Every company has and uses a predictive hiring system—whether they know it or not. Let's elaborate on a concrete definition of what a predictive hiring system is...
Successful predictive hiring models require three key components: comprehensive data collection, statistical analysis of historical outcomes, and continuous model refinement. Organizations should start by identifying which candidate characteristics actually correlate with job success in their specific context. This involves analyzing past hiring decisions, employee performance data, and retention patterns to build evidence-based predictive hiring models rather than relying on assumptions or traditional hiring practices.
Modern predictive hiring assessments have expanded beyond traditional criteria to include more sophisticated evaluation methods. Some less popular characteristics that are starting to gain traction include:
Now let's look at how this all works together in a common hiring situation.
Beth is a recruiter at Acme, INC. She needs to hire an entry-level sales rep in the next 30 days for one of the teams she supports. From here, she has a standard process to fill the position that might look something like this.
This is the system or process that Beth uses to find and hire the "best" candidate for the job.
To start, she has to understand what the "best" candidate looks like, so she talks with Jane (the hiring manager) and asks: "So what are you looking for in this sales rep position?"
Jane replies with a list of characteristics that Jane believes will lead to the "best" candidate. The list looks something like this:
"I need someone with ...
Beth writes these characteristics down and starts sourcing candidates based on the characteristics she received from Jane. As candidates start applying, Beth screens their resumes against the list of characteristics and accepts candidates to interview that "look good on paper."
The candidates are brought in and interviewed by Jane and others who ask additional questions to assess characteristics of candidates. They might say "Tell me about a time you were in a stressful work situation and how did you handle it?" to gauge how well this person can handle stress.
Once candidates are all interviewed, all the data is gathered in one place, often a conference room table, where the hiring manager and team review resumes, interview notes, and interviewer feedback to determine who is the "best fit" for the job.
This conversation illustrates how most predictive hiring tools rely on subjective manager input rather than data-driven insights. Jane replies with a list of characteristics that Jane believes will lead to the "best" candidate.
This predictive hiring process represents a typical workflow used by most organizations. This is the system or process that Beth uses to find and hire the "best" candidate for the job.
With all the data gathered on the final candidates and the stakeholders in the room, everyone has a chance to voice his or her prediction about the candidate and what data was used to make that prediction.
"I think Sally is the right person for this job. She has 2 years of sales experience, has been in some stressful situation at work, and I think she would work well on the team."
"My concern with Sally is that while she has sales experience it's B2C sales and we need B2b. I think Greg is our guy. He did great in the interview, has 1 year of B2B experience, and can start immediately."
This goes on until Jane, the hiring manager, takes in all the data from the team and then ultimately makes her own prediction about who will be the best fit for the role.
There are several common problems with the manual approach laid out in this example. Here are a few...