Recruitment analytics are becoming increasingly important to recruiters and HR managers. With the help of recruitment analytics, you can make better, data-driven choices when it comes to sourcing, selecting and hiring. In this 6-part series, we explain all there is to know about recruitment analytics, step by step. In Part 1 we look at the basics: what is recruitment analytics?
When we talk about recruitment analytics, we mean detecting, interpreting and simplifying meaningful patterns for sourcing, selection and hiring. This means using data to find and explain certain patterns in data. For example, when new employees leave the company within the first three months, this could indicate a mismatch between the job description and the actual role or a poor onboarding process.
Level 1: Operational reporting
Operational reporting mainly looks at the past. It is based on existing data, which are then analysed to determine what they mean for the company. Metrics such as cost-of-hire, source-of-hire, applicants per job opening, selection ratio, time-to-fill, time-to-hire, hiring manager satisfaction, and many more are regularly consulted.
By using a strong recruitment system (ATS), all the mentioned metrics are relatively easy to capture. When an ATS is implemented in your organisation, it is usually easy to link a dashboard to this system. In future parts of this series, we will list the best metrics and how to build a strong recruitment analytics dashboard.
Level 2: Advanced reporting/analytics
At level 2, the relationships between different variables are regularly examined. Advanced reporting on skills acquisition, for example, can identify which skills are in demand in a company to guide future vacancies. Advanced reporting does not yet require advanced statistical tools, but rather the combination of multiple data sources.
When companies go digital, for example, they have less need for administrative staff for tasks such as printing, transcribing and copying, and instead need more technically minded people. Advanced reporting can be a useful tool in detecting this problem and finding the right applicants. Another example has to do with candidate experience. To evaluate this, the various phases of the recruitment cycle must be evaluated and the candidate experience must be measured or collected. This can be done via surveys that are included in the or through surveys via advanced reporting.
Level 3: (Predictive) analytics
Predictive analytics represents the most advanced type of recruitment analytics. Not only does this approach analyse data, it evaluates data to make predictions about the future. In recruitment, the data that can be measured using predictive analytics is as broad as your company’s unique inputs and information sources. Fundamentally, any candidate or process-related data can be collected, analysed and measured.
A strategic review of capacity analytics may reveal, for example, that employee productivity lags around the holidays. In that case, HR may suggest that the company offer extra incentives, such as a performance-related bonus, to maintain productivity at that time of year.
In the 6-part series Recruitment Analytics, we’ll take you step-by-step and teach you everything there is to know about recruitment analytics. This was part 1.
In part 2 we will discuss: The best Recruitment Metrics to optimise your recruitment process