesn’t flow, through a network (they can be a broker or a bottleneck, or both, depending on context).  When we look at Twitter Conversation Networks, individuals with a high level of Betweeness serve as ‘bridges’ to clusters of interacting individuals (after all, that’s what we’re looking at: actual interaction and conversation!)  The node of interest above is Jennifer McClure, and the edges that connect her to others are colored in red (just as they were in our first visual).  I did this to illustrate that generalized visuals (even without tweaking) can show us nodes that stand out as key connectors – it’s not rocket science all the time.  Why is Jennifer important within the particular context of #HRFL10?  Because she has 40 individuals mention something to her (and/or RT her).  Given that the above map only shows 335 edges (or mentions/RTs), that means Jennifer is involved in ~12% of all the interaction.  Even if you don’t do the math, you can just look at the map and identify her as someone who is likely very close to one or several people whom you’d like to recruit to your new team.

For another visual that really illustrates just how important Betweeness is, see the following.  Imagine the power of connecting with the 5 most ‘Between’ people on this map (those with the biggest pictures).  Out of the random 301 phone calls you could make (which is the number of people reflected here), would you believe that 5 alone could earn you immediate access to everyone?  You’d essentially be targeting the broker to a particular talent cluster (signified by the tight groupings around the larger pictures.)  And better, how about the fact that you’d have a warm lead?  If you’re in the world of Recruiting, I don’t need to tell you the power of having someone open a door for you instead of trying to open it on your own.  There is a tremendous amount of time spent by Executive Search firms trying to identify and nurture relationships with these people, especially those who share openly.  They’re termed “Bird Dogs”, and I can assure you that every single search starts with a “Who might you know for this role?” phone call to them.  When you hear about Executive Recruiters filling jobs with 3 or 4 phone calls, this is what they’re describing.

Why is this important to you?  Because through following interaction patterns online, you can accurately identify what relationships you need to build.  It’s like a referral program on steroids.  We’re removing randomness and “luck”.  We’re stacking the deck in our favor, and utlimately, that’s what success is all about. Imagine a referral program where you could go back to an employee and reward them for their efforts (say for a blog post that was tweeted and re-tweeted, ultimately resulting in 2 hires through a ‘See Our Open Positions’ link on the blog), even if they didn’t hand carry you a resume?  Through what is described here, it’s truly possible.  Do you think that would bolster your referral program?  You bet!

P.S. Not all interaction today can be mapped, such as our cellphone calls and emails, etc. (although this greatly enhances our research if we’re open to this data).  The “Social Web”, however, is moving us in a direction where more and more of our interation is transparent (hey, for an example, think about what the massive push toward Facebook Connect is all about, not to mention what our interaction patterns reveal about us!)