This blog provides 14 important HR metrics examples. HR metrics are indicators that enable HR to track and measure performance on different aspects and ultimately predict the future. However, not all HR metrics are created equal. In this blog, we will provide some of the most valuable HR metrics examples.

Examples include the number of software developers, the staffing pattern over the life cycle of the software, cost, schedule, and productivity. Some metrics belong to multiple categories. For example, the in-process quality metrics of a project are both process metrics and project metrics. Software quality metrics are a subset of software. 64 Essential Testing Metrics for Measuring Quality Assurance Success. Software testing metrics are a way to measure and monitor your test activities. More importantly, they give insights into your team’s test progress, productivity, and the quality of the system under test. 8 Essential Software Development Metrics for Team Productivity. Data driven software development team leaders know that there is magic and insight in their team data. With the right information at the right time, great managers can identify which teams need help, spot and fix troublesome bottlenecks, and prevent team burnout and attrition.

Even though this list provides some essential HR metrics, you might come up with a great number 15! You are welcome to suggest additional HR metrics in the comments below.

HR metrics examples in recruitment

1. Time to hire (time in days)
An important metric for recruitment is the ‘time to hire’. This is the number of days between a position opening up and a candidate signing the job contract. It’s an excellent way to measure the efficiency of the recruitment process and provides insight into the difficulty of filling a certain job position.

There’s also the time to fill metric. This metric takes the same starting point but takes the date the candidate starts working as the end point.

2. Cost per hire (total cost of hiring/the number of new hires)
Like the time to hire, the ‘cost per hire’ metric shows how much it costs the company to hire new employees. This also serves as an indicator of the efficiency of the recruitment process.

3. Early turnover (percentage of recruits leaving in the first year)
This is arguably the most important metric to determine hiring success in a company.
This early leaver metric indicates whether there is a mismatch between the person and the company or between the person and his/her position. Early turnover is also very expensive. It usually takes 6 to 12 months before employees have fully learned the ropes and reach their ‘Optimum Productivity Level’. According to a 2014 Oxford Economics report, the lost output cost over this period averages £30,000 ($43,700) for new hires.

4. Time since last promotion (avg time in months since last internal promotion)
This rather straightforward metric is useful in explaining why your high potentials leave.

HR metrics examples related to revenue

5. Revenue per employee (revenue/total number of employees)
This metric shows the efficiency of the organization as a whole. The ‘revenue per employee’ metric is an indicator of the quality of hired employees. Check this Business Insider article to view how the top 12 tech companies in the world score on this metric.

6. Performance and potential (the 9-box grid)
The 9-box grid appears when measuring and mapping both an individual’s performance and potential in three levels. This model shows which employees are underperformers, valued specialists, emerging potentials or top talents. This metrics is great for differentiating between, for example, wanted and unwanted turnover.

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In another article, we wrote about the qualitative and quantitative ways to measure employee performance. Metrics include Net Promoter Score, management by objectives, number of errors, 360-degree feedback, forced ranking, etc.

7. Billable hours per employee
This is the most concrete example of a performance measure, and it is especially relevant in professional service firms (e.g. law and consultancy firms). Relating this kind of performance to employee engagement or other input metrics makes for an interesting analysis. Benchmarking this metrics between different departments and managers/partners can also provide valuable insights.

8. Engagement rating
An engaged workforce is a productive workforce. Engagement might be the most important ‘soft’ HR outcome. People who like their job and who are proud of their company are generally more engaged, even if the work environment is stressful and pressure is high. Engaged employees perform better and are more likely to perceive stress as an exciting challenge, not as a burden. Additionally, team engagement is an important metric for a team manager’s success.

Other HR metrics examples

9. Cost of HR per employee (e.g. $ 600)
This metric shows the cost efficiency of HR expressed in dollars.

10. Ratio of HR professionals to employees (e.g. 1:60)
Another measure that shows HR’s cost efficiency. An organization with fully developed analytical capabilities should be able to have a smaller number of HR professionals do more.

11. Ratio of HR business partners per employee (e.g. 1:80)
A similar metric to the previous one. Again, a set of highly developed analytics capabilities will enable HR to measure and predict the impact of HR policies. This will enable HR to be more efficient and reduce the number of business partners.

12. Turnover (number of leavers/total population in the organization)
This metric shows how many workers leave the company in a given year. When combined with, for instance, a performance metric, the ‘turnover’ metric can track the difference in attrition in high and low performers. Preferably you would like to see low performers leave and high performers stay. This metric also provides HR business partners with a great amount of information about the departments and functions in which employees feel at home, and where in the organization they do not want to work. Additionally, attrition could be a key metric in measuring a manager’s success.

13. Effectiveness of HR software
This is a more complex metric. Effectiveness of, for instance, learning and development software are measured in the number of active users, average time on the platform, session length, total time on platform per user per month, screen flow, and software retention. These metrics enable HR to determine what works for the employees and what does not.

14. Absenteeism (absence percentage)
Like turnover, absenteeism is also a strong indicator of dissatisfaction and a predictor of turnover. This metric can give information to prevent this kind of leave, as long-term absence can be very costly. Again, differences between individual managers and departments are very interesting indicators of (potential) problems and bottlenecks.

As you can see there are a lot of different examples of HR metrics. While some metrics are easier to implement than others, all of them provide insights into the workforce and HR. Combining these insights will prove vital for making substantiated decisions with proven impact. In a recent blog, we wrote about HR metrics related to employee retention, absenteeism, and learning & development effectiveness.

Because of the interest in this topic, we decided to create a course on strategic HR metrics. Check it out!

So, what are HR metrics exactly?

Before you start to work with HR metrics, it’s important to make sure you understand how metrics can work for you. What are HR metrics?

Human Resource metrics are measurements that help you to track key areas in HR data. The most important areas are listed below. In this list of HR metrics, we included the key HR metrics examples associated with those areas.

1. 1. Organizational performance
      • Turnover percentages
      • % of regretted loss
      • Statistics on why personnel is leaving
      • Absence percentages and behavior
   2. HR operations
      • HR efficiency (e.g. time to resolving HR self-service tickets)
      • HR effectiveness (e.g. perception of HR service quality)
   3. Process optimization
      Process optimization helps to analyze how we do what we do in Human Resource Management. The HR metrics and analytics in this area focus on changes in HR efficiency and effectiveness over time. These HR metrics and analytics are then used to re-engineer and reinvent what is happening in HR. This helps to optimize the Human Resource delivery process. Process optimization metrics are next-level. They are still very rare in modern organizations as they require a very high level of both data maturity and analytics maturity.

To learn more about HR metrics and how to implement them in your organization, check out our strategic HR metrics course.

• Software Quality Management

• Useful Resources

• Selected Reading

Software metrics can be classified into three categories −

• Product metrics − Describes the characteristics of the product such as size, complexity, design features, performance, and quality level.

• Process metrics − These characteristics can be used to improve the development and maintenance activities of the software.

• Project metrics − This metrics describe the project characteristics and execution. Examples include the number of software developers, the staffing pattern over the life cycle of the software, cost, schedule, and productivity.

Some metrics belong to multiple categories. For example, the in-process quality metrics of a project are both process metrics and project metrics.

Software quality metrics are a subset of software metrics that focus on the quality aspects of the product, process, and project. These are more closely associated with process and product metrics than with project metrics.

Software quality metrics can be further divided into three categories −

• Product quality metrics
• In-process quality metrics
• Maintenance quality metrics

Product Quality Metrics

This metrics include the following −

• Mean Time to Failure
• Defect Density
• Customer Problems
• Customer Satisfaction

Mean Time to Failure

It is the time between failures. This metric is mostly used with safety critical systems such as the airline traffic control systems, avionics, and weapons.

Defect Density

It measures the defects relative to the software size expressed as lines of code or function point, etc. i.e., it measures code quality per unit. This metric is used in many commercial software systems.

Customer Problems

It measures the problems that customers encounter when using the product. It contains the customer’s perspective towards the problem space of the software, which includes the non-defect oriented problems together with the defect problems.

The problems metric is usually expressed in terms of Problems per User-Month (PUM).

Where,

PUM is usually calculated for each month after the software is released to the market, and also for monthly averages by year.

Customer Satisfaction

Customer satisfaction is often measured by customer survey data through the five-point scale −

• Very satisfied
• Satisfied
• Neutral
• Dissatisfied
• Very dissatisfied

Satisfaction with the overall quality of the product and its specific dimensions is usually obtained through various methods of customer surveys. Based on the five-point-scale data, several metrics with slight variations can be constructed and used, depending on the purpose of analysis. For example −

• Percent of completely satisfied customers
• Percent of satisfied customers
• Percent of dis-satisfied customers
• Percent of non-satisfied customers

Usually, this percent satisfaction is used.

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In-process Quality Metrics

In-process quality metrics deals with the tracking of defect arrival during formal machine testing for some organizations. This metric includes −

• Defect density during machine testing
• Defect arrival pattern during machine testing
• Phase-based defect removal pattern
• Defect removal effectiveness

Defect density during machine testing

Defect rate during formal machine testing (testing after code is integrated into the system library) is correlated with the defect rate in the field. Higher defect rates found during testing is an indicator that the software has experienced higher error injection during its development process, unless the higher testing defect rate is due to an extraordinary testing effort.

This simple metric of defects per KLOC or function point is a good indicator of quality, while the software is still being tested. It is especially useful to monitor subsequent releases of a product in the same development organization.

Defect arrival pattern during machine testing

The overall defect density during testing will provide only the summary of the defects. The pattern of defect arrivals gives more information about different quality levels in the field. It includes the following −

• The defect arrivals or defects reported during the testing phase by time interval (e.g., week). Here all of which will not be valid defects.

• The pattern of valid defect arrivals when problem determination is done on the reported problems. This is the true defect pattern.

• The pattern of defect backlog overtime. This metric is needed because development organizations cannot investigate and fix all the reported problems immediately. This is a workload statement as well as a quality statement. If the defect backlog is large at the end of the development cycle and a lot of fixes have yet to be integrated into the system, the stability of the system (hence its quality) will be affected. Retesting (regression test) is needed to ensure that targeted product quality levels are reached.

Phase-based defect removal pattern

This is an extension of the defect density metric during testing. In addition to testing, it tracks the defects at all phases of the development cycle, including the design reviews, code inspections, and formal verifications before testing.

Because a large percentage of programming defects is related to design problems, conducting formal reviews, or functional verifications to enhance the defect removal capability of the process at the front-end reduces error in the software. The pattern of phase-based defect removal reflects the overall defect removal ability of the development process.

With regard to the metrics for the design and coding phases, in addition to defect rates, many development organizations use metrics such as inspection coverage and inspection effort for in-process quality management.

Defect removal effectiveness

It can be defined as follows −

$$DRE = frac{Defect : removed : during : a : development:phase }{Defects: latent : in : the: product} times 100%$$

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This metric can be calculated for the entire development process, for the front-end before code integration and for each phase. It is called early defect removal when used for the front-end and phase effectiveness for specific phases. The higher the value of the metric, the more effective the development process and the fewer the defects passed to the next phase or to the field. This metric is a key concept of the defect removal model for software development.

Maintenance Quality Metrics

Although much cannot be done to alter the quality of the product during this phase, following are the fixes that can be carried out to eliminate the defects as soon as possible with excellent fix quality.

• Fix backlog and backlog management index
• Fix response time and fix responsiveness
• Percent delinquent fixes
• Fix quality

Fix backlog and backlog management index

Fix backlog is related to the rate of defect arrivals and the rate at which fixes for reported problems become available. It is a simple count of reported problems that remain at the end of each month or each week. Using it in the format of a trend chart, this metric can provide meaningful information for managing the maintenance process.

Backlog Management Index (BMI) is used to manage the backlog of open and unresolved problems.

$$BMI = frac{Number : of : problems : closed : during :the :month }{Number : of : problems : arrived : during :the :month} times 100%$$

If BMI is larger than 100, it means the backlog is reduced. If BMI is less than 100, then the backlog increased.

Fix response time and fix responsiveness

The fix response time metric is usually calculated as the mean time of all problems from open to close. Short fix response time leads to customer satisfaction.

The important elements of fix responsiveness are customer expectations, the agreed-to fix time, and the ability to meet one's commitment to the customer.

Percent delinquent fixes

It is calculated as follows −

$Percent :Delinquent: Fixes =$

$frac{Number : of : fixes : that: exceeded : the :response :time:criteria:by:ceverity:level}{Number : of : fixes : delivered : in :a :specified :time} times 100%$

Fix Quality

Fix quality or the number of defective fixes is another important quality metric for the maintenance phase. A fix is defective if it did not fix the reported problem, or if it fixed the original problem but injected a new defect. For mission-critical software, defective fixes are detrimental to customer satisfaction. The metric of percent defective fixes is the percentage of all fixes in a time interval that is defective.

A defective fix can be recorded in two ways: Record it in the month it was discovered or record it in the month the fix was delivered. The first is a customer measure; the second is a process measure. The difference between the two dates is the latent period of the defective fix.

Software Testing Metrics Examples

Usually the longer the latency, the more will be the customers that get affected. If the number of defects is large, then the small value of the percentage metric will show an optimistic picture. The quality goal for the maintenance process, of course, is zero defective fixes without delinquency.