Picture this: You spend countless hours scouring candidates’ resumes, drawing up a shortlist, interviewing applicants, and coming up with a few outstanding potential hires.
Despite all your best efforts, subjectivity can influence your decision-making – and you end up with a bad hire.
But thanks to HR technology, you no longer need a crystal ball to find the best candidate.
A data driven recruiting strategy, used in tandem with talent assessments, removes the guesswork and empowers you to hire objectively and eliminate bias from your hiring process.
In this article, we cover what data driven recruiting is, how it can benefit your business, and seven best practices to leverage this method as part of your overall talent acquisition strategy.
In data driven recruiting, you collect and analyze data from your talent pool – such as their skill levels and personality traits – and use it to accurately identify the candidates with the highest potential for success.
This talent acquisition trend encourages a data-first approach throughout all the different stages of your hiring funnel, including:
Sourcing: Track data from different talent sources (i.e., job boards, career pages, or social media) to see which yield the best candidates
Selection process: Use talent assessment data to objectively evaluate applicants’ skills and find those best suited to the role
Recruitment planning: Use data to pinpoint skills gaps or weak areas in your organization to better target your recruitment efforts
Measuring hiring efficiency: Use data to gauge your efficiency level through metrics, such as cost per hire, and compare with industry standards to find areas for improvement
Data driven hiring relies on quantifiable metrics and evidence-based insights to evaluate candidate suitability and predict performance.
For instance, when hiring a software developer, the traditional hiring process focuses on what’s listed on the candidate’s resume, how they perform during an interview, and how the interviewer perceives the candidate.
Ultimately, these factors are not the best determinants of how an applicant will perform on the job.
Leveraging data driven methods, such as talent assessments, increases your chance of getting the right hire the first time.
For instance, you can use coding skills tests to evaluate job applicants and only interview candidates with the required language skills.
As you repeat this process, you generate even more data, which hiring managers and recruiters can use to gain even better insights into key hiring metrics.
Tracking hiring key performance indicators (KPIs) is the best way of deriving valuable insights into the effectiveness of your talent acquisition efforts. It's also the surest way of ensuring you get a return on your data driven recruitment investment.
However, first, you need to know which KPIs are crucial to your hiring journey.
Speed-based recruitment metrics: Speed metrics track the time to achieve certain hiring milestones like time to hire.
Quality-based recruitment metrics: These measure the overall quality of the hiring process. Quality-based metrics include interview-to-offer ratio, candidate quality per source, retention rates, and time to productivity.
Cost-based recruitment metrics: Cost-based metrics track the financial outlay aspect of the recruitment process, such as cost per hire, advertisement cost, or the most effective talent source.
Up until recently, finding applicants based on traditional hiring methods had been the norm for hiring managers and recruiters. This approach falls short on various accounts involving:
Candidates claiming to have certain skills, which you later find out they don’t have
Unconscious biases creeping into the process, such as favoring candidates from certain universities or backgrounds
Lack of a standardized scoring system, leading to subjectivity in the process
Inability to track effective sourcing methods, leading to high recruitment costs
Data driven recruiting’s ability to help you identify hiring inefficiencies is one of the main reasons it’s making a lasting impact on the HR world.
By systematically analyzing data at every stage of the recruitment process, a data driven approach identifies bottlenecks like excessive time to hire, elevated hiring costs, or high dropout rates.
Organizations can then take targeted actions to streamline their hiring processes, reduce these inefficiencies, and make more informed hiring decisions.
For instance, your KPIs may reveal that although your job postings are getting a lot of views, the ratio of applications you’re receiving to those views is relatively low. This tells you that you may need to work on crafting better job descriptions.
Ditching traditional hiring methods and adopting a data driven approach to recruitment simplifies your hiring process, enhances efficiency, and ultimately proves more cost-effective.
Here are six concrete benefits of implementing data driven recruiting and HR analytics in your hiring plans:
Adopting a data driven hiring strategy can supercharge your diversity hiring by identifying potential barriers in your hiring practices.
For instance, your data might reveal that female candidates have an abnormally low success rate in your interview process. This could tell you that unconscious biases might be affecting interviews, making it more difficult for women to land the role.
You can then devise corrective measures to improve your interview process, such as creating a structured interview approach and training your interviewers on unconscious biases.
A data driven strategy also empowers you to monitor your funnel in real time to establish the types of candidates applying to join your company. If you’re attracting less diverse candidates than you’d like, you can act in good time and still meet your recruitment targets.
One company that is excelling in this data driven hiring approach is Johnson & Johnson.
The company has analyzed demographic data to identify a number of hiring gaps and has deployed several initiatives to inform a more inclusive hiring process. These include a program aimed at women in IT roles and substantial support for people with disabilities.
Data driven hiring enhances your organization’s ability to anticipate hiring needs and respond to business cycles and operating environments.
Proactive recruiting entails sourcing candidates when you don’t need them immediately but expect to need them at a later stage to fill upcoming roles or replace retiring employees.
Placing your finger on the number of candidates you require is difficult without data. There’s a real chance of overhiring and introducing a higher cost burden, or underhiring and stressing your existing workforce.
Insights from your talent acquisition analytics platform help HR managers and recruiters align their hiring plans with current business needs.
For instance, data driven recruitment tools parse historical data to create precise hiring strategies, like predicting success rates or employee turnover.
Data also gives you a clearer view of your spending. You can see if you’re overspending or not spending wisely on recruitment – whether through agencies, internal processes, or outsourcing – and then rectify it in your next hiring plan.
Every company wants to bring the best talent onto their team.
However, traditional recruiting methods frequently fall short in this category because they’re susceptible to unconscious biases and rely heavily on preconceptions rather than hard data.
Also, the allure of a candidate during an interview doesn’t always translate into stellar performance on the job.
By harnessing the power of data through, for example, a 16 Personalities test, you can see which personality types lead to the highest quality hires in your workforce.
Data from skills and personality tests provide insights into a candidate's strengths and traits and help you identify technically qualified candidates likely to excel in the organization.
A good case study is Axelerated Solutions, a Saudi Arabian IT & business consulting company that relied on manual resume screening for job candidates.
After screening, the company would then schedule multiple interviews with shortlisted candidates. Because manual screening was slow and inefficient, too many candidates would get moved forward, resulting in a long interviewing process and drawn-out time to hire.
Axelerated shifted to TestGorilla to automate the screening, testing, and scoring process. The organization only interviewed the most qualified candidates instead of investing time in individuals who weren’t the right fit.
By adopting a data driven approach to recruitment, the company saw a much higher passing rate for employees on probation, with all candidates hired using this method integrated into the company’s workforce.
One common challenge for hiring teams is estimating how long it will take to fill a job.
A data driven approach enables you to optimize your hiring process by giving you an accurate estimate of how long it takes to fill a position and helping you detect any slow spots in the process that could be sped up.
Here’s how Revolut, a global leader in the financial services industry, did it.
The company faced the challenge of recruiting for language skills, relying only on a time-consuming, expensive manual assessment.
This process also led to a negative candidate experience, with candidates waiting for a long time to get results.
Using data from language proficiency tests, Revolut automated and streamlined its hiring process, relieving the recruitment team of laborious manual processes and drastically reducing the time taken to assess candidates.
Top-notch candidates are often spoiled for choice, being inundated with recruiters and job offers.
If your hiring process is drawn out and cumbersome, these sought-after individuals may bypass your organization and move on to the next enticing opportunity.
The solution is to elevate the entire candidate experience by streamlining it, ensuring that applying for a position with your company is a breeze.
Data driven analytics shine a spotlight on the elements that influence a candidate’s experience.
Whether it’s the time from the first contact or the duration between different stages, you get a clear picture of factors either bolstering or diminishing the chances of the right candidate withdrawing their application.
Armed with this knowledge, you can craft strategies to reduce the risk of losing talent and enhance your candidate relationship management strategies.
A stellar candidate experience is especially critical in hiring Gen Z, whose lives and career preferences revolve around the internet. This generation places a premium on speedy access to information, a key plank of a great candidate experience.
Recruiters often find themselves in situations where candidates have similar qualifications, experiences, and skills, making it difficult to make a clear choice.
In such cases, relying on gut feelings or emotions may seem like the only option.
However, this approach introduces subjectivity and bias into the hiring process, leading to suboptimal decisions.
This is where data driven recruitment steps in – it goes beyond surface-level assessments and takes a more analytical approach.
By analyzing various data points – i.e., candidates' past performance, skills, and personality traits – you can find valuable insights into your hiring trends that may not be immediately apparent.
For example, you might find that candidates with self-starter personalities tend to stay longer with your company and ultimately have more success within your entrepreneurial culture.
Also, data analysis might discover that a candidate possesses the same character traits as your organization's top performers, indicating that this applicant has the potential to thrive in your company.
Adopting a data driven approach provides hiring managers and recruitment teams with the tools required to improve their decision-making during the hiring process.
Here are 7 best practices for getting the most out of your recruitment analytics:
1. Invest in the right tools
Choose the right mix of tools and software to efficiently power your data driven recruitment
2. Hire the right data experts
Rely on experts to ensure data accuracy, mitigate bias, and derive actionable insights for HR professionals
3. Collect highly relevant data
Focus on the most important metrics for every stage of the hiring process
4. Deliver a positive candidate experience
Ensure your data strategy removes inefficiencies and bottlenecks that ruin your candidates’ experience
5. Examine past hiring initiatives for insights
Look back for success stories, correlations, and practices that worked in the past, and replicate them in your hiring
6. Understand your data’s limitations
Look out for data limitations, such as historical or algorithmic bias, that can influence hiring decisions
7. Take action on your talent acquisition insights
Use data driven insights to drive a culture of continuous improvement in your recruiting efforts
To create a firm foundation for HR data analysis, start by choosing the right tools and software. The right tools depend on your current business needs, but it’s always wise to consider scalability to cater to your growing business.
Examples of tools to enhance your data driven hiring include:
Applicant tracking system (ATS): An ATS sits at the foundation of data driven recruiting. It enables you to track job applications and collect, store, and organize applicants’ information for easy access.
Skills testing platforms: Talent assessment tests help you accurately evaluate a candidate’s skills and personality to understand how they align with the open role.
Feedback and survey software: Collecting candidates’ feedback gives you the insight to identify challenges in your hiring process and further optimize it for efficiency and a better candidate experience.
Recruitment dashboard: A hiring dashboard visualizes the hiring process, making it easier for you to spot areas of improvement or where the process works effectively.
Data security and privacy tools: These tools enable you to keep applicants’ data safe and compliant with relevant data laws.
Good data tools facilitate the entire data driven recruitment process by both collecting the data for you and keeping it properly organized.
Because technology evolves, be sure to invest in frequently updated tools to avoid the costs of having to switch suppliers.
If you’re looking to get the most out of your data, having the right experts is non-negotiable.
Data experts play a pivotal role in data driven recruiting by:
Enhancing data collection and management: Data experts ensure that you’re collecting the right data rather than meaningless fluff. They also understand data privacy regulations and help organizations comply with data protection laws, ensuring that candidate data is safe.
Ensuring quality data: Data quality is essential in data driven recruiting. Data experts can implement data validation processes, clean and preprocess data, and identify and rectify issues with data accuracy and completeness.
Enabling action-oriented data analysis: Data experts are skilled at analyzing data to extract meaningful insights. They can use statistical techniques and machine learning algorithms to identify patterns and trends in candidate data, helping organizations make more informed hiring decisions.
And what better way to find a data expert than with a data driven method, such as a talent assessment – for example, the Working with Data test.
You must ensure you have the right data to make truly informed hiring decisions.
Begin by collecting data at every stage of the recruitment process, from listing the job to onboarding your new hire.
Give particular focus to your talent sources – where do you find the most potential candidates, and which sources result in the most successful hires?
For instance, analyze how many candidates found you on job boards versus those who visited your career page or those who were employee referrals.
You can monitor:
How far applicants from each source tend to move in the hiring process
How long it takes for these new hires to be productive
How long they stay with the company
This way, you can pinpoint your best recruitment channels and focus your efforts on the most successful ones.
In addition, keep an eye on the trends rather than one-off instances, which can be misleading.
Monitoring trends empowers you to see the bigger picture, avoid making panic-induced adjustments, and benchmark current initiatives against your past hiring efforts and, most importantly, against competitors and industry leaders.
Data driven hiring should ideally remove the inefficiencies of a manual hiring process.
For this reason, keep candidate experience at the front of your mind when designing and implementing a data driven hiring strategy.
Here are some ways that data driven recruiting helps with delivering a better candidate experience:
Personalized communication: Use data to tailor communication to candidates – for example, send personalized emails or messages that acknowledge their specific skills, experience, or interests.
Efficient application process: Analyze data to identify bottlenecks or pain points in your application process and streamline it accordingly.
Interviewer training: Use data to assess the effectiveness of your interviewers. Are they consistently rating candidates accurately? If not, think about a learning and development plan to get them up to speed.
Candidate feedback: Collect feedback from candidates through surveys or structured interviews to understand their experiences and improve the hiring process for future applicants.
Data driven onboarding: Extend the data driven approach to onboarding best practices by analyzing feedback and performance metrics to enhance the onboarding experience.
Examining past hiring initiatives through a data driven lens and applying the lessons learned enables you to optimize your recruitment processes.
This way, you make more informed decisions and ultimately attract and hire top talent more effectively.
In addition, analyzing data enables you to identify past initiatives that were successful and those that fell short.
The key is to look for correlations between specific strategies or practices and positive outcomes to know what worked before. Examples of correlations you can establish include:
Talent source and time to hire
Screening methods and performance
Talent source and diversity
Application method and candidate experience
For instance, if referrals are posting better performance than direct hires, you can introduce a formal employee referral program to take full advantage of this channel.
Additionally, if social media recruiting is bringing in more diverse candidates, you can double down on this channel to bring more diversity to your talent pipeline.
Your recruitment data might contain certain limitations that you need to be aware of.
Example of data limitations includes:
Bias in historical data: If historical hiring data contains biases like gender or racial bias, using that data to make future hiring decisions can perpetuate these biases.
Limited data sources: Data driven recruiting relies on the availability of data, but not all aspects of a candidate’s suitability for a role can be easily quantified or measured.
Data quality: The accuracy and completeness of data can be a major limitation. Inaccurate or outdated data can lead to incorrect hiring decisions. Data can also be incomplete, especially for passive recruiting.
Algorithmic bias: Data driven recruiting often relies on algorithms to make decisions. These algorithms can introduce their own biases, depending on the data they are trained on.
To address these limitations, take a thoughtful and balanced approach to data driven recruiting, combining data insights with human judgment.
For instance, speak directly to candidates to find out their experience in any other stage of the hiring process. This way, you can combine their insights with what your tools are telling you – resulting in a more balanced view of your organization’s efforts.
Data driven recruiting means nothing if you don’t act on the data output.
So use data driven insights to drive a culture of continuous improvement in your recruiting efforts.
Your portal could have bottlenecks if your data shows candidates leaving your online application. Identify and deal with these barriers to increase job applicants.
Look at your retention and turnover rate data, which provide a direct measure of the quality of your hiring decisions – if these metrics turn out to be concerning, perhaps it’s time to go with a new hiring approach.
You could try investing in skills-based hiring, which leads to you hiring higher-quality employees who are more likely to feel confident in their work, leading to higher job satisfaction and reduced turnover.
Lastly, it’s possible you luck out and find that all your systems and metrics are pointing in the right direction. In this case, don’t feel compelled to change anything straight away just for the sake of it – however, always keep your finger on the pulse of your data because your metrics can change quickly and catch you off guard.
It’s worth taking a moment to learn about three companies leading the way in mainstreaming a data driven approach to hiring.
Here they are:
Develops profiles of employees likely to leave the company and deploys timely and targeted interventions to minimize talent loss
Uses machine learning and artificial intelligence to help candidates find their most suitable roles
Walgreen Boots Alliance (WBA)
Relies on data to gain insights into different aspects of their process, such as employee wellness, diversity, turnover, and hiring trends.
Employee turnover caused by burnout and poor work-life balance has been a big problem in the tech industry.
To nip this problem in the bud, Microsoft leverages human resources analytics to develop profiles of employees more likely to leave the company. These profiles, derived from statistical data, tell Microsoft that direct university hires leave the company faster than other groups.
The company then uses these profiles to develop targeted interventions to improve retention rates for this cohort. Measures taken include assigning mentors to help these employees chart their career paths and enhance workplace communication.
Microsoft's approach underlines the value of collecting and segmenting hiring data, which helps them reduce attrition rates by more than half.
Amazon has used machine learning and AI in talent acquisition for a while now. This includes helping candidates find the right job, offering online assessments for flexibility, and ensuring fairness and legal compliance.
Job seekers often face the challenge of sifting through numerous job postings to find positions that match their skills, qualifications, and career goals.
Machine learning at Amazon assists these candidates by providing them with personalized recommendations and suggestions based on their unique profiles and preferences.
For instance, when Amazon interns and fresh college graduates ask about full-time positions, the system suggests roles that align with their interests, skills, and experience.
The HR department at Walgreens generates quarterly reports on the overall wellbeing of the organization.
It continuously monitors trends in its workforce, keeping close tabs on employee wellness, diversity, equity, and inclusion, and turnover rates.
HR professionals use the insight gained from this data to make informed decisions about where they should focus the company’s efforts and budget.
The Walgreens’ HR team also uses the same data tools to monitor the impact of incentives on employee retention and to identify workforce gaps in the job market.
Finally, the organization uses people analytics to manage employee schedules and assess employee performance to fully understand and optimize its workforce planning.
Talent acquisition technology gathers all your important people data in a single location, letting you track important factors, such as quality of hire, the success rate of candidates from a certain source of hire, and the overall efficiency of your hiring process.
By monitoring these trends, you no longer have to resort to trial and error to find an approach that works. You can recognize successful initiatives through historical data and build upon your past wins.
Skills tests bring another layer of objectivity to the hiring process.
For example, the Data Science test is perfect for hiring candidates well-versed in statistics, machine learning, and deep learning to supercharge your data driven recruitment efforts.
Next, learn more about how data can enhance your organization's strategic workforce planning by empowering you to better identify skills gaps and future capacity needs.
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