If you're in HR, you've probably noticed that hiring has become quite complex lately.
It's not just about posting job ads and hoping for the best anymore.
Today, analytics is playing a very important part in how we deal with data and plan ahead.
If that sounds a bit technical – we are here to break it down in a way that's easy to understand and apply to your everyday work.
Let's begin!
The current approach to data in HR
Think about your typical workday.
You're probably dealing with a stack of resumes, trying to schedule interviews, and attempting to make sense of all the feedback from hiring managers.
It can feel overwhelming, right?
Many HR professionals still rely heavily on their instincts and traditional methods when making hiring decisions.
While there's certainly value in experience and intuition, there's also a wealth of information available that often goes unused.
Many HR departments are sitting on mountains of data without realizing how helpful it could be.
This information comes from various sources:
- Application forms
- Interview assessments
- Performance reviews of past hires
- Employee turnover rates
The challenge?
Figuring out how to use this information effectively. Why does this matter?
Well, a Deloitte study showed that companies using people analytics in hiring not only speed up their processes but also make better-quality hires, reducing ghosting and turnover rates
They're more likely to find the right people for their teams, keep them around longer, and ultimately, contribute more to the company's success.
Key metrics for planning your recruitment strategy
Now, let's talk about some specific numbers that can really help you improve your hiring process.
These aren't just random statistics – they're valuable insights that can guide your decision-making and help you become more efficient and effective in your role.
9 most important metrics to track in HR
1. Time-to-hire: Tracks the number of days from job posting to offer acceptance, helping to streamline your hiring process and avoid losing top talent.
2. Cost-per-hire: Calculates total recruiting costs divided by the number of hires, offering insights into your recruitment budget and spending.
3. Quality of hire: Measures the effectiveness of new hires based on their performance, retention, and manager satisfaction after 6-12 months.
4. Source of hire: Identifies where your best candidates come from (job boards, referrals, etc.) to optimize recruitment efforts.
5. Candidate experience score: Evaluates how candidates perceive your hiring process, helping to improve satisfaction and employer brand.
6. Offer acceptance rate: Tracks the percentage of accepted job offers, signaling whether your offers are competitive and appealing.
7. Time in pipeline: Analyzes the time candidates spend in each stage of your hiring process to identify and remove bottlenecks.
8. Diversity metrics: Monitors the diversity of candidates at each stage and among hires, promoting inclusive hiring practices.
9. Predictive analytics: Uses data to forecast future hiring needs and identify candidates likely to succeed based on historical performance.
Time-to-hire
This metric measures how long it takes from the moment a job opening is posted to when a candidate accepts an offer.
It's important because lengthy hiring processes can lead to losing top candidates to other companies.
To calculate time-to-hire:
- Start date: When the job was first advertised
- End date: When the candidate accepts the offer
- Formula: End date - Start date = Time-to-hire
Industry averages vary, but aim for 30-40 days for most positions.
If your time-to-hire is longer, look for bottlenecks in your process.
Are you taking too long to review resumes?
Is scheduling interviews a challenge?
Identifying these issues can help you streamline your hiring process.
Cost-per-hire
This metric helps you understand the financial investment required for each new employee.
It includes both external costs (like job board fees or agency costs) and internal costs (like the time your team spends on recruiting).
To calculate cost-per-hire:
- Add up all external and internal recruiting costs for a period (e.g., a year)
- Divide by the number of hires made in that period
For example, if you spent $100,000 on recruiting in a year and hired 20 people, your cost-per-hire would be $5,000.
Understanding this number can help you budget more effectively and identify areas where you might be overspending.
It can also help you decide whether certain recruiting channels are worth the investment.
Quality of hire
This is a bit trickier to measure, but it's crucial.
After all, the goal isn't just to hire quickly or cheaply, but to find great employees who will contribute to your company's success.
You can measure quality of hire by looking at:
- Performance ratings of new hires after 6-12 months
- Retention rates of new hires
- Hiring manager satisfaction
For example, you might survey hiring managers 6 months after a new hire starts, asking them to rate the employee's performance on a scale of 1-10.
You could also track what percentage of new hires are still with the company after a year.
Source of hire
This metric tells you where your best candidates are coming from.
Are they finding you through job boards, employee referrals, your company website, or somewhere else?
To track this:
- Record where each applicant first learned about the job opening
- Look at which sources produce the most hires
- Compare the quality and retention rates of hires from different sources
You might find, for instance, that while you get a lot of applications from job boards, your best hires actually come from employee referrals.
This could lead you to invest more in your referral program.
Candidate experience score
In today's job market, candidates have choices.
A poor experience during the hiring process can cause great candidates to lose interest in your company.
To measure this:
- Survey candidates after they've gone through your hiring process
- Ask about their satisfaction with communication, the application process, interviews, etc.
- Calculate an average score
For example, you might ask candidates to rate various aspects of their experience on a scale of 1-5, then calculate an overall average. Aim for a score of 4 or higher.
Offer acceptance rate
This measures the percentage of job offers that are accepted by candidates.
A low acceptance rate might indicate issues with your compensation packages, or problems with how you're selling the role or company to candidates.
To calculate: (Number of accepted offers / Number of total offers) x 100 = Offer acceptance rate
Industry averages are around 80-90%.
If yours is lower, it's worth investigating why candidates are turning down your offers.
Time in pipeline
This metric looks at how long candidates spend at each stage of your hiring process.
It can help you identify where bottlenecks are occurring.
To measure:
- Track how long each candidate spends in each stage (resume review, phone screen, interviews, etc.)
- Calculate averages for each stage
For example, if you find that candidates are spending an average of two weeks waiting for a decision after their final interview, that might be an area to improve.
Diversity metrics
A study by McKinsey found that companies with diverse workforces are 36% more likely to financially outperform their less diverse counterparts.
Many companies are focusing on improving diversity in their workforce.
Tracking metrics related to diversity in your hiring process can help you see if you're making progress.
Consider tracking:
- Diversity of your candidate pool at each stage of the hiring process
- Diversity of your final hires
- Retention rates for diverse hires
Remember, improving diversity often requires looking at your entire hiring process, from where you source candidates to how you conduct interviews.
Predictive analytics
This is a more advanced use of HR analytics, but it's becoming increasingly important. Predictive analytics use historical data to make predictions about future outcomes.
In hiring, this might involve:
- Using past performance data to predict which candidates are likely to be successful in a role
- Analyzing turnover patterns to predict which employees might be at risk of leaving
- Forecasting future hiring needs based on business growth and typical turnover rates
For example, you might find that employees with certain skills or experiences tend to perform better in specific roles.
This information could help you refine your screening criteria for future hires.
The difference between actionable insights and heaps of data.
Also read: Important recruiting metrics you need to measure (here)
Now that we've covered these metrics, you might be thinking, "That's a lot of data to collect!"
And you're right.
But here's the thing: having a lot of data isn't the same as having useful information.
The key is to turn that data into actionable insights.
An actionable insight is a piece of information that tells you what to do next. It's not just a number or a statistic – it's a clear direction for improvement.
For example, let's say you've calculated your time-to-hire and found that it's 60 days on average.
That's data, but it's not particularly actionable on its own.
But if you dig deeper and find that candidates are spending an average of 20 days waiting for feedback after their final interview, that's an actionable insight.
It tells you exactly where to focus your efforts to speed up your hiring process.
Here are some tips for turning data into actionable insights:
1. Look for patterns and trends: Don't just look at numbers in isolation. Compare metrics over time or across different departments or roles.
2. Ask "why" and "so what": When you see a number that stands out, ask yourself why it might be happening and what impact it's having on your overall hiring success.
3. Prioritize: You can't act on every piece of data. Focus on the metrics that align most closely with your overall business goals.
4. Set benchmarks: Compare your metrics to industry standards or your own past performance to understand what "good" looks like for your organization.
5. Involve others: Share your data with hiring managers and other stakeholders. They might see patterns or opportunities that you've missed.
Also read: Data-driven recruiting: What it is and how to use it for hiring (here)
Why it's time to connect your recruitment software with your HR analytics solution
If you're using separate systems for recruiting and HR analytics, you're probably spending a lot of time manually transferring data from one system to another.
This not only takes up valuable time but also increases the risk of errors.
Integrating your recruitment software with your HR analytics solution can bring several benefits:
- Real-time data: Instead of waiting for monthly or quarterly reports, you can access up-to-date information whenever you need it.
- Comprehensive view: You can see the entire employee lifecycle, from application to hire to performance and beyond, all in one place.
- Better forecasting: With more complete data, you can make more accurate predictions about future hiring needs.
- Improved candidate experience: Integrated systems often allow for smoother, faster processes, which candidates appreciate.
- Time savings: Automated data transfer means less time spent on administrative tasks and more time for strategic thinking.
Speaking of modern HR solutions, let me introduce you to Kula ATS - a game-changer in the world of recruitment.
As a cutting-edge, all-in-one Applicant Tracking System, Kula addresses the core challenges recruiters face daily. From sourcing and screening to scheduling and analytics, Kula's native AI handles it all in one seamless platform.
Take a look at what we've built and experience the future of recruitment firsthand!
9 ways HR analytics can improve your hiring process
9 ways HR analytics can improve your hiring process for your infographic:
1. Identify and optimize sources of candidates: Track and analyze the performance and retention rates of hires from different recruitment channels.
2. Optimize job descriptions: Use data to craft job descriptions that attract more qualified candidates by analyzing the language of high-performing job posts.
3. Predict candidate success: Analyze the traits of top performers to build predictive models for identifying candidates likely to succeed.
4. Enhance diversity and inclusion: Use analytics to track diversity metrics at each stage and identify potential biases in your recruitment process.
5. Improve candidate experience: Collect and analyze candidate feedback to continually refine the hiring process and increase satisfaction.
6. Reduce bias in hiring decisions: Utilize data to detect patterns of bias and implement corrective actions, such as standardized interviews.
7. Forecast hiring needs: Leverage historical data on turnover and growth to accurately predict future hiring requirements.
8. Optimize the interview process: Analyze which interview questions and assessments best predict job success and refine your interview strategy accordingly.
9. Improve offer acceptance rates: Study accepted and declined offers to identify factors influencing acceptance and adjust your offer strategy.
Let's dive deep into 9 specific ways you can leverage HR analytics to transform your hiring process:
Identify and optimize your best sources of candidates
HR analytics allows you to track and analyze where your best hires come from.
This goes beyond simply counting the number of hires from each source – it involves examining the quality, performance, and retention rates of hires from different channels.
How to implement:
- Track the source of every applicant and hire
- Analyze performance metrics of hires from each source after 6 months and 1 year
- Calculate the cost-per-hire for each source
- Determine the retention rate of hires from each source
Example insight: You might discover that while job boards bring in the most applicants, employee referrals lead to hires who perform better and stay longer. This could prompt you to invest more in your employee referral program.
Optimize your job descriptions for better candidate attraction
Data-driven insights can help you craft job descriptions that attract more qualified candidates.
By analyzing application rates, candidate quality, and even the language used in your top-performing job posts, you can refine your approach.
How to implement:
- Use text analysis tools to identify common phrases in high-performing job posts
- A/B test different job titles and descriptions
- Track application rates and quality of candidates for different versions
- Analyze the correlation between specific words or phrases and application quality
Example insight: You might find that job descriptions emphasizing professional development opportunities attract more high-quality candidates than those focusing primarily on required qualifications.
Predict candidate success and improve selection
By analyzing the characteristics and experiences of your top-performing employees, you can create predictive models to identify candidates likely to succeed in your organization.
How to implement:
- Identify the key performance indicators (KPIs) for each role
- Analyze the backgrounds, skills, and experiences of your top performers
- Use machine learning algorithms to find patterns and create predictive models
- Apply these models in your candidate screening process
Example insight: You might discover that for sales roles, candidates with a combination of customer service experience and participation in team sports tend to outperform others.
Enhance diversity and inclusion in your hiring process
HR analytics can help you identify and address potential biases in your recruitment funnel, ensuring a more diverse and inclusive hiring process.
How to implement:
- Track diversity metrics at each stage of your hiring process
- Analyze drop-off rates for different demographic groups
- Use blind resume screening techniques to reduce unconscious bias
- Implement diverse interview panels and track their impact
Example insight: You might find that female candidates are disproportionately dropping out after the first interview round. This could prompt you to investigate potential biases in your interview process or job descriptions.
Improve the candidate experience
By collecting and analyzing data on candidate satisfaction throughout the hiring process, you can continually refine and improve the experience for applicants.
How to implement:
- Send surveys to all candidates (both successful and unsuccessful) after each stage of the process
- Track metrics like application completion rates and time-to-complete
- Analyze correlation between candidate experience scores and offer acceptance rates
- Use feedback to make iterative improvements to your process
Example insight: You might discover that candidates who receive timely feedback after interviews are 30% more likely to accept job offers, prompting you to prioritize quicker post-interview communications.
Reduce bias in hiring decisions
Analytics can help identify patterns of bias that might not be immediately apparent, allowing you to take corrective action.
How to implement:
- Use AI-powered tools for initial resume screening to focus on skills and experience
- Implement structured interviews with standardized scoring
- Analyze hiring decisions against diversity goals
- Provide unconscious bias training and track its impact on hiring decisions
Example insight: Data might reveal that certain interviewers consistently rate candidates from particular backgrounds lower, indicating a need for additional training or changes to the interview process.
Forecast hiring needs with precision
By analyzing historical data on turnover, business growth, and seasonal trends, you can more accurately predict future hiring needs.
How to implement:
- Track employee turnover rates by department and role
- Analyze historical hiring patterns and correlate with business metrics
- Consider external factors like market trends and planned business initiatives
- Use predictive modeling to forecast future hiring needs
Example insight: You might predict that based on historical data and planned expansion, you'll need to hire 15% more software developers in the next quarter, allowing you to start recruiting proactively.
Optimize your interview process
Analytics can help you identify which interview questions or assessments are most predictive of on-the-job success.
How to implement:
- Track which interview questions are asked and how candidates are scored
- Correlate interview scores with subsequent job performance
- Analyze which interviewers' assessments are most predictive of success
- Use data to standardize and refine your interview process
Example insight: You might find that candidates who score highly on problem-solving questions tend to perform better in technical roles, leading you to emphasize these questions in future interviews.
Improve offer acceptance rates
By analyzing data on accepted and declined offers, you can refine your approach to extending offers and increase acceptance rates.
How to implement:
- Track all elements of job offers (salary, benefits, job title, etc.)
- Analyze which factors correlate most strongly with offer acceptance
- Compare your offers to industry benchmarks
- Survey candidates who decline offers to understand their reasons
Example insight: Data might show that flexible working hours are more strongly correlated with offer acceptance than slightly higher salaries, influencing how you structure future offers.
Remember, the key to success with HR analytics is not just collecting data, but turning that data into actionable insights.
Each of these strategies should be implemented with clear goals in mind, and the results should be continuously monitored and refined.
It's also important to note that while HR analytics can provide powerful insights, it should not replace human judgment entirely.
The most effective approach combines data-driven insights with the experience and intuition of skilled HR professionals.
This not only benefits your organization by bringing in better talent more quickly and cost-effectively, but it also improves the experience for candidates, enhancing your employer brand in the process
