What is HR Analytics? Definition, Examples, Metrics

With the advancement of technology, human resource management has changed dramatically in recent years.

Although HR professionals focus primarily on the human aspect of running a business, they have become increasingly reliant on technology.

58% of businesses use HR technology to find, attract, and retain top talent. The report also indicates that 74% of companies plan to increase spending on HR technology.

But why are businesses investing in HR technology, specifically HR analytics? In this article, we will discuss exactly this.

Expert Advice

HR analytics tools allow HR teams to analyze and interpret employee performance, engagement, and retention data. HR professionals can make informed hiring, training, and development decisions and identify improvement areas. The analytics tools allow HR teams to monitor workforce trends and identify potential issues before they become major problems. HR teams can be more efficient, data-driven, and proactive in managing their workforce using HR analytics tools.

By the end of this article, you will better understand what human resource analytics is, the importance of human resource analytics, and how you can get started with it.

What Is HR Analytics?

HR analytics is a data-driven method that helps businesses make better-informed decisions about HR functions.

Also known as human capital analytics, HR analytics focuses primarily on the HR department of an organization.

It involves measuring and analyzing a range of HR metrics, including

• Time to hire
• Time to fill 
• Offer acceptance rate
• First-year turnover rate
• Revenue per employee
• Involuntary turnover rate
• Average absenteeism rate
• Training expense per employee
• Training efficiency
• Human capital risk

These insights help businesses fill vacancies faster, improve retention rates, reduce expenses, and boost organizational efficiency. 

Why Do We Need HR Analytics?

Now that we know what is HR analytics, let us understand why it is important.

Why do most organizations need specialized HR data and analytics when they already possess it? Can’t HR technology just look at the information they already have?

Raw data doesn’t offer any useful information for business outcomes. It resembles gazing at a gigantic spreadsheet filled with numbers and words. The data appears worthless without structure or purpose.

When raw data is arranged, assessed, and evaluated, it provides essential insight. It assists in answering queries like:

• What trends may employee turnover reveal?
• How much time will it take to recruit staff?
• How much investment is required to bring staff up to full productivity?
• Which staff is likely to leave in the coming year?
• Do training and growth activities influence worker efficiency?

With information-backed evidence, companies can concentrate on implementing required modifications and strategizing future endeavors.

With the capacity to respond to critical organizational challenges without speculation, it’s natural that many corporations that use analytics in HR correlate enhancements in performance to HR initiatives.

What Are the Benefits of HR Analytics?

As the HR industry continues to meet the demands of the remote working and digital era, HR professionals need to leverage data to meet their strategic goals.

In a nutshell, workforce analytics transforms HR management by enabling HRs to: 

• Make data-backed decisions
• Create better recruitment strategies
• Test the effectiveness of these strategies

Here’s an in-depth overview of the importance of analytics in HR. 

1.  Improve Employee Retention

HR analytics measures metrics that can help you improve employee productivity and retention, ultimately leading to cost savings (related to recruitment). 

For example, you should track the retention rate by source as a recruiter. As you collect this data, you may find that candidates from certain sources (e.g., referrals) stay in the organization longer than other sources (e.g., advertisements on job portals).  

With these insights, you can prioritize candidates who come via referrals over other sources, increasing the retention rate.  

2. Enhance Your Recruitment Strategy

Apart from helping you increase the employee retention rate, analytics can also enable you to improve your recruitment strategy. 

For instance, analytics gives you insights into hiring duration, hiring effectiveness, offer acceptance rates, applicants hired by sources, and more. 

You can use the information to focus on sources with the most hires (previously) and a high acceptance rate to improve your time to fill and save on recruitment expenses.  

Similarly, you can use the average hiring duration to look for potential candidates before the position becomes vacant. 

3. Create a Better Employee Experience

Human resource managers do more than find suitable candidates. They also focus on enhancing the employee experience continuously. 

The better the employee experience, the higher the retention rate. 

Metrics like attendance, absenteeism, and productivity can provide HR professionals with a better understanding of the overall employee experience. They can use this information to find areas of improvement and create data-driven insights to enhance employee experience and satisfaction. 

HRs can also use these insights to optimize compensation, benefits, training, and leave policies to meet employee expectations. 

Pros And Cons Of HR Analytics

HR analytics are quickly becoming a coveted complement to HR practices.

Data acquired consistently across the organization has no value unless gathered and evaluated, making human resources analytics a great tool for tangible insight that never existed before.

However, while HR analytics can potentially elevate HR practice from the functional to the conceptual level, it is not devoid of its obstacles.

Pros and Cons

A data-driven approach allows for more accurate decision-making, reducing organizations’ need to rely on instinct or speculation.

Retention plans can be established with an improved awareness of why people depart or remain with an organization.

Engagement among workers can be raised by evaluating data on employee behavior, like how employees communicate with coworkers and customers, and assessing how procedures and the surroundings can be improved.

By analyzing and comparing data from present employees and potential applicants, recruitment and hiring may be better customized to the organization’s actual skill set needs.

HR data habits and patterns can be forecasted using predictive analytics, allowing organizations to proactively maintain efficient staff.

Several HR departments do not have the statistical and computational skills to cope with massive datasets.

Various company organizational and monitoring systems complicate data aggregation and comparison.

Access to high-quality information can be a problem for organizations lacking up-to-date systems.

Companies need access to high-quality research and monitoring software to use the obtained data.

Tracking and gathering additional information with emerging technologies (e.g., cloud-based systems and wearable gadgets) could cause ethical concerns.

How Does HR Analytics Work?

Analytics comprises various elements that are dependent on each other.

1. Data must be initially gathered to acquire the analytical solutions that human resources analytics guarantees.
2. The data must then be tracked and compared to other data, like historical data, standards, or averages.
3. It aids in the recognition of patterns or developments. The outcomes can be examined in the analytical stage.
4. Finally, apply insight to business decisions.

Let’s see how the entire process works. 

1. Data Collection

The first essential element of HR data and analytics is gathering and keeping track of high-quality data.

The data must be easily accessible. It should have the capacity to be linked to an accounting system. You can collect the data from existing HR systems, training, and growth systems, or unique data-collection methods like cloud-based systems, smartphones, and wearable devices.

The various kinds of data you may collect include:

• Employee performance
• Training
• Engagement
• Promotion and salary history
• Retention
• Turnover 

2. Measurement 

The data starts with an ongoing assessment and comparison method at the evaluation stage, commonly known as HR metrics.

HR analytics compares acquired data to previous norms and corporate standards. The procedure cannot be based on just one instance of information. Rather, it relies on an ongoing flow of data throughout time.

A comparative benchmark is also required for the data. For example, how can a company determine an absentee range if it is not defined first?

The key metrics monitored in HR data and analytics include:

• Organizational Efficiency

Data is gathered and analyzed to comprehend absences, turnover, and acquisition outcomes.

• Process Improvement

This section combines data from organizational efficiency and operational indicators to determine areas for process enhancement.

• Operations 

The efficacy and productivity of HR’s daily activities and initiatives are evaluated using operational data.

3. Analysis

The analytical phase examines the results of measurement reporting to detect patterns and trends which might have an organizational impact.

Depending on the desired output, several analytical procedures are applied. These are predictive analytics, descriptive analytics, and prescriptive analytics.

Predictive Analytics uses statistical techniques to analyze previous information to foresee future hazards or possibilities.

Descriptive analytics only involves evaluating previous data and determining what may be changed.

Prescriptive Analytics improves Predictive Analytics by anticipating the consequences of anticipated results.

Predictive HR Analytics

Predictive analytics examines past data to estimate the future. The data is collected and utilized to develop future forecasts about workers or HR operations.

It can range from forecasting the applicants who will be more effective in the organization to predicting who will walk away in a year.

Algorithms can recognize patterns, creating future behaviors using complex statistical methods. These future patterns indicate potential hazards or possibilities companies can use when making decisions for the future.

Examples in HR Analytics

So, how do you use HR analytics in organizations?

Let’s take two common HR analytics examples for company issues.

Example 1: Recruitment

Organizations look for people with the appropriate talent and traits that complement the organization’s work environment and performance needs.

Sorting through many resumes in the hiring process and choosing based on basic data is time-consuming, especially when potential applicants may be disregarded. 

For example, one organization may discover that creative thinking forecasts performance better than pertinent job experience.

Analytics in HR can:

• Allow for the rapid and automatic collection of potential data from various sources.
• Examine multiple factors, like possibilities for development and adaptation to culture, to acquire an in-depth awareness of potential customers.
• Identify applicants who have characteristics similar to the organization’s top performers.
• Avoid habitual prejudice and provide an adequate opportunity for all applicants; with a data-driven method for recruiting, one person’s attitude and opinion can no longer influence applicant evaluation.
• Establish metrics on the amount of time it requires to recruit for specific roles inside the organization, allowing teams to be more ready and aware when an opportunity to hire arises.
• Provide historical data on instances of over-hiring and under-hiring to help organizations establish better long-term hiring strategies.

Example 2: Turnover

When employees leave,  reports or data are collected on individual circumstances, but there is no way of understanding if there is an underlying explanation or pattern for the turnover.

Organizations require this information to avoid churn becoming an ongoing problem, as turnover is costly regarding discarded time and profit.

People analytics has the potential to:

• Collect and evaluate prior turnover data to uncover trends and patterns that indicate why employees depart.
• For a greater awareness of the state of present employees, gather information regarding employee behavior like efficiency and involvement.
• Link both kinds of information to comprehend the reasons for turnover.
• Contribute to developing a prediction model for better monitoring and flag workers who may fit the established pattern of employees who have quit.
• Create plans and make decisions to improve the workplace environment and worker engagement.
• Determine trends of worker involvement, satisfaction, and productivity.

Examples Of HR Analytics Metrics

We discussed the key metrics that human resource analytics measures. Let’s understand them in detail.

1. Time to Hire

 It is the number of days it takes to approach and onboard a candidate. This data can help recruiters determine why it takes so long to hire a candidate, enabling them to create a better candidate experience. 

2. Time to Fill

 Like time to hire, time to fill measures how long it takes between advertising a job opening and hiring a candidate to fill that position. Measuring time to fill helps recruiters identify areas that take the most time (e.g., finding the right candidate) so they can optimize their recruiting strategies.

3. Offer Acceptance Rate

As the name suggests, it indicates the percentage of candidates that accept the offer. It can be calculated by dividing the number of accepted offers by the number of offers given in a given period. Anything above 85% is a good ratio. If it is lower than that, you might need to dig deeper to understand why candidates are not accepting your offer.

4. First-year Turnover Rate 

It indicates the percentage of employees who leave within their first year (or as soon as they complete one year). It can be calculated by dividing the number of employees who resign within one year by the total number of employees in your organization.

5. Revenue Per Employee

 You can calculate the average revenue each employee generates by dividing the company’s income by the total number of employees in the organization. It helps determine how efficient the employees are.

6. Involuntary Turnover Rate

Terminating an employee (for breach of the code of conduct or any other reason) is considered involuntary resignation. You can calculate it by dividing the number of employees who left involuntarily by the total number of employees. This metric is directly linked to the recruitment strategy and should be used to improve the quality of hires.

7. Average Absenteeism Rate

It helps determine employees’ productivity and happiness. A high absenteeism rate indicates they’re unhappy in the organization (or might face personal problems). HRs need to identify people with a high absenteeism rate and sit with them to understand their issues and devise a solution.

8. Training Expense Per Employee

It can be calculated by dividing the training expenses by the number of employees who received training. You’d want to minimize it while ensuring the training is effective. 

9. Training Efficiency

 It helps determine whether the training program is effective or needs changes. Calculating the training efficiency includes analyzing multiple factors, including employee performance improvement, test scores, and upward transition in employee roles.

10. Human Capital Risk

includes employee-related risks, such as lack of specific skills, lack of qualified employees for any given role, chances of employees leaving the organization, etc.

What Data Does a Human Resource Analytics Tool Need?

The data human resource analytics tool needs can be divided into two categories:

• Internal data
• External data

However, to get the most out of a human resource analytics tool, you need to ensure that you’re collecting the right data and that it is accurate. 

Let’s understand the type of data you need to collect. 

Internal Data

refers to the data available within the HR department. Here are some metrics you can get from the HR department.

1. Average employee tenure
2. Employee compensation
3. Employee training records (including expenses)
4. Performance appraisal 
5. Reporting structure
6. High-value, high-potential employees
7. Any disciplinary action against an employee
8. Employee attendance records

The only challenge in HR data and analytics is ensuring data accuracy. Therefore, you must ensure that the data you’re collecting (and providing the analytics tool) is as accurate as possible.  

External Data

Any data collected outside of the HR department is considered external data. It also includes data from outside the organization, offering a global perspective that working solely with company data cannot. 

Here are some types of external data that you’ll need. 

1. Financial data: It is crucial to track the company’s financial data, including the revenue and profit margin. This will help calculate metrics like revenue per employee and the cost of hiring. 
2. Passive data from employees: Employees continuously generate data to help them determine their organizational happiness. For instance, their social media posts (especially LinkedIn) can be used to guide HR data analysis. 
3. Global data includes economic, political, and environmental factors that might impact employee behavior. These insights can help you guide your HR strategies in a way that internal data cannot. For instance, 2021 saw such high resignation rates due to the pandemic that it was termed “The Great Resignation.”

These insights together can help predict how employees might react to similar situations. It also enables you to determine trends related to voluntary and involuntary turnover. 

Role of HR Analyst in Every Organization

HR analysts is vital in collecting and analyzing HR processes and data. They also ensure every process aligns with the organization’s policies and objectives.

Here is a detailed overview of the role of HR analysts:

• Working with the HR team to identify and resolve HR-related issues.
• Collecting and sharing information on new employment regulations, labor laws, and government regulations.
• Creating and analyzing employee surveys, exit interviews, and staffing policies.
• Helping with job audits and HR investigations.
• Providing support and advice to different departments in the company regarding HR policies and best practices.
• Assisting the HR team with optimizing operating policies, guidelines, and systems to ensure best practices are followed throughout the organization.
• Creating assessments for potential and current employees.
• Managing HR reports filed by employees.
• Analyzing employee relations within the organization.
• Monitoring competitor practices to give suggestions to the upper management.

HR analysts also keep tabs on new employees and evaluate their performance. They can also decide to find replacements for employees who are not performing as they should.

How to Get Started with HR Analytics?

While you will need an HR data analyst to get the most out of HR analytics, you can also consider training existing HRs in human resource analytics and data. 

Here are some tips to help you get started with HR analytics.

Step 1. Prepare Your Team

The first step to adopting human resource analytics is ensuring everyone is on the same page.

It begins with involving C-suite executives in discussing the need for HR analytics. You will need to make them understand the benefits of analytics in the HR department and how it can benefit your organization.

You also need to prepare your team for the change. They should be able to deal with the amount of data (and analyze it) to measure the progress.

Step 2. Involve Data Scientists

who are crucial in helping you benefit from HR analytics. They can monitor the quality and accuracy of the data, helping you get up-to-date, accurate insights. Furthermore, data scientists can guide HR professionals in using the data to their benefit.

Step 3.  Start Small

One of the best ways to start with HR data analytics is by implementing a small project. For instance, begin with tracking one or two metrics and analyze how effective the insights from the analytics platform are. Gradually, you can implement analytics in the entire HR department.

Step 4. Ensure Legal Compliance

You must ensure legal compliance to avoid breaching employees’ rights and privacy. Here are some legal considerations to keep in mind when implementing HR analytics.

• Taking consent from employees about the data you’re collecting.
• Ensuring employee privacy and anonymity.
• Establishing goals for data collection and informing employees about the same.
• Ensuring data privacy and security (from unauthorized access).
• Location of the HR analytics vendor where the data will be stored and complying with their local laws.

Furthermore, you should ensure transparency concerning the type and amount of data you collect.

Step 5. Choose an HR Analytics tool

Last, but not least, you will need a robust HR analytics tool to streamline everything. Compare the best HR analytics software to get a higher return on investment.

What to Look For In an HR Analytics Tool?

To help you choose the right HR analytics tool, we have curated a list of things you must look for.

1. Real-time Analytics

Opt for an HR analytics tool that provides real-time insights into necessary metrics. This will help you make decisions quickly and ensure you’re atop everything in the HR department.

2. Role-based Access and Collaboration

To ensure data privacy and security, you will need a tool that displays information based on roles and functions. For instance, a CHRO might want to see more high-level data, whereas an entry-level HR may not need access to all the information.

3. Predictive Analytics

Instead of spending hours organizing data and generating insights, let the technology do the work. Predictive analytics help you prepare for unknown future events. For example, it will help predict if the current termination rate will increase in the coming months. This way, you can anticipate and prepare rather than react.

4. Easy Visualization

Working with data can be tiresome. However, with a smart and interactive visualization dashboard, you can understand data better and identify trends quicker. Besides, charts and graphs help you easily explain the data to non-technical people.

5. Intuitive UI and UX

There’s no point in investing in a difficult tool. Not only you’ll have to spend hours learning it, but you’ll also have to spend a lot of time finding the necessary data.

One way to ensure the tool is easy to use is by trying it. Most HR analytics tools offer a free trial, so you can get a gist of the software before investing in it.

6. Reliable Support

No matter how easy the tool may be, you will likely run into problems now and then. Or you may require help in configuring a new data set.

This is why choosing a platform that offers all-around support is essential. It would be great if they offered live chat and phone support, as they are among the fastest support channels.

Summing Up

Human resource analytics has become more important than ever. It fuels various HR strategies, including recruitment, retention, and employee experience. However,  you must ensure the data you feed to the data analytics tool is up-to-date and accurate. Also, don’t forget to choose the best HR analytics platform for your business.

Priya Naha

Priya is a seasoned writer with expertise in the SaaS industry. With over five years of experience in the field, she deeply understands the requirement of this specific industry, its techniques, and tools. Her articles, whitepapers, and blog posts are known for their clarity, practicality, and actionable insights. Priya also possesses in-depth knowledge of SEO and content marketing, which enables her to develop effective strategies to increase website traffic and improve brand visibility.