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Business speaks the language of numbers.

Analyzing data is one of the best ways to make informed decisions. This is especially true when it comes to recruitment, where using predictive analytics can save companies a lot of time and resources.

With recruitment analytics, you can identify the right candidates for your vacancies and decide faster where to find them. In this blog post, we’ll be looking at what recruitment analytics is, how predictive analytics works in recruiting and what metrics you need to measure.

What Is Recruitment Analytics?

Recruitment analytics is not higher mathematics but a convenient and understandable way to make the work of a recruitment team easier and more efficient.

This is a set of methods for business evaluation of recruiting processes. Such statistics help HRs and recruiters to assess costs and risks, select suitable candidates and spend more time communicating with applicants and employees.

What Is Predictive Analytics in Recruitment?

Predictive analytics is a form of data analysis that uses historical data and other information to predict future outcomes or events.

In recruitment specifically, predictive analytics can be used to define the most relevant recruitment sources and time-to-hire for specific positions, retention and turnover rates, etc.

By gaining insights from this data, you can improve your recruitment processes, making them more efficient and cost-effective, as well as ensuring that you hire the best candidates for each role.

What Can Be Evaluated and Improved Using Predictive Analytics?

Predictive analytics can be used to evaluate many aspects of your recruitment process including applicant tracking systems (ATS), job descriptions, candidate profiles, interview processes, and more. You can use predictive analytics to identify any gaps in your current recruiting strategy and find ways to close them by targeting specific skill sets or demographic groups with tailored job postings or other strategies.


You can also use predictive analytics to optimize your ATS so that it filters applications automatically based on predetermined criteria such as skillsets or experience level. This saves recruiters time in sorting through applications manually and ensures only the most qualified candidates make it through the first round of interviews.

What else you can improve?

Select candidate sources: the amount of candidates each channel generates (job boards, LinkedIn, referrals, etc.).

Optimize the duration of hiring for different positions: the average time-to-hire for administrative and technical positions.

Control recruiter’s performance: how many days it takes for a recruiter to lead the candidate through the job pipeline

Tackle hiring challenges: where bottlenecks in the hiring process regularly occur, what effects they have, and how to fix them.

What Metrics Do You Measure in Recruitment Analytics?

To get meaningful insights from your recruitment analytics data, measure key metrics, including:

  • time-to-hire (the amount of time it takes from posting a job ad until an offer has been accepted),
  • quality-of-hire (the rate at which new hires become successful employees), and
  • cost-per-hire (the total cost associated with filling a vacancy).

Measuring these metrics will allow you to track how effective each recruiting strategy is over time so that you can adjust accordingly if needed. Additionally, measuring metrics related to diversity, such as gender representation within different roles, will help ensure that all potential candidates are given equal consideration during the hiring process.  

Check out our blog post - 15 Best Metrics And KPIs For HR!

How to Begin Using Predictive Analytics in Recruitment?

If you haven’t already started using predictive analytics for recruiting purposes, now is a perfect time! The first step in getting started with predictive analytics is collecting all relevant data about your current recruitment processes from sources like applicant tracking systems (ATS), job boards, surveys, or social media platforms like LinkedIn or Facebook. Once you have collected all relevant data about your current recruiting practices, it’s time to start analyzing it using various software tools to gain valuable insights into what changes need to be made for your organization’s recruitment efforts to become even more successful than they already are.  

Using an ATS

An Applicant Tracking System is a software platform designed to streamline recruitment by automating certain tasks. An ATS can help you manage your entire recruitment pipeline from sourcing and screening candidates — all while providing valuable insights into your recruiting practices. By integrating predictive analytics into your ATS, you access even more valuable insights about your potential candidates.  

Some ATSs have incorporated analytics tools. For example, Axterior recruitment platform allows team leaders to track the performance of recruiters (and recruitment efficiency, respectively) by total jobs open, closed, and archived, and control the KPIs by jobs, target date, days, and job pipeline.

Example of recruiter performance tracking

These insights can be used to inform decisions throughout the hiring process—from pre-screening applicants all the way through onboarding new hires.


Predictive analytics is quickly becoming an essential tool for recruiters everywhere due to its ability to provide valuable insights into how effective their current recruiting strategies are while also helping them identify potential areas where improvements need to be made for those strategies to become even more successful than before.

Are you ready to test the potential of the ATS in recruitment analytics? Get instant access for free!

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