In the movie “The Matrix” there’s a scene where Laurence Fishburne says to Keanu Reeves, “The Matrix is everywhere. It is all around us. Even now, in this very room. You can see it when you look out your window or when you turn on your television. You can feel it when you go to work … when you go to church … when you pay your taxes.”
That’s basically the premise of big data, where the potential in recruiting is in getting good candidates to respond.
You see, we’re surrounded by things that collect data on us: phones, tablets, cameras, soon “black boxes” in cars, and of course browsers. All this data supposedly offers unprecedented awareness of people’s actions and attitudes. As of 2012 about 2.5 exabytes (billion gigabytes) of data are created each day — much of it from retweets by followers of Lady Gaga. That number is doubling every 40 months or so. Given this ocean of data, the thinking goes something like this: big data potentially offers great competitive advantages because it allows companies to make better predictions and smarter decisions.
An article in the Harvard Business Review sums it up: “We can target …. areas that so far have been dominated by gut and intuition rather than by data and rigor. As the tools and philosophies of big data spread, they will change long-standing ideas about the value of experience, the nature of expertise, and the practice of management. Smart leaders across industries will see using big data for what it is: a management revolution.” That may well be the case, but revolutions have a record of failing far more often than succeeding — leaving a lot of debris and scapegoats.
For some businesses it seems to work.Amazon’s and Google’s success is largely tied to their ability to make use of big data. With big icons of success and such lofty expectations the temptation is to believe the hype and jump in with both feet.
Back to what I mentioned up top — getting good candidates to respond — it’s all about getting those postings and other content to those who are most likely to be qualified and will be most interested. A version of this is available from eQuest that targets job postings to career sites based on candidate behavior.
While that’s useful, the real potential for big data is based on building interest graphs — a map of a person’s interests. Sites and products based on interest graphs would know what appeals to a person, eliminating the need to search through news sites for the stories, or shopping sites for the products, because the site would know as soon as a person went to it what their likes and dislikes are.
This is what has been built into Google Now. It tries to anticipate what a user wants to do and provides content traffic alerts, weather, train schedules, music etc. based on the person’s activities and interests.
Applied to recruiting, this could recommend job postings in a meaningful way, instead of the near-often useless keyword matching that happens today. It would be even more useful if the postings were targeted at people most likely to be receptive to them. This isn’t exactly difficult to do; some indicators are already available. For example, people who start receiving a lot of recommendations on LinkedIn in a short period of time are more than likely starting to look for a job. Analyzing conversations on Facebook or Tweets can suggest that a candidate is unhappy with their current work situation … or very happy, in which case calling them may be a waste of time.
The same can be done with content created to attract candidates. Knowing a person’s interest graph can allow for that to be tailored specifically to the candidate, making it much more likely to build engagement.
Article Continues Below
How mature is your hiring process? Answer these 5 questions and find out.
The above suggestions for big data may make what’s possible look more like a protest march than a revolution. That’s because the challenges in using big data are formidable. Google and Amazon are successful because they have the means to collect the data. Most employers, even very big ones, don’t have the means to collect enough information on candidates to develop a big data strategy. Vendors of services like eQuest that aggregate huge amounts of data on candidates can do so, but for others there just isn’t enough there to do much of anything. The typical employer can only collect data on candidates entering their career site, which includes how they behave on the site and the resume they submit. A LinkedIn profile doesn’t add much here because the ATS treats it as a static document.
What’s needed is the ability to collect data from candidates on an ongoing basis in multiple settings — conversations on Facebook, tweets, blogs, profiles, etc. — to build the interest graph. It isn’t easy to do this and combine the data. It usually requires the candidate’s willing participation. Trying to do this with publicly available data is unreliable. Pivotlogix is one company that ha built a product to combine candidate data from all sorts of sources, social and others, but only if candidates agree to give them access.
Change the Tune
Today the theme song for most recruiters is the Moody Blues’ “I know you’re out there somewhere.” The first part sums it up pretty well:
I know you’re out there somewhere
I know I’ll find you somehow
And somehow I’ll return again to you
Big data offers a way to change that, but it requires a new way of thinking. A lot of what’s new and improved in HR technology tends to get used as old and abused. People like to stick with the familiar — so it was with job boards, which largely replaced print ads, and employer pages on Facebook which replicated corporate web sites.
Instead of trying new ways to find and engage candidates, what’s likely to happen is that the data gets used just to post jobs. That’s likely to disappoint, as advertisers on Facebook found out; the social media ad platform company Mediabrix estimates that the click-through rate for ads on Facebook is just 0.05%. Even improving that by a factor of 10 based on a better understanding of people’s behavior means that the CTR would only be 0.5%.
Focusing on building engagement would be better. If you can change the tune to the one in the title of this article, you’ll know more about who you’re reaching and be more certain that they’re likely to respond.