9 Examples of How Big Data Will Help Talent Acquisition Teams

9 Examples of How Big Data Will Help Talent Acquisition Teams

Mike Roberts

recruiting with big data“The more I learn, the more I realize how much I don’t know.” Albert Einstein famously said this, and it comes to mind when we talk about big data.

As new examples of how companies are using emerging technology to improve talent acquisition efforts continue to surface, it becomes more apparent that the recruiting field has merely begun benefiting from what big data and advanced analytics have to offer. Clearly, there’s a lot to be learned, but also some action that can be taken now.

Big data is the concept of transforming massive structured and unstructured data sets into actionable intelligence. Last week, we defined it in the context of talent acquisition. And this week we’re following up on that discussion, going beyond explaining big data and into the ways in which it may actually be used in the future.

It’s important to note that few talent acquisition teams are really leveraging big data today, because most are still in the learning phase. And while there is no standard for how to use big data in recruiting (or any other job function really), the following nine examples should help get your mind moving in the right direction.

1. Better predict hiring needs and move beyond “just-in-time” recruiting

One of the biggest challenges recruiters face is balancing time to fill with the quality of hires. And the less lead time they have to fill the position, the more risk there is around finding incorrect candidates. Big data sources such as employee assessments, departmental performance, and even overall economic indicators coupled with statistical modeling can be used to help recruiters get more predictive with hiring, moving away from “just-in-time” hiring.

2. Optimize hiring funnel flow performance to fill positions more efficiently

By identifying places where your conversion rates are low, you can then start to make adjustments like shortening time to an offer or questions on an application page. For recruiting organizations that hire thousands of people per year, there are so many different avenues for identifying optimization opportunities from sourcing candidates all the way through extending an offer.

3. Identify correlations between recruiting performance and business performance (and vice versa)

It would be incredibly powerful information if we could find connections between the ability to efficiently fill positions with quality candidates and the collective impact to broader business performance. And the same could be said in the reverse scenario—understanding how improved performance enables recruiters to do their jobs better.

4. Understand the connection between brand perception and recruiting performance

Even the best recruiters can have a challenge filling positions with talent if the company’s image or brand is suffering. Big data sources such as social media (tweets, Facebook posts, etc.) can be analyzed alongside recruiting performance indicators to really zero in on what that impact could be.

5. Identify the characteristics of candidates that lead to quality and retainable hires

What recruiters really want is for talented individuals to not just join their organization, but to not leave at the first sight of a better opportunity. With statistical modeling and advanced analytics, companies can determine the characteristics of hires that are doing a standout job, while also being more likely to stick around. With that information, recruiters can be more proactive in their decisions.

6. Better understand business units/divisions/hiring manager needs for competencies and skills

Recruiters shouldn’t be just looking at overall business needs, but also needs by department or business unit or some other variable, and how employees with particular characteristics (backgrounds, ambitions, and competencies) meet those needs. This information can lead to less attrition, lower cost per hire, and a number of other benefits.

7. Correlate applicant source with long-term employee performance

What many recruiters have come to realize is different applicant sources lead to different qualities of hires. The challenge is determining which of these sources—particular job boards, social media networks, and so on—delivers the best candidates and employees. Analyzing source information with employee quality data (assessments, reviews, etc.) can tell recruiters this information.

8. Deliver more relevant, personalized job postings to talent networks

With many companies today offering the option to opt into a talent network, recruiters now have the ability to essentially market their requisitions to their database. By using big data analytics, recruiters could deliver more personalized and relevant positions to particular people, rather than just blanketing (a.k.a. spamming) their database with every possible opening.

9. Identify existing employees more suited for roles in other departments

While we tend to discuss recruiting strategies for identifying new candidates—outside of the organization—it’s important to not overlook the potential goldmine of talent that may be right beneath our noses. Big data analytics can uncover connections between current employees skills and other in-house openings. Doing so can make for happier employees, while also reducing turnover and cost per hire.

Interested in recruiting analytics and the future of big data in talent acquisition? Sign up for the Data Driven Recruiter blog.

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