In a recent post on predictions for 2016, I noted that the key area in which I expect to see more from LinkedIn next year is in data utilization. This is based on what we've seen in 2015, particularly around a recently announced upgrade to their Recruiter platform - at their Talent Connect 2015 event, held back in October, LinkedIn detailed an update to Recruiter which will enable hiring managers to use LinkedIn's vast professional databanks to find better matches for their open positions, based on the career history, education and any number of other data points of their existing employees.
The system, in essence, works like this:
Let's say have someone on your team who's perfect, who fits all your requirements, does the job exactly as you'd like. The new Recruiter tools will enable you to use that employee's LinkedIn profile as a template in order to find people with duplicate skill sets, experience, education, etc. The system will then sort applicants by best match, based on those data points, helping recruiters find the most suitable candidates, making the recruitment process easier, more logical and less susceptible to our own, subconsciously biased, judgement. That's not to say everyone makes bad calls in this process, but recruitment still comes down, in large part, to gut feel. The new tools reduce the burden of such selection.
This is a relatively simple use of LinkedIn's data, but simple as it may be, it's likely only the first step in a much bigger movement for LinkedIn. While this system is a fairly straight-forward and logical data matching process, through the expansion of that same procedure, LinkedIn has the capacity to change the game on recruitment and talent acquisition. Using their ever-expanding network - which has now surpassed 400 million members worldwide - LinkedIn has more career history and education path insights than any platform in history. That database is incredibly powerful, more valuable than anyone likely fully comprehends at this stage. Through this, LinkedIn could provide not only simplistic matches, in like-for-like candidates, but they could also highlight ideal career paths, perfect team structures, best individualized staff fit for your business. And today, LinkedIn's taken another step on the data utilization pathway, announcing an update to their Job Postings to provide more in-depth data and insight into each role - another small addition in itself, which also provided a window through which to view the wider possibilities.
In a new post on the official LinkedIn Blog, LinkedIn Senior Product Manager Vidya Chandra details how the new additions will work and will help in the job seeking process. Chandra notes that oftentimes, listing a job's requirements doesn't provide enough context, and that job seekers also want information on things like:
- What connections (if any) do you have at the company?
- Does the company have a history of hiring people like you?
- Who will you work with if you get the job?
Such details can provide significant insight, and as such, the new additions to job postings will help job seekers learn more about the advertised role and their potential fit with the company.
First off, LinkedIn will now show job seekers all the connections they have to the advertising company on job postings.
As per the announcement:
"According to a recent LinkedIn survey, 89% of career builders networked while seeking their current job. Of those who networked with former colleagues, 53% reported those networking efforts as having effectively helped them land their job. And those who reached out to a former boss reported it as 61% effective."
The feature will make it easier for job seekers to get further context and insight into the job and company from people they know, which is a great addition to the process.
Second, LinkedIn will now also show job seekers employees at that company with similar roles to the one being advertised, including brief notes on each person's background and skills.
LinkedIn's research has found that successful job seekers are "9X times more likely to research current employees of hiring companies". This new addition makes that process easier, giving job seekers a better understanding of where they might fit, and whether the company has a history of hiring people with their skills and experience.
And third, LinkedIn will also provide an 'Inside Look' listing for each prospective employer, including info on company growth rate, average tenure and top schools and companies they hire from.
"When we asked professionals who recently landed a new job about the process, the #1 obstacle they faced was not knowing what it's really like to work at the company. With our new Premium insights, we aim to make crucial intel about a company more transparent."
The tools add significant context to the job seeking process, and will help potential candidates better understand what employers are looking for, and how they fit into each profile. But on top of this, the new tools provide more context on LinkedIn's wider plan for improving the recruitment process, a further overview of the data and insights LinkedIn has and can use to better match candidates to positions. Given their vast trove of data points and career histories, it's not hard to imagine that, one day, LinkedIn might be able to give you a complete overview of your ideal career path, based on your skills, attributes and interests.
Sounds far fetched? They're already doing this - back in 2014, Re/code writer Kurt Wagner, then writing for Mashable, detailed how LinkedIn's data science team constructed a full, customized overview of how his career is projected to pan out, based on their data banks. And that was almost 18 months ago - you can only imagine the advances they've made on this front now.
Your Future Self
Given its position as the professional social network, LinkedIn is in a unique position in this regard. While these smaller starting points are interesting, they're not setting the world on fire - companies aren't re-assessing their entire recruitment process based on LinkedIn's ability to cross-match candidates or conduct random hypotheses about potential career journeys. But this is only the beginning. While everyone would like to assume that their personality traits and choices are entirely unique and different to everyone else in the world, there are similarities, there are common themes in our choices that, on a large enough scale, start to reflect trends and patterns - pathways that can, eventually, be mapped.
The problem with data accuracy, generally, is scale. One person making a decision to watch TV after reading a newspaper means nothing, but a million people following the exact same process is indicative. In this same sense, one person switching from one job to another at a certain point in their career is an anomaly, but thousands of people doing the same is a trend. LinkedIn, with 400+ million career profiles on file, can track this - and not only solid trends like this that stand out, but more subtle shifts, progressions that occur within different communities, that are the result of different education backgrounds or regional upbringings. On a wide enough spectrum, LinkedIn has the capacity to map very specific career shifts, trends and behaviors - and subsequently, given enough input, LinkedIn may also be able to provide an accurate career path based on those same shifts.
As noted above, LinkedIn's data team has been experimenting with this for some time. LinkedIn's 'University Finder' tool is another great example - using straight data-matching, University Finder aims to help students connect with their ideal career path by showing them the universities that are the best match for their future intentions, based on what they want to do, where they want to work and where they want to live.
The next level of this is predictive analytics and AI matching - not only will students be able to find out which universities are most commonly attended among the people already doing the job they're aiming for, eventually, LinkedIn will be able to show them, based on intricate data-matching, exactly what career they should be pursuing, what will make them most happy (based on average career longevity and time spent with the same employer) and what will best enable them to realize their wider life goals.
On the flip side, such a system could also lead to a significant boost in productivity and job satisfaction levels for employers - a recent report from the National Bureau of Economic Research found that computer algorithms may actually make better hiring decisions than people.
"The researchers looked at the employment record of 300,000 low-skill service sector workers across 15 companies. The jobs had low retention rates, with the average worker lasting just 99 days, but researchers found that employees stayed in the job 15% longer when an algorithm was used to judge their employability."
"The researchers also looked to see if those hired by human managers against the algorithm's recommendation had higher productivity to counter their short tenure, but found that this was not the case. Hiring against the machine's recommendations went completely against better outcomes, the authors said."
Again, this is small scale, these are only the first steps. But the indicators are there, the data capacity is within reach. In this regard, LinkedIn's moves towards data analytics of this type make perfect sense.
It reminds me of a scene in the TV show Futurama, when Fry, the main character, first wakes up 1,000 years in the future. Part of the employment process of the future is that everyone is implanted with a computer chip which, based on their individual's make-up, identifies what job they're best suited for. Such a reality may not be as far of as it seems - maybe not in a prescriptive sense, but in a predictive way. Right now, LinkedIn could likely provide a fairly accurate prediction of what career you're best suited to. Once employers start using that same data as an indicative measure of best-fit employees, such projections could become imminently more valuable.
The next step for LinkedIn, I suspect, will be the wider integration of employee tracking and categorization to improve projections for job listings. For example, using one, single, ideal employee may not be the best way to go - effective teams generally require a range of different personality types and approaches which, when combined, produce optimum results. As such, I suspect LinkedIn will soon extend the capacity of the Recruiter platform to find best matches based on team make-up - businesses will be given the opportunity, via Recruiter, to categorize their employees' LinkedIn profiles by team. This will then enable businesses to use their best performing teams as templates - so when you go looking for a new team member, LinkedIn will be able to match the traits and make-up of those ideal teams in order to recommend best-fit candidates for each business unit. If accurate - and given the data they have to work with, it will be - that innovation will provide a big boost to recruiters and will further evolve LinkedIn's Recruiter offering, making it the best fit tool on the market, and it'll likely take a large chunk of business from other job-listing websites as a result.
So how far away are we from a time where algorithms will be able to map out our entire, ideal career paths for us, guiding us towards our happiest and most productive working journeys? Not as far as you might think, and while there'll always be anomalies - artists, for example, who hit it big, which is outside the norm, in terms of career progression - it's certainly not hard to see how LinkedIn would be best placed to lead the charge.
You might doubt it, it may seem like the stuff of science fiction. But in the immortal words of Bruno Mars - "Don't believe me just watch..."