By: Raghav Singh, SPHR, Director, Reporting and Analytics, Korn Ferry Futurestep
Hiring employees has typically been a very mechanical process in order to comply with myriad goals like non-discrimination, diversity, etc. Assessments are used in some cases to try and predict performance on the job, based on demonstrated skills or competencies. All of this has some value, but it rarely leads to creating a high-performing team. A key reason is that the typical hiring process provides little or no information to help determine how the candidates that are being considered will perform as part of team. There’s also the fundamental problem with our approach to hiring where the focus is on evaluating candidates for individual performance, when in fact, most jobs require people to work with others.
Hiring the most qualified candidates does not guarantee that the result will be a high-performing team. Professional sports is rife with examples of superstars that could not succeed as a team, and the same is true in other fields. The Navy Seals have learned from decades of putting together teams tasked with extremely difficult assignments that a collection of the most qualified people often does not make for a great team.
The Age of Machine Learning
With the help of machine learning technologies we’re developing analytical models that can help determine how candidates will perform as part of a team. Predicting if a person will be successful in a team is mainly about understanding how well they fit in, and their motivation for the job. In the past, the challenge has been one of defining the culture of the organization, which may not be the same everywhere. But now we can use data from social networks to know if a candidate will fit in with a group. Using the profiles of people in a team, it’s possible to predict if a person will make friends with them. That’s a good proxy for evaluating fit and eliminates any need to define the culture.
Analytics can also predict the impact of adding a person on a team’s productivity. It may substantially increase the team’s overall productivity or have no effect at all. It may even be negative. This can also be turned around to figure out what kind of a candidate should be hired so that a team can achieve certain goals.
But knowing if a candidate will fit in is only relevant if they have the motivation to do so in the first place. A machine learning system can identify people who are most likely to consider a solicitation for a job; in other words, those who are more motivated to change jobs or accept a new one. There’s an abundance of data on social networks and other places that can be tapped for this purpose. For example, Google’s Timeline tracks your every move (check it out) and can be used to accurately determine a person’s commute. A candidate with a long commute is more likely to respond to a solicitation than someone who has a short one, especially if the former travels through heavy traffic. Combine a candidate’s travel information with other data — such as remarks posted on social networks that can be indicative of their engagement levels in their current job — and you’ll very likely boost your response rate. Currently Google doesn’t let anyone see other people’s timeline, but that’s by no means guaranteed to continue. The cell-phone providers all have the same data as well and already sell it for targeted marketing campaigns.
We’re at the very early stages of using analytics to evaluate candidates, but the field is advancing rapidly. Machine learning models are being continuously improved and can already make a range of predictions about people in the context of employment including the likelihood of undesirable behaviors. Employers that incorporate these approaches to their hiring processes should understand the value and the risks, but will likely gain an edge in building high-performing teams.
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