Sourcing your best hire comes with some major challenges: spending too much, too little, and using the same sites over and over with little to no efficiency. Oftentimes those challenges can be fixed by improving the process. By fine-tuning your sourcing strategy you are more likely to reach your target applicants.
So, when it comes to your recruitment sourcing strategy and finding the right candidate for your job listing, there are several factors to consider.
Before you even get to the candidate sourcing step of the recruitment process, you have to tackle an important task: perfecting the job listing and requirements listed. Specificity is good for candidate sourcing, but sometimes an overly specific list of requirements can be just as off-putting to a candidate as a job listing stuffed with vague terms. This means that finding the right blend—informative, but not too dense—is key to effective sourcing. But it doesn’t stop there. Let’s say you’ve done your due diligence, market research, and internal alignment and have landed on a job description that finds the perfect balance between specificity and generality. You’re sure that any qualified candidate who sees this listing will apply. But how can you be sure that your job gets in front of the right person during the candidate sourcing phase?
One of the biggest challenges facing recruiters today is the sheer number of online job boards that exist. This can be great for job seekers but can make it difficult for employers to understand exactly where the candidates they want are spending their time. And so it makes it difficult to manage efficient, cost-effective candidate sourcing. Hopefully, you’re already using various AI recruiting tools that will provide you with data from these sites: such as which job sites perform the best based on the job type, how many candidates fall off in the process and where, and so on.
With this information, targeting the right candidates should be simple. Right? That’s partially true—if you’re using the right tools for your recruitment sourcing strategy. That’s because with more data comes a new challenge: collecting, organizing, and analyzing all those numbers so you can actually use them to make smart decisions. This is where AI and machine learning come into play.
Unless your HR department has its own team of highly specialized data scientists, it’s unlikely they’ll be able to analyze data in the same efficient way that AI software built for that purpose can. That’s because AI recruitment software is not just analyzing the real-time data that comes from how your open role is performing on various job boards, it is also taking into account massive amounts of historical data from how similar jobs have performed across various online job boards. By combining all this information, an AI candidate sourcing strategy can help you get in front of the most qualified applicants. The software will not only be able to predict where your open listing will perform the best, but it can also help you make real-time decisions based on if the job is performing as expected—whether it’s performing worse or better than predicted.
An AI sourcing strategy has many advantages. It…
Makes all your working dollars more efficient, by dynamically allocating budget to the best performing job sites
Can speed up your candidate sourcing and recruitment process by delivering more qualified candidates more quickly
Analyzes historical and real-time data so your job listing gets in front of qualified candidates—without any guesswork from you
Is able to accomplish all the above for all your open jobs simultaneously—freeing up your time and brain space
For many people, if they hear the words Big Data and audience targeting, they’ll likely think of advertising. In our modern age, marketers are able to gather valuable data on their intended audience that can range from broad demographic info to specific online behaviors. Marketers use this data both to target their ideal audience and tailor their message.
You may be wondering why we’re talking about advertising when we should be focusing on recruiting and recruitment analytics—but as all smart HR and talent acquisition specialists know, effective recruiting requires its own kind of marketing. So while it might be strange to associate numbers—and a lot of numbers at that—with the recruitment industry, which is first and foremost about people, it all makes sense when you start to look at things a little more closely. Big Data recruiting is a tactic that is here to stay.
Essentially, recruiters use data in a similar way that marketers do: to target their ideal audience now and learn how to target their audience more effectively in the future. But in order to target candidates effectively, recruiters need access to a lot of data—Big Data. As most recruiters know, it has become increasingly difficult to effectively source candidates, what with all the new and varied online job boards—not to mention social media, where candidates also spend their time. Because of this, having access to Big Data is not just a “nice-to-have,” it’s a crucial factor in effective recruiting.
Now that you know the basics of why recruitment analytics and Big Data are important to recruiters, let’s look at how those recruitment analytics are used. The practice of Big Data recruiting is a powerful approach to candidate sourcing and filling your open roles because of the way that recruitment analytics are used. With access to Big Data, predictive models and algorithms can be created that are capable of automating complex processes and making informed decisions that used to require human thought. Not only does AI save time when paired with Big Data, but it also saves crucial brain space. AI recruitment software makes data-driven decisions for you, so you and your HR team can focus on people-driven decisions.
Big Data is not just helpful for real-time reporting, but it is also crucial when it comes to predictive analytics. A prime example of this is the NAVi algorithm in pandoIQ, which takes a massive database of past job ad performance information and analyzes more than 199 billion data points across 5.4 million historical job ads. pandoIQ uses Machine Learning to identify key attributes that can be used to predict your job ad performance. From there, automated algorithms and recruiters alike can use predictive models built off Big Data analytics to make more informed decisions about what to do next.
From the way we work to the places we work, things change over time—and we all know that they can change quickly. Big Data is able to capture and reflect these changes. This constant stream of information enables self-learning algorithms to constantly get smarter and automatically adapt. Time is often of the essence in effective recruiting, which is why Big Data recruiting and its counterpart in AI software aren’t going anywhere.
Programmatic job advertising platforms like pandoIQ use AI-enabled algorithms and Big Data to mine years and years of historical performance data across millions of job ad campaigns. With this information, pandoIQ is able to determine the ideal targeting strategy for each job type. And these proprietary algorithms are capable of doing much more than just determining the best places to advertise your jobs. The algorithms can even determine how long a job ad should be promoted on a specific site before it will no longer get results. But all you really need to know? AI analyzes all the data so you don’t have to. And the end result is faster and better candidate sourcing and audience targeting.
Smart recruiters are turning to AI candidate sourcing — so they can make even smarter decisions.