It’s impossible to make good business decisions without having access to relevant data. This also applies to HR departments. Human Resource analytics, among others, helps improve talent retention and positively impacts hiring decisions.
In today’s article, we’re going to answer the following question – what is HR analytics. We will also take a closer look at some of the most common HR data metrics, tell you how HR data analytics works, and briefly discuss its pros and cons. Let’s dive right in.
What is HR Analytics?
HR Analytics relates to creating insights about how investing in human capital assets impacts business success in terms of revenue generation, expense reduction, risk mitigation, and strategic plan execution. For this purpose, statistical methods are applied to integrate HR, talent management, financial, and operational data.
Now that you know what HR analytics is, it’s worth taking a look at how you can benefit from it.
How can you benefit from HR Analytics?
Using human resources analytics is associated with numerous benefits, here are some of the most common ones.
- HR data analytics improves retention
HR analytics comes in handy while identifying your retention rate. Not only does it tell you how many employees leave, but it also lets you in on the reasons behind their departure or why some of your workers have decided to stay. Onboarding new hires takes a lot of time while losing employees generates significant costs such as hiring costs, productivity losses, training costs, etc. As an example, by leveraging HR analytics, IBM was able to establish that employees with the highest retention rate at the company have a Life Sciences degree and work in the lab, whereas their sales executives were the ones who were most likely to switch jobs quickly.
Source: Unsplash
- Better hiring decisions
HR analytics enables better recruitment choices by looking at historical data. As in the above-mentioned IBM example, it helps with spotting certain patterns. For example, if you hired 12 candidates out of which 4 came from a particular source of candidates and it turned out they didn’t perform as well, it might mean you shouldn’t hire someone from a similar environment again.
- Boosted employee experience
HR analytics creates a positive impact on the employee experience – it speeds up recruitment, and candidates don’t have to wait as long for feedback. This, in turn, translates into a better candidate experience, which improves your brand image and gives your company a competitive edge.
- More training opportunities
HR analytics plays an important role in spotting skill gaps within an organization. Not addressing them on time can create significant costs as well as result in productivity losses. Using a solution like TalentBoost can help HR identify which skills the business is missing, or find employees internally who can quickly pick up the necessary skills and send them to training. TalentBoost also helps in evaluating whether the training brought anticipated results.
What are some HR Analytics examples?
Let’s now take a look at HR analytics examples to better illustrate how you can benefit from it.
Recruitment
Recruitment goes beyond hiring employees with the right skill set. It’s also necessary to find people who fit the company culture, which can be a very time-consuming process – especially in IT.
In fact, according to DevSkiller data, the costs of hiring the right tech candidate can be as high as $60,000 in productivity loss, recruitment, and contractor cover. Having access to data which, for example, includes success indicators will significantly reduce time-to-hire, as you’ll know which candidates to keep an eye out for.
In recruitment, predictive analytics is especially beneficial because it focuses on the future performance of your candidates. This will be covered in more detail in section “HR predictive analytics”.
How HR analytics can help:
- By automating candidate data collection from various sources
- Building detailed candidate profiles by gaining access to variables like developmental opportunities and cultural fit
- Discovering candidates who display the same characteristics as your top-performing employees
- Getting metrics on time-to-hire for specific roles, which will help you in succession planning.
Turnover
More often than not, when an employee resigns, you don’t know the reason why they’ve decided to leave unless you conduct exit interviews. Not knowing why employees leave will result in a high attrition rate.
How can HR analytics help:
- Collecting and analyzing historical turnover data to discover trends and patterns showing why employees quit
- Getting data on employee behavior like productivity and engagement to understand the state your workforce is in
- Understanding the factors behind high employee turnover through data correlation
- Developing a predictive model to keep track of and flag employees who might be at risk of quitting
- Building strategies for improving the work environment and as a result boost engagement levels
Human Resource Analytics – how it works
HR analytics is built of four components. These are:
- data collection
- data measurement
- analysis
- implementation
Step 1: Data collection
Decide what data points you have and what exactly you want to take away by the end of the process. Is it about understanding employee engagement rates or calculating your time-to-hire? Or maybe about measuring your employees’ satisfaction with recent training? The data points you should consider in your evaluation include:
- employee performance
- demographic data
- employee retention
- salaries
- onboarding & training
Keep in mind that data comes in two forms – qualitative and quantitative. Only the latter can be easily imported into your HR analytics’ tool. Qualitative data is descriptive in nature (for example, answers to open-ended questions), so if you want to include it in your analysis, it can be quite time-consuming (or in some cases impossible) to transform them into a numerical value.
Source: Unsplash
Step 2: Data measurement
At this step, you focus on continuously comparing data against a set of standards (HR metrics). These can be, for instance, comparing this year’s data with that of a previous year or corporate and/or market norms.
In order for your human resource analysis to be successful, you cannot measure a single batch of imported data. Rather, you need to engage in the measurement continuously as new data keeps flowing in.
In the next section, we discuss a few recommended metrics you should include in your HR data analysis.
Step 3: Analysis
This is the moment you start reviewing the results from your research. The goal of this step is to identify recurring patterns and how they impact your business.
Depending on what you want to find out, your analysis can go in three directions: descriptive, predictive, and prescriptive analytics.
While descriptive analytics focuses solely on analyzing what had already happened, predictive analytics aims to foresee what might come for your business next. Prescriptive analytics takes both past events and future assumptions to derive the best recommendations.
Step 4: Implementation
At this stage, you will need to decide how and where to apply your insights. For instance, did you notice a trend in your employee turnover? Or maybe you found that 20% of your highest-performing employees come from employee referrals?
Think of how you can use this data to improve your HR department performance and boost your results next time you perform an analysis.
On top of doing regular checks, it’s worth keeping an eye on changes as they unfold. With talent management software, you’re able to see exactly how many people joined and how many left, as well as look at what they have in common.
Source: TalentBoost
HR data analytics – what metrics to consider
Not sure what data to measure? Below are our top HR metrics recommendations.
Revenue per employee
It measures how much money each employee generates for the business. It’s calculated by dividing the total company revenue by the current number of employees.
Job offer acceptance rate
The percentage of candidates who say “yes” to the company’s job offers. It’s measured by dividing the number of accepted job offers by the total number of offers available within a given time period.
Training expenses per employee
Measures the average cost of training a single employee.
Voluntary turnover rate
The percentage of employees who willingly decide to leave the organization.
Involuntary turnover rate
The percentage of employees whose contracts were terminated due to layoffs, workforce reduction, or poor performance.
Time-to-Hire
It’s the amount of time that passes between initially contacting a candidate and accepting the employment offer. This is especially important in tech, where competition is high but the process tends to be long. According to Dice data, it takes on average 39 days to hire a software developer, 40 days to hire a UI developer, and 46 days to hire a data engineer.
Absenteeism
Refers to frequent absences from work, which go beyond the acceptable time-off like vacations, sickness, or family emergencies.
Advantages and disadvantages of human resource analytics
Let’s take a look at the pros and cons of conducting HR analysis.
Advantages
Spotting underperforming areas in your HR operations. For instance, if you notice that your time-to-hire has extended by 30% for most of your recruiters, this might indicate an issue in the hiring process that needs to be addressed. Sometimes automating parts of the process or swapping the order of your recruitment process steps can bring about an amazing result. As an example, our customer ImpacTech is able to hire developers with 2.5x fewer interviews.
Source: DevSkiller
- Eliminating the guesswork. Implementing HR analytics will give you the means to validate your hypotheses and assumptions before implementing any changes in your hiring and talent management processes.
- Improving employee satisfaction & engagement rates. Analytics will not only help you improve your HR team’s performance metrics. It will also positively impact all other team members. If your human resources team has access to insights such as the turnover rate or employer satisfaction survey results, they’ll be equipped to improve the employee experience. As a result, your team will be more motivated to perform at their best.
- Improving the recruitment process. If you conduct HR analysis correctly, you’ll be able to track data such as job acceptance rates and average time-to-hire per position. However, you can also go a step further and include post-interview surveys for your candidates to learn how you can better improve the candidate experience and attract the right talent.
- Predicting trends or employee behavior. By making the most of HR predictive analytics, you’ll be able to act upon telltale signs, such as a drop in employee satisfaction survey results. This will allow you to fix minor issues before they escalate or strategize for the inevitable, such as your key employees leaving the company.
How about the potential downsides of HR analysis?
Disadvantages
- Missed opportunity or deriving untrue insights, if the HR team is using a complex tool and isn’t skilled in data analysis.
- Potential security issues, if analytics and third-party tools that have access to sensitive information don’t fulfill the right security standards. Luckily, this threat can be avoided by using tools with sound security procedures in place.
- Potential ethical and legal issues, if the data you track breaches privacy or accesses private employee information. For instance, think about the thin line between collecting insights from a public vs private Slack channel – if you ever consider analyzing communication, you must get consent from your employees and clear it with a lawyer!
How to avoid these and other mishaps and ensure that you’re making the most of your data? By leveraging the power of predictive analytics and using the right HR analytics tool – for instance, TalentScore.
HR predictive analytics – TalentScore
If you want to ensure that you make the best hiring decisions and retain your employees longer, we recommend using a tool like TalentScore.
TalentScore leverages an AI Benchmarking Engine that takes into account factors such as work sample coding test scores, test difficulty levels, as well as the time it takes candidates to complete tasks. All this will allow you to predict candidate performance with an astounding 85% accuracy!
Source: DevSkiller
Summary
HR analytics enables organizations to become proactive in their use of data. Rather than only fixing previous issues, companies can be more creative with their future and solve potential issues before they happen. HR analytics can be used for both recruitment and retention – collecting and analyzing data for both of these gives you a full picture.
In order to make the most of your employee and recruitment analytics, we recommend that you use TalentBoost and TalentScore. By using these solutions, you’ll have access to a whole range of insights, such as skills gaps in your teams, training opportunities, time-to-hire, and other performance predictors that will help you attract the very best talent.
Image credit: Pexels
