This blog is part of a blog series on Intelligent Recruitment Automation in which we explore its relevance for the recruitment industry, the technologies used and the potential use cases.
The rise of a global pandemic has shown how adoption of technology can surge. Also in the recruitment business, people are adapting to digital first interactions and recruitment professionals see the necessity for the use of digital means. The recruitment industry has always been a people business. But partial automation has shown to be beneficial, and sometimes a prerequisite, when you want to rapidly scale up teams.
The biggest concern for CEOs remains attracting and retaining talent. And recruitment professionals, also during times of Covid-19, mention the biggest priorities to be building candidate pipeline (66%) and filling current requisitions (53%). Most organizations respond to this challenge by focussing their efforts on finding active job seekers by searching for people who are open to work and by posting their jobs on job boards. But with this focus on active job seekers the question arises; ‘Am I increasing my chances to find the best candidates or am I merely creating noise and more work for myself?’ Looking at the complete global workforce, just 20-30% are active talents, people who are actively looking for a job and are the people who typically end up on a job board. The absolute majority however, 70% of the workforce, is passive talent who are not actively looking for a job but who mostly are open to discuss opportunities that are relevant for them. With this almost five times bigger potential talent pool there is a good reason why 83% of talent acquisition professionals see passive sourcing as an important source of hire and 73% of them indicate that when they focus their efforts on passive sourcing they find higher-quality candidates. Focussing on passive talent can give a great return, but the challenge is to find matching talents in the billions of candidate profiles available.
83% of talent acquisition professionals see passive sourcing as an important source of hire
For fast growing tech companies for instance, this search is focussed on people with a certain skill (mostly tech and commercial related) but also with certain personality traits (e.g. “Growth Mindset”). When you are sourcing for a specific type of talent, searching on existing platforms like LinkedIn can be an enduring task with a diffuse talent base, limited search filters and incomplete data (e.g. engineers usually don't spend much time on LinkedIn). Just on LinkedIn only there are already 800 million users. Indeed has 100 million, GitHub 40 million, Xing 19 million, Stack Overflow 10 million, not even to mention Facebook, Dribble, Twitter and other platforms... Eventually you don’t need millions of people for the actual job, you need one. Because of these reasons 47% of recruiters say they cannot find enough qualified candidates, let alone finding the best. So how do you find the right person in billions of profiles? Most recruiters (78%) think data intelligence would make them more successful at their job. And it makes sense to adopt intelligence in making sense of the vast realm of data so you can be more accurate in your search and qualification.
Talent acquisition professionals and leaders still spend most of their time on searching and screening candidates
The rise of new technologies has created a great deal of new opportunities, but the technical terms flying around don’t help to better understand which technology to adopt, let alone how. In this blog series we introduce the concept of Intelligent Recruitment Automation that acts as a framework to understand the application of intelligent technologies in the context of the recruitment cycle, mostly applied to sourcing and screening (the absolute majority of recruiters, 80%, indicate that recruitment automation is most useful in sourcing and screening process). Intelligent Recruitment Automation describes a set of techniques used for automation of users, tasks, systems and robots that includes the use of analytics and Artificial Intelligence (AI) to make automated and intelligent decisions. AI is an umbrella term that basically refers to intelligence demonstrated by machines as opposed to intelligence demonstrated by humans (natural intelligence). AI includes a collection of technologies like Machine Learning, Natural Language Processing and Predictive analytics. In the following blogs we will further outline the meaning of these technologies for recruitment.
Gone are the days that knowing how to do a Boolean search made you a good recruiter
The potential of Intelligent Recruitment Automation is massive. Among the most mentioned benefits that talent acquisition professionals experience are an improved quality of hire, a reduced unconscious hiring bias and a reduced time to hire. Some examples of companies that have adopted Intelligent Recruitment Automation are Dutch scaleups Picnic and Mollie.
When Picnic, one of the fastest growing tech startups of Europe, was rapidly scaling their operations they were in need of delivery drivers. They adopted performance marketing techniques to reach the right candidates through google and social media marketing. Through their data driven automation approach they have scaled the yearly applications from 0 to 30.000 within 1,5 year.
Another example is Mollie, a fintech startup that had over 30 open tech positions to fill with limited recruitment resources. They used a cross-platform talent search engine to find software engineers across platforms like LinkedIn, GitHub, Stack Overflow and personal websites of engineers in one place. Within two weeks Mollie had found and hired four people for hard to fill tech positions.
Important note here is that the use of automation in the recruitment process doesn't always result in the desired results. Amazon's AI recruiting tool for example had to be cancelled because their Machine Learning algorithm showed bias against hiring women.
The undisputedly successful use cases are still rare. Some organizations choose to focus on applying Intelligent Recruitment Automation to merely “speed-up” the process, but this usually turns out to neglect the need of personalization in the interaction with candidates in the process. Merely speeding up things is usually also accompanied by a decreased accuracy and an unpleasant candidate experience.
The biggest potential of intelligent automation is in situations where you want to analyse and process big sets of data that a human cannot do because of the mere volume of data (and people’s limited ability to process and remember data and patterns in a short amount of time). We distinguish five primary areas in the talent acquisition cycle based in which you can apply Intelligent Recruitment Automation: Sourcing, Screening, Outreach, Interviewing and Relationship Management. Important to realize is that some tools provide intelligent automation across multiple of the mentioned areas.
In the following blogs of this series we will go through some of the use cases in more detail. But some of the main use cases are:
Candidate Relationship Management: keeping track of and nurturing established candidate relationships using a smart ATS (like Beamery).
In this blog series we will further uncover Intelligent Recruitment Automation. We will dive deeper into the technology being used. We’ll learn how especially fast growing companies can benefit from Intelligent Recruitment Automation and how you can start small in matter of days.
Highlights of the Dutch ecosystem start of 2021
Many fast growing tech companies are looking for sales executives. Tech sales execs usually sell either an unproven product, in an unproven market or both at the same time. In this blog we provide an example of the sales exec job description.