Recruitment Analytics: The 3 Levels to Optimize Recruiting

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Recruitment Analytics: The 3 Levels to Optimize Recruiting

Recruitment analytics, also known as recruiting analytics, plays an increasingly important role for recruiters and recruitment managers. Recruitment analytics can help to make better, data-driven choices when it comes to sourcing, selection, and hiring. In this article, we will explain what recruiting analytics is, and how to get to value-adding analytics in three steps.

What is recruitment analytics? A definition

Before we dive into the potential that recruitment analytics offers, let’s first look at what recruitment analytics is.

Recruitment analytics is the discovery and interpretation of meaningful patterns for sourcing, selecting, and hiring. This means that data is used to find and explain patterns in data. For example, if new hires leave within the first three months, this may indicate a mismatch with the job description and the actual role, selection mistakes or a bad onboarding process. This is an example of recruitment analytics.

Recruiting analytics can answer many other questions, including:

  • Which sourcing channel provides the best candidates?
  • What is the cost of hire for a position?
  • What do my best candidates have in common?
  • Where in my recruitment funnel do most candidates drop out?

Being able to answer these questions is essential to improve decision-making in recruitment.

Common data sources for recruitment analytics include the Applicant Tracking System (ATS), Customer Relationship Management (CRM) systems, Human Resources Information System (HRIS) data, satisfaction surveys, brand data, and data from the advertisement platforms used for job advertisement and branding.

Recruitment reporting and analytics

When we look at recruitment analytics, there are three levels we can distinguish. These are operational reporting, advanced reporting, and (predictive) analytics. The levels are based on the commonly seen HR analytics maturity models.

Level 1: Operational reporting

In level 1, recruitment analytics is descriptive. They represent the well-known core recruiting metrics. Metrics include the cost of hiring, source of hire, applicants per job opening, selection ratio, time to fill, time to hire, hiring manager satisfaction, and more. For a full overview of the best-known recruiting metrics, check the linked article.

All the measurements listed are fairly easy to capture using an ATS. If there is an ATS implemented in your organization, it is usually fairly straightforward to connect a dashboard to this system, define metrics, and report on them. All these measurements look back at historic data already present in the system and no, or very few, additional calculations are required to report this data.

Level 2: Advanced reporting

Level 2 represents advanced reporting. Reporting on these measurements still doesn’t require advanced statistical tools but it does require the combination of multiple data sources to be generated.

An example is the candidate experience. In order to assess the candidate experience, different phases in the recruitment cycle will have to be mapped and the candidate experience needs to be measured or otherwise collected. This can happen through surveys integrated into the candidate software or through separate questionnaires.

Other metrics include financial recruitment metrics, like the cost per candidate per sourcing channel, sourcing channel conversion, recruitment funnel conversion, the amount of money saved, minimum slate, employer branding, and so on.

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Level 3: Analytics

Level 3 represents strategic and predictive analytics in recruitment. Strategic analytics includes segmentation, statistical analysis, and the development of people models. Predictive analytics involves the development of predictive models and strategic and scenario planning.

An example of strategic analytics is segmentation in job advertising and the deployment of programmatic advertisement. In programmatic advertisement, target groups for a job opening are defined and then targeted through multiple online sources. In this case, the ad spent (per click or per thousand impressions) should be closely monitored and when needed, adjusted. Because of the segmentation, different advertisements can be tested against different job-seeker segments in an effort to optimize conversion and lower cost.

Other examples of analytics are the prediction of an ideal candidate profile, predicting the time to hire, and optimization of the recruitment funnel.

An example of such an optimization is a case study on chatAssess, a chatbot that was implemented to optimize the application process for Rolls-Royce. The team at Rolls-Royce meticulously tracked conversions of online applications so they could exactly determine the impact of implementing chatAssess.

After the implementation of the tool, completion rates of applicants increased tremendously, with the completion rate of hires from the Business stream going up from 74% to a whopping 96%!

chatAssess application process optimization

In this case, chatAssess is a technology solution. And whereas the ability to track data (e.g., completion rates of an online application) would be considered a level 1 recruiting measurement, the ability to measure drop-out rates throughout every sub-step of the application for different segments and measuring if conversion improvements are statistically significant, definitely qualifies as a level 3 recruitment measurement.

This example also shows that the distinctions between operational reporting, advanced reporting, and analytics, are not black and white. Oftentimes, you will have to go through different levels in order to gather the most insight from your date.

A new perspective on Recruiting analytics

Another perspective on recruiting analytics is given by Andrew Gadomski, founder of Aspen Analytics and one of the world’s top experts in recruiting analytics. Gadomski is the lead instructor on the Academy to Innovate HR’s course on recruitment metrics & analytics.

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To explain these different levels, Gadomski uses a skiing analogy. In skiing, there are three kinds of slopes. Green, blue, and black. Similarly, in recruitment analytics, there are also three levels. Companies usually start at the blue slope and once they mastered these measures, go on to blue and black.

The more advanced levels of recruiting analytics require more data and also more advanced processing of data. Whereas green measurements are reported on in a quarterly or monthly report, advanced metrics like the effectiveness of job advertisements should be tracked daily as every single click on the advertisement can cost the company tens of dollars.


Using this framework, Gadomski is able to clearly explains the evolution of these metrics, and how any organization can move from green measurements on to blue and black measurements.

For more information, check out the Recruiting Metrics & Analytics course!

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