Data-Driven Recruiting: Benefits, Process, And Best Practices
Someone wise once said, “In God we trust, everyone else brings data”. In today’s world, with all the tools at our disposal, there’s no excuse for not using data to improve the recruitment function.
Use this guide to help you start your journey toward data-driven recruitment.
What Is Data-Driven Recruitment?
Data-driven recruitment is an approach to hiring that leverages metrics and recruitment analytics to inform and improve the recruitment process.
By using data, companies can make more informed decisions to improve all aspects of the hiring process, from sourcing candidates to guiding the training and development of team members.
5 Benefits Of Data-Driven Recruitment
Aside from the obvious benefits of making any audit of recruiting processes easier, here are some other ways data-driven recruitment will help your organization.
1. More efficient hiring process
Metrics such as recruitment funnel speed will help you identify any bottlenecks in your recruitment process and increase efficiency.
For example, you might identify that it’s taking too long for hiring teams to give interview feedback and this is causing candidates to disengage.
You can also keep track of data such as sourcing or marketing channel effectiveness (number and quality of candidates) to concentrate your resources on the most fruitful activities and lower costs.
For example, at my last company, there were a lot of platforms and job sites to choose from but my budget was not infinite and neither was the hiring team’s capacity to interview all the candidates.
So, I looked at the candidates we hired and candidates that went to the final interview (which was the culture interview) to recognize which platforms yielded the technically strongest candidates.
That was the baseline the hiring team and I decided to go for as things like cultural and behavioral fit are something that only our interview process can assess.
2. Better candidate experience
A more efficient hiring process will result in a better candidate experience. Also, data can be qualitative so collecting candidate feedback is a useful way to help improve the hiring process and impress candidates.
When I was recruiting at Twitch, for example, we took our hiring process efficiency very seriously, especially as we were translating US-focused hiring practices into the EMEA and APAC regions.
I delved into the data which showed that in EMEA candidates are less open to the uber-fast-paced style of hiring characterizing the US market because they have longer notice periods.
To them, the process we were trying to run for 1-2 weeks was way too rushed and not respective of their time and circumstances.
Had I not run NPS surveys for quant data and interviews for some more in-depth qualitative data, I wouldn’t have known why a few candidates we were really keen on pulled out (we ended up re-engaging them and hired two of those cases!).
3. Mitigating hiring bias and making more objective hiring decisions
We all have biases, including our new AI colleagues. For example, skills tests, work sample assessments, and structured interviews can be standardized across all candidates, reducing the influence of subjective opinions.
Data can also help identify the most effective channels for sourcing diverse candidates.
For example, I remember one situation where we had an 80% drop off of candidates between the first and the second stage.
After reviewing the interview feedback vs job requirements, we discovered that the first interview was run by one of the Finance managers and the second by the actual hiring manager who was the VP of finance.
The Finance managers kept putting people through who had the exact same profile (same school, qualifications, and similar companies) as them, but the role required a different set of skills which were not assessed at all.
This uncovered the familiarity bias this interviewer was operating under, which was news even to them!
Watch out for patterns like these because they can turn your diverse top-of-funnel into a completely homogeneous end-of-funnel.
4. Better planning
Looking at benchmark metrics such as average turnover rate and average number of applicants needed to fill a certain role helps with hiring planning and allocating resources.
The old adage of you having to screen 10 candidates to get one hire is a great start, but if you have more specific or senior roles that may be closer to 20 candidates screened per hire and even more.
In my current company, I screened 25 candidates to make one very specialized Staff engineer hire and now I know that this is the new benchmark should we need to replace or hire another person of this seniority.
5. Improve employee experience
Data that you gather from candidates can help improve the employee experience for existing talent. For example, are candidates put off by the lack of flexible working options, and might this also be impacting turnover as well?
How To Incorporate Data Into Your Hiring In 3 Steps
1. Choose the right data and metrics
Start by determining the key metrics and key performance indicators (KPIs) that are relevant to your hiring goals.
You can get as granular as you want here but, as I highlight in my article on recruitment metrics, it’s best to start with a few key metrics and then start to dig into more advanced ones as your capabilities develop.
Key metrics include:
- Time-to-Hire: The average time it takes to fill a position.
- Number of hires (vs. number of open positions): How many hires you make per month/quarter/year vs your headcount plan.
- Conversion ratio: How many candidates pass each stage vs how many candidates progressed to the stage.
- Recruitment funnel speed: How long candidates spend in each stage of the funnel.
- Cost-per-hire: The total cost associated with hiring a new employee.
- Source of hire: The effectiveness of different recruitment channels (job boards, social media, referrals, etc.).
All of these metrics exist relative to each other. I like to start with the conversion ratio because it gives me an idea of which other metrics to monitor more closely to uncover the full story.
For example, if the conversion ratio is strong but there's still a long time to hire, then look at the number of candidates. Is the top of the funnel strong enough? Is the recruitment funnel speed fast enough?
If the top of the funnel isn’t strong enough (like in many seed-stage startups including mine), then that should be the main priority as the funnel speed may be immaterial if there aren’t candidates to push through.
2. Decide how to collect data
Next up it’s deciding how you’re going to collect your data and analyze it. In reality, you’re going to need some form of recruitment analytics software for this.
It’s likely your applicant tracking system (ATS) is where the most recruitment data is collected and many solutions come with predesigned, often customizable, recruitment dashboards to help present data.
Another useful ATS feature is opt-ins like creating a candidate survey for NPS or capturing candidate feedback.
You can combine the data collected in your ATS with data from your HRMS to provide a fuller picture e.g. how many candidates from a certain recruiter are failing probation or go on to be high performers?
3. Monitor and optimize
Keep track of your recruitment metrics and KPIs to assess the effectiveness of your hiring process.
Use the tools to highlight trends and patterns in the data to identify areas for improvement. For example, if your time-to-hire is longer than desired, you delve deeper and figure out how you can streamline certain stages.
4 Data-Driven Recruitment Best Practices
1. Develop clear metrics
A lot of people get confused between the Time to Fill and Time to Hire metrics. Technically speaking, Time to Fill is the period between you opening a position and hiring someone.
Time to Hire is the time it took for the specific candidate you hired to move from application to hire.
This demonstrates the need to always clearly define metrics, as well as how it’s calculated and what your data sources are.
2. Use different types of data
I touched on this above but wanted to reiterate again. Most of the data you’ll be using is quantitative, meaning that it can be represented numerically.
However, don’t neglect qualitative data such as feedback from candidates or hiring teams. This is just as valuable and can lead to improvements that benefit retention such as improved total compensation.
As Sarah Lovelace, VP of People at Airbase, emphasizes, “By approaching talent management with a blend of data and analysis, we can make informed decisions and effectively communicate them to the broader organization.”
3. Present data in a meaningful way
Data visualization is an essential part of data-driven recruiting. When presenting data to drive action, it’s important to think carefully about the data and also the conversation you’re trying to prompt with your audience. This is where creating internal personas comes into play.
Personas are used by marketing teams to paint a picture of their target customers when building out marketing campaigns, and they’re a useful tool for data visualization too.
For example, not everyone needs to know everything, and each stakeholder will require different levels of help to analyze what’s being presented to them.
An example might be the difference between the kind of data you’re presenting to a hiring manager to improve a certain aspect of the hiring process vs a member of the C-Suite to get the OK for a new recruiting software tool.
For further guidance here, I highly recommend Liam Reese’s excellent article on HR data analytics.
4. Keep good data hygiene
You need good-quality data that represents reality. I once had an interim consultant “close off” all the roles that were previously open and not marked as hired and claimed that they placed 80 people over the course of 4 months… in a company of 90 people.
Had the teams previously closed off the roles when they were indeed closed, it would have reflected reality better.
For reasons such as this, I recommend implementing some data best practices to maintain high quality.
The best way to do this is to follow up regularly and check in with your team and the hiring managers to help them build the habit of entering and updating data.
For example, to make sure screening calls are entered into the system set a KPI around them and check in regularly.
If you want to make sure that hiring managers enter their feedback, check in after every interview and read that it is good quality feedback.
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