Use Talent Analytics to Guide Employee Selection
We’ve all heard that age-old saw. The lesson is pretty straightforward: don’t always take things at face-value; take the time to find the deeper significance. That is never more true than when it comes to hiring an applicant who is expected to make a significant business impact. And it is ESPECIALLY true in the high-volume hiring environment of most customer-facing operations.
For the most part hiring happens fast and furious, especially this time of year, and it’s easy – and understandable – to give most candidates only a cursory look before making the offer. After all, you have seats to fill, right? A quick glace at a resume – this candidate has experience? Check! Maybe a brief face-to-face interview – the candidate looks presentable? Check!
Recruiters often rely on their well-earned experience and instinct to decide which candidates are likely to become excellent contributors. And, through no real shortcoming of character, they are reluctant to embrace technology as an aid in making a superior hiring decision. After all, they know what constitutes a superior candidate, right? Well, maybe not always.
The advent of predictive talent analytics is a powerful weapon in the recruiting team’s arsenal. And the team that fails to use all the tools at its disposal runs the risk of sub-optimal quality-of hire and poor stakeholder satisfaction.
Predictive talent analytics is a fairly straightforward concept – correlate the pre-hire behaviors of excellent performers, and seek to find applicant who exhibit those behaviors. In practice however, it requires a rigorous, systematic, cross-departmental approach where these key business outcomes are regularly collected and linked to each employee’s pre-hire profile.
Find the common characteristics of excellent performers and it’ll be like winning the candidate lottery – hire those folks and you’ll be more likely to achieve your business goals. Likewise, you’ll have some who fail to meet expectations. Uncover the common traits of those folks and put applicants who share them at the bottom of your list. Sounds hard? It really isn’t, with the right technology.
Advanced machine learning techniques can make quick work of discovering these correlations and can be a great help to your recruiting teams by prioritizing their candidate review. Focus on those who exhibit the traits of excellent performers and leave those who don’t for last. Pretty soon you’ll see that quality-of-hire will increase and the recruiter’s job will correspondingly increase in value.