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We know the HR landscape has undergone rapid changes in technology over the past decade, from manual processes to programmatic advertising featuring automation and built on AI. The goal? Get more applicants faster and at lower cost--i.e., improve the three main driving KPIs in recruitment marketing: Time-to-Hire, Cost-per-Hire, and Quality-of-Hire. Having more applicants should mean you have a better pool of talent to sift through to fill your next open position, not that you’re spending more money to sift through a bigger pile of unqualified candidates and wasting time.
Current tech trends are bringing improved efficiency and quality of candidates to talent acquisition teams. According to a survey by TA Tech, companies are using a greater reliance on programmatic recruitment technologies featuring automated processes, targeted job advertising, and smart (data-driven) spending, and are increasingly engaging passive candidates through proactive recruitment. Trends designed for these purposes, like automation, predictive data, and analytics, will continue to shape the changing landscape of HR management, and in 2019 these trends will be bigger than ever.
Automation is probably already baked into your HR system in some form. Let’s take, for example, a simple keyword search. Think through the two opposing ends of the spectrum: manual process vs. AI. You can type in any word you think will match the skill you’re looking for when you sift through applicant resumes. But it will take a long time to get eyes on all those resumes, and you’ll miss something. Eyes and wrists get tired, and there’s only so much time in any search.
AI tech, on the other hand, can perform the same function and do it better, faster, and more reliably, using natural language processing to expand keywords to synonyms—and that is just one small kernel in the silo of processes that go into a single job search. The speed and agility of AI assistance would stack across the multiple job searches that talent acquisition teams perform. Many teams automate processes where they can, but we currently have ad tech that can automate these processes across the board, synthesizing the various processes together and optimizing recruitment campaigns as a whole.
If we break down programmatic job platforms into its various parts and pieces, you can see the bigger picture. Pandologic’s programmatic job platform, for example, uses a targeting algorithm that works in concert with its classification algorithm. That means when you have your next job description ready to go, the program uses the job type to find the best location for job seekers of that type. The platform also uses a “prediction algorithm” using billions of data points from historical job ad campaigns to locate places where these types of jobseekers actually look. Every click of a button a candidate makes is usable data that will help AI further target job seekers in real time and help monitor your job ad’s performance.
This is sophisticated stuff, and it’s much better than saying, let’s try Indeed again because it seemed to work before. The smart targeting performed by algorithms will scour the internet across multiple sites, job boards, and social media to find your next hire.
The biggest bulk of any recruitment budget comes in job ad spend, which is approximately 30% of the budget. When employers rely on gut-checks and guesswork, these dollars can be wasted—but even when your team uses tough internal recruitment metrics, you are still operating with limited data. AI ad tech can maximize your job ad spend—and you don’t even have to break out your calculator. Our programmatic job platform uses your budget to assess budget allocation; this process works in concert with an optimization algorithm and a dynamic bid algorithm, spreading your dollars across multiple job ads and putting the money and the ads where they most need to go to maximize the success across the entire campaign.
And of course, this entire platform, while sifting through Big Data, will keep tabs on analytics and new information that comes in to optimize efficiency and effectiveness over the campaign. This is the essence of what is called “machine learning”; it is how the program collects and adjusts to new data in real time to maintain the success of job ad campaigns, not just for the future, but for the now.
This is a fast-moving train. When competition for talent is high, employers who embrace the efficiencies through ad tech will reach the best quality candidates first. As we move into 2019, companies that don’t want to be left behind better jump on board in order to keep up.