You already know we live in a tech-driven world—which means you probably also know the…
Chances are, the next hire you will make is checking their mobile device, using a wireless network, or running social media software-right now. For you, that means constant streams of information you can use in order to find and hire the best candidates for your open positions. Over the past decade, companies have seized on big data analytics to create efficiencies across all industries. For recruiting, there's still further to go; the rise of the internet led to the rise of job boards, while later social media and advanced tracking technology has led to not only reaching more candidates but also finding better, more reliable hires.
You have access to so much information before a candidate even walks in the door for an interview. But as big data grows bigger, how can your talent acquisition team leverage this data to their advantage? Rather than view all that data as extra grunt-work for recruitment, how can you make the data work for you?
A bigger pool from which to find the perfect candidates
Sourcing via job boards requires sifting through a lot of data. This prevents you from being able to access an aggregate view of your job performance. But we are now at the point where AI recruiting tools can do the work for you and eliminate data silos, as they sift through information across whichever sites you prefer to bring you the right people, from the right places with the right credentials. The more information that's out there, the more fuel there is to feed AI technologies in order to pinpoint the exact candidates you seek for open positions.
And the more big data there is, the more accurate AI-enabled algorithms can be. After the predictive algorithms are set and fine-tuned, data is the fuel that drives AI to update these algorithms for environmental factors such as supply/demand and job seasonality. The manual process is thus eliminated and you can focus your resources on other things. The best part is we are just beginning to see its potential. Your team can use AI tools to seek out the exact traits and skills you want in a hire-big data plus smart tech will yield big smart data that saves you time and money. For recruitment industries that will mean more effective, efficient and smarter recruiting.
More specific and targeted ads for passive candidates
So how can you find those quality candidates using social media on their smartphones right now? They need to see and apply for your job opening-and you need to be able to find the resumes and get the attention of passive candidates who may not be actively searching for jobs. This is where AI technology combined with big data can be particularly helpful, via tools like pandoIQ. Using the patent-pending NAVi algorithm, more than 199 billion data points have been analyzed across 5.4 billion individual historical job ad campaigns to determine the scientific formula and key attributes that predict job ad performance. What this means is predictive algorithms will use mass amounts of data to predict job performance across your ad campaigns.
While it is true that businesses have used data to creative predictive models for quite some time, only now are we seeing AI update these predictive models for environmental factors. Vast amounts of historical data allow us to make the best ad placement decisions using these AI controlled algorithms -which can then, in turn, create insights and recommendations for better ad performance.
So, for example, if you're planning a new job ad campaign, big data analytics is key to figuring out which job ad vendors to use or which sources you want to use to consolidate your data while simultaneously figuring out which sites will perform and which will be an unnecessary dent in your recruitment budget.
Meanwhile, a business can use data, and the ability of AI to analyze that data, not only to create predictive models but also to try out different approaches and learn quickly what works and what doesn't. While businesses always try to replicate successes and learn from failures, AI can help learn quicker and use predictive analytics to make every campaign successful.