Recruitment is often seen as a form of qualitative decision-making, but implementing clear measurables can improve outcomes. Data-driven hiring uses metrics and analytics to make unbiased, well-informed, and, above all, efficient hiring decisions. It reduces subjectivity and may result in better hiring outcomes. 

At the same time, the human element still has a role. Data-driven recruitment does much of the heavy lifting in candidate selection, but human resource professionals use their interpersonal skills and critical thinking to monitor and guide the process.

Where to use data in the hiring process

The use of data analysis for recruitment spans different phases of the recruiting process, with human oversight and intervention at critical points. Areas where you might leverage data include:

Candidate sourcing

When searching for candidates, you can use programmatic advertising to spread the word. Data shows how well the strategy is working, allowing you to readjust the parameters you are using when selecting platforms for job advertising. 

You can also leverage data when reviewing your existing talent database. Perhaps you already have saved the details of previous applicants who may be desirable candidates. Data analysis helps you to identify the people you should reach out to. 

For a deeper look at how AI enhances programmatic recruitment strategies, read our article on AI and programmatic advertising.

Resume screening

If your recruitment drive has resulted in an avalanche of applications, you need to comb through them to find people with the right skills for the role you are working to fill. Data analysis allows you to identify your top applicants. Use your experience as an HR professional to evaluate results and adjust your analytics parameters as needed. 

Analysing preliminary interview data

Preliminary interviews help recruiters determine whether candidates are truly interested in an advertised post. For example, people may apply at random without considering factors like whether they would be willing to relocate if they were to be appointed. 

Preliminary interviews can be tailored to identify potential deal-makers and deal-breakers. Whether they are automated or in-person, the data you gather can be analysed to show you who the best-fit candidates may be.

Gauging candidate experience

Candidate experiences will affect your reputation as a recruiter or employer. Ideally, all candidates should feel valued, even if they were unsuccessful. Data gathered from surveys can help you identify areas for improvement. Since surveys and analysis of results can be automated, you can gain valuable insights in return for little effort.

Post-hire evaluation

Data analysis for recruitment is only as good as the parameters implemented in candidate shortlisting. Find out whether there is a correlation between the data points analysed and successful candidates. Identify the data features that successful candidates share. This allows you to fine-tune recruitment data analysis to achieve even better results in the future. 

Key metrics to track

Apart from using data analysis to help you recruit the right people, you can use it to evaluate how data-driven recruiting impacts your own efficiency. Metrics you might implement include time to hire, effectiveness in sourcing candidates, successful hires, and evaluation of candidate experience.

Your aim is to find highly suitable candidates as quickly as possible. If you are leveraging data effectively, you should be able to reduce the average time to hire while cutting recruitment costs, finding the right people to fill roles, and offering excellent candidate experiences.

Implementing data analytics in the recruitment process

With the right tools to help you, implementing data analysis in recruitment should be relatively straightforward. The results you achieve when advertising posts will be among the first to come under the microscope.

Choose tools that allow you to examine conversion rates to see how many applicants you are gaining. Analyse your talent pool. Most roles should attract suitable applicants across demographics. If you are not attracting a broad demographic, you may need to adjust your advertising strategy.

Besides ensuring that you are attracting an extensive pool of candidates, you should assess their potential suitability for the role. Your data analysis tools should be compatible with your Applicant Tracking System (ATS).

Using simple analytics dashboards, you should be able to set parameters for data analysis and achieve instant results. However, your role as a recruiter cannot be fully automated, so be sure to check them and search for anomalies that may indicate a need for adjustment.

The result should be a shortlist of candidates well worth any hiring manager’s attention. Once again, you should consider how you are using data and implement adjustments if necessary.

Benefits of data-driven decision-making

Human decisions are prone to bias, shaped by emotion and experience. Data helps counter this by offering objective, fact-based insights that lead to better hiring outcomes.

When analysing data, we can gain a fact-based analysis of the results our strategies achieve. This helps us to adjust them to achieve better results. In data-driven hiring, this type of analysis and adjustment model can be implemented throughout the recruitment process. 

Apart from this, using data makes us more efficient, particularly when we can use technology to interpret it. For example, it can take days to sort through resumes, and fatigue plays a role in how we select or reject candidates. Data analysis tools can turn this lengthy process into a near-instantaneous action. All that remains for recruiters to do is to check results and search for new ways to use data if results seem in need of improvement. 

Finally, using data allows recruiters to improve and innovate as they go. The changes they make are driven by results and data. They can test and refine their ideas quickly instead of adopting a wait-and-see approach. 

Overcoming challenges in data-driven recruitment

Data analysis is a tool, and how well it works depends on how it is used. Human oversight remains essential to achieving meaningful outcomes in data-driven decision-making. Overcoming challenges in data-driven recruitment is still an entirely human task that allows recruiters to implement their unique skills and judgement. 

For example:

• Poor–quality or incorrectly formatted data can lead to false conclusions. The symptoms can be identified in the results you achieve, and the solution lies in effective data management
• Over-reliance on data can lead to sub-par outcomes. Balance data against intuition, but also consider your intuitive reactions and self-regulate against bias
• Integrating data into HR systems can pose a challenge. The compatibility of your data analysis tools with your ATS will be a key consideration
• Data and algorithms can be biased. When using tech tools, look for evidence of bias and take steps to mitigate it. For example, the University of Melbourne found that its AI tools were downgrading women and applicants who had recently taken parental leave. 

No matter how much easier leveraging data makes your role as a recruiter, never forget that your skills are irreplaceable. Use them to create synergy between human skills and data analysis.

Success stories in data-driven hiring

Overcoming challenges and using technology to complement human skills can lead to success. Technology, when coupled with human oversight, has repeatedly proven its worth in talent acquisition and data-driven hiring.

Saving time and money while reaching the right candidates

Data-based advertising strategies can be extremely efficient. A Broadbean client typifies the results we can achieve with programmatic advertising. Our client reports reduced advertising costs, better talent attraction, and up to 30 minutes of time savings per advertisement posted. 

Reducing time to hire and cost per hire

It is no surprise that tech companies have been among the first to implement data-driven hiring. Big-name tech firms are reporting up to 30 percent reduction in time to hire and as much as 40 percent reduction in cost per hire.

Benefitting from a wide range of demographics

Employing people from a variety of backgrounds allows firms to benefit from a wider range of perspectives. Case studies show that companies are acquiring people who match a wider range of demographic profiles by implementing data-driven candidate screening systems. 

Achieving successful hires while boosting employee retention

Another prominent firm, faced with a high volume of applicants and expressions of interest, says it has successfully used data to predict candidate success. Apart from reducing time to hire, this led to a 30 percent increase in employee retention. 

Leveraging technology for data-driven recruitment

If you are investigating data-driven recruitment in the hope of benefitting from the many advantages it can offer, you are far from alone. However, you may find yourself considering a dizzying array of tools offered by a wide range of companies.

When selecting tools, focus on proven effectiveness, system compatibility, and overall ROI. Broadbean began as a programmatic advertising specialist and has since expanded to offer a unified suite of recruitment analytics and automation tools, designed to integrate seamlessly with your ATS and workflows.

With trusted partners and a focus on measurable outcomes, Broadbean provides the infrastructure needed to turn data into action. Explore how our connected ecosystem can support your recruitment goals — request a demo today.