Recruitment data analysis: What to track and why

Nearly two-thirds (66%) of HR leaders agree that data analytics has transformed how they hire and retain talent. Yet fewer than one in five companies use it consistently to make people decisions.

If your organisation is one of them, you’re leaving a lot on the table. You have a treasure trove of information at your fingertips. And once you know how to better use it, you can do things like: 
 

• Fill open positions faster 
• Improve the quality of your new hires 
• Save money on recruiting expenses 
• Enhance the candidate experience from application through onboarding 

So, where do you start? We’ll walk you through the basics of recruitment analytics and how to put them to work in your organisation. This can help you to build stronger teams and connect recruitment data to business goals.

Key takeaways

• HR analytics in recruitment helps you uncover actionable insights from all phases of the hiring process. 
• There are three distinct levels of recruitment analytics: operational reporting, advanced reporting and predictive analytics. 
• Recruitment analytics can make your hiring process more efficient by highlighting bottlenecks and opportunities for cost savings. 
• The best insights come from tracking metrics across the entire hiring journey, not just one piece of it. 

What is recruitment analytics?

Recruitment analytics is the practice of gathering talent acquisition data from your recruiting software and other sources and finding meaningful patterns within it. From there, you can use those insights to make sound people decisions and continuously improve your hiring strategy. 

That last part — using insights to take action — is what separates analytics from simple reporting. Reporting tells you what happened. Analytics helps you to understand why and what to do next.  

Here are three tiers of recruitment data reporting and analytics: 

Operational reporting

Operational reporting involves sharing core recruiting metrics. They’re your baseline, telling you what’s going on in your hiring process at any given time. They include:  

• Cost of hire
• Source of hire
• Number of applicants
• Selection ratio (the number of candidates hired vs. those who applied) 
• Time-to-fill
• Time-to-hire
• New hire turnover rate

You can generally get these metrics without any complex calculations. Your applicant tracking system (ATS), human resources information system (HRIS), payroll system and finance department are all natural sources. 

Many applicant tracking systems will give you some of these numbers automatically. And if yours pulls in finance data, you might be able to see an estimated cost of hire without doing any math. 

Tier outcome: Identifying a recruiting-related trend 

Advanced reporting

Advanced reporting is a bit more sophisticated. And it means collecting and synthesising data from several sources. 

Examples of these types of metrics and their corresponding data sources include (but aren’t limited to): 

Metric	Sources you can use to calculate
Candidate experience	Candidate engagement scores Survey scores at different phases of the hiring process  Application completion rate  Offer acceptance rate
Quality of hire	At the one-year mark: Employee performance rating  Hiring manager satisfaction score  Employee engagement score
Onboarding effectiveness	At the 90-day mark: Employee performance rating  New hire retention rate   Training completion rate  Employee engagement score

 
This is where reporting begins to move into analytics. Once you understand a trend, such as why your offer acceptance rate is slipping or why your 90-day retention looks so different across departments, you’re ready to do something about it. 

Tier outcome: Understanding a trend you discovered 

Predictive workforce planning and recruitment analytics

Your reports do a good job of explaining what has happened, but advanced workforce planning and recruitment analytics use that data to forecast what’s likely to happen next and get ahead of it. With the right tools, you might be able to predict things like: 

• Future staffing needs 
• Time-to-hire 
• Which candidate is most likely to succeed 
• Which job ad will be most effective 

Much of the data needed to make these predictions exists in your ATS and HRIS. But some information could come from your strategic workforce planning system, surveys (employee and customer) and performance data. Modern talent acquisition software also includes AI-assisted capabilities, such as screening tools that automatically highlight the top candidates for each open role, saving your recruiters hours of manual work. 

Tier outcome: Learning from the data to make predictions 

How recruitment analytics improve efficiency

Recruitment analytics can give you the leverage to make meaningful improvements across your entire hiring process. Here are some suggested focus areas: 

• Time-to-hire: Identifying (and correcting) hiring process bottlenecks and posting ads on the best-performing job boards can help to get new talent in the door faster. 
• Hire quality: Drawing data-backed correlations between how candidates perform during the hiring process and how they perform once hired can help you to build an ideal candidate profile. 
• Candidate experience: Pinpointing where candidates drop off during the hiring process or express dissatisfaction can help you course correct. 
• Workforce planning: Knowing when staffing needs will hit can help you ramp up your hiring efforts in advance. 

What to track with recruitment analytics

Your recruiting process generates a lot of data. Here are 10 of the most important metrics to monitor in your recruitment analytics dashboard: 

What to track	What it is	What it might imply
Time-to-fill	The days between opening a requisition and making a hire	Long hiring processes could indicate a bottleneck at a certain stage.
Time-to-hire	The days between a candidate entering the pipeline and accepting a job offer	A long stretch between events can mean your candidate outreach needs improving.
Offer acceptance rate	The percentage of candidates who decide to join your team when invited	A low acceptance rate can be due to salaries below market rate.
Quality of hire	How well a new employee performs in their role and fits in with the company culture	Subpar hire quality could mean you need to adjust your screening and onboarding procedures.
Cost-per-hire	How much it cost to recruit a new team member	A high cost-per-hire might indicate that you need to source candidates differently or find a way to shorten the hiring process.
Source of hire	Where you found the candidate (or the candidate found you)	Sources of hire should be regularly reviewed for quality and value.
Pipeline conversion rate	The percentage of candidates who make it through each stage of your hiring process	A low pipeline conversion rate might mean that you need to adjust your hiring process to prevent candidate drop-off.
Candidate experience	How candidates perceive your hiring process and employer brand	A poor candidate experience could indicate your hiring process is too long or your recruiter’s outreach is ineffective.
Early turnover rate	The percentage of hires that leave your company within the first year	A high early turnover rate may indicate that your onboarding process needs improvement (onboarding software can help) or that there was a mismatch between your company’s and the candidate’s expectations.
Diversity hiring data	The demographics of applicants, candidates and new hires	A lack of diversity in your talent pool could indicate bias in your hiring process.

Building your recruitment analytics programme

Here are some best practices for building a recruitment data analysis programme that moves the needle: 

1. Set recruiting goals, such as reducing the time-to-hire or improving the candidate experience. From there, you’ll be able to determine which metrics to prioritise.
2. Choose the best recruitment analytics tool for your needs and budget. Ideally, your dashboard will feature real-time updates and role-based views and can pull data directly from any systems you already rely on. 
3. Audit and clean up your data sources, such as your ATS and HRIS, regularly. That way, your analytics tool can draw valid, useful conclusions from the data. 
4. Roll out the new technology slowly and train all users thoroughly. If you’re brand new to recruitment analytics, start by running reports and learning what every metric means. 
5. Experiment with the system’s predictive capabilities. If it features machine learning, it should learn over time, improving the accuracy of its forecasts. 

The right reporting and analytics capabilities can turn your hiring data into one of your most valuable strategic assets. 

Frequently asked questions

What is recruitment analytics?

Recruitment analytics involves finding and interpreting patterns in talent acquisition data to inform hiring decisions and process improvements. Teams typically use three levels of recruitment analytics that build on one another: operational reporting, advanced reporting and strategic or predictive analytics. 

How can recruitment analytics help you to hire faster?

Recruitment analytics can help you to hire faster by yielding insights into hiring process bottlenecks (e.g., a manager who takes too long to provide interview feedback) and ineffective candidate sourcing channels. Once you know the problem, you can take deliberate steps to fix it and get your positions filled more quickly. 

What are the most important recruitment metrics to track?

Without knowing your specific organisation and company goals, it’s tough to say which recruitment metrics are the most important to track. But time-to-fill is a good one to keep top of mind, no matter your industry or growth stage. 

What is the difference between recruitment analytics and HR analytics?

HR analytics is an umbrella term that encompasses all people-management analytics across the employee lifecycle. Recruitment analytics fall under that umbrella but focus on data specifically related to hiring and onboarding new team members. 

How do you track time-to-fill vs. time-to-hire accurately?

Time-to-fill is the time elapsed between opening a job requisition and hiring an employee. Time-to-hire is the time that elapses between when a candidate enters the talent pipeline and accepts a job offer. You can track both metrics by looking at timestamps in your ATS. 

What should a recruitment analytics dashboard include?

Your recruitment analytics dashboard should be customisable and include all the metrics and functionality that are important to your company. The view should be based on the user’s role, so the insights presented tie directly into their work. Depending on your dashboard capabilities, you might also want to receive real-time updates on key performance indicators (KPIs). 

Find out how you can lighten the load with a single AI-powered people platform.
Let's see it