3 Ways Predictive Analytics Help You Hire Better

We’ve been inundated with bewildering big data for years now. Thankfully, predictive analytics has emerged to help us put that data to use in ways that drive business results. In talent acquisition (TA), predictive analytics is a tool that helps companies hire better and faster. In today’s highly competitive talent market, doing so is key to securing the talent you need. So, how does predictive analytics empower better hiring?

  1. Workflow prioritization. Predictive analytics takes everything your recruiters, hiring managers, and sourcers do, all the applications, all calls, every single action and aggregates it on a daily basis. Certain “triggers” are applied so that it indicates what actions your people need to make each day to keep job orders on track. For example, if your goal is to have a new hire start in 60 days, that means by day 45 you need to have an offer accepted, you need final round interviews day 35-40, interviews scheduled day 20-25, and hiring manager initial feedback within the first two weeks. By using an “assembly line” type of logic, every step is mapped out. Alerts are placed on the key dates or activities, and daily dashboards show you where you might be getting off schedule. This way, TA teams always know every day how to prioritize their workflow to address the most pressing issues first.
  2. Workforce planning. The second power of predictive analytics lies in its ability to take data and show you where you get your best hires from, how long those people stay, where they go when they don’t stay (your competition), what time of year you get the best hires, and what talent pools your talent comes from. By looking at themes and cycles, you can establish critical information that lets you pinpoint where to spend budget, as well as predict where and when you will get the best results. For example, if a company hires their best talent in May and June of each year, are they getting fresh college grads or is this just when people are looking for new work? Predictive analytics will examine the historical data and answer this for you. Then you can determine whether or not you should delay hiring until those months. Predictive analytics will help you determine who is converting and why so you can target the right people.
  1. Process improvement. The third power of predictive analytics is in its ability to help you drive efficiency and process improvement. The data on how well your team is performing, as well as how well your process is working can be used to make adjustments, tweak efficiencies, and drive better ROI. When you use A/B testing, the data sets will show you what works best, and you can optimize your teams, processes, and planning for maximum efficiency and performance. Great predictive analytic tools will also have TA best practices baked into them so that you can continually benchmark and have game plans to address challenges.


Bottom line: predictive analytics makes your work smarter and more efficient and results in faster, better hiring. When you are evaluating predictive analytic tool vendors, be sure to understand that your system needs to be able to provide the data to the tool and not all systems have this capability. Talk with your IT department to ensure your system is compatible with the vendor of your choice. Secondly, look for a vendor who has expertise in TA so that the tool will be tailored to the types of datasets that your team needs to succeed. Your next competitive advantage may lie in the data you already have, so be sure you start to leverage predictive analytics to empower your hiring results.

  • http://www.medievalrecruiter.com/ Medieval Recruiter

    “Workflow prioritization”

    Simply having a dashboard that lets you know when you’re about to miss a deadline doesn’t help you hit that deadline anymore than any other kind of reminder.

    Workflow prioritization

    The information mentioned in this section only helps if you know EHY your best hires come from this place or that, or at this time of year vs another. And most times it will just be plain chance as few companies hire enough people to get an adequate sample size to draw conclusion from. What they will really get is the ILLUSION of information and the ILLUSION of informed decisions, as they cherry pick ‘data’ that confirms the biases they already hold about hiring.

    This is not ‘predictive’ analytics, it’s ‘correlative’ analytics.

    • Emily Egan Gordon

      I agree that analytics can be used or twisted to create a story that is not as factual as one would hope. Having a small sample size can be a problem, having someone that has a bias armed with lots of data can also be a problem. The goal is to use what we can, when we can, what is factual, and is helpful. Some companies struggle with forecasting openings and some with knowing where high performance talent originates from. The overall point of this was to say you COULD use analytics to help find this information… and so much more. Arguably, data is used and abused for good and for evil. The great debate about HOW data is used is a great article I would love to see written. Maybe we could collaborate and get a robust and multidimensional story out there. Let me know!

      • http://www.medievalrecruiter.com/ Medieval Recruiter

        I appreciate the offer, but I like doing things alone.

        The underlying problem is that how data is used IS the problem, and it’s one you can make black and white right/wrong judgments about. But, people are soft wired to make wrong decisions, because over generalizing patterns and correlations with regard to risk aversion is what’s kept our species alive and allowed it to evolve to dominate the planet. Human beings are wired to see patterns where there are none, and we are wired to jump immediately from correlation to causation even when there is no such link. And most people simply will not hear you when you explain how wrong that is. And even in case where they do hear you, they want to still ascribe some level of usefulness to such moves even if there’s absolutely none.

        For example, you can show as much data as you want to a manager to prove to them there’s no causative relationship between having a degree and performance in a particular job, and most simply will not believe you. And of those who do believe you, the majority will still think demanding the degree is a means of hedging their bets, or playing it safe, while in reality they’re not doing any such thing, AND concurrently shrinking their pool of potential applicants in a completely arbitrary manner, with associated but largely unseen opportunity cost.

        Put as much data as you want into the hands of such people, overcoming their biases and preconceptions is still the major hurdle, and they’re much more likely to use such data to bolster their wrong beliefs than they are to question them. That’s essentially why almost no one changes their minds in politics. You can give them data galore to show they are wrong, it doesn’t matter, because they are evaluating it from an established ideology, or system of beliefs. You need to move THAT before facts will matter to them. So, all the data in the world will generally not change bad management practices for most people. They’ll just discount what doesn’t fit their existing world view, overvalue what can plausibly support it, and proceed as if nothing has changed.

        Because it hasn’t.

        • Derek Gillaspy

          When you buck the “norm”, you are by definition taking a risk. If you hire people “just like everyone else”, when they don’t work out – it was because of the candidate. But when you hire someone that doesn’t fit the “norm”, and it doesn’t work out – then it is because YOU made that hire.

          This is why we have so many disenfranchised groups – Millennials that don’t work in tech, mothers returning to the workforce after 3+ year absences, convicted felons and software sales MEN over age 55 (especially Indian and caucasian).

          Many hiring managers have written off these groups completely….and there are others of course that I’m missing (such as college degrees it seems you were discussing above).

          In the tech world, if you took a group of 10 students with a computer science degrees from Stanford university (~$200,000 to get), and 10 students that went to ‘Hack Reactor’ 13 week bootcamp (~17,000 to get) – guess which group hands down can write better code?

          Education is being disrupted in many fields, but where you other millennials as the hiring managers (often the case in Tech), you get a radically different result (meaning the manage will take the bootcamp student over the Stanford student).

          Why is this? This is where Medieval really nailed it above – it is because they don’t have this embedded hiring code that says a hire must look a certain way. They are innovative in that they are ignoring tradition (Stanford) and choosing results (Boot camp).

          Tough to replicate to something like picking financial analysts or entry level marketers when we can’t even agree what good looks like for those roles.