The Do’s and Don’ts of Data-Driven Recruiting

Remember when a recruiter’s role was defined by identifying, screening, and hiring qualified professionals?

Okay, so though that part of the definition still stands, there’s no denying it has expanded significantly. Today’s recruiters are now defined by a variety of responsibilities outside of the traditional scope of talent acquisition. Being a successful recruiter now means being someone who understands digital marketing, someone who can think like a salesperson, and, most recently, someone who can collect and interpret recruitment data.

Making use of recruitment data (or engaging in people analytics, as it’s often referred to) is perhaps the most impactful new aspect of recruiting. According to HR analyst Josh Bersin, 2016 is the year that data analytics won’t just be a “nice-to-have” part of recruitment, but rather, “your ability to harness and understand the data about your people is becoming core to HR’s mission in 2016.”[1]

Recruitment Analytics to Drive Success

Why let data drive recruitment? In short: it increases recruiting success rates. It’s predicted that by 2021, hiring success will improve by more than 300 percent due to the use of data analytics in the hiring process.[2] By using data to inform recruitment, organizations will be able to find better candidate matches, more efficiently. In fact, LinkedIn found that when talent acquisition teams used data to hire, they were two times more likely to improve their recruiting efforts and three times more likely to reduce costs, while improving efficiency.[3]

Applying data analytics to recruiting can also help organizations:

  • Identify what’s working
  • Identify what’s not working
  • Red flag recruitment risk factors
  • Improve employee retention
  • Limit hiring process errors
  • Predict hiring needs
  • Gain visibility to all aspects of recruiting performance

A study from Gallup that measured the impact of using data to identify top talent suggests that when “companies select the top 20% most-talented candidates for a role, they frequently realize a 10% increase in productivity, a 20% increase in sales, a 30% increase in profitability, a 10% decrease in turnover and a 25% decrease in unscheduled absences.”[4]

Already, 75 percent of HR leaders say analytics are important to business success.[5] Sixty-five percent of senior leaders say they risk becoming irrelevant and/or uncompetitive if they don’t utilize big data.[6] And 90 percent of CEOs say it’s important for HR leaders to understand workforce analytics and apply that understanding to their recruitment strategy.[7] According to a study conducted by the Harvard Business Review, 57 percent of companies expect to integrate data across multiple systems within the next two years.[8] Company leadership understands that data matters.

Yet, despite the recognition of its importance, actually making use of recruitment data has been slow to catch on. Only 14 percent of organizations use advanced analytics to make talent decisions.[9]  LinkedIn finds that three in four recruiters don’t use data at all.[10] Perhaps most surprising is that the companies we’d expect to be ahead of the curve on analytics—i.e. enterprise-level companies—aren’t. Of companies that indicated they were using analytics to drive their recruitment, 87 percent had less than 2,500 employees.[11]

So what’s holding companies back? It may start with the need for a better understanding of the do’s and don’ts of data-driven recruiting.

Do: Acquire a Holistic View of Recruiting Data

The leading obstacle to achieving better use of data, metrics, and analysis by HR and talent management professionals in surveyed organizations was “inaccurate, inconsistent, or hard-to-access data requiring too much manual manipulation”.[12]

When data about the recruitment process is disparate and disjointed, recruiters, hiring managers, and executives are challenged to fully measure the success of their efforts. This means organizations should be looking at data for all parts of the recruitment process: recruitment marketing performance, screening efficiency, and onboarding effectiveness, as well as the performance of all of their integrated providers, like those that conduct assessments and background screenings.

To maximize use and understanding of recruitment data, however, organizations need their recruiting technologies to be connected, with data from each technology accessible in a consolidated, organized location, such as a talent acquisition system of record.

By achieving such a holistic view of recruitment analytics, organizations will be better able to recognize trends and make smarter decisions to improve ROI, such as by linking applicant source with long-term employee performance.

Don’t: Stop at Data Collection

Collecting recruitment data isn’t necessarily a new concept in HR. Many recruitment software providers generate basic recruiting metrics, like average time to fill. Though organizations may report on this information and make use of it in a reactive capacity (such as noting that cost-per-hire was too high in Q1), they’ll need to move away from simply collecting data to effectively acting upon it—in other words, letting it drive decisions.  

One of the most impactful ways organizations can do so is via predictive analytics.  When organizations use data to make predictions about recruitment, they become empowered to not just identify recruitment strengths and weaknesses, but proactively address them. For example, with a predictive analytics approach, existing data can be used to forecast what applicant volume will be next year at a given time, giving companies the ability to start finding ways to either raise or lower it. Though only four percent of recruiters currently use data for predictive purposes, the clear benefits of the approach suggest adoption will increase.[13]

Do: Ensure Reporting is Highly Configurable

Reports about recruitment data should be flexible enough to consider a variety of variables. For example, if there’s a need to pinpoint which sourcing channel generated the most hires in Sales from April to June, HR shouldn’t be limited by reports that only track time on a monthly or annual cadence, or that can’t break down hires by department.  Without the ability to configure input variables—as well as decide in which format reports will be generated—analysis remains less complete and less effective at guiding recruitment strategy.

Reports should also be configurable to individual users. Hiring managers will likely have interest in different recruiting metrics than recruiters, and one recruiter will benefit from a specific report variation more than another. By allowing users to configure reports to their unique needs, and quickly access the reports from an individual dashboard that provides real-time updates, it becomes possible for users to keep a pulse on the metrics most impactful to them.

Don’t: Opt for a Separate Data Analytics Solution

Data-driven recruitment doesn’t have to require the adoption of separate data analytics technology, or the creation of a separate data analytics department, for that matter. Why? Software that supplies robust quantities of recruiting metrics already exists. It’s a capability built right into leading software solutions on the market today.

Plus, creating divisions between HR and data analytics, however collaborative the worlds are intended to be, could create inefficiencies, result in misinformation (or lack of information flow between data experts and HR), and foster redundancies.

Simply put: organizations will be best positioned to leverage recruitment data when they keep the collection and analysis of that data closely tied to HR. That’s because HR is best equipped ask the right questions to generate the right reports, and contribute appropriate context for analysis.

Does this mean individual recruiters need to become data analytics experts? Not at all.

Firstly, advanced recruitment software makes the generation and presentation of recruiting metrics easy to comprehend. Software solutions like iCIMS feature reporting centers and dashboard that are intuitive, user-friendly, and feature a wealth of information that’s clearly labeled and easy to digest.

Secondly, if necessary, pulling in an HR data specialist that’s dedicated to analyzing recruitment data can be a reasonable and effective supplemental solution, particularly if HR leadership could benefit from an additional set of eyes to analyze data on a macro-level. In other words, recruiters need not fear that their organizations will soon demand from them predicative analytics expertise.

Do: Broaden Your Scope of Analysis

Are you tracking which devices job seekers use to visit your career portal? Do you have an idea of how long it takes individuals to complete each stage of your application, or at which stages job seekers abandon your application? What percentage of candidates make it through each part of your engagement funnel—from paid advertisements, to your website, to your career portal to your application?

These questions move beyond what Manpower Group Solutions refers to as transactional metrics, and asking them can result in more specific, effective ways to improve recruitment efforts. Surface-level analysis, the kind that might just look at average time to fill, is certainly a place to start, but companies seeking a competitive edge will maximize efforts by broadening the scope of analysis.

Doing so starts with asking “why” when confronted with each new metrics report. Why did time to fill remain stagnant in the first three months of the year? Why are more candidates engaging with us on Facebook, rather than our paid advertisements on LinkedIn? It’s the “why” questions that will expand the breadth of analysis and uncover new, more impactful, recruitment components to track.

Don’t: Overlook Developing a Formal Strategy

Successful data-driven recruitment starts with identifying what’s to be learned from data. In what ways do you hope data analytics will improve your recruiting and organizational outcomes? By establishing overarching goals, and breaking those down into quarterly, monthly, and ongoing goals, organizations can build out a formal analytics strategy. This will organize and shape how recruiters use data, how resources are managed to respond to that data, and how information is reported up to an executive team.

“Transactional metrics (such as time to fill, time to hire, and cost per hire) remain important, as they allow employers to establish early success measures. However, many employers and recruiters have relied exclusively on these traditional metrics, resulting in missed opportunities to raise the bar. Transactional measures reveal quantity, not quality, which suggests the need for something more.”
Manpower Group Solutions

Though the specific structure of a data-driven recruitment strategy will vary from company to company, all strategies should make it possible to clearly showcase how recruitment is driving business results. Being able to point to specific numbers and trends can help HR departments more clearly communicate how they contribute to company growth and success, which can ensure that things like HR budget, staff, and resources continue to increase as necessary.

Lastly, when developing a formal strategy, it’s not a bad idea to involve your sales and finance teams in some capacity. After all, these are the folks that have been using data to drive their decisions for years. How has their approach to data analytics yielded success, and what elements of that approach could be pulled into HR?

In many ways, data will drive the future of recruiting. It currently drives the majority of other business functions; there’s no reason HR won’t—or can’t—embrace data analytics, too. Recruitment technology already generates significant quantities of data, technology providers are continuously finding new ways to make that data more accessible, and HR teams benefit from data analytics when they make use of it.

While all of this could suggest that technology is again replacing jobs once done by humans, that’s really not the case. Across the board, data is being used by businesses to inform decisions, not replace the people that carry them out. In other words, data-driven recruitment won’t be taking the “human” out of “Human Resources”. What it will do, is maximize those resources and drive recruitment success.

How iCIMS Can Help

iCIMS is the leading provider of talent acquisition solutions that help businesses win the war for top talent. iCIMS empowers companies to manage their entire hiring process within the industry’s most robust Platform-as-a-Service (PaaS). Built on the foundation of a best-to-market talent acquisition software suite, iCIMS’ PaaS framework, UNIFi, allows employers to expand the capabilities of their core talent acquisition technology by integrating with the largest partner ecosystem in talent acquisition to help them attract, find, screen, and manage candidates. Offering scalable, easy-to-use solutions that are backed by award-winning customer service, iCIMS supports more than 3,500 contracted customers and is one of the largest and fastest-growing talent acquisition solution providers.

[5] Bersin by Deloitte, “Talent Analytics: From Small Data to Big Data”

[6] Capgemini & EMC: Big & Fast Data: The Right of Insight-Driven Business

[9] Bersin by Deloitte, (no longer online)

[13] Deloitte Global Human Capital Trends 2014

 [RG1]Interested in opportunities to add in supplemental graphics/visual aids wherever applicable.