The Use of Data Analytics in Recruitment

HR teams who can harness big data and use analytics in recruitment will find the most success. We’re in the age of social recruiting, and with lots of data and candidate profiles to wade through, finding the best hires by relying on traditional methods of recruitment alone may be similar to looking for an elusive four-leaf clover.

Thankfully, modern technology has made it possible to harness lots of data, with analytics now applied in the recruitment field.

The term “data analytics” may seem daunting, but looking at the big picture, it’s a promising change from traditional hiring that HR teams should consider and learn to adapt with.

HR analytics has several advantages compared to the traditional recruitment model. As you gain insight into analytics, you’ll learn why it’s high time to adopt it in your organization. It’s also best to gain an overview of transitioning into predictive analytics with the right kind of tools and data.

Predictive Hiring Is Gaining Traction

Predictive analytics makes use of data to project the future success of candidates based on patterns among current employees. Here are some of the advantages:

  • Unlike traditional hiring, which only relies on manually screening resumes and intuition-based interviews, predictive hiring uses data and algorithms to recommend the most fitting candidates. Think of the process this way: when you watch Netflix or YouTube, the same type of videos in your history appear in your feed as recommendations.
  • With the use of data and algorithms to choose potential employees, the most recommended people are directly forwarded to hiring managers. This process saves time and prevents wasted opportunities because applicants with the most potential are not rejected just because of bias.
  • Freelancing giant Upwork says that predictive analytics is useful in improving time to hire and retention. Companies use data on performance and personality profiles to identify employees that are likely to leave or stay and use their findings to proactively resolve problems.
  • Predictive hiring weeds out potential bad hires based on skills, experience, and behaviour. Avoiding bad hires saves the company from having to deal with bad employees that may affect productivity, client satisfaction, and culture in the long run.
  • It impacts the training and development of current employees. Data on the best-performing employees can help HR and management in training and assisting other employees to improve their performance and overall productivity.

Key Points from HR Analytics

The impact of top talent is real. Consider this: the best developer at Apple is nine times more productive than their counterpart in other companies. The best transplant surgeon at a top hospital has six times a better success rate than counterparts in other hospitals. If companies embrace data analytics, leveraging human capital data in hiring people can be done efficiently and quickly.

Here are several important key points that are beginning to emerge with the use of data analytics in recruitment:

  1. Seemingly useful information from resumes may be misleading and may cause companies to hire the wrong people.

Google found that GPAs are not indicative of good employee performance. That’s why the company also considers potential employees’ soft skills, such as leadership, collaboration, humility, adaptability, and capacity to learn and re-learn new things.

Algorithms used in predictive hiring can be used to assess fundamental skills that HR staff may miss if they only base their decision on predictable resume data.

  1. Predictive hiring considers a bigger pool of candidates to find the best hires.

Reaching a wider pool of candidates doesn’t mean that your HR team must headhunt for potential hires in campuses and job fairs. Leaders in HR analytics have created tools to identify the best candidates based on objective and data-driven hiring methods, giving them the opportunity to reach high-fit candidates

For instance, LinkedIn has created a game-changing strategy to target the best recruits without simply relying on chance. They skipped the career fair altogether, pinpointed their target candidates, and built personal connections with them before they were targeted by other competitors.

  1. Every company has a unique hiring profile, even for similar jobs.

Although a job title may seem similar in one company to another, the job description may vary and companies may have different needs and criteria for hiring new employees.

Key Steps in Data-Driven Recruitment

Many organizations may get discouraged in using analytics, but this should not be the case. There are different ways to get started.

Conduct a strategic assessment to identify the needs of the company and how it can benefit from using predictive analytics. Identify key people in the organization that should be involved or if an outside expert is needed. Once the assessment is complete, make a roadmap to identify actions to take into consideration.

  1. Identify and set the required data.

First, set a meeting with your hiring manager to define and refine your target pool using data. You can “clone” high-performing employees by using their education, skills, and experiences as criteria. Then, agree on a talent pool after tweaking for what you need.

  1. Choose the right tools in predictive analytics and reporting.

There are a lot of business intelligence (BI) tools used in predictive analytics. They are mostly used in marketing, sales, and related fields, but they are also applied in HR analytics. The following BIs are the most popular options.

  • This BI tool specializes in rich visualizations and is designed to connect with data sources such as SAP, Excel, and more. Tableau’s popularity stems from its ease of use, so users don’t have to learn a technical language. It also features drag-and-drop visualizations for easy analysis.
  • Microsoft Power. Companies looking for a cost-effective and scalable tool may consider using Microsoft Power. It lacks the analytics and visualization capabilities of Tableau, but it’s fully integrated with Excel and Microsoft Azure, making it a powerful BI worth considering.
  • What’s unique about QlikView is it processes data using system RAM, enabling this BI to provide greater speed compared to traditional databases.
  1. Create an operating model for analytics.

You need a robust and well-defined operating model to integrate predictive analytics in your organization. Do this by building an operating model for analytics and adopting the strategies used by companies with a large talent pool.

  1. Create a proof of concept.

After identifying your target data, the tools you need to gather and present data, and creating an operating model, demonstrate the value of adopting predictive analytics. There should be a proof of concept as to the scale of using analytics in HR and its potential ROI.

  1. Put your recruitment plan into action.

What comes next is putting your strategy into action. Prioritize candidates that are likely to respond, and reach out and contact the candidate. The trick here is to pipeline proactively and reach out to potential candidates by making them aware of your organization and highlighting its strengths.

  1. Iterate the process

Predictive hiring is a learning process. As you accumulate and interpret data, you will learn what information is valuable and how to make decisions from the insights you’ve gathered.


Don’t be afraid to test the waters. Although traditional recruitment may have worked for your company in the past, adopting new trends and using data to your advantage may create a total shift in your organization for the better.

With the fast-changing landscape of recruitment in the 21st century, it’s important to adapt and keep up. Who knows, you might just find the people who will help your company grow all the more.

Author Bio: Danielle Canstello is party of the content marketing team at Pyramid Analytics. They provide enterprise level analytics and business intelligence software. In her spare time, she writes around the web to spread her knowledge of the marketing, business intelligence and analytics industries.

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