Competitive Analysis & Competitive Intelligence, Recruiting Technology

Recruiting Technology: Using Predictive Analytics in the Recruiting Process

Wouldn’t it be nice to have some way to assess the likelihood that a given candidate will stay with the organization long term—or to have at least a way to predict which candidate is more likely to succeed in your organization if more than one seems qualified for a given role?

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The field of predictive analytics has tools that aim to answer those types of questions.

What Is Predictive Analytics?

Predictive analytics, as the name implies, seeks to make predictions based on data analysis. Typically, a large data set is analyzed to look for trends and create forecasts or predictions of outcomes based on various factors. This idea—predicting future outcomes—is what sets predictive analytics apart from other forms of data analysis. Predictive analytics seeks to not just understand but also use data to determine what future behaviors might be able to be predicted in advance so that we can make better decisions with the data we have.

Predictive analytics is used across many industries, and you’ve certainly already been influenced by it, even if you didn’t realize it. For example, credit bureaus use a form of predictive analytics when they review creditworthiness. They’re looking at data that show past behavior as a way to predict the future likelihood of paying on time.

The recruiting process is another place where predictive analytics can be utilized. Say, for example, a recruiter wants to know which applicants are most likely to stay with an organization for more than 5 years. He or she could assess all of the previous hires and compare each of their qualifying characteristics and see which ones did and did not stay with the organization that long. With enough data, trends usually emerge.

These data can be sourced from applicant information you already have on file, in many cases. They can also be gathered via surveys of current employees. Going forward, the organization can also use applicant questionnaires as part of the screening process—which will be both a source of data and a means of comparison for that individual. [Note: Predictive analytics works best when you have a large data set to work with. For smaller organizations, getting a large data set will be tougher and may require the team to have many more years’ worth of data in order for the results to be meaningful.]

Now that we’ve laid out what predictive analytics entails, stay tuned for part two, where we’ll outline how predictive analytics can be used to enhance the recruiting process.