What is HR Analytics?
Analytics is an essential component of HR responsibilities, especially in the digital age, where data has become more important than ever. Having the right information at hand enables HRs and managers to better manage their teams. It helps them have an accurate overview of their workforce, enabling them to streamline employee management, while predictive analytics even helps with future trends prediction.
Let us understand this essential feature for companies, as the data collected with HR analytics helps efficiently manage the most valuable resource for any company, its employees.
What is HR Analytics?
HR Analytics is the process of collecting and processing data from HR operations to improve company processes. As a result, HR analytics collates numerous metrics related to various aspects of human resources such as recruitment, performance, payroll, expenses, attrition, and more.
The HR professionals are tasked with analyzing the metrics and improving the processes to ensure the achievement of organizational goals while keeping the staff engaged and satisfied with their company. Based on HR analytics, HR and management can also develop various initiatives to improve the overall employer branding of the organization.
What is the Importance of HR Analytics?
From the section ‘What is HR Analytics?’, we can understand that it helps with data capture and analysis. With the help of this data, HR and management can make informed decisions, leading to a better workplace. It enables them to make the changes requested by their staff, streamlining their daily operations. It enables them to optimize their HR operations, resulting in an engaged workforce.
Some of the major factors elevating the relevance of HR analytics for organizations are:
- It enables the HR professionals to understand the current performance of their staff, identify the skill gaps, and upskill them effectively.
- It helps them identify the gaps in company operations and streamline them with the use of technology like HR chatbot.
- It enables HR to understand the diversity and inclusivity of the employees and make changes to be more inclusive.
- It helps measure the employee engagement rate and strategize initiatives to take it to the optimum level.
- It helps analyze the interpersonal relationships between managers and their subordinates and improve them.
- Predictive HR analytics can also provide future trends prediction capabilities to HR professionals to avoid major issues.
Evolution of HR Analytics
HR analytics has its origins in the early 1970s when the concept of ‘The Measurement Imperative’ was proposed to measure the effectiveness of HR operations. The data capture mechanisms were manual and hence, were not as effective as later generations; however, the idea of using data to capture the impact of HR activities was born in this decade.
In the 1990s, the idea became more robust with the introduction of dedicated mechanisms to capture the required data for HR analytics. It was soon transformed into ‘bench-marking’, where companies would set a theoretical benchmark and try to attain them. However, it soon fell out of practice due to the impracticality associated with ‘theoretical’ benchmarks.
The early 2000s saw the re-emergence of the concept of HR analytics due to the advancements in computers and technology. Automated tools were developed to collate the required HR data and hence, analyzing them became easier. Since then, HRs all over the world have been using different kinds of HR analytics software to effectively evaluate their staff metrics and optimize them to ensure employee engagement.
Data Types for HR Analytics
There are various data types required for HR analytics. The data required for HR analytics can be sourced from various options such as:
1. Internal Data
Internal Data refers to the data that is generated within the organization. Since the data is collected from within the organization, it is one of the most relevant parts of the HR analytics process. Hence, one should exercise extreme caution while collecting the internal data for HR analytics as this data should be error-free.
Some of the common examples of internal data are employee compensation and appraisal records. Similarly, employee performance, attrition, hiring, training metrics, etc. can also be included in internal data. Additionally, internal data also consists of talent management metrics, work culture data, leadership analytics metrics, and more.
A data scientist might be required to collate the data and make meaningful inferences from it if the size of the company is huge, which would result in various kinds of internal data generated, which would be overwhelming for HRs. Hence, modern organizations often invest in a dedicated HRMS to ensure accurate data collation and error-free processing.
2. External Data
External Data refers to the data generated outside the organization, which can be collated by understanding its impact on internal data. External data can be further subdivided into:
Financial Data: Financial Data refers to the data related to company finances such as revenue per employee, cost per hire, training costs, etc. Analyzing these metrics will enable the HR professionals to lower the company costs by optimizing the processes related to these costs.
Passive Data: Passive Data refers to the data generated by the staff when they work with the organization. This data can be gathered from various sources such as their feedback, employee surveys, etc. Passive data can be instrumental in understanding the numerous factors affecting employee performance, employee engagement, job satisfaction, and more.
Historical Data: Historical Data refers to the data generated by extrinsic factors that are beyond the control of the organization. These can be generated from economic, political, and other external conditions. For example, the recent global pandemic led to redefining the ‘workplace’ with companies moving to remote and hybrid work culture, which necessitated the requirement of advanced metrics to measure employee performance.
3. HR Analytics Implementation
The process of HR analytics implementation can be highly varied depending on the nature of the business and the size of the company. It can also change depending on the various factors included in the metrics as well as the complexity of the various departments within the company.
However, the following steps are required for setting up HR analytics correctly within any organization:
4. Set the Objectives
The first step is to identify the goals the HR wish to achieve with HR analytics. It should align with the organizational goals as well. By clearly defining the goals, HR teams can understand the different kinds of data that need to be analyzed. For example, a start-up would be more interested in understanding how to lower costs, while an established organization would be interested in streamlining its activities.
5. Identify Data Sources
Once the goals are identified, the next step is to identify the various data sources which can provide the data required for the analysis of the company and the employees. Since data is central to HR analytics, you should ensure that the data sources are accurate and provide error-free data.
6. Choose the Analytics Model
The HR analytics model used to track and analyze the various metrics should be decided once the data sources are identified correctly. These models should ensure accurate data capture while being capable of highlighting any data outliers for optimum usage of HR analytics. The HR analytics model should also facilitate the achievement of organizational goals.
7. Organize Data
Once the data is collated, the data analytics model should be able to organize it correctly to transform it into meaningful information. The data collected using the HR analytics system will be meaningful only when converted into information, and hence, it is essential to organize the data effectively.
8. Analyze Information
Since the data is converted into meaningful information, HR and management can deduce various kinds of statistics from it. By analyzing the information at hand, the HR teams can identify patterns that enable them to understand their workforce better. It also allows them to develop various strategies to optimize their company operations and achieve organizational objectives.
9. Develop Dashboards
Once the data is analyzed, it needs to be converted into visual representations such as graphs and charts to make it easier to understand for all stakeholders. Hence, it is essential to develop intuitive dashboards and report-generation tools that provide a pictorial representation of the data.
10. Deploy the Analytics System
Once the HR analytics system is ready, you should deploy it within a department to ensure that it is working as expected. You should verify the various analytics models used and recheck the metrics being measured and their accuracy. You should also verify the various dashboards and ensure that the system is working correctly.
11. Train the HR Teams
The next step in deploying HR analytics is to train the HR team members to use it effectively. Since the HR teams are required to use HR analytics tools daily, they should be well-versed in its usage. Hence, you should conduct training sessions to ensure that the HR staff can analyze data effectively using the HR analytics tools at their disposal.
12. Evaluate and Reiterate
Once the HR analytics system is in place and the HR teams have started using it, you will have to face issues with unique scenarios that you may not have anticipated while developing it. Hence, it is essential to evaluate the system periodically and make changes to include all possible scenarios. It will help in creating a robust HR analytics system, using which you can develop a robust employer brand.
Common HR Analytics Metrics
Now that we have a robust idea of the HR analytics deployment method, let us check out the common HR metrics which are vital for the success of an organization. By paying attention to these metrics, you can make the required changes, ensuring improved employee satisfaction and a better workplace.
The most relevant HR analytics metrics include:
➔ Revenue Per Employee
The ‘Revenue Per Employee’ is a metric that provides the value of revenue generated by each employee. It is calculated by dividing the total revenue generated for a period by the total number of employees working in the organization during that duration. Since it is a financial metric, it enables the HR teams to understand how efficient is their organization in terms of employee productivity.
➔ Training Cost Per Employee
Another major metric for HRs of an organization is the ‘Training Cost Per Employee’, which is the total cost incurred in training an employee. It is calculated by dividing the total amount spent on training by the number of employees trained. It helps understand the effectiveness of the training management system and make changes if required.
➔ Voluntary Turnover Rate
Voluntary Turnover Rate refers to the number of employees who have left the organization voluntarily. It is the ratio of the employees who left voluntarily with the total employees in the organization at that period. It helps the HRs get an idea of the employee experience in their organization and curb attrition.
➔ Involuntary Turnover Rate
Involuntary Turnover Rate is the total number of employees who left the organization involuntarily, which includes termination. It is calculated by dividing the employees who were forced to leave by the number of employees in the company during that duration. Having this figure handy helps the HR teams optimize their recruitment strategy to hire better individuals.
➔ Time to Hire
Time to Hire refers to the total time spent between a candidate showing interest in a vacant position within the organization to when they were placed in that position. Having this metric enables the HR teams to fill any gaps in the recruitment process and improve their efficiency to hire faster.
➔ Offer Acceptance Rate
Offer Acceptance Rate refers to the rate of accepted job offers within a specific duration. It is calculated by dividing the total number of offers accepted by the candidates by the total number of offers shared in that duration. This metric gives an idea of the effectiveness of the compensation and benefits packages.
➔ Absenteeism Rate
Absenteeism Rate refers to the number of days employees remain absent within a specific duration. It is calculated by dividing the number of days employees remain absent by the total number of working days within the same period. It helps the HR teams understand the overall satisfaction rate within the organization, and make any changes if required.
➔ Human Capital Risk
Human Capital Risk refers to the collective personnel risk a company undertakes while conducting its business. It includes multiple employee-related risks such as the lack of leaders, lack of talented staff, relationship metrics, compensation-associated risks, and more. Having a robust idea of the human capital risk within the organization enables the HR teams to mitigate it effectively, and HR metrics enable them to quantify it.
➔ Retention Rate
Retention Rate refers to the number of employees who are retained effectively. It is calculated by dividing the total number of employees who remain at the organization at the end of a duration by the total number of employees who were working at the company at the start of the duration. It is usually measured in percentage.
Benefits of HR Analytics
The most relevant benefit of HR analytics is the vast amount of information the HR and management can collect and analyse for improving business operations. It results in better decision-making, leading to optimized processes, and paving the way for a better employer brand.
Hence, let us understand the multiple benefits of HR analytics:
1. Better Decision-making
HR analytics provide the HR teams and the management with data that is both insightful and actionable. As a result, they can make effective decisions, that create a positive impact on their workforce. Additionally, data-backed decisions can also enable them to benchmark their strategies, helping them understand the difference they made by quantifying their output.
2. Increased Employee Loyalty
HR analytics also provide deeper insights into the various employee metrics which would be inaccessible to the HR teams in its absence. Hence, it enables them to gather in-depth insights into the needs and wants of their staff. Fulfilling these requirements will increase the trust and faith of the staff members in the organization multiple times.
3. Better Employee Experience
Similarly, with the help of HR analytics, the management can make changes to the workplace to enhance the overall employee experience. Since the employee experience starts with recruitment, HR analytics can play a significant role in improving the overall experience for the recruit through their life within the company.
4. Better Recruitment
According to 2024 Global Talent Trends study conducted by Mercer, 56% of Indians mentioned enhancing the user experience for attracting and retaining talent to be a key focus area. HR analytics plays a key role in improving recruitment as it provides insights into the current hiring strategies of the company.
It enables the recruiters to collate the data and streamline the hiring process to ensure a better overall candidate experience. It results in better quality talent as well as engaged employees who can be provided with a better employee experience from their first day onwards.
5. Predictive Analytics
Similarly, due to advancements in technology, HR analytics can also provide predictive analytics which analyses the previous patterns and can make accurate predictions about employee behaviour. For example, if the employee’s behaviour points to disengagement, it can be an indicator of the employee planning to leave the organization. However, with the help of predictive HR analytics, HRs can understand whether they are planning to leave and make changes to ensure their retention.
6. Better HR Performance
Similarly, since data is essential for robust employee management, having the wealth of information gathered using HR analytics enables the HR teams to optimize their operations. Since HRs are required to work with data, having a dedicated system to visualize and analyse this data enables them to make meaningful deductions resulting in improved efficiency.
7. Reduced Attrition
HR analytics also prove fundamental in reducing attrition as it helps HR gather data on employee exit through the use of surveys and interviews. Hence, they can analyse the patterns and reasons for leaving employees and make changes to ensure that the same reason doesn’t become the cause of another employee leaving.
8. Increased Productivity
Since employee productivity is dependent on various factors such as work culture, work equipment, office environment, etc. gathering the data on these metrics can be handled with the help of a robust HR analytics system. With the backing of this data, HR leaders can improve the factors that positively impact the productivity of the staff, making it essential for HRs.
9. Improved Engagement
All the factors we discussed until now help improve employee engagement within the company. Additionally, it can also help in understanding which departments have a higher level of employee engagement and the reason behind it. They can implement better strategies once they understand the reason for other departments which have lesser engaged staff.
Types of HR Analytics
There are 3 major types of HR analytics based on its application:
1. Diagnostic Analytics
Diagnostic analytics helps in diagnosing the root cause of various issues. It includes metrics like turnover rate, which helps you understand that something is wrong with the company’s hiring or the employee experience. Hence, you can diagnose the issue and make changes to the relevant process to ensure a better experience for your employees.
2. Descriptive Analytics
Descriptive analytics helps with existing data and pattern identification within the organization. Analysing the current and past data will help you identify major challenges within the company as well as understand what is going well. Hence, descriptive analytics plays a major role in learning from existing data and applying the inferences to create better employee experiences.
3. Predictive Analytics
Predictive HR analytics relies on past data to identify patterns and predict future trends within the organization. It uses various advanced technologies like machine learning, artificial intelligence, data modelling, etc. to ensure that the predictions are as accurate as possible. Since it can predict the future up to an extent, HRs can use predictive analytics to take the necessary course of action to avoid bad outcomes.
Modern HR Analytics Trends
The future of HR analytics is technology-driven as data collation and manipulation have become the forte of modern technologies like big data and artificial intelligence. These technologies will be used extensively in the future to analyse employee-generated data and predict future trends.
So, let us understand the modern HR analytics trends which are here to stay:
➔ AI and Automation
AI and automation have already started making their presence felt in the HR industry. With its faster processing and simplified user experiences, AI will be at the forefront of HR analytics since it can easily handle large databases. Combined with AI, HR analytics will be more streamlined with AI doing most of the work for the HR teams such as analysing the patterns, predicting future trends within the company, and more.
➔ Virtual Onboarding
Another major impact will be in onboarding. Companies are already toying with Virtual Reality (VR) and Augmented Reality (AR) to ensure that their recruits have the best onboarding experience possible. With companies trying out bleeding-edge technologies to provide their recruits with the best experience, virtual onboarding will soon become mainstream.
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➔ Simulation Training
Similarly, employee training will have a radical redefinition, thanks to the data provided by HR analytics. Employees can be provided with a snapshot of their work periodically, with AI suggesting personalized training options if required. Similarly, AI can also evaluate the training sessions and report their effectiveness to ensure that it is completely optimized.
➔ Personalized Performance Management
Another avenue for personalization will be performance management with companies employing the best systems feasible to ensure that they capture performance data as accurately as possible and feed it to HR analytics algorithms. Using the reports generated with this database, companies can provide their staff with all the amenities and the best tools they require to undertake their jobs quickly and efficiently.
➔ Predictive Reporting
Additionally, predictive reporting will be a major part of the HR meetings as these reports will highlight possible issues so that HR teams can resolve them as soon as possible. With pre-emptive measures taking the front seat, HR leaders will have the option to avoid any disaster and reallocate their time to streamline the company operations strategically.
