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Data-driven recruiting: What it is and how it’s done

Data-driven recruiting is becoming one of the buzzwords of the recruiting world.

But… what is it? And, more importantly, how do you implement it at your organization without feeling overwhelmed?

This post dives into the four steps of the recruiting process and how you can use data to improve each one along the way.

What is Data-Driven Recruiting?

Data-driven recruiting is an approach to recruiting new talent where hiring decisions are based on specific, measurable job-relevant skills and criteria in a consistent manner. This approach stands in contrast to how much recruiting is done today: using resumes as a proxy for skill, using inconsistent criteria, and opening the door for bias. to creep in.

Many top organizations like Uber, Robinhood, and Facebook have adopted data-driven recruiting to go beyond resumes in their hiring processes and consistently identify top candidates based on skills, not pedigree of education or past employers.

Benefits of Data-Driven Recruiting

A few of the benefits of data-driven recruiting for Talent Acquisition and hiring teams are:

  • Reducing bias in the talent selection process
  • Increasing diversity in recruiting funnel
  • Optimizing recruiting processes for key metrics like time-to-fill
  • Improving quality of hire

The Data-Driven Recruitment Process

Step 1: Engagement

At the top of the funnel it’s about engaging applicants. How do you even get people in your funnel: inbound and outbound. Inbound is casting a wide net while outbound is spearfishing. When it comes to the top of funnel, you need to have data to know where to focus your efforts. 

Some of the things you should measure at the top of the hiring funnel include:

  • How many total applicants applied to each job
  • What job boards are bringing the highest quality of applicants
  • What recruiters or sourcers are bringing the best candidates

This will help you decide whether to focus on inbound or outbound recruiting. It’s not always mutually exclusive, but you want to ramp up the best channels first to maximize your ROI. 

Step 2: Evaluation

Once you have someone engaged, then it becomes the evaluation stage. When we talk about evaluation, we definitely don’t mean resume evaluation. Resumes can introduce bias into your hiring process and have you focus on skill proxies rather than actual skills. That’s why we always recommend implementing an objective framework-based assessment as the very first step after an applicant shows interest in your company. 

Many organizations don’t do assessments or technical interviews until late in the hiring funnel. Why would you spend time interviewing folks who aren’t actually qualified? Instead, implement data-driven recruiting into your evaluation stage early!

You also don’t have to just have one assessment. Implement a baseline assessment as the first step of your hiring process but also take the time to craft logical, skill-based questions for the phone and in-person interviews. You want to make sure you have a consistent hiring process where you’re asking all candidates the same thing. Otherwise, you’re injecting potential bias into the process which could catch up with you in the long run. 

Tips for Drafting Interview Questions

When it comes to the interview stage of the hiring process, it is important to avoid bias that can impact the objective nature of data-driven recruitment. Here are some best practices for drafting data-driven interview questions:

DO: Learn about bias in interview questions. Culturally-specific examples in coding tasks, for instance, may confuse candidates who hold the necessary job-relevant skills but don’t know your specific reference.

DO: Develop tasks specifically designed for the role you’re interviewing for. Realistic interview questions provide the best signal of a candidate’s job-relevant skills for interviewers, while also creating a positive candidate experience.

DO: Ensure consistency with structured interviews. This requires pre-interview prep, defined interview questions and interviewer roles, and structured de-brief procedures.

DON’T: Ask different questions to different applicants of the same position. This prevents consistent and fair comparison across candidates.

DON’T: Speak like a robot. Interviews are an important opportunity to connect with candidates on a human level—even in highly structured interviews.

Step 3: Closing Candidates

What percentage of candidates are you closing? You should dive into this in as much detail as your data allows. Look at the offer-to-hire ratio as well as onsite-to-hire ratio. Then slice the metrics by the channel they came in. You’ll start to be able to get a sense of the which channel ultimately gives you the best return on investment. 

There are a few reasons why your close rates could be less than fantastic. Some of these include:

  • Focusing too much on pedigree (university, previous employer)
  • Using the wrong channels for recruiting
  • Not having realistic compensation expectations

Understanding the type and source of every candidate will help you double down on the areas that are working.

Step 4: Post-hire Analysis

At the end of the day, if you end up hiring people who leave quickly or perform poorly, your entire data-driven process was for nothing. This piece of data-driven recruiting tends to be overlooked because it falls in between the cracks of talent acquisition and talent retention teams. 

Don’t let these teams go down the acquisition versus retention rabbit hole. Instead, create a collaborative data-driven process that will benefit both teams by understanding the traits of outstanding employees and how you can identify them from the hiring process.

Leverage Data in Recruiting with CodeSignal

Ready to learn more about how CodeSignal can help you transform your technical hiring processes to be more data-driven? Schedule a demo with one of our assessment and interviewing experts.

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