Recruitment KPI and HR analytics

The last decade has been characterized by one of the deepest and pervasive evolutions of the digital world: big data management.

In an increasingly global and interconnected world – governed by data – are analytics skills and technologies that transform raw data into valuable information for business decision makers (C-level executives and HR Managers); today it is possible to obtain a competitive advantage thanks to prompter and more up-to-date decisions: data-driven.

To undertake a digital transformation process of the HR department, it is necessary to change the methods of data collection and management and to implement tools and technologies to support each one’s recruiting activities.

Idea: if I can trace a series of data and information (analytics), capable of defining homogeneous pictures of the scenario in which I operate (recruiting), I will have an advantage in the phase of research and selection of profiles.

Here’s an example: Knowing that a source of recruitment for a specific position can guarantee me a greater number of candidates in line with my research, allows me to optimize times and resources needed for the whole recruitment process.
In this way we switch from a “push” strategy based on massive recruiting marketing to a “pull” strategy that provides a specific allocation of resources on the most effective source of recruitment.

In an increasingly digital perspective, organizations have started to use technological tools to optimize talent acquisition and better manage CVs: ATS.

The Applicant Tracking System (ATS) is a tool able of building up candidates’ database that can be consulted on demand in a quick and effective way, optimizing the time and resources to dedicate to recruitment.

However, the needs of the HR sector are experiencing a constant evolution, which requires the adoption of increasingly innovative tools; for this reason, we are no more talking about ATS but about ARS: Applicant Ranking System.

By definition, KPI recruiting involves a series of data to be analyzed; it is therefore important to define exactly which types of data we should have within our recruiting software.

Let’s see them together:

• personal data (now present in all modern and fundamental ATS for a recruitment process);
• data of origin of the candidate;
• professional data;
• assumptive data;

Once identified and collected, all these data are analyzed and crossed to define your strategy.

Recruitment analytics are critical. We are not only talking about being able to determine the first contact (e.g. when the profile completes the offer registration format), but also to establish when the candidate comes back to register for a new position or when he / she accesses his / her personal area; in short, being able to trace the candidate journey.

The most relevant data that a recruiter can analyze is the recruitment rate by source of recruitment.

In this article we only wanted to focus on talent acquisition processes but KPI strategies can also be applied in the daily management of coworkers (defining, for example, strategic drives starting from performance analytics).

In conclusion we can say that: if the basis of KPI strategies is the ability to be able to determine and collect HR analytics, the analytics themselves base their survival on the existence of HR software – such as talent management software – capable of collect and sort the data received.

Our advice? Define a KPI digital recruitment strategy supported by tools capable of converting big data management from difficulty to opportunity.