Hiring GlossaryWhat are predictive analytics in recruitment?
Understanding predictive analytics in recruitment
Want to get insight into how your talent acquisition (TA) team is likely to perform in the future? One of the best ways to achieve this is with predictive analytics.
What exactly is predictive analytics, and what does it have to do with recruiting? Here’s how Michelle Yoshihara, Senior Manager, Talent Planning, at Greenhouse, defines this term: “Recruitment analytics involves looking at the information you have within your ATS or recruiting software – whether it’s from an old search or sourcing strategy – and using that to predict what might happen with future searches.”
You can use predictive analytics for tasks like getting an idea of how long your time-to-fill might be for a particular role or surfacing the candidates in your database who are most likely to get hired.
In order to tap into the power of predictive analytics, you need access to historical data from places like your applicant tracking system (ATS), which could include CVs, assessments and past performance. In addition to this data, you’ll also need statistical modeling tools that can identify patterns and correlations and use them to predict future outcomes.
Compared to traditional hiring methods, predictive analytics is faster and more efficient, and can be less biased. This is because predictive analytics relies on objective data while traditional methods tend to depend on hiring teams’ subjective assessments, which can be inconsistent and biased. For example, a hiring manager might have a preference for candidates who have played on a sports team or who went to a specific university, even if those qualities don’t necessarily correlate to success on the job.
The advantages of implementing predictive analytics
If your TA team has been getting by without predictive analytics, you might wonder whether this is something you really need. But as your company grows, it becomes more important to have scalable hiring processes – and that means less reliance on manual work and more leaning into automation and other efficiency-boosting tactics.
Here are a few of the ways predictive analytics can help.
- Managing hiring managers’ expectations
Have you ever felt like a hiring manager was asking for the impossible? With predictive analytics, instead of just guessing, you can use data to come up with an educated estimate about how long it will take to fill a specific role or how many people you need on your team to support making a given number of hires. You can share examples of how long it took to fill a similar role in the past or how many candidates you interviewed before making an offer. This data-driven approach helps you manage expectations and can limit some of those requests that are out of touch with reality.
- Enhanced candidate selection and fit
When you have access to historical hiring data and candidate profiles, you can use predictive analytics tools to identify the skills and experiences that successful hires tend to have. You can then use this information to guide a structured hiring process so you’re surfacing the most relevant candidates and assessing them on the qualities that will make them more likely to succeed in the role.
- Reducing time-to-hire and cost-per-hire
Because predictive analytics tools can automate recruiting tasks like CV screening, they can speed up the hiring process and reduce time-to-hire. Plus, making use of recruitment analytics means you’re building a strong pipeline to source from in the future, which means you don’t have to start every search from scratch – and this can shave days or even weeks off the hiring process.
Rosa Gandler, Senior Director, Revenue Operations, at Greenhouse, said, “With a complete, centralised candidate record, you can source from your current database of individuals to find candidates you may want to consider for future openings. Reconsider previously rejected candidates, confident that you have all the relevant information.”
Some predictive hiring tools can also tell you which sources generated the most qualified candidates in the past, so you can spend your recruiting budget more effectively and avoid sinking too much into sources that aren’t likely to lead you to make any hires.
- Improved employee retention and job satisfaction
With the insights you gain from predictive hiring tools, you’re making hiring decisions based on what’s likely to make a candidate successful in a role. This reduces the chances that you’ll hire someone who’s not a good fit for the role or your company (which often leads to attrition and more time and resources spent backfilling that role). In other words, the investment you make in selecting the right candidate pays off in terms of happy employees who tend to stick around.
Steps to implement predictive analytics in your hiring process
If you’re interested in trying out predictive analytics, you first need to have a place where you’re collecting data on your hiring process – this will most likely be your ATS or other software that you’re using to manage candidates or employees.
But you also need to ensure that this data is reliable. Here’s how Rosa Gandler, Senior Director, Revenue Operations at Greenhouse, explained it: “Having reliable data allows you to make predictions off of your previous hiring data and can inform strategic changes to your process.”
Data can tell all kinds of stories, so next you’ll want to take some time to clearly define what you’re hoping to learn from your data. For example, you might want to search your database to surface past candidates who’d be a good fit for a role that’s about to open. Or maybe you’d like to be able to give your finance team an accurate prediction of when you expect a particular role to be filled. Depending on what your main goal is, you can define the type of data you want to collect and start to establish some benchmarks.
Finally, you might want to consider how you can layer in additional tools to take your predictive analytics skills to the next level. For example, Greenhouse integrates with several predictive analytics tools, like PredictiveHR, which predicts talent outcomes and financial impacts to business outcomes, or The Predictive Index, a tool that offers candidate assessments and targeted interview questions to help you find candidates who meet the needs of the role.
Real-world applications of predictive analytics in recruitment
How are real-world recruiting teams making use of predictive analytics? Many of them start by collecting data and establishing best practices for tracking it consistently. Based on the information they gather, they can begin to make predictions about what’s likely to happen in the future. Adam Ward, former Head of Recruiting at Pinterest, said, “Being able to track metrics that matter within Greenhouse, such as number of phone screens, on-site interviews and referrals, helps us be more predictive and forward-thinking in our hiring process.”
Other teams are using recruiting analytics to cut through high volumes of applications and surface the most promising candidates. For example, the NFL’s recruiting team used to be overwhelmed by thousands of applications. The AI-powered Talent Filtering feature in Greenhouse helps the team quickly surface qualified candidates using smart, keyword-driven suggestions based on each job description tailored to their unique Greenhouse data. Sourcing is now faster and more intuitive. Plus, the NFL’s TA team has cut time-to-fill by 24%, going from 63 days to just 48. Read more about the NFL’s story here.
Here at Greenhouse, we’ve been investing in predictive analytics with Offer Forecast. This tool leverages machine learning to ensure roles are on track based on target offer acceptances and start dates. It means recruiters are better equipped to prioritise tasks and manage resource allocation since they know which roles are on track and which ones need more attention. It also gives hiring managers visibility into the health of their job openings and helps finance teams accurately forecast salary spend and plan accordingly.
Key considerations for adopting predictive analytics
If you’re interested in adopting predictive analytics in your recruitment processes, there are a couple of tips that will help you be as effective as possible. First, make sure your data is accurate and reliable. Because predictive models rely on historical data, incomplete or inaccurate data can lead to skewed results.
Next, make sure that you’re following data privacy regulations like GDPR and CCPA to protect candidate information. If you don’t already have them in place, establish clear policies regarding data collection, storage and usage to ensure your practices are transparent and ethical.
Finally, be aware that artificial intelligence and machine learning are likely to be major influences on the future of predictive analytics. These technologies promise to enhance predictive capabilities by analysing vast amounts of data more efficiently. In the coming years, we can expect to see even more advanced tools to improve candidate matching, reduce time-to-hire and enhance diversity in hiring practices.
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