Getting smarter at predicting talent

SUMMARY:

Data-driven decisions drive better talent hiring at recruitment outsourcer Mindfield.

Cameron Laker
Cameron Laker

The difference between good companies and great companies is having employees that are switched on, engaged and aligned with the culture and aligned with the values – those are the companies that can go on major hyper-growth. If you have really poor people working with your customers every day, it doesn’t matter how good the strategy is.

That’s the view of Cameron Laker, chief executive of Canadian recruitment process outsourcing firm, Mindfield, which specializes in industries with high-volume recruitment needs, such as retail outlets and restaurants.

Mindfield is using cloud-based predictive technology from OutMatch to help its customers pinpoint those workers that match their core values and will help their companies grow.

While the company had already been using a base-level behavioral assessment tool to filter candidates, it lacked the full, end-to-end analytics the company was looking for. As Laker explains:

We had an assessment tool which would tell us whether they were good or not good, but we were struggling to find out what happened after that.

Laker wanted a tool that not only speeded up selection but one that looked beyond this initial assessment to make more accurate predictions of long-term success.

OutMatch does that by working out the behaviors of high performers in a particular role. But rather than this being a one-off, fixed set of behaviors, the platform continuously learns and grows. Data such as candidates’ attitudes to the job – whether the job matches expectations, for example – together with their managers’ views are all fed into the system. The more hires a customer makes, the more it learns.
That means, says Laker, that for customers hiring thousands of workers a year, the tool:

could get really smart, really fast, because of the sheer volume.

Using the tool for the last 18 months or so has helped Mindfield make smarter sourcing decisions, enabling it to quickly reject unsuitable candidates, which can be a time consuming job for recruiters with high volume of hires. As Laker points out:

If you’re managing 1,000 jobs per year and of their jobs 30 applicants per job, you’ve got 30,000 people to sift through.

That takes care of the bottom 20% to 25%, but an equal priority is to identify the top 20% of performers, which is where OutMatch is really making a difference, according to Laker:

Through OutMatch we’ve got a library of 900 profiles and we configure and calibrate a certain job type or title to one of the validated profiles in the library. That enables us to get right out-of-the-box a really accurate assessment tool that’s not just looking for generic behaviors, but is looking for behaviors that are proven to be associated with performance in that job.

In a restaurant, for example, behaviors needed for front-of-house servers will be different than the behaviors required for cooks working behind the scenes.

A cook in a chain restaurant many need to be able to follow particular cooking instructions to ensure what they serve matches other restaurants in the group. So sought-after behaviors in this case might be someone good with attention to detail, takes pride in following steps and consistency. Laker explains:

It’s the weighting and calibration of each of those that can predict the likelihood of performance in that job.

Making a difference

The tool is making a big difference to the way it chooses appropriate candidates for clients, notes Laker:

It’s been fantastic at helping our recruiters identify who they should be spending time with. It’s proving to really drive a lot of efficiencies in how we manage the front end of the recruiting process i.e. who should we be interested in in the applicants who came in.

It also bought in a lot of data about regional differences in talent pools and to help the company increase it’s the numbers of people in that talent pool. Laker explains:

So we can look at average scores for different roles and regions and identify where we’re really good from a talent pool perspective and where we’re thin. So, we use the tool to identify if there are regions or spots where maybe the average applicant is lower and that gives us an indication maybe we have to ramp up our advertising or sourcing activities in that market.

Another major benefit, believes Laker, is that it’s helped Mindfield to improve the predictability and consistency in the types of people the company hires:

It’s added a level of scientific evidence to the why behind why you hire someone and it’s really a bout behaviours rather than experience.

Often the data can reveal a very different profile from the perceived wisdom within a company about the qualities of a successful worker.

Laker’s vision for the future is to be able to use the OutMatch platform to provide insights into customers’ workforce, looking at the quality of hire and engagement of those hires and how those people progress in the business:

Ultimately, we want to provide insight into their workforce they’ve never had before, to better understand the workforce today and clear strategies on how to improve it.

Data may take the subjectivity out choosing the right candidate, but there is still a place for gut-feeling. OutMatch chief executive, Greg Moran, says:

We never tell a client, ‘Forget about you’re hiring process just rely 100 % on what we’re doing’. So much of this comes down to human interaction and the fit between the hiring manager and the candidate. You can have someone who matches up perfectly, but the hiring manager and him/her just don’t like each other.