HR analytics is a method of data analysis that can be used to make informed decisions about hiring employees or for personnel management. It involves linking and analyzing data that is constantly generated within the company.
The data used describes the past and present of the company in figures. For HR analytics, these figures are used to create statistical models and provide information on correlations or causal relationships. In this way, development trends can be uncovered and used to create forecasts by extrapolating trends. Ultimately, the data enables better-founded predictions to be made regarding future recruiting.
HR analytics can be divided into people analytics and recruitment analytics (also known as data-driven recruitment). In both cases, data is collected and analyzed.
People analytics works with the data of employees who already work for the company. This can be data from the HR system or a combination of this data with data from other departments. In this way, highly differentiated key performance indicators (KPIs) can be calculated.
Recruitment analytics, on the other hand, evaluates data that is generated within the recruitment funnel (especially on the careers page of the company website).
HR analytics also has a data protection dimension, as it uses personal data - including sensitive personal data in some cases. All data should therefore be processed anonymously so as not to violate the personal rights of employees. In view of the sensitivity of the processed data, the works council and data protection officer must give their consent before working with it.
Every day, companies generate large amounts of data relating to their employees. This data can be analyzed using people analytics.
This is usually done by formulating hypotheses about current issues and examining them using statistical methods. The data can then be used to determine key figures that are very useful for HR management.
Some very basic key figures of People Analytics
For example, people analytics can be used to determine the characteristics of employees who will remain with the company for longer based on existing data. In this way, the HR department can search more specifically for candidates who have the same characteristics and, for example, reduce the fluctuation rate and therefore recruitment costs in the future.
If the fluctuation rate is very high, this can also be countered by improving the employee experience in order to save recruiting costs and training costs for new hires. Companies can also use employer branding to counteract a high fluctuation rate in the run-up to recruitment. This is because it provides information about the corporate culture and prevents unsuitable candidates from applying in the first place.
Analyzing the reasons for termination can also clarify the question of how an increased fluctuation rate comes about.
In this way, people analytics supports the HR department in the development of new procedures and thus serves as a decision-making aid. This is because the data enables better-founded predictions to be made regarding future recruiting, personnel development and the effectiveness of personnel marketing. The effectiveness of measures such as further training can be determined using HR analytics.
Especially when HR analytics combines the data from the HR department with that of other departments in the company, the success of a training measure for service employees can be examined with regard to the development of customer satisfaction, for example. This makes it possible to determine which training measures are effective and which are not. Other influencing factors can also be identified that are suitable for further increasing customer satisfaction.
Recruitment analytics is used for the goals and purposes of recruiters. This primarily involves analyzing data from the Applicant Tracking System (ATS) and from the company's job page. This is where the data relevant to recruiting is collected.
The most important key figures that can be derived from the ATS for Recruitment Analytics include Time to Hire and Cost per Hire.
Time to hire is the time that elapses between the job being advertised and the vacancy being filled. If you measure this key figure, you can identify pain points in the recruitment funnel. This is important because if you want to get the best talent on the market, you have to hire quickly. Long recruitment times can lead to the best candidates dropping out.
In order to achieve improvements in time to hire, the average time to hire must first be calculated. The reporting function of the ATS can be used for this.
The time to hire can then be calculated for different roles/positions to be filled, hiring managers, departments and recruiters.
When broken down in this way, bottlenecks and potential savings in the recruitment processes can be identified very quickly. For example, does CV screening take too long or is there a particular manager who always gets in touch with applicants very late?
The second important point for data collection is the company's career website.
The following questions can be answered here: How many applicants have visited the company's career website? At which of the different stages of the application process do candidates come to the company's website? Do applicants switch to job boards or application modules during the process, when are they back on the company's own careers website and how many have abandoned their application after all? Do candidates read the job advertisement to the end? Do candidates also visit other pages on the company website?
If candidates do not apply on their own, recruiters have to turn to sourcing. This can again be determined on the company's careers page: Where (from which channel) did most, the best or the rather weaker applicants come from?
If you know which channels produce the best results, you can focus your recruiting efforts on them and avoid wasting resources on measures that are not effective.
How do you do that? By using specific URLs for each channel so that it can be traced from which website the applicant was forwarded to the company. These links contain so-called UTM codes, which are appended to the end of URLs. They are generated separately for each channel and can be analyzed in Google Analytics. This makes it possible to determine the exact source of each click on the company's job page.
UTM.io is a free tool for generating UTM codes. Bit.ly generates UTMs only in the paid version.
Other important KPIs in the area of recruiting are
A particularly important key figure is the visitor to applicant conversion rate, which indicates how many of the visitors to a job page actually apply to the company. A conversion rate of 11% is a good average. Anyone who achieves more has obviously done something very right. All values far below 11% give food for thought and should result in an improvement of the employer branding or the job page.
If visitors do not stay on the job page, the page must be optimized. A heat map can help here, which uses eye tracking of test subjects or the mouse movements and scrolling behavior of real users of the job page to deduce which areas of the page are perceived little or not at all or immediately precede the abandonment of the application process.
By making the candidate journey as pleasant as possible, only a few applicants are lost during the application process due to abandonment.
When recruiting generations Y and Z, for example, it is particularly important in this context that the career page can also be displayed and used responsively on common smartphones.
All relevant KPIs along the candidate journey can be measured with analytics tools. They can be displayed clearly with widgets in the dashboard of an analytics tool so that numerous key figures can be monitored simultaneously.
A case study for the successful introduction of HR analytics is the uvex group, a manufacturer of protective clothing in the work and sports sector from Franconia in Fürth, whose products (e.g. protective goggles for professional athletes) can be seen at numerous sporting events.
An analysis of the current situation was carried out by the HR department employees in order to gain an overview of the target groups and recruitment channels. This was based on an evaluation of the applicant management system, which tracked recruitment, the number of applications and the type of application received.
One problem with recruiting at uvex was that it was unclear in retrospect through which channels successful applicants had reached the company website. It was therefore not possible to evaluate the success of certain recruiting channels at uvex.
This problem was solved with the help of Google Analytics. The points that uvex can now monitor and evaluate include the conversion rate, error messages on the application form and the scroll depth.
Google Analytics is already used by many companies for user tracking on the company website, so you can quickly get started with Recruitment Analytics with an account.
Matomo is an open source tracking tool that can be used to evaluate your own career site. How does Matomo differ from Google Analytics?
Both tools measure website traffic in a similar way. Matomo is more complex, even in the free version, and must be installed on your own server. Google Analytics, on the other hand, is easy to install. Both tracking tools offer the availability of live data and other desired data. As expected, Matomo does not allow the integration of Google Ads, whereas Google Analytics does.
Matomo comes with an Advanced Privacy Control, has a GDPR Manager and stores data on its own server, while Google is not GDPR-compliant by default.
The data that is often available in the company is a good basis for analyzing problems and making predictions. The available tools can be used to gain a precise overview of applicant behavior on your own career site. This can be used to quickly identify opportunities for improvement.
HR analytics can answer interesting questions about efficiency in recruiting: Can the time-to-hire be shortened further? What costs are incurred by the company per vacancy and where can savings be made?
Important trends are identified. The results of the analysis should be translated into instructions for action within the company. However, this first requires acceptance of the results of the analysis. The results often have to be "sold" within the company. This is because it is sometimes necessary to overcome attitudes that have been entrenched for years.
Ultimately, it can be said that HR analytics, with its analysis and forecasting options, saves costs and time for recruiting. HR analytics is a valuable decision-making aid along the way.
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