HR analytics, also used interchangeably with people analytics or talent analytics, can transform the way HR initiatives affect business outcomes.
Strategic use of HR analytics enables HR departments to impact HR functions and strategies that directly affect revenue, expenses, risk, and planning.
For example, on the one hand, HR tech companies can leverage public web data to help fuel HR analytics software and services.
On the other hand, organizations are able to take the actionable insights gained from HR analytics software to implement revenue-building strategies surrounding human capital.
Let’s take a closer look at HR analytics, its data sources, benefits, and trends for 2022.
What is HR analytics?
HR data analytics involves data analysis pertaining to potential candidates and existing employees to examine their influence on overarching business goals.
While human resource data appears to cover a broad range of information, HR data can be broken down into three categories: HR management system data, people data, and firmographic data.
Not unlike other use cases, HR analytics leverages these data categories in a variety of ways.
However, it is important to note the overarching goal of HR analytics is to help smooth people-related business operations, not just to help the HR department.
How does HR analytics work?
HR analytics works by collecting, analyzing, and interpreting workforce data to improve decision-making in human resource management. The process begins with gathering data from multiple sources, including HR information systems (HRIS), employee engagement surveys, performance management tools, and payroll records. This raw data is then cleaned, processed, and analyzed using various analytics techniques: descriptive analytics to identify past trends, predictive analytics to forecast future workforce behaviors, prescriptive analytics to recommend HR strategies, and diagnostic analytics to determine the root causes of workforce challenges.
Organizations leverage data visualization tools, HR dashboards, and AI-driven insights to make sense of this information and apply findings to areas such as hiring, retention, productivity, and workforce planning. By implementing HR analytics, companies can make proactive, data-driven decisions that improve employee performance, reduce turnover, enhance engagement, and align HR strategies with overall business objectives.
Importance of HR analytics
HR analytics is a data-driven approach to human resource management that helps organizations optimize their workforce, improve employee performance, and make strategic business decisions. By leveraging data insights, HR teams can enhance talent acquisition, boost employee engagement, and drive business success. Below are key reasons why HR analytics is essential.
- Improves recruitment and talent acquisition by Identifying the best talent sources, reducing time-to-hire and cost-per-hire and analyzing candidate success predictors to make better hiring decisions.
- Reduces employee turnover by identifying key factors contributing to employee dissatisfaction, such as low engagement levels, inadequate career growth opportunities and poor management practices.
- Enhances employee performance and productivity. Tracking employee performance data allows HR teams to identify top performers and understand what drives their success, pinpoint skill gaps and create targeted training programs and recognize trends in productivity across different teams and departments.
- Increases employee engagement and satisfaction through employee feedback surveys, sentiment analysis of workplace communication and real-time monitoring of engagement trends.
- Optimizes workforce planning and cost management. HR analytics provides insights into workforce trends, enabling organizations to forecast future talent needs, optimize workforce distribution and reduce labor costs by identifying areas of inefficiency.
- Strengthens diversity, equity, and inclusion (DEI) initiatives. HR analytics helps track DEI metrics such as gender and ethnic diversity ratios, pay equity across demographics and inclusion sentiment based on employee surveys.
- Ensures compliance and reduces legal risks by monitoring working hours, wages, and overtime compliance; tracking diversity and non-discrimination policies; maintaining accurate employee records for audits and legal compliance.
HR analytics is essential for modern businesses, providing data-driven insights to optimize workforce management, enhance employee experiences, and drive organizational success. By leveraging HR analytics, companies can make more informed decisions, reduce costs, and build a highly engaged and productive workforce.
Types of HR analytics
HR analytics can be categorized into different types based on their purpose and function. These analytics help HR professionals understand past workforce trends, predict future behavior, recommend actionable solutions, and diagnose the root causes of HR challenges. The four key types of HR analytics are descriptive, predictive, prescriptive, and diagnostic analytics.
Descriptive HR analytics
Descriptive HR analytics focuses on analyzing historical HR data to identify patterns and trends in workforce management. It answers the question: "What happened?" by compiling data from sources such as employee records, performance reviews, and attendance logs.
HR teams use descriptive analytics to track key HR metrics such as employee turnover rates, absenteeism trends, hiring success rates, and engagement levels. This type of analytics provides insights into past workforce behavior, helping HR leaders recognize trends that can impact future HR strategies.
For example, a company might analyze past turnover rates across different departments and identify that certain teams have consistently higher resignation rates. This insight can help HR teams investigate potential causes, such as poor management or lack of career development opportunities, and take corrective action.
Predictive HR analytics
Predictive HR analytics helps organizations anticipate future HR challenges by using statistical models, machine learning, and historical data to forecast workforce trends. It answers the question: "What will happen?" and allows HR teams to make proactive decisions rather than reactive ones.
Companies use predictive analytics to:
- Forecast employee turnover by identifying risk factors such as job dissatisfaction, low engagement, or salary discrepancies.
- Predict hiring success by analyzing past recruitment data and determining which candidate attributes lead to long-term employee retention.
- Estimate workforce demand by analyzing business growth patterns and future skill requirements.
For example, organizations use predictive HR analytics to determine which employees are most likely to leave the company. By analyzing engagement levels, compensation data, and job satisfaction scores, the company can take proactive steps to retain high-performing employees before they decide to resign.
Prescriptive HR analytics
Prescriptive HR analytics takes predictive insights a step further by offering recommendations on what actions HR teams should take to improve workforce outcomes. It answers the question: "What should we do?" and provides data-backed solutions to HR challenges.
This type of analytics suggests strategies for:
- Improving employee engagement by identifying which factors drive motivation and satisfaction.
- Enhancing workforce productivity by recommending training programs tailored to employee needs.
- Reducing turnover by personalizing retention plans for employees at risk of leaving.
For example, if predictive analytics identifies a high turnover risk in a specific department, prescriptive analytics can recommend interventions, such as mentorship programs, salary adjustments, or leadership training to improve employee satisfaction.
Companies that leverage prescriptive analytics can take proactive steps to optimize HR practices, ensuring long-term workforce stability and efficiency.
Diagnostic HR analytics
Diagnostic HR analytics focuses on explaining why workforce trends occur by examining patterns in HR data. It answers the question: "Why did this happen?" and helps HR teams uncover the underlying reasons for workforce challenges.
Organizations use diagnostic analytics to:
- Investigate high employee turnover rates and determine whether salary dissatisfaction, lack of career growth, or poor management is the cause.
- Analyze poor employee engagement scores to identify whether workload, company culture, or leadership style is impacting morale.
- Understand hiring challenges by reviewing recruitment processes to see if job descriptions, candidate assessments, or interview procedures are contributing to hiring difficulties.
For example, if a company experiences a sudden decline in employee engagement scores, diagnostic analytics can analyze internal surveys, performance reviews, and exit interviews to determine whether the issue is related to a lack of career development opportunities or increasing workload pressures.
By understanding the root cause of HR issues, organizations can develop targeted strategies to improve employee satisfaction and overall business performance.
HR analytics plays a crucial role in workforce management, and each type of analytics—descriptive, predictive, prescriptive, and diagnostic—offers unique value. By leveraging these four types of HR analytics, companies can make data-driven HR decisions, enhance employee experiences, and create a more efficient and productive workplace.
How to get started with HR analytics?
The HR analytics process, similar to workforce analytics and other internal company analytics processes, involves three major steps: data collection, evaluation, and implementation.
Today, many companies outsource this process to HR technology companies, which provide businesses with automated tools and software to help smooth this process.
According to AIHR, some of the most popular tools utilized internally and externally by HR tech companies include R, Python, Tableau, and Excel.
Additionally, to expand on this process, let’s take a look at the steps involved in HR analytics.
Define HR goals
The first step in the HR analytics process is to identify the key HR challenges and objectives that the organization wants to address. Without a clear goal, collecting and analyzing data can become overwhelming and ineffective. HR teams must determine the specific workforce issues they want to improve.
For example, if a company struggles with high attrition rates, its HR analytics goal might be to reduce voluntary turnover by 20% within the next year by identifying factors that influence employee resignations
HR data collection
For many companies, the data collection process involves both the retrieval of internal and external data. Internal data collection might include collecting and storing qualitative information about employees’ names, ages, interests, and even survey responses.
External or third-party data collection might consist of purchasing human capital-related data from public web data providers or HR tech companies.
Further, external data is helpful for recruiting, understanding a competitor’s workforce, and fueling AI-based HR tools that need larger samples of data beyond just one company’s internal data.
Evaluation
The evaluation process involves not only HR analysts but also other HR team members, as well as HR software and tools.
HR tools and software are able to help human resources by collecting and analyzing people data, performance data, and business data, generating insights on the impact current systems, strategies, and team members have on the company's overall success.
The evaluation process for quantitative data can be processed quickly with software; however, qualitative data, such as survey results, take longer to analyze but are just as important.
Likewise, it is important for HR teams to develop an evaluation process that dedicates ample time to utilizing AI-based tools and software for quick insights and unpacking more lengthy qualitative information.
Implementation
After the collection and analysis of HR-related data, HR teams are able to implement the actionable insights generated from the above steps.
This might look like implementing more team-building events, increasing the number of employees at a given company, or even simply adding more amenities in work offices.
Continuously optimize strategies
HR analytics is an ongoing process that requires continuous monitoring and refinement to ensure long-term success. After implementing HR initiatives, organizations must track the impact of these strategies by measuring key HR metrics and adjusting approaches as needed.
Ways to continuously optimize HR analytics strategies include:
- Regularly updating HR data to keep insights relevant and accurate.
- Measuring the impact of HR programs
- Collecting employee feedback to assess how HR changes affect workplace morale.
- Refining predictive models to improve workforce forecasting.
- Benchmarking against industry standards to stay competitive.
Key metrics for HR analytics
There are many HR metrics that HR professionals use to enable data-driven insights and seek to develop strategies for performance improvement. Some of them are listed below.
Employee turnover
Turnover, also known as employee churn, is a measure of how long employees work at a particular company before termination of employment.
When analyzing this number over time, organizations are able to determine their average turnover rate.
This provides the company with insights on how to improve this number, ultimately increasing employee retention and saving money and resources on recruiting, training, etc.
Cost-per-hire
Cost-per-hire is the measurement of how much it costs a company to hire a new employee. According to Zippia, it was found that in 2022 companies spent on average $4,700 to hire new employees.
As with understanding and improving employee turnover, tracking this number is crucial, as it can provide direct information about how much money is either being wasted or properly dedicated to the recruiting and training processes within an organization.
Time-to-hire
Time-to-hire measures the time it takes to fill the position once the candidate has applied for the job. It's similar to time-to-fill, the only difference being the starting point.
With time-to-fill, the beginning of the process starts when the job openings are published.
Acceptance rate
Acceptance rates are calculated by dividing the number of job candidates given a job offer by the number of job candidates who accepted a particular company’s job offer.
Acceptance rates provide rich insights into how to optimize recruitment and talent sourcing strategies for future hires.
Employee retention
The opposite of employee turnover is the measure of employee retention. HR analytics measure employee retention by dividing the number of total employees that remained employed at a particular company by the total number of employees that were hired during a given period of time.
Specifically, this information is useful for better understanding workplace health, engagement, and even the quality of hire.
Human resources analytics implies a significant correlation between the quality of hire and employee retention, as the quality of hire is measured with various data points.
For instance, according to Achievers, organizations that provide consistent recognition see a 34% increase in employee retention, directly improving one’s employee retention rate.
Demographics
In addition to collecting quantitative data surrounding hiring, recruiting, and training processes, HR analytics is also interested in analyzing qualitative information about current and potential employees.
For example, companies are able to collect and analyze their current employees’ demographic information such as age, gender, experience level, and external interests to generate insights about the overall cultural make-up of the company.
Further, suppose HR professionals for a particular company notice a lack of younger employees that might have fresh or different perspectives on outreach and marketing.
In that case, it is in the company’s best interest to expand the age range of its employees, as a diverse workforce offers many revenue-generating benefits.
Company culture
Similar to measuring demographics, getting a better understanding of company culture relies on the collection of qualitative employee information.
For instance, some HR professionals collect data via regular surveys from employees, record check-ins between employees and their managers, and track employee engagement with team building activities, etc.
Productivity (Capability)
Lastly, but certainly one of the most crucial HR analytics metrics is employee productivity, sometimes referred to as employee capability.
Employee productivity is measured in a variety of ways, such as time-to-productivity, employee capability scores, competency scores, etc.
For example, a company with a high employee turnover rate might also be interested in measuring time-to-productivity, as a high turnover rate coupled with a high time-to-productivity rate can lead to a chaotic and unstable workplace, negatively affecting the overall productivity of the company.
HR analytics examples
IBM's predictive analytics for employee retention
IBM faced challenges with employee turnover and sought to proactively address this issue using predictive analytics. By developing AI models that analyzed various factors including job roles, performance metrics, and personal demographics IBM could predict with 95% accuracy which employees were at risk of leaving the company. This allowed HR to intervene with personalized retention strategies, such as tailored career development plans and mentorship opportunities, resulting in improved employee retention rates and significant cost savings.
Google's project oxygen: enhancing managerial effectiveness
In the early 2000s, Google initiated Project Oxygen to determine the impact of managers on employee performance and satisfaction. By analyzing performance reviews, feedback surveys, and other data sources, Google identified eight key behaviors exhibited by effective managers:
- Is a good coach
- Empowers the team and does not micromanage
- Expresses interest in and concern for team members' success and personal well-being
- Is productive and results-oriented
- Is a good communicator—listens and shares information
- Helps with career development
- Has a clear vision and strategy for the team
- Possesses key technical skills that help advise the team
Implementing training programs focused on these behaviors led to significant improvements in managerial performance and employee satisfaction. This data-driven approach underscored the importance of effective management in fostering a positive work environment.
Unilever's AI-driven recruitment process
Unilever transformed its traditional recruitment process by integrating artificial intelligence to handle initial candidate screenings. The company implemented AI tools to analyze video interviews, assessing candidates' facial expressions, tone, and word choice to evaluate suitability for roles. This approach reduced the hiring process duration, saved approximately 70,000 hours of interview time annually, and enhanced the candidate experience by providing quicker feedback.
Microsoft's analysis of remote work patterns
Microsoft conducted an extensive study analyzing the impact of remote work on collaboration and productivity. By examining communication patterns, such as email and meeting data, the company discovered that remote work led to more siloed communication and a reduction in cross-team interactions. These insights prompted Microsoft to implement strategies to foster collaboration across teams, such as virtual team-building activities and enhanced communication platforms, aiming to mitigate the challenges of remote work.
These examples illustrate the transformative power of HR analytics in addressing various organizational challenges, from enhancing management practices to optimizing recruitment and retention strategies.
Data categories & sources for human resources analytics
There are three main categories used to describe HR analytics data: human resources management systems data, people data, and company data.
Further, many organizations refer to these data types as human capital data. When combined, these three data categories can provide businesses with hundreds of data points about their employees, company-wide structures, and financial data.
When appropriately analyzed and implemented in conjunction with successful existing HR strategies, all of which can ultimately improve more than just employee retention and satisfaction.
Strategies and implementations derived from analytics can also improve revenue and sales.
Human resource management system data
HR management system (HRMS) data, also known as a human resource information system (HRIS), is internal data stored and managed within a human resource software system.
It includes data such as individual employee information, benefits information, payroll information, time cards, confidential employee and employer documentation, and more.
With HRMS data, companies are able to perform human resource analytics processes, including predictive HR analytics.
For example, companies utilize HR management systems such as Oracle and PeopleSoft to log and oversee recruiting, talent management, entrance interviews, exit interviews, employee compensation, and benefits information, just to name a few.
People data
People data covers a broad range of data categories, sources, and data fields. Nonetheless, HR analytics enables HR professionals to utilize people data such as:
- Feedback surveys
- Company employee reviews
- Professional development information
- Absence information
- Wellness information
HR leaders can use this data to gain deeper qualitative insights into specific indicators for retention, turnover, employee satisfaction, and work environment, to name a few examples.
Also, you could use employee data collected from publicly available web sources.
With this data, you will receive relevant data points such as employee experience, name, location, education, connections, employment length, and more.
As a result, you will be able to enrich and scale your existing candidate data.
What's more, you won't ever need to worry about the freshness and accuracy of the data. At Coresignal, it's our primary goal to always keep the data fresh.
Company data
Company data, also sometimes referred to as firmographic data, is useful within HR analytics as it can provide insights into company-wide organizational structures, revenue, sales, and more.
In addition to firmographic data, which is typically purchased externally from verified third parties, some companies utilize company data that is internally collected within the organization’s larger project management systems and CRM.
Wrapping up
In all, using HR analytics offers a gateway for companies to expand and improve their human capital and overall people-related functions and strategies. Which, in turn, could affect business outcomes.
However, in order to see real growth and progress from utilizing HR analytics, companies must harness public web data and HR analytics tools to implement the actionable insights generated from such resources.
