Traditional recruitment methods often rely on subjective factors and intuition and require a lot of manual work, time, and resources. By leveraging data analytics tools, companies can improve the efficiency, cost, and quality of the hiring process. Ultimately, this helps them build a high-performing workforce and stay ahead of the competition.
This article aims to guide business owners and hiring managers new to data-driven recruitment. It illustrates the steps and best practices they need to apply via a case study of Workwolf, a data-driven hiring platform.
Table of Contents:
- What Is Data-Driven Recruitment?
- Benefits of Data-Driven Recruitment
- How to Incorporate Data into Hiring
- Data-Driven Recruitment: Next Steps
What Is Data-Driven Recruitment?
Data-driven recruitment uses data and analytics to make informed hiring decisions, reduce bias, and improve the quality and efficiency of the process. It goes beyond the sourcing and hiring of candidates.
Data-driven recruiters track various metrics to evaluate past and improve future performance. By constantly collecting and analyzing data, they eliminate guesswork and make the process more effective.
Benefits of Data-Driven Recruitment
The benefits of a data-based hiring approach are numerous.
- Quicker time-to-hire: Automating parts of the recruitment process will help you find suitable candidates much faster. Plus, it saves your HR team hours of manual work and frees up time for other activities.
- Lower costs-per-hire: Data-driven recruiting may require a significant upfront investment, but ultimately, it saves a lot by making the process quicker and more efficient.
- Reduced bias in hiring decisions: Every hiring decision is subjective, but the negative impact can be reduced based on data-driven insights.
- Identifying hiring issues: Tracking and analyzing key recruitment metrics over time allows us to identify areas for improvement.
- Establish and monitor compliance with best practices for recruitment: The continuous improvement of your hiring process is possible only through rigorous data analysis.
How to Incorporate Data into Hiring
Transforming your entire recruitment process and transitioning to data-driven recruitment may initially seem overwhelming. That’s why we focus on the basic steps of recruitment. We describe optimizing them using data and analytics, what you must do in-house, and how to leverage third-party solutions to facilitate your efforts.
Step 1: Define your goals.
Before you begin looking for candidates, determine your recruitment KPIs. Consider your current recruitment procedures and joint issues and identify the weak points in your process.
What do you aim to achieve with a data-driven approach? Do you want to improve hiring decisions' quality and efficiency or reduce the manual work, cost, and time-to-hire?
Focus on a few objectives and measure your performance on each component before applying the data-driven recruiting best practices. This will allow you to assess your progress afterward.
Step 2: Define the job requirements.
Next, create your ideal candidate profile. Do you seek someone with an analytical mind? Are excellent communication skills crucial for the job? Do you want your new employee to be independent or team-oriented? Define how to assess a role’s skills, qualifications, and characteristics.
If you’re unsure which characteristics make someone a good fit for your team and the role, you can ask your top performers to take Workwolf’s psychometric assessment Packfinder. This will allow you to benchmark candidates against your top employers during selection.
More importantly, it will objectively measure applicants’ suitability and reduce bias. In addition, it will allow you to track and monitor your recruitment process, identify pitfalls, assess the quality of hires, improve your procedures, and track progress.
Step 3: Develop a sourcing strategy.
There are various ways to look for employers: posting job ads, using social media to advertise and contact candidates, employing referral programs, etc.
Workwolf offers another data-driven solution. You can upload your job postings and advertise on several job boards simultaneously. The software then provides a list of candidates and a match score indicating their suitability for the job. It calculates the score by analyzing your job ad and applicants’ profiles—an efficient usage of analytics in recruitment and a time-saver.
Step 4: Screen the candidates.
This stage of the recruitment process—going through all submissions, analyzing them, and selecting suitable candidates—is tedious and time-consuming. Using screening software is the best way to reduce the time-to-hire.
Workwolf automates and improves the screening process using data science and analytics. With its help, you can proceed directly to the next stage with suitable candidates instead of going through thousands of applications.
Additionally, you can compare applicants to your current employees by inviting both parties to take Workwolf’s psychometric test. This will improve the quality of hires significantly.
More importantly, this stage is vulnerable to bias, and Workwolf’s technology eliminates both conscious and subconscious hiring biases by automating the applicant filtration process. The platform allows you to filter candidates based on their pre-existing education, skills, and qualification benchmarks created using psychometric data from over 30 million professionals with different socio-economic backgrounds, ethnicities, ages, experiences, incomes, etc.
Moreover, the technology can hide the applicants’ name, address, gender, background, experience, and employment record, leaving employers with only a psychometric profile result that predicts the candidates’ performance potential and fit for the job.
Step 5: Know the limitations of data.
Data-driven recruitment may reduce hiring bias but opens up other vulnerabilities, especially when working with third-party solutions. Privacy and security should be top priorities.
Workwolf uses end-to-end encryption to retrieve data and allows employers and candidates to control information transfer by granting, denying, or revoking access to information. It relies on blockchain technology to ensure the security and integrity of data during the recruitment process.
In addition, you must ensure that the provided information is reliable. Verifying your candidates’ credentials can be expensive and time-consuming, but it reduces the risk of mis-hires. And for some professions, checking criminal records and verifying records of employment and qualifications is a must.
Workwolf’s databases include verified candidates. And if you’re recruiting via other hiring sources, you can still use its background check services.
Step 6: Track your progress.
To complete the recruitment cycle, return to step 1: recruitment KPIs. Data-driven recruitment is an ongoing process, not a one-time procedure. Evaluate performance on every component and measure your progress toward your goals. Do it continuously to assess your strategies’ success and identify new areas for improvement.
You can apply HR analytics even when you aren’t recruiting. For example, employee retention rates, satisfaction, and productivity are linked to organizational performance. Measuring them will help you identify areas for improvement and positively impact the entire business.
Data-Driven Recruitment: Next Steps
Once you’ve covered the 101 of data-driven recruitment and feel comfortable with the approach, consider hiring an in-house data analyst. Better yet, educate your team on using analytics for recruitment.
365 for Business offers corporate data science and data analytics training. Upskill your recruitment team with beginner-friendly courses and become a data-driven organization. Request a free demo and try our program for free.
