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Using numbers to influence recruitment decisions may sound a little soulless. Shouldn’t you rely on human emotions more than stats when sourcing people to join your organisation? Isn’t a candidate’s magnetic personality and positive first impression more meaningful than their ability to answer multiple-choice questions at speed?
The reality is that analytics in recruitment will help you hire smarter and reach your people goals faster. You’ll receive valuable insights into your hiring funnel processes and predict how candidates will fare once they've joined your team. And by doing so, you’re setting everyone up for success — what’s more human than that?
By the end of this guide, you’ll understand which recruitment metrics to track, why you’re tracking them, and feel confident overall about the power of analytics in shaping your recruitment process.
What is analytics in recruitment?
Recruiting analytics uses data science to measure different aspects of your hiring strategies related to budget, talent management, diversity, and more. From these measurements, you’ll refine your processes and deliver a better ROI for your business.
Of course, analytics is nothing new in the corporate
world. Companies have used the term "big data" since 2005 when Roger Magoulas coined it at the O'Reilly Strata Conference. Since then, software developers have launched numerous data-driven technologies to help companies augment their business decisions—including those related to recruitment and talent management.
Why do we need analytics in recruitment?
If you’re not convinced about the power of data analysis, consider these five reasons to incorporate number-crunching into your recruitment processes.
1. Predict future job performance
Your top priority as a recruiter is hiring someone who can do the job well. But how can you predict performance beyond relying on gut feelings? Or trudging through historic experience listed on resumes in the hope it will translate into future success? The answer is analytics.
Back in 1998 (when the Spice Girls were huge, and Justin Timberlake was the dorky one in N-Sync), Frank Schmidt and John Hunter published a meta-study pulling together 85 years of research on how to assess candidates.
Their research revealed the top predictive validity scores — the most likely indicators of how a candidate will perform in their job. As you can see from the below recruiting analytics, work sample tests are the most useful signal.
2. Be proactive about talent management
Analytics allows you to track attrition and predict future staffing needs. This data can cement your decision about when it’s best to hire internally or externally, identify core areas of skill gaps in your workforce, or measure performance against industry averages.
With the right information, you'll anticipate which talent needs will likely arise and when. This can help you decide whether or not it's time to invest in training and development, create a targeted recruitment campaign, or hire specialist skills in the form of part-time or contract workers.
3. Identify bias in your hiring process
Checking your recruitment data enables you to identify potential blind spots in your process and give everyone an equal chance of receiving a job offer. Bias may include:
- shortlisting candidates with certain educational backgrounds
- asking unstructured interview questions during the hiring process
- using gender-biased language in job ads
Data analysis allows you to monitor and interpret any hiring process trends and ensure you're not inadvertently discriminating against certain groups.
4. Improve your employer brand
Recruitment data provides actionable insights into how well you're attracting qualified candidates. It measures the success of your employer brand and pinpoints any areas where you can improve your messaging or enhance the candidate experience.
The bottom line: Potential candidates are always excited to apply to a company with a positive employer brand.
5. Boost workforce diversity
Recent Glassdoor research reveals that 32% of job seekers would avoid applying to a company lacking diversity in its ranks.
Analytics will increase the volume of candidates you attract from underrepresented backgrounds and, equally as important, how many progress through your hiring funnel to join your teams.
| Mission / Values Alignment | Culture Fit Alignment |
|---|---|
| Passion for your company’s missionA shared approach to working/collaborating | A shared educational, cultural or professional backgroundSimilar demographic (age, social class etc)Mutual hobbies/interests |
| Mission / Values Alignment | Culture Fit Alignment |
|---|---|
| Passion for your company’s missionA shared approach to working/collaborating | A shared educational, cultural or professional backgroundSimilar demographic (age, social class etc)Mutual hobbies/interests |
| Mission / Values Alignment | Culture Fit Alignment |
|---|---|
| Passion for your company’s missionA shared approach to working/collaborating | A shared educational, cultural or professional backgroundSimilar demographic (age, social class etc)Mutual hobbies/interests |
12 recruitment reporting metrics to track
Analytics in recruitment is a broad concept, so let’s get specific about some of the recruitment reporting metrics you can track and take action on.
1. Employer brand
Social listening is a powerful tool for understanding potential candidates' perceptions of your employer brand.
Research suggests that 68% of millennials review platforms like Glassdoor, Indeed, and Blind to check out what others say about a company before applying for a job there. Start by tracking your company score on these platforms to understand the lay of the land.
2. Source of hire
Monitor where your best applicants come from — for example, job boards, recruitment agencies, employee referrals, etc.
You'll identify which sources to invest more of your time and money in, so you can repeat the cycle for future hires.
3. Volume of applications
It might make sense for you to know how many candidates apply for your roles, especially if you're concerned your job posting doesn't have the reach you intended or you want to monitor whether your job descriptions appeal to your target audience.
While these are both important factors, volume of applications can also be a vanity metric. Remember: The quality of applications outweighs the quantity received.
4. Diversity metrics
If you claim to be an equal opportunity employer, prove it by tracking the diversity of candidates who apply for your roles. This data will refine any initiatives or programs you rely on to nurture a diverse and inclusive workplace.
Here's an example of how we track DE&I metrics in Applied. You can also view reports on disability pay gaps, socio-economic diversity, and more.
5. Assessment scores
Skills-based hiring focuses on testing high-quality candidates on key parts of the job before extending an offer of employment to them. In Applied, track and rank scores from:
- Cognitive aptitude tests
- Work samples
- CVs
- Structured interviews
- Numeracy tests
6. Time to hire
Time to hire is the amount of time between when the first candidate applies to when the successful person accepts your job offer. The longer this is, the more expensive the process and the more likely you are to lose good candidates to your competitors.
As a benchmark, LinkedIn data reveals that hiring can take as long as 49 days in some industries.
7. Time to fill
Time to fill is similar to time to hire, but it’s measured from when the role is created to when it’s filled. For both of these recruiting metrics, use the DATEDIFF function in Excel to track yours.
8. Cost per hire
Recruiting costs are expensive — recent SHRM statistics show that the average cost to fill a vacant position is 90 to 200% of that person’s annual salary. When totting up your own overall cost, factor in:
- Recruiting team time
- Hiring manager time
- Candidate sourcing
- Candidate screening
- Third-party fees (agencies, job boards, assessment tests)
- Onboarding costs (tools, training, software licenses)
- Referral bonuses for existing employees
9. Offer acceptance rate
Track the percentage of candidates who accept your offer over time. If you notice a decline, see how this correlates with industry trends, such as the volume of candidates in the market, benchmarked compensation data, and flexible working standards.
10. Candidate experience
Ramp up your candidate experience by tracking the quality of their journey. Set expectations upfront, keep them in the loop with timely updates, and measure sentiment throughout the process to improve this metric.
11. Employee retention
Retention is one of the most important measurements to take note of. Jobvite reports that 30% of new joiners will quit within the first 90 days of starting a new job which is an expensive headache for recruitment teams.
Predictive analytics forecast retention by using algorithms to highlight which employees are most likely to quit. It uses data such as employee feedback or performance scores to highlight which employees haven’t connected with their role or your company culture.
From here, you can get ahead of the resignation letter with well-timed interventions that persuade your employees to stay.
12. Quality of hire
Quality of hire is the holy grail metric for recruiting teams — and it’s largely based on the new joiner’s performance, engagement, and productivity. Ask the following questions to create and rank your quality of hire scores:
- How long does it take for them to get up and running in their role?
- How quickly do they hit their KPIs?
- Are they staying with the company longer than the average employee?
- Do they achieve higher-than-average customer service scores?
- Do they make lower-than-average errors?
6 best practices for using analytics in recruitment
Before you wade in and start analysing the heck out of all the data, follow these best practices to maximise your strategy.
1. Define your recruitment goals
Start by identifying your pain points — what problems do you want to solve? Don’t try to tackle everything at once, but create a shortlist of business goals you want to achieve, complete with timelines for each.
- Example 1: You want to increase diversity in leadership positions within 1 to 3 years.
- Example 2: You need to lower your hire turnover rate over the next quarter.
- Example 3: You need to decrease the number of candidates rejecting your job offers immediately.
2. Create a hypothesis
Use your instinct or industry trends to craft a hypothesis.
- Example 1: You feel you’re not receiving quality applications because candidates perceive your employer brand negatively due to posts on social platforms.
- Example 2: You believe you’re not holding onto high-potential new joiners because your onboarding process is too long and clunky.
- Example 3: Your quality of hire is suffering because you’re not testing candidate skills sufficiently during screening.
3. Select your priority data
Not all data will be relevant to your hypothesis, so be selective about the metrics you track.
Example: Reduce turnover by monitoring metrics like:
- Retention
- Candidate experience
- Assessment scores
Review your data consistently, and set alerts for any changes or anomalies in your data sets.
4. Incorporate storytelling into your data analysis
Raw data can be overwhelming, so learn how to extract meaningful insights from the numbers and use them to demonstrate your point.
If you need to present the data to stakeholders such as your HR leaders, managers, or C-suite execs, include stories about your insights to make the numbers more memorable and persuasive. Incorporating visuals such as graphs, charts, and infographics will unravel complex concepts too.
5. Understand data regulations
Be aware of your responsibilities in collecting, analysing, and distributing sensitive candidate or employee data. This may vary depending on your business location and your company size.
Remember: Always check with your compliance and legal teams to ensure you fully adhere to any data protection laws.
6. Plan for your future hiring needs
Valuable recruitment insights are a golden opportunity to prepare for the future. Whether you're expanding into new business areas or have identified a skills gap you need to bridge, use data as a catalyst to take action.
Implement your data-driven recruitment analytics strategy
With the right talent acquisition platform, collecting and analysing data is a cinch.
Applied allows you to track the data that matters on autopilot. Our key features include:
- Skills-based shortlisting: Identify the best candidates for your role using skills-based questions.
- Structured interviews: Conduct science-backed, data-driven interviews and compare results consistently.
- CV scoring tool: Ask focus questions to zoom in on the details.
- DEI role reporting: Empower your hiring teams with a full recruiting analytics suite, including ethnic, gender, and disability tracking, to understand how minority candidates progress through your hiring funnel.
- Job description analysis tool: Address gender imbalance in your organisation by detecting implicit bias in your job descriptions.
- Cognitive testing: Predict job performance with assessments that test critical, role-specific skills
- Numerical skills testing: Identify numerical reasoning skills for high-volume roles
- Global recruitment strategy reporting: Turn candidate sourcing into science by generating stats that optimise your budget, processes, and quality of hire.
Ready to incorporate some business intelligence into your hiring decisions?
Applied’s purpose-built recruitment automation system will gather, rank, and present the data you need to take charge of your recruitment process. Book in a free demo today.
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