Throughout all areas of business, data has become a source of highly prized information. As HR professionals increasingly see the inherent value in data, many are seeking to understand more about what HR analytics is and how data analysis methods can boost departmental strategy.
Are you making the most of your staff data? If you’ve not reached this stage yet, keep reading to discover the potential of HR analytics.
What is HR analytics?
Human resources analytics is a field that deals with analysing employee data and applying analytical processes within HR.
Gartner defines this technique, also known as HR Analytics as the collection and analysis of a company’s employee data to improve performance and business results.
This allows the organisation to measure the impact of a set of HR metrics, such as the time-to-hire or retention rate, on the commercial objectives.
The difference between HR analytics, People Analytics and Workforce Analytics
Are HR Analytics, People Analytics and Workforce Analytics interchangeable? They are closely related, but there are slight differences between them. To see what these are, let’s review their definitions:
- HR Analytics: this method focuses on the human resources metrics themselves, such as the time-to-hire, training costs or leaving rate. These data belong to the human resources department.
- People Analytics: people analytics is a more generic concept and can encompass individuals outside the organisation, such as clients. It is, however, used frequently in the field of HR.
- Workforce Analytics: this term refers to all an organisation’s employees (fixed, temporary, freelance, consultants, etc).
As we can see, depending on the context and our objective, some of these terms can overlap. For example, employee performance and productivity data belong to both HR analytics and workforce analytics.
The benefits of HR analytics
HR analytics can help an organisation’s management process data that they were previously unaware of, implement new measures and, in general, make better business decisions.
- Improve employee retention: analysing certain metrics and obtaining a deeper understanding of employee opinion through staff satisfaction surveys, can provide many clues as to why workers decide to leave a company voluntarily. This can help us put certain preventative measures in place to try to lower the leaving rate. Nielsen managed to retain 40% of its “flight risk” employees and increase the chances of employees staying with the company by 48%.
- Increase worker output: thanks to data, team leaders and the HR department can find out what motivates the staff and identify what is stopping them from reaching their full potential. It’s a case of going one step further than the performance review to detect patterns and design an improvement plan.
- Help create better remuneration and incentive programs: a detailed data analysis can reveal if the benefits the company is offering translate into better performance or increased retention. They can even tell us, for example, if wage increases make little difference to certain groups (which can save the organisation a lot of money).
- Improve employee engagement: with data we can find out how people are feeling and adapt workspaces to create a pleasant experience for individuals.
- Facilitate HR planning: predictive HR analytics lets us make fairly accurate projections on staff fluctuations. If we can estimate when critical roles will be leaving, we will be more prepared to intervene. In the same way, it will be much easier to identify staff excesses or shortages in a specific area.
- Cost savings: clear, objective information lets us make better decisions and undertake practical actions, impacting in turn on company finances.
The process of an HR analytics project
If you want to get results you can use, the HR analytics process should be approached meticulously, following a series of steps.
1. Identification of the problems to be resolved: the starting point must always be the company’s requirements or the questions they want the data to answer.
2. Design the investigation: the next step involves deciding how we will extract these data, from where, and what analytics we want to explore.
3. Data identification: we need to find reliable data sources that give us the information we need for our research.
4. Advanced statistics: we can calculate the metrics we want using statistical formulae.
5. Automation: the last step involves automating the whole process, from extraction to data calculation.
The main HR analytics metrics
For a company’s metrics to have a real impact on the business, they need to be agreed by the HR team and management. However, here are some of the most common in HR analytics:
- Revenue per employee: this is obtained by dividing the company’s revenue by the total number of employees. It measures the organisation’s efficiency.
- Offer acceptance rate: divide the total number of job offers accepted by the total number of offers made in a given period to obtain the rate. A high rate, over 85%, is a good result. If it is lower, perhaps you need to review the organisation’s talent acquisition strategy.
- Training spend per employee: just divide the total training spend by the number of employees that participated in a course. It is interesting to compare this figure with the next metric.
- Training efficiency: it is not easy to calculate, but you can estimate it by taking into account various factors, such as performance improvement and the number of promotions within the organisation.
- Voluntary leaving rate: calculate this rate by dividing the number of employees that resigned from the company by the total number in the workforce. This metric can give us an indication of internal problems.
- Time-to-hire: the number of days from when you contact a candidate to when one accepts a job offer. This figure can be very important for recruiters.
- Absenteeism rate: divide the number of lost days by the total expected number of working days to measure the rate. This figure can tell us about our employees’ general health and if they are happy at the company.
How to implement HR analytics in your department
If you want to start using data to derive information and improve your organisation’s performance, you can introduce HR analytics into your department by following these steps:
1. Make a plan
Identify the company’s requirements or commercial objectives to determine how analytics can help improve these aspects.
It is important to identify which metrics can provide valuable information, so you achieve the results you want and harness the strategic weight of HR within the organisation.
2. Involve a data scientist
Data scientists have the required expertise to evaluate and find the best analysis solution. They can help monitor the quality and accuracy of the data and support HR professionals to use them in the most beneficial way. Moreover, they can provide training for the team involved so they have a better understanding of the process.
3. Start slow
Establishing a data culture in a company is not an easy task. Neither is persuading all parties to see the business value of analytics. Therefore, start slowly with small projects. You can see these as “quick wins”, that generate tangible short-term results that also have a significant impact on the organisation.
4. Guarantee compliance with data protection laws
Get up to speed on the legal situation regarding data protection and avoid issues with your employees’ privacy. In this aspect, it is also essential to be transparent about how the company will collect and use the data. If you think it is necessary, consult a legal expert to help you comply with the current legislation.
5. Use HR analytics software
The best way to coordinate HR analytics is via custom software which simplifies, consolidates and automates the processes. In this way, the team responsible only has to focus on analysing and interpreting the information. The tool takes care of extracting and visualising the data.
The advantages of using HR analytics software
HR Analytics software is essential to reliably monitor the company workforce for the following reasons:
- Ease of use: they are very easy tools to use and don’t require long training sessions. They are designed so any professional, without analytics expertise, can extract information and interpret it.
- Consolidated data analysis: the information is kept on a single platform, even though it comes from different avenues. This helps to visualise the data, as well as providing access to old data to make forecasts.
- New functions: analytics software is constantly evolving and updating to offer new functions that respond to changing HR requirements, such as security, report storage, useability, etc.
- Cost reduction: software usually has a lower cost attached than other solutions that the company can design.
- Time savings: automating data extraction and visualisation processes saves a great deal of time for the HR team, who can focus on other more valuable tasks like strategy or introducing improvements.