Hiring the right people allows your business to thrive, but when hiring goes wrong, the consequences can be steep.
According to Harvard Business Review, 80% of employee turnover is caused by poor hiring decisions. Plus, the average cost of a bad hire is USD 17,000, with some estimates reaching as high as USD 18,700 when you factor in recruitment, training, and lost output.
With stakes this high, it’s not surprising that more and more companies rely on data-driven recruiting to find the right candidates.
Let’s break down exactly what data-driven hiring is, explore some real-life examples, and share best practices to help your business hire the right people.
What is Data-driven Recruiting?
Data-driven recruiting is all about using measurable insights to make objective, evidence-based hiring decisions.
Instead of relying solely on resumes, cover letters, gut feelings, or checklists, data-driven hiring uses a variety of criteria and analytics to optimize every step of the recruitment process. This includes tracking metrics like time to hire, source of hire, candidate quality, and even employee retention.
Analyzing data from hiring efforts enables talent acquisition teams to identify what’s working and what’s not, resulting in better hires and reduced turnover. In fact, 64% of talent professionals say data-driven recruiting improves retention and hiring quality, according to LinkedIn’s Global Recruiting Trends Report.
In short, data-driven recruiting gives your hiring team the information they need to find the right people more efficiently.
Data-driven recruitment examples
Here’s what using data to improve hiring looks like in action.
- Optimizing job postings: Analyze data from previous job ads to determine which headlines and keywords brought in the most qualified applicants.
- Identifying the best places to hire: By monitoring where the best candidates come from — such as job boards, LinkedIn, or employee referrals — you can allocate recruitment budgets more effectively.
- Reducing hiring bias: Data-driven recruitment relies on using consistent structured interviews where each candidate is asked the same question and scored in the same way, making it easier to compare candidates fairly.
- Improving the time and cost of hiring. Tracking these two important metrics over the long-term makes it possible for HR teams to identify parts of the hiring process that need improvements. For example, they may discover that engineering candidates take longer or are more costly to hire compared with other roles, and they can use available data to determine why this is.
Why You Need Data-driven Recruiting
Whether you're a small, growing startup expanding into new markets or an established company managing a global team, data-driven hiring offers many advantages, including the following:
1. Efficiency
International hiring comes with added layers of complexity, such as compliance with local labor laws, language and cultural differences, time zones, and more. A data-driven hiring approach to global hires can streamline this process by helping you focus only on the candidates who match the profile of high performers in your company.
In fact, data-driven talent acquisition teams are twice as likely to improve their recruiting process. And with the help of recruiting analytics, you can cut down on unnecessary interviews and prioritize the most relevant candidates — regardless of geography.
2. Cost Savings
Hiring mistakes are costly — the average cost of a bad hire is USD 17,000, as we noted above. And a data-driven hiring process can help you avoid bringing on the wrong person by using evidence-based methods to predict candidate success.
Data-driven recruiting also reduces turnover-related expenses, such as onboarding, training, and lost productivity.
Learn more about the costs of hiring internationally.
3. Scalability
As businesses scale and expand into new countries, hiring needs often fluctuate, but a data-driven approach to hiring makes it easier to scale recruiting efforts up or down, depending on your needs.
This is especially easy if you partner with an Employer of Record (EOR) with access to international hiring data because you’ll be able to evaluate candidate availability and cost-efficiency in real time. This allows you to make informed hiring decisions without setting up a legal entity in each country.
4. Diversity and Inclusion
Traditional hiring methods often suffer from bias, such as ageism, racism, or sexism, but data-driven recruiting makes it easier to identify patterns of bias in the hiring process and take action to improve them.
Employing data-backed practices like structured interviews, blind screening tools, and standardized assessments can improve fairness and transparency and increase diversity — and that can affect your bottom line.
In fact, research from McKinsey found that companies in the top quarter for ethnic and gender diversity are 25-36% more likely to outperform their peers financially.
5. Competitive Advantage
Top talent is always in demand, especially in fields like tech. And in such competitive landscapes where speed to market and innovation are crucial to winning, hiring the right talent is one way to guarantee you get a leg up on the competition.
Research shows that companies using data-driven hiring practices like predictive hiring analytics report a 20% improvement in quality of hire.
6. Alignment with Business Goals
“The fastest hire isn’t the best hire. And the cheapest hire isn’t the best hire. It’s all about the result — the business impact,” says Ross Baron, Head of Recruiting for Western Europe at Tiktok.
This means that a successful hire is one whose contributions help the company achieve its goals, and nothing helps with this like data-driven hiring.
Data-driven hiring ensures that recruitment efforts align closely with the company's strategic objectives. This alignment is a critical component of a company’s success as it enables the organization to build a workforce that’s highly skilled and strategically positioned to achieve its goals.
Learn more about hiring a remote team across borders.
How Do You Gather Recruiting Data?
The first step in gathering recruiting data is to identify what you want to measure, whether that’s source of hire, time to hire, cost per hire, diversity metrics, or any number of important information.
Once you know what metrics are important to your company, you need to determine where that data lives — and how to collect it consistently.
There are a variety of application tracking systems available to help you collect and analyze recruiting data, including Greenhouse, Lever, and Workable.
These platforms track everything from how long a job stays open to how many candidates make it through each stage of the hiring funnel. They also help record candidate touchpoints, such as where applicants came from, how they performed in interviews, and why they were hired or rejected.
In addition to tools like these, you can also gather this information from a human resource information system, candidate surveys you conduct, interview feedback forms, and more. Your HR team may also have a wealth of post-hire data, including retention rates and performance reviews, that can offer useful insights into the results of hiring decisions.
This is all valuable in-house data, but many companies also have social-listening tools like Brandwatch, Hootsuite, and Sprout Social to monitor online conversations about their brands, identify talent trends, and engage candidates. Companies can then use this qualitative data to refine their hiring practices.
For business expanding globally, an Employer of Record, or EOR, is another option for gathering recruiting data. EORs often have insights into local labor markets, compensation benchmarks, and talent availability.
And because they manage so many parts of the employment lifecycle — including hiring, onboarding, payroll, and benefits — they generate a lot of data that can help evaluate the success of international hires. They can also help identify and hire top talent in the future, especially since many EOR platforms integrate with HR and applicant tracking systems.
Best Practices to Launch a Data-Driven Recruitment Strategy
Now let’s take a look at the steps you can take to bring data-driven hiring to your company.
1. Use predictive analysis for candidate screening.
Rather than sifting through resumes and conducting endless interviews, harness the predictive power of data to identify the most promising candidates.
Here’s how it works: Predictive analytics leverages historical data to predict a candidate’s chances of succeeding at your company. It does this by analyzing the candidate's qualifications, skills, and past performance within the context of your industry and company needs.
This makes it easy to identify candidates who aren’t only great on paper but are also likely to excel in specific roles.
2. Conduct structured Interviews.
A data-driven recruitment strategy thrives on structure, especially when it comes to evaluating candidates fairly, and one of the most effective ways to do this is through structured interviews.
Here’s how it works: Instead of asking different questions to different candidates, use a standardized set of questions delivered in the same order. Pair this with a clear scoring system to evaluate answers, and you’ll ensure every applicant is measured against the same criteria.
This approach doesn’t only reduce bias, but it also provides you with valuable data to improve hiring practices over time. For instance, you can analyze interview responses against employee performance data to determine which questions most accurately predict success in the role.
Check out these tips for interviewing international candidates.
3. Design a skills assessment.
Did you know that 78% of job applicants misrepresent or think about misrepresenting themselves on their applications and resumes? With that in mind, it’s a good idea to implement a data-driven skills assessment to vet candidates.
Skills assessments involve tests or exercises designed to gauge a candidate’s competence by evaluating their knowledge and ability to execute core tasks related to their role. For instance, a data-driven skills assessment for a software developer might involve coding challenges or debugging exercises.
The results from such an assessment provide concrete data on a candidate's coding proficiency, problem-solving skills, and ability to work under pressure.
4. Improve diversity and inclusion metrics.
Creating a culture of data-driven hiring also involves tracking and analyzing data related to diversity metrics at various stages of recruitment. These metrics might consist of information on gender, race, ethnicity, age, and other relevant characteristics.
By tracking diversity metrics, you get a clear and quantifiable view of the diversity landscape within your candidate pool. This allows you to understand the composition of applicants, interviewees, and the individuals you hire.
Over time, analyzing these metrics can help you spot possible biases in your hiring process, which you can correct to create a more inclusive hiring system. For instance, you might notice that older applicants are less likely to progress through the hiring process, which might signal age-related bias.
You can then investigate and correct this by implementing strategies ranging from assembling a diverse panel of interviewers to reviewing job descriptions for language that may discourage older applicants.
5. Employ post-hire performance tracking.
Post-hire performance tracking is one of the pillars of data-driven hiring because it provides an understanding of how hiring decisions translate into on-the-job descriptions. You then use these insights to reshape your hiring process for better results.
For example, if candidates from a particular source (e.g., a specific job board or university) consistently perform well, it might be worth investing more in that talent pipeline.
Post-hire performance tracking transforms data into actionable knowledge, allowing companies to refine their hiring criteria and fine-tune their processes. It also aligns their talent acquisition strategies with evolving business needs.
Use Data-Driven Recruiting to Hire Internationally
According to Career Builder research, 74% of employers have hired the wrong person for a position, and this eye-opening figure underscores the importance of applying data-driven metrics when assembling a team — whether at home or abroad.
Data-driven recruiting provides the insights you need to make smart, scalable, and efficient hiring decisions that are backed by evidence, and an EOR like RemoFirst has the experience and data you need to employ and manage employees as your company expands globally.
We allow companies to employ workers in 185+ countries and to manage and pay contractors in 150+ countries — and we have the data-backed approach to international recruitment that you need.
Schedule a demo to learn more.
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