Recruitment Analytics: 16 Key Metrics and How to Use Them
Published: Nov 4th, 2023
In today's digital hiring realm, analytics help us discover, interpret, and communicate information, patterns, and statistics. Data analysis has become such a crucial tool for running an organization that it's tough to imagine a world without it.
Recruitment analytics (also known as recruiting analytics or hiring analytics) can help you optimize one of your most critical business processes: finding, hiring, and retaining employees.
According to a report by LinkedIn, 84% of recruiters believe this type of information analysis will become all but ubiquitous by 2025. But what is data analytics in recruitment, exactly? Do you need it, and if so, how can it help your organization?
Ahead, answers to these questions, along with tips for getting started with recruiting analytics and key recruitment metrics every hiring manager should know.
What are recruitment analytics?
Recruitment analytics is the practice of using data-driven metrics — such as application completion rate and time-to-fill — to optimize your business's talent sourcing, short-listing, interviewing, and hiring processes. The idea is to make things more efficient and effective while improving the experience of all parties involved.
Recruitment analytics can help you improve your recruiting process and ultimately hire better team members faster and at lower cost. What gets measured gets improved."
Key recruitment metrics from your recruitment data analysis
Hiring metrics call on recruitment data analytics to measure the success, efficiency, and satisfaction of your recruiting process. The most common metrics are:
- Source of hire
- Sourcing channel effectiveness
- Sourcing channel cost
- Applicants per vacancy
- Application completion rate
- Candidate diversity
- Selection ratio
- Offer acceptance rate
- Time to fill
- Cost per hire
- Quality of hire
- Cost of reaching OPL
- Time to productivity
- First year attrition rate
- Candidate satisfaction
- Hiring team satisfaction
Read on to find out what each recruiting analytics metric means and how it can be used to optimize your talent acquisition process.
1. Source of hire
In analytics recruiting, source of hire is the source (or sources) that attract potential candidates and inform them of a vacancy. It could be job boards, a posting on your company website, employee referrals, a virtual job fair, or an outsourced recruiter.
2. Sourcing channel effectiveness
Sourcing channel effectiveness measures the conversions of each medium. Conversions can include clicks to the job description page, completed online applications, emailed resumes, or the number of hires per channel. This is one of the most important recruitment analytics metrics to track because it reflects your basic system health.
3. Sourcing channel cost
If you use multiple sources of hire, analyzing the cost of each one alongside its overall effectiveness can help you determine which ones are worthwhile. For example, if 100% of your hires are found by an outsourced recruiter, you may not need to use job boards to fill openings.
4. Applicants per vacancy
Applicants per vacancy is a pretty self-explanatory recruiting analytics metric. It's the number of people that apply for each open position at your company through all sources of hire.
A notably high number of applicants for a vacancy may indicate a few different things. It could mean the particular role is in high demand, that it's an entry-level position with few background requirements, or that your job description was a bit too broad.
If your job description was too broad, be a bit more thorough when outlining the qualifications, preferred experience, and description of your ideal candidate. This will likely result in fewer low-quality applicants, which can save the hiring team substantial time.
5. Application completion rate
Application completion rate is the time it takes each applicant to apply for the role. This recruiting metric mostly pertains to companies that require candidates to apply on their company website.
If you notice a high number of people dropping out before completing the application, it might mean the process is too long or that your interface isn't user-friendly. You want to ensure the best talent applies. Analyzing this recruitment analytics metric can help you avoid losing qualified candidates along the way.
Recruitment Analytics Facts
6. Candidate diversity
Recruiting data analytics can also provide valuable insights into candidate diversity. Adding an option to disclose ethnicity, gender, disability, and veteran status on the application can help you prevent discrimination and ensure your staff accurately represents the population.
7. Selection ratio
Sometimes called the submittals-to-hire ratio, selection ratio is the number of people hired divided by the total number of applicants. With only one opening and a notably high number of candidates, the ratio of this recruiting analytics metric can quickly approach zero.
8. Offer acceptance rate
Offer acceptance rate is crucial in determining recruitment success. It's calculated by dividing the number of people that accepted a job offer by the total number of offers.
For instance, if your first choice of candidate declined the offer but the second person accepted, the offer acceptance rate would be 50% (1 out of 2 offers accepted).
9. Time to fill
Recruitment time to fill is the time it takes to source, interview, and hire an employee to fill a vacancy. It is usually measured by the number of days between posting an open position and a candidate accepting the job offer.
10. Cost per hire
Cost per hire is the total cost of recruiting, interviewing, and hiring a candidate. A number of expenses are factored in, including job board fees, hiring a professional recruiter, performing background checks, and the actual hours spent sifting through resumes and interviewing candidates.
11. Quality of hire
Recruitment analytics can provide valuable information about the quality of each hire. The data can assess information for a hired employee's resume, such as their education and credentials, as well as their first-year performance ratings.
12. Cost of reaching OPL
Cost of reaching OPL (optimum productivity level) is the total expense incurred for getting a new hire up to speed. This includes considerations like onboarding, training, purchasing equipment, and their prorated salary during this time. Depending on the organization and the position, the total cost of hiring and reaching OPL can range from $500 to over $10,000. Track this recruiting analytics metric to improve overall hiring efficiency.
13. Time to productivity
Time to productivity is a key recruitment metric that measures how long it takes to reach OPL. It starts on a new employee's first day of work and ends when they're fully trained and able to contribute to the company with a reasonable amount of support and supervision.
14. First-year-attrition rate
First-year-attrition rate tells you the percentage of employees who leave your company within the first year after being hired. Also known as turnover rate, this recruitment analytics metric includes those who resign and those who are laid off or fired.
15. Candidate satisfaction
Candidate satisfaction tracks applicants' overall impression of the recruitment process, including filling out the application, phone screenings, in-person interviews, and communication throughout each step.
While satisfaction is relatively abstract and thus a little trickier to measure than other metrics, you can conduct optional surveys to gather candidates' sentiments about their experience. This metric is important for both hired talent and those who aren't selected, as it gives them an impression of your brand and what it may be like to actually work for your company.
For example, if your hiring process took several months and involved poor communication from the hiring team, it could leave the person who receives a job offer with a bad impression of your organization. In the end, this could make them decline the offer or help convince them to resign within the one-year mark. This recruiting analytics metric can tell you if you’re turning away great candidates.
16. Hiring team satisfaction
Similarly, you can get an idea of the hiring team's overall satisfaction with the process. On the whole, are people feeling like their time was well spent and that the best person for the job was ultimately hired? Recruiter metrics like this can help you answer these questions and make adjustments to optimize the process.
Getting started with recruitment analytics
If you want your company to compete effectively in your industry, achieve steady growth, and thrive in the long term, recruiting analytics is the name of the game. To get started, you'll need to:
- Choose an analytics solution
- Collect relevant data
- Use data visualization
Here's what to know about these three essential components of recruitment analytics.
Choosing an analytics solution
As with all other forms of business analytics, there are several tools and platforms you can use for recruitment analytics. The right solution depends on several factors, such as your industry, the size of your company, and the specific metrics you're using.
Having said that, a few of the most popular recruiting analytics tools include Erecruit, Phenom People, Saba, Yello, Bullhorn Canvas, SmashFly, TalentLyft, and Kallidus Recruit.
Collect relevant data
Next, you'll want to figure out which data to collect and which metrics to use. Bear in mind there's no one-size-fits-all approach to recruiting analytics, so your data analysis won't look the same as the next company's.
For instance, your company might want to figure out which source-of-hire the best candidates are coming from. In that case, you'd want to collect data on all candidates and how they were notified of the vacancy, as well as the resumes and performance of those you hire.
Use data visualization
With a long list of possible metrics and nearly infinite statistics to analyze, making sense of it all can seem overwhelming at first. That's where data visualization comes in.
The tools and platforms mentioned above typically include a recruiting analytic dashboard. This lets you clearly see the information, organize your key metrics, identify trends, and interpret what the numbers mean. You can see what's working and what isn't, and ultimately, take action or adjust your hiring approach based on what the data is telling you.
Data visualization tools can turn a sea of numbers into clear insights that guide your hiring effort with laser-sharp precision."
Using predictive analytics to better plan your recruitment
Talent acquisition metrics can be leveraged to better plan for the future. As such, predictive analytics call on statistical data to determine the likelihood of certain events occurring during the hiring process.
Of course, it's impossible to make precise predictions on all accounts. When running a company, you can almost always expect some level of unforeseen occurrences.
However, you can use this information to figure out how much time and money is required to fill a vacancy with a high-quality candidate and a low turnover rate. Further, you can make the process more efficient by determining which sources-of-hire are the most fruitful for your business.
Why you should automate data analytics recruitment
As a business owner, hiring manager, or professional recruiter, it's in your best interest to streamline the process of attracting, screening, and hiring candidates. By offering an organized solution for collecting, tracking, measuring, and analyzing relevant data, recruitment analytics is a critical component of modern talent acquisition.
Recruiting analytics is a vital tool for reducing your turnover rate and competing in your industry. With the right recruiting metrics template, you can identify patterns, find meaning in statistics, create a plan for making everything more efficient, and employ data-driven decisions about hiring employees.
Contact us for more information, or watch a demo to see how it works.