The life of a recruiter is never dull—it often requires juggling multiple deadlines, handling hiring manager expectations, keeping employees informed on the progress of their referrals, headcount planning… the list goes on and on! The idea of introducing data and analytics as yet another item to your already crowded to-do list can seem intimidating, but that doesn’t have to be the case. In fact, by knowing which data to collect and when, you can manage expectations, optimize workflows, and take a more strategic approach to your role.
Let’s take a look at how you can use data and analytics to measure your recruiting performance in real time.
A pipeline report offers you a snapshot of the recruiting funnel for a job within a selected timeframe. Greenhouse Director of Talent Acquisition Jacqui Maguire recommends pulling a pipeline report every time you open a new role and then weekly throughout your search. This action helps you provide your hiring manager with updates on their open roles. Below are a few of the types of data you can collect in a pipeline report. Note: the specific examples we’re mentioning here come from the Greenhouse pipeline report. We recommend choosing an applicant tracking system (ATS) that will track candidates’ progress through stages of the pipeline in order to give visibility into your recruiting pipeline at any moment so you and your hiring managers are always in the know.
Prior to opening a role, it’s very helpful to pull historical data on how the same role (or a similar role) has performed in the past. This allows you to make educated guesses about the number of candidates you’ll need in each stage and how long the hiring process will take. Knowing how many candidates are needed in the application review stage in order to make a hire will let you calibrate when to dial down your sourcing efforts and shift your focus to the candidates who are already in the pipeline. The power of this type of data will make your talent acquisition team more efficient.
The information you get from historical data also helps to manage hiring managers’ expectations. For example, if you need to hire 5 sales reps, and data shows you that it takes 30 phone screens to make one hire, share that data insight with your hiring manager so they know it will take up to 150 phone screens (or 75 hours of their team's time!) to hit this hiring goal. Being transparent about this type of information with the hiring manager will allow you to create a realistic strategy based on your team size and bandwidth.
Conversion rates from stage to stage
Conversion rates or “pass rates” track which percentage of candidates “pass” from one stage of the pipeline to the next. Greenhouse Senior Recruiter Ariana Moon shares that a conversion rate that’s too high can be a red flag. Looking at a pipeline report that showed a 92% conversion rate from take-home test (THT) to onsite interview, she writes, “Immediately I’m asking myself, is the THT stage filtering out enough unqualified candidates? If not, how can we better vet candidates before we bring them onsite?”
And, on the other hand, a conversion rate that’s too low can also be problematic. Ariana continues: “At Greenhouse, I once experienced the opposite problem. The THT stage had a pass rate of only 10%, which was resulting in very few onsite interviews and a very anxious hiring manager. So after digging deeper, the hiring manager and I concluded that certain aspects of the test were filtering out candidates too aggressively.”
Average number of days in stage
This metric is pretty self-explanatory: It allows you to see how quickly candidates are moving from one stage to the next.
Keeping your eye on this metric will help you identify any inefficiencies in your process and fix problems in real time. For example, how long are candidates spending in the take-home test stage? You don’t want to let them hang out there for too long and risk losing momentum to make it through the rest of the pipeline.
You can also use the average number of days in stage metric to benchmark the efficiency of your recruiting coordinators. Coordinator efficiency has been notoriously difficult to pin down, so having averages for how long candidates are “sitting” in a stage for an active role can be an indicator of how quickly your coordinators are able to schedule. Speed and timely communication are vital to getting offers signed in a candidate’s market, so it’s important to monitor coordinator speed through the pipeline in real time.
The total hours metric is calculated by taking the amount of time you’ve estimated a specific stage, e.g. “Initial Screen,” should take and multiplying it by the number of Initial Screens you’ve scheduled. So if you’ve determined that initial screens take 30 minutes and you’ve conducted 20 initial screens, the total hours would be 10.
This is another metric that’s helpful in setting expectations with your hiring manager when you open a new role. You can show hiring managers just how many hours they and their team members can expect to spend interviewing candidates. If you have 5 open roles, for example, and you know it generally takes about 20 initial screens to make one hire, this adds up to 50 hours conducting initial screens (10 hours per role multiplied by 5 roles). Hiring managers can then use this information to plan out their team’s workload and hiring plan and make adjustments as necessary.
Another helpful type of data for recruiters is interviewer calibration. You probably won’t need to pull this data on a regular basis, but it comes in handy when you have a group of interviewers who provide conflicting feedback on a specific candidate. We—and all of our interviewers, presumably—are human. Even when we add structure to our process, there will always be an element of emotion and bias, so having the ability to calibrate interviewers comparatively can aid decision-making.
If you find yourself in a situation where most interviewers have passed a particular candidate while one interviewer said, “Definitely not,” you can look at interviewer calibration to understand more about this particular interviewer. Have they only ever given “Strong yes” or “Definitely not” answers? If so, you can dig a little deeper with that interviewer to find the root of their “Definitely not” and perhaps it can be taken with a grain of salt. Or, if they’ve never given that answer to any candidate before, you’ll probably want to check in and find out why they had such a negative impression of this candidate.
Taking the next steps toward data-driven recruiting
In this post, we’ve looked at some of the ways you can use data in a tactical way to measure your pipeline on a regular basis. You can use this information to make real-time changes and adjustments to specific aspects of your hiring process, and to keep your team and your hiring managers up to date on the progress you’re making with specific hires. If you’re looking for other ways to become more data driven with your approach to recruiting, it’s also helpful to take a step back and consider the bigger picture of what your data is telling you.
Want to learn which data sets to look at on a quarterly basis and how this can inform your recruiting strategy? Be sure to download our eBook, “The 5 Recruiting Key Performance Indicators.”