When it comes to screening and selecting candidates, we tend to throw around a pretty basic distinction: good candidate vs. bad candidate.
Good candidates have the desired experience and education, and they are poised and confident both on the phone and in person. Bad candidates fumble and don’t give good answers and sometimes wear sunglasses to job interviews.
Come on, you’ve had a candidate who wore sunglasses during an interview, haven’t you? It happens.
Why do we so often find ourselves looking at failed hires and saying, “But he was such a good candidate!”? Clearly, good vs. bad is not an adequate way to measure candidate viability.
A far more meaningful mindset for looking at job candidacy is degree of fit. That is: How well does this specific individual fit this specific role, organization, and culture? When you know these answers, your track record of good hires improves, in terms of both employee performance and retention.
The problem lies in figuring out degree of fit. It’s complicated — more complicated than looking at a stack of resumes and conducting interviews. You need a way to evaluate a broader array of information at a higher level to understand degree of fit. This is where people analytics comes in.
1. Analytics Allows You to Spend Less Time Deciding Whom to Hire While Improving Your Hiring Success Ratio
Hiring successfully requires looking at a lot of information — resumes, assessments, background checks, and so on. Analytics allows you to combine these data sources into a single algorithm that gives you the degree of fit for the position, the team, and your company’s culture. You’ll be able to take a complex data array and streamline it into something much simpler. You’ll simultaneously save time and get better results.
2. Analytics Enables You to Use Employee Engagement Data and Exit Interview Information to Improve Overall Retention
You are likely to find that certain personality attributes and performance competencies correlate with greater satisfaction and retention (relative to degree of fit with the job and the organization), or you may discover that employees who have taken part in one kind of on-the-job training show higher engagement levels than others. Analytics lets you input, sort, and get feedback from this type of information much more simply than you could have in the past.
Getting retention right will save your company time and money; getting it wrong means bleeding talent and knowledge and trying to regain lost productivity. As with hiring, predicting retention requires looking at multiple data sources such as personality traits, engagement survey results, performance ratings, 360-degree reviews, and skills training. You can do a deep dive into retention with analytics to figure out what mix of variables leads to the highest job satisfaction, as well as pinpoint the indicators that someone is about to leave.
Using analytics can also allow you to crunch this information to determine what kind of training is needed and what kind is most effective at improving retention. Instead of depending on hunches and guesswork and trying to sort piles of disparate data sources on your own, you can combine your information with analytics to get more powerful recommendations.
3. Using Analytics Means You Can Always Justify Your Recommendations With the Kind of Predictive Data CEOs Look for When Making Important Decisions
The days of operating off gut instincts are over. Most CEOs today will not make critical business decisions without hard data. And, as always, the HR seat at the C-level table remains tenuous. To retain, or even increase, influence over company direction, HR professionals must have data to back up their recommendations with evidence.
HR initiatives have often been hard to quantify in the past, but analytics is coming to the rescue. When you can show there is strong data to support your conclusions, you’ll find it easier to get money allocated for better training, coaching programs, team building, and a more advanced, technology-based recruitment methodology.
This is only the beginning of how analytics will be able to support better recruitment, retention, and engagement — which is another way of saying greater productivity and profitability. Now is the time to begin integrating the data-driven approach, if you haven’t already.
David Solot, PhD, is the analytics product manager at Caliper.