Hiring managers leverage unbiased data analytics to make informed decisions from a pool of candidates. A robust tool for recruitment analytics transforms scattered data points into statistics useful for recruiters across hiring levels. HR analytics for recruitment is an excellent tool for streamlining resources and gaining valuable insights.
You can also apply data analytics to shape workforce strategies around building morale, engagement, and retention of employees. Let’s learn how this process unfolds!
What is recruitment data analytics?
Recruitment data analytics uses past and present company data to make smarter, data-driven hiring decisions. The role of HR analytics in recruitment and selection lies in exploring every aspect of the business, converting data into actionable insights, and making better hiring choices.
In a departure from traditional hiring methods, the advent of data analytics allows for a broader investment into measuring, tracking, and analyzing candidates to land the finest choice for a role.
Why choose recruitment analytics?
Using the right tools keeps you one step ahead of your competition and increases your chances of finding the right talent for your organization. Automating primary screening through data analytics in recruitment has an immediate impact on the hiring costs of the employees. Data-driven recruiting can save up to 23 hours of manual labor a week by pre-screening and shortlisting candidates. In addition, it increases the possibility of actual policy changes in the later stages of onboarding talent.
Fortune 500 companies such as Cisco, Google, and Deloitte have eliminated the chances of mishaps in the hiring process by using recruitment analytics. As per a poll on LinkedIn, 77% of talent professionals have attested to using analytics in planning their workforce.
How recruiters can use recruitment data analytics for better hiring
Nearly every forward-thinking organization uses analytics in recruitment to bring efficiency to its hiring process. A significant improvement in the quality of hire has a lasting impact on the company’s workforce turnover.
Here’s a five-step guide on how to use recruitment analytics for better hiring:
When hiring fresh talent, selecting metrics to judge them on is critical. Your KPI (key performance indicator) must be relevant to the organization. They should also be relevant to the open roles. Recruitment analytics simplifies the process by using data points to filter the candidates with the most suitable application. Some other insights to check for are the quality of leads, application analysis, and source of hire.
Hiring analytics can target selected areas of improvement in their talent drive and optimize them for future use. For example, if the biggest hurdle of your hiring process is an in-person interview, you can improve it by adding another layer of screening for selected candidates.
Once you have selected the metrics to work with, the next step is collecting relevant data. Fastening this process is counterproductive, as identifying trends throughout is the best way to assimilate data from different sources. Recruitment analytics helps you by studying your previous hiring cycles and finding loopholes.
By highlighting your KPI, you can narrow the process down and focus only on the most urgent areas of improvement.
Now that you have the data to work with, the biggest challenge is to present it in a correlative form. This data will help prepare a competitive analysis. The best way to showcase analytics in recruitment is through easy-to-use and customizable dashboards. These long and elaborative presentations enable the tracking of critical metrics across the board.
A sophisticated visual board reveals the low-hanging fruit of the process and helps identify areas that need improvement. Furthermore, organizations can transfer verified leads onto their profile platforms with the help of AI. SpringRole, a verified professional profile platform, is an alternative to the old Excel slides and yellow pads.
A mountain of data is relevant only when analyzed through the desired metrics. The next step is feeding accurate and relevant information from candidates and specifying the KPIs to improve the selection process. A broadened analysis of data enables predictive analytics. It further helps predict the performances of candidates. The predictions are made based on their work experience and employer reviews.
Predictive analytics in recruitment plays the role of HR in selection by taking over all of the grunt work. It channels data through trends to produce actionable insights which would help recruiters. Here, the quality of data demands critical caution. Because crowding the field with multiple pointers will deteriorate the process and produce nothing of value.
Getting your hands on the actionable insights is a battle half-won. Now begins the hefty task of applying the data points to improve your recruiting process. You can go through the findings in steps.
For example, suppose the largest glare in your hiring process is a low-quality talent pool. In that case, your actions must be all about improving the visibility of your job postings. You can start with reevaluating your advertising strategy and reworking the contents of the listing to make it impressionable for high-quality job seekers.
Or, say your organization is suffering from an embarrassingly low retention rate — this can be a call for overhauling the entire onboarding process to seek more suitable matches for your listed role.
To sum up
If you are running an organization, the use of recruitment analytics in talent acquisition will improve your hiring process. It will also strengthen workforce retention and lower the operational costs of an onboarding cycle. By getting your hands on the right tools and data and following this manual, you too can revolutionize the human resource management of your organization.