Machine learning in recruitment isn’t coming, it’s here

When we started Elevate 7 yrs ago, we had a vision for what we thought might be a simple app that connected people with jobs. It is easy, it’s a simple problem right: here’s some jobs, here’s some people, away we go, and boom, we’re all successful internet entrepreneurs…little did we know!

Fast forward to now, and we have a very large Enterprise software application on our hands; it is responsive, has mobile apps, and incorporates the latest in machine learning and data science-led research.

We realised around 4 years ago that purely having a route to attract candidates wasn’t going to cut it. The recruitment industry, whilst it has its many detractors, is a tough industry to succeed in,  because the art of being a successful recruiter – and it is an art – is a hard one to master. As someone who’s been in the game for almost 20 years there’s a lot I take for granted about how it works, and there’s obviously been many changes during those time.

In 1998 we used to fax CVs; you’d have to wait in the queue to send them to customers – now we’re building automated recruiting bots that can interview people, transcribing voice to text and then running some NLP across the answers to create a set of scores.  Quite a leap.

Total Talent Management (TTM)

We needed more than simple matching. We needed to deconstruct the mind of a recruiter and tell the customer who’s the best person for the job; in the same way a recruiter would. Which led us down the data science path and eventually, in 2017 onto the pinnacle of the whole application, TTM.

Total Talent Management or TTM, is a phrase we first came across around 3 years ago. It’s been widely used since but the number of firms actually attempting to do something with it has remained very minimal, until now. We see the next 12 months as an awakening moment where organisations are finally starting to realise they have so much data to hand, often in disparate sources, but by connecting it and energising it, they can start to streamline talent in a way that’s never been done before.

Until now, what’s happened is customers have “reacted” to demand; we need a body to fill this space, and more often than not the 1st route is to look externally. That’s changing. It’s often said that people are the most important part of an organisation which is mostly true, so we found it hard to understand that once an organisation reaches a certain size, they lose track of what those same people actually “do” and more importantly “can do”.

By using a much deeper skills and experience profile than you’d get in a traditional ATS or HRIS, firms can start to uncover the hidden skills and talents that exist within their organisations before they even need to consider going external. If there truly aren’t the skills in house, then you’ve a validated approach and justification for looking for a new hire. Using data, firms can now judge whether a role should be a full-time or contingent hire, based on actual population forecasts, backed up by real data. See what the market’s paying, and review all the potential applicants, build an external talent pool at the same time as an internal talent pool, blending the results to give a truly holistic view of who’s the best fit for a given piece of work.

We’ve had many hundreds  – possibly thousands of people find work via Elevate through a plethora of clients, ranging from small 1 man band startups to global corporations and Government.  Faster hires, high quality and based on statistics, not guesswork. This is the key. We now have 35 staff across 3 locations, a smart new office in a cool part of town, and for the first time, a ping pong table – corny I know. We even had to pay someone to come and put it together. We can build a machine learning algorithm, but we can’t put a table tennis table together. Hmmm.

We’re excited about the next 12 months; some significant change is coming to the recruitment industry and we’re proud to be helping drive the change


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