Competitive Analysis & Competitive Intelligence, Recruiting Technology

Recruiting Technology: How to Use Predictive Analytics to Enhance Your Recruiting Process

In part one, we introduced the idea of using predictive analytics in recruiting. Let’s take a look at some of the ways to do exactly that.

analytics

Source: marchmeena29 / iStock/ Getty

Here are some ways predictive analytics can be used during the recruiting process. It can be used to:

  • Assess which of the qualification requirements were most likely to correlate with good job performance over time. It can be tough to know which quality is most important when it comes to how successful someone will be. Data can help to understand that. With this knowledge, the organization can focus more directly on those characteristics during the recruitment process and perhaps focus less on qualifications that had less of an impact than expected. This means recruiters can be more efficient by focusing on the factors that matter.
  • Factor in which soft skills will have the most impact on how successful a candidate will be. Just like the previous example, organizations can look at the soft skills that had the greatest impact on future success for an employee and then focus more on finding candidates with those skills. This could even include using the results of personality assessments in a way that helps to predict how successful a given candidate will be. The recruiting team will be better able to assess combinations of soft skills and experience to see who truly is the best fit.
  • Be more analytical in candidate comparisons. Candidates may seem equally qualified but have differing backgrounds. One may have held more positions that utilized the necessary skills; another candidate may be more motivated to climb the career ladder. Which candidate is likely to be the better choice over time if they seem otherwise equal? Using data sets, this type of prediction can be made.
  • Estimate which candidates are more likely to accept a job offer. No one wants to waste time with rejected offers. Data can be gathered about previous candidates who have and have not accepted such offers to predict who is more likely to in the future.
  • Be more objective in utilizing information gathered during background screening. With aggregate data comparisons, you can see which types of things in a candidate’s online presence may be more likely to predict how successful an applicant might be within the organization.
  • Assess the most successful recruiting methods based on past data. For example, how often should candidates be contacted? Which talent sources are most likely to have candidates who will be successful in a given role? These types of questions can help the recruiting process to be more efficient by focusing on the avenues most likely to bring fast results.
  • More accurately screen out candidates who “look good on paper” but are actually not likely to do well in the organization. This could reduce the number of bad hires and thus reduce the cost involved with replacing them.
  • Speed up the hiring process because it allows for more objective (and thus faster) comparisons of candidates.

Predictions are exactly that—predictions. They’re not foolproof. There will always be exceptions. But with large enough data sets, recruiters can make more informed decisions about the likelihood of candidate success, thus improving their overall rate of successful hires.

Employers should also be careful in how data are utilized—it would be far too easy to use data in ways that could appear to be discriminatory if they are not used wisely and carefully. Think through any predictive analytics program to be sure you’re not screening for things inappropriately or screening out candidates based on data that should not be used in the decision-making process.