The Benefits of Data-Driven Talent Acquisition

The Benefits of Data-Driven Talent Acquisition

If you’re one of the many HR teams out there not using data analytics in your talent acquisition and recruitment processes, then you’re missing out on valuable and critical information. 

A data-driven approach to recruitment is an absolute must have. Especially if you hope to consistently and efficiently hire the best talents. 

Without it, it’s like flying completely blind and hope for the best.

Names like Google, Cisco, Sprint, and Deloitte all use recruitment analytics to drive their decision making and hiring processes, and do so with industry-leading success.

Here are some of the benefits of using data analytics in recruiting new talents:

  • It provides objective transparency into the effectiveness and value of your recruitment activities.
  • It helps keep track of high-potential applicants, allowing you to actively nurture possible future hires.
  • It unlocks the potential to learn from and optimize processes.
  • It enables proactive recruitment (rather than reactive recruitment) to drive better and more timely hiring decisions.

What is data-driven recruitment?

Data-driven recruitment refers to making objective hiring decisions based on a variety of data sources beyond the usual screening, interviewing, and extending job offers. 

Data-driven recruitment use a range of recruiting metrics to track how successful the recruitment process is and use the insights to optimize the entire process.

Furthermore, collecting and analyzing data in your recruitment process eliminates guesswork and biases to ensure that you’re selecting the right talent for the role.

The recruitment technology within your HR tech stack, like your ATS or your candidate assessment solution, is a great source of data. Other sources of data include interview scores, Google Analytics, social media analytics, and more.


Image source

Applying data-driven recruitment best practices to your recruiting process results in many benefits for your organization. 

Data-driven recruitment examples

You can use data in different aspects of the recruitment process to enable data-driven decision-making. 


Monitoring the sources you receive your applications from is a critical recruitment KPI and a good example of how to use data to make decisions.

It helps you track the effectiveness of different channels (job boards, ads, agencies, social media, your own career page), which also allows you to calculate your cost per hire.

Whichever source brings in more quality candidates, that’s where you need to put more money.

For example, consider increasing your budget on niche job boards if you get more qualified applicants than from Google or Facebook ads.

Or, if you’re not receiving qualified applicants from LinkedIn, you need to reduce your budget there or scrap this out from your plan.

Candidate experience

Candidate experience is the applicant’s overall user experience of the hiring and onboarding process. It includes time spent between each recruitment phase: from initial contact to the screening process through the interview and the job offer to the introduction of the new employee to the organization. It can be measured using the Net Promoter Score via a candidate experience survey. 

Candidate experience can also include the applicant’s perception of your career site or page and the content therein. Measuring time spent on particular pages or content, top landing pages, exit pages and or what order the applicants are viewing your content in between each recruitment phase. 

Analyzing this type of data will help you to better understand your potential applicants and help you optimize your applicant’s journey by creating content that is more effective in attracting the right type of talent.

Recruitment planning

Mining your recruitment data will help you determine what to do with it. 

Here are examples of common recruitment challenges that relevant data will help you uncover and ways how to address them: 

  • If you need to cut your expenses, focus on cost-associated KPIs like cost per hire, job advertising performance, or the number of applications per source or channel. 
  • If you want to speed up your recruitment process, concentrate on speed-related metrics like time to fill, time to hire, and time to productivity. 
  • Or, if you want to have more highly-skilled employees, pay attention to quality-related metrics like source of hire, candidates per hire, new hire turnover, and new hire retention rate. 

Why is data-driven recruiting important?

There are plenty of reasons why you should adopt a data-driven approach in recruiting: 

Speeding up the hiring process

Collecting data will help you understand where the bottlenecks are in your recruitment process so you can work on whether to remove them or automate them. 

You need to draw out your hiring process by first gathering details like: 

  • Applicant drop off rate
  • Average time to hire for a particular job
  • Recruitment methodologies involved: job ads vs. job boards, recruitment events vs. employee referrals 
  • Duration for each hiring step: How much time do you spend on sourcing? How much on interviews? 

For instance, if you find out that a lot of applicants drop off right at the start of your application, you can make the application process easier for them. Let them just upload their resume and remove the online application form from your job posting. Or, if your interview process takes a long time due to scheduling issues, you can look into automating the interview scheduling.

These insights will give you a more accurate estimation of your time to hire. And with this information, you can confidently advise stakeholders of realistic timeframes.

Reducing hiring costs

Data-driven recruiting shortens your time to hire and helps you make better use of your recruitment budget. 

Aside from tracking your source of hire, you can also check on your current recruitment tech stack to see if it is actually helping with your talent acquisition efforts.

For instance, maybe you don’t need video interviewing software if you’re targeting local candidates who can just walk into your office for an interview. 

Improving candidate experience

Data-driven recruitment is not just about using the latest HR technology. It’s about the process used to make the candidate experience more straightforward and quicker. 

Only 17% of companies ask for candidate experience feedback in multiple stages of the recruiting process. Doing that helps you understand which stage you need to improve first.

Making the process faster, smoother, and fairer is also something your candidates will appreciate.

Being proactive instead of reactive

Using data to forecast your hiring needs and inform your hiring plan lets you proactively prepare for what’s to come instead of reacting to the situation.

You can track the annual employee turnover rate, business expansions (new teams, new locations, new product offers), and internal mobility (like promotions or lateral transfers). 

If historically, X employees are going to leave the organization within this period, we’d need Y amount. This is to guarantee an available recruitment budget whenever there’s a need to immediately hire new employees.

Forecasting hiring time is also critical to set expectations when a new employee comes in. This is especially important for critical positions like new managers responsible for heading new departments during a product launch. 

Boosting quality of hire

Employing the best people substantially contributes to your company’s success and profitability. 

There are different approaches to using data to select highly qualified candidates. Work sample test scores and general mental ability assessments predict future job performance better than resumes and interviews. 

Another way is to focus on the sources that yield quality candidates. Gather data like top performers, turnover rate, and productivity levels. Compare these data between different hiring sources (social media, job boards, recruitment agencies) to determine which recruitment channels generate the best quality candidates. 

By gathering and analyzing these types of information, you’re creating data-driven ways to improve your quality of hire. 

Setting up the recruiting team for success

With a data-driven recruitment model, you can build a repeatable procedure that enables you to make sound hiring decisions. 

Recruiters and hiring managers are more confident with the selection process because they have actual data to prove they’ve made the right choice. 

Data-driven teams also operate more effectively with lower costs, making them more productive and adding more value to the organization.

Data-driven recruitment best practices

Now that you know the benefits of data-driven recruitment, let’s have a look at some best practices that you can apply at your organization: 

1. Select relevant metrics and identify data sources

It’s easy to get lost when you look at the different data points your recruitment software is collecting for you. To avoid getting overwhelmed, determine which metrics would be the most helpful in improving your recruitment process.

Review your overall business goals to develop a list of metrics that will help you achieve these goals. 

Then, identify where you are going to get this data from. Your data can come from your ATS, Google Analytics (e.g., for source tracking), or candidate survey feedback. You can also track your budget data, hiring manager satisfaction, and more.

2. Build a recruitment dashboard 

A recruitment dashboard helps you visualize the data, spot trends, and identify issues quickly. It shows data on applicants, campaigns, and budgets.

Investing in a recruitment dashboard allows you to see problems and opportunities quickly. 

3. Determine actions to take 

Using data in recruitment doesn’t only mean tracking it but also acting on it. 

For instance, if you notice that you started getting a more significant share of low-quality candidates, you can check where they’re coming from (e.g., a specific job board) and stop promoting your opening there.

Or, if you notice you are getting more applications directly from your careers page, consider investing in search engine optimization (SEO) to bump up your website on the search engine results page.

4. Consider data limitations 

Data is not foolproof. 

Harvard Business school conducted a survey and found that 88% of executives said they’re aware their HR tools reject qualified candidates. 

The rejection is that job descriptions have too many criteria and create a long list of required skills for the algorithms to check for in resumes. The algorithms end up declining many qualified applicants who may be missing just a couple of skills from the list.

Another reason is having a work gap in their resume for more than six months. These work gaps may represent other legitimate life obligations or events like military deployments, pregnancy complications, caregiving obligations, or illness.

When using any data, understand that it doesn’t tell the whole story. You should dive deeper into the ‘why’ of what you’re finding in the data.

As a recruiter, you still need to interpret your own findings and rely on your knowledge and experience to make the right recruitment decisions. 

5. Compare the before and after states and iterate 

The data will be most helpful if you know what the state was before you implemented any changes and what it is after.

Is your plan bringing you the desired results? If not, what can you do to improve?

Data will help you see your successful and failed hiring strategies to improve your future recruitment efforts. 

A final word

Implementing data-driven recruitment requires more than just gathering some data.

You need a mindset change and start working in a new way to fully reap the benefits of data-driven recruiting.

An excellent place to start is to audit what data you’re already collecting through your HR technology and think of how this data can help you improve your recruitment process.

Not sure where to start? Let TAlytic help, reach out to us today for a free consultation.

Comments are closed.