What is HR Analytics?

Updated on December 22nd, 2021
What is HR Analytics?

HR analytics is the process of collecting and analyzing data to help you make better decisions about employees. HR analytics deals with analyzing employees, employee performance, productivity, and employee interactions.

HR analytics can provide helpful insight for making decisions about hiring new employees, firing some existing ones, or promoting your organization. It can boost the performance of your employees by examining their aptitudes. This blog post will discuss human resource analytics, how an HR professional can use data to make better decisions for the company, and many more. If you're interested in learning via video, then watch below. Otherwise, skip ahead.

 

How HR Professionals Use HR Analytics

Human resource professionals are experts who deal with people analytics and workforce analytics. They are adept in human resource management. They manage the people who work in a company. HR professionals use data analytics and contribute toward the growth of the organization by implementing effective human resource management procedures.

They employ various HR analytics software for recruiting new employees, training staff, administering payroll, and managing employee benefits. They train staff members to work according to the company's policies and boost its progress.

Types of HR Analytics

Human resource analytics is a data-driven analytical process that focuses on better human resources management. It focuses on making an organization more competent than its competitors. Following are the types of HR analytics that HR teams employ to help their organizations:

Descriptive Analytics

Descriptive analytics deals with the process of collecting and reporting data on what has already happened. Descriptive analytics focuses on data that is already available and what you can learn from it. It is useful in the analysis of historical data patterns, which helps determine future actions.

For example, If you want to estimate the turnover rates in the future, you need to have an idea about the past data. It also helps you learn the possible causes of the departure of employees so that you can take action to reduce it.

Diagnostic Analytics

Diagnostic analytics aims to determine the causes of certain problems. Identifying the issues is necessary for solving the problem. For example, if you encounter a high turnover rate in your company, diagnostic analytics will help you understand the possible causes of that high turnover rate. The high turnover rate might result from some company policies, or it might be because of the low salary packages. Picking up the right cause will help you change your company policies to reduce turnover rates.

Predictive Analytics

Predictive analytics deals with forecasting future events by using historical data patterns. It helps determine the probability of something happening in the future by analyzing previous data. It is obtained through various statistical techniques, like machine learning, data modeling, and artificial intelligence.

This type of analytics is widely used to predict the success or failure of future projects. By predicting the probability of a future occurrence, you can reduce the chances of failure and thus improve your organization's growth.

For example, you can use predictive HR analytics to determine which employees are more likely to quit their jobs within the next few months. HR managers can take the necessary steps to retain these employees by analyzing this data. This information can also enable them to plan the future workforce requirements to support growth in the organization.

Prescriptive Analytics

Once HR professionals predict the business outcomes of a particular practice, the next step is to recommend required actions to achieve goals or prevent failure. It is where prescriptive analytics comes to help them.

Prescriptive analytics deals with proposing suggestions for future actions according to the prediction made via predictive analytics. The suggestion proposed by prescriptive analytics are based on data analytics and thus more reliable.

It involves statistical modeling to determine the best course of action for achieving success in the future. It also involves simulation of proposed actions to understand their consequences. This type of analytics is widely used by companies for making strategic decisions.

For example, prescriptive analytics can suggest actions required to achieve the goal of reducing the turnover rate within the company. HR managers can act upon these suggestions to improve their workforce planning.

What is HR Analytics Metrics?

HR analytics involve several different metrics concerning HR data that you can analyze to help improve an organization. Following are the key metrics that human resources analytics deals with:

Revenue Generation Per Employee

Revenue generation per employee is one of the most critical metrics. This metric calculates how much revenue an organization can generate through each employee. It helps determine how much an organization is investing in each staff member and how much profit it is getting in return.

Training Cost Per Employee

Training cost per employee involves a cost that an organization spends on training its employees. It is measured by dividing the total training costs of a training program by the number of employees receiving the training.

Employee Voluntary Turnover Rates

Employee voluntary turnover rates measure the number of employees who have voluntarily left an organization within a particular period. It provides information on how many qualified employees are leaving at any point in time. It is measured by dividing the number of employees who have left a company by the total number of employees.

Employee Involuntary Turnover Rates

Employee involuntary turnover rate measures the number of employees fired or terminated by an organization within a specific period. It is calculated by dividing the number of employees that a company has fired by the total number of employees. Assessment of this metric is used to determine whether the company has too many employees or is maintained at an optimum level.

Time to Recruit New Employees

Time to recruit is one of the most crucial factors for a firm's progress. This metric calculates how much time an organization requires to find a new staff member if it loses its current employees. It includes the time taken by the company from the advertisement of vacancies to hiring a new individual. It helps determine the efficiency and effectiveness of an organization's recruiting process.

Job Acceptance Rate

The job acceptance rate is a metric that measures the number of individuals who accept a job offer by an organization. It helps determine how many people are attracted to a particular organization and its offerings. It is calculated by dividing the accepted number of job offers by the total number of jobs offered by a company.

Employee-Related Risks

Employee-related risks are metrics that measure the lack of specific skills in a company's workforce. It helps determine whether an organization has enough employees with the proper skill set. It also deals with how much an organization is exposed to risks regarding its workforce.

Absenteeism Rate

The absenteeism rate is one of the essential HR analytics metrics. This metric deals with the number of staff members who miss working for a day or more in a month. It excludes those on leave, sick, or other types of time off. It helps determine the unavailability of an organization's employees and its impact on the organization.

How Do HR Leaders Implement Predictive HR Analytics?

HR Leaders can use predictive analytics for effective workforce planning and management. The following are the steps that they have to follow:

Identifying Goals of an Organization

The first thing that HR managers should do is identify their goals. They have to determine what does the organization wants to achieve in the near future. It may be an increase in revenue, maintaining the profit margin, or hiring more people.

Working on any project without proper goals is like working in the dark with no light. The goal should be well defined, measurable, and achievable within a given period. HR professionals analyze various HR metrics that contribute to organizational goals.

Gathering Data from Various Sources

Collecting relevant data is very important for predictive HR analytics. Data has to be gathered from various sources. It may include surveys, employee directory information, CRM databases, internal documents like recruitment files, etc. Accurate data collection is necessary for data analysis and drawing information from it.

Understanding Data Science

Understanding data science is necessary for predictive HR analytics. Data analysis enables HR managers to predict whether proposed solutions will be helpful for the organization or not.

Predictive analytics requires a thorough understanding of data science, how it works, the kind of problems it can solve, and the most common data mining techniques. If an HR professional doesn't have enough knowledge of data science, they have to take the help of data scientists.

Processing The Data

HR leaders should know how to process and handle the data. Collected data is useless if the HR department can't process it. It is why HR professionals should use advanced data analytics tools and software that help with data processing.

They have to identify the tools they need to process large datasets. They should know which algorithm is suitable for the problem at hand. They have to clean and prepare the data before putting it in a learning algorithm.

Solving Employee Issues

The HR department needs to detect the root of the problem. They need to find data related to the problem and employees' traits. They have to decide what kind of data is relevant for solving a particular issue.

HR managers use predictive analytics to find and resolve issues related to the workforce. They use various HR metrics to forecast employee recruitment and retention. They also utilize it for predicting employee turnover. They apply the right kind of analytics to get better results.

Effective Workforce Management

Managing human resources and making them grow is another important goal of an organization. HR managers develop various plans to achieve the desired results. Differences between current and future skill requirements can be identified with the help of predictive analytics.

Greater insight into current workforce abilities, performance, and diversity can be achieved with human resource analytics. Predictive analytics helps in forecasting the future workforce needs, requirements and gaps. It can also be used to track employee productivity to help make the right decision at the right time.

How can HR Analytics Help Shape Your Business?

Human resource analytics has recently gained much attention with its immense potential to help businesses in the business industry. By analyzing data obtained from various sources, like reports, surveys, exit interviews, and learning management systems, HR analytics helps companies improve. Following are the ways it is helping companies and organizations across the globe:

Improving Recruitment Process

The recruitment process is strenuous and lengthy. It also involves high costs. Organizations can use HR analytics to improve their hiring process.

Keeping track of the recruitment process and tracking different KPIs like the number of applicants, the time required to fill a position, cost per hire over a period of time can help HR managers understand where time and money are being spent. They can also easily find the best fit for a position by looking at past performance, competencies, and performance indicators.

Employee Retention

Another major challenge faced by businesses is employee retention. Managers have to ensure that the new hires fit in with the company culture, get along well with their team and work efficiently with their colleagues. They often fail to meet these requirements, resulting in a high turnover rate.

HR analytics helps managers understand the reason behind high turnover rates. They can use this information to take the necessary steps to ensure employee retention.

Employee Training and Development

By analyzing the needs of a company, HR analytics helps find areas where development is required. It also identifies which employees need to be trained. By analyzing data related to the skills gap among employees, managers can decide upon necessary training programs that will help employees acquire new skills.

Predicting The Impact of HR Practices

HR analytics helps companies make predictions based on historical data and identify high-level trends and patterns. It allows managers to predict the consequences of the proposed practice.

Using HR analytics to analyze the impact of a new practice is an excellent way for managers to have an idea of how their decisions will affect the company. An accurate prediction can help companies analyze the financial impact of a particular practice. It will help them create the action plan accordingly.

Aligning Strategies with Business Goals

By predicting the impact of practices, HR analytics give management an opportunity to align strategies with business goals. It enables companies to understand which practices are working and allows them to make changes accordingly. HR professionals can better understand the impact of business initiatives and work on the alignment of action plans accordingly to improve efficiencies.

Employee Engagement

HR analytics assists companies in exploring the employee engagement factor and its effect on the business. It can help businesses understand which departments have a higher level of engagement and how the level of engagement is affecting their output. By analyzing this factor, businesses can implement policies that focus on employee engagement.

Better Decision Making

HR analytics helps the HR department collects information, analyze and benchmark their performance. Organizations can now use data and analytics in order to make business decisions that would otherwise be a too difficult and time-consuming process. They can make smart decisions based on real-time data to enhance the bottom line of their companies.