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Recruiting by Numbers
By Matt Ferguson
Aug. 26, 2015
The rise of data analytics in human resources and recruiting has many industry pros as conflicted as they are excited.
There is a lingering anxiety that the intuition and interpersonal skills paramount to successful talent acquisition programs may be subverted or replaced by computers and complex algorithms. Proponents, on the other hand, argue that data analytics will reinforce the role of recruiters and should be embraced as a key tool of the trade.
While I wholeheartedly believe the latter sentiment will win out, it’s clear there is enough uncertainty to necessitate a broader discussion around when and how big data should be used to advance talent acquisition goals.
Currently, 52 percent of hiring managers and recruiters rate their proficiency with workforce analytics as poor or fair, according to a spring 2015 survey by CareerBuilder along with Harris Poll. In fact, only 1 in 7 HR departments regularly use big data in recruitment strategies and half never do. I frequently hear HR leaders say the benefits of data analytics are unclear, and even if research yields intriguing findings, the time and effort spent on such efforts isn’t worth it.
Source: CareerBuilder CEO Survey, 2015, Harris Poll
Do these skeptics have a point?
It depends on the context. Most small companies don’t need an analytics function in their HR department, especially if the firm is only making a handful of hires annually. The same is true for firms that have a well-established pipeline of skilled candidates, such as the manufacturer that has strong relationships with vocational schools and community colleges. In short: Companies that don’t have complex recruiting or talent management challenges can probably survive without data analytics tools.
For most midsize and enterprise companies, however, an issue-free recruiting strategy is the exception and not the rule.
The average talent acquisition team is beset by numerous recruiting hurdles, such as preventing applicant drop-off, determining the correct mix of talent sources and, of course, filling the pipeline with enough skilled candidates. Even as the number of monthly job openings in the U.S. reached record levels over the summer, the pace of hiring continues to lag, according to the U.S. Bureau of Labor Statistics. Our survey found that 50 percent of HR managers currently have open positions for which they cannot find qualified candidates and that the time it takes to close vacancies for some high-level STEM jobs — science, technology, engineering and math — regularly crosses the three-month mark. (Editor’s note: The author works at CareerBuilder.)
The connection between a slow recruitment process and stalled company growth is real. Projects and initiatives critical to the business are hindered or never happen, and companies frequently have to delay hiring in one area because of vacancies in others.
Data analytics, however, can kick-start new solutions to these problems by revealing previously hidden patterns and insights.
There are three major uses of workforce data analytics that have proven highly beneficial. Data helps recruiters and HR leaders learn the market, set expectations and gain influence within their organization and evaluate the effectiveness of current strategies.
Learning the Market
It is easy to lose sight of the fact that recruitment ostensibly functions on the same principles as any marketplace: Firms compete for scarce resources and pay for those resources per the market rate. Economists don’t rely on gut feelings when evaluating global economies; nor should talent acquisition pros when evaluating regional and national labor markets.
Labor market supply and demand data, previously confined to clunky government websites, is now packaged in a variety of software applications that help companies answer essential questions with empirical authority: How many candidates are graduating in my market in a specific major? How many workers with a specific job title are looking for new work? What are real-time, geographically adjusted compensation metrics for workers in various occupations?
The answers to these questions can help HR leaders determine whether they need to advertise in new markets, realign job descriptions or adjust compensation to improve candidate pools. Moreover, workforce planning isn’t just about the head count companies need now, or even in the next one to two years for that matter.
Consider this: In the next two decades, the U.S. will see tens of millions of baby boomers retire from the workforce. There is a similarly sized millennial cohort set to fill the void, but not in the same occupational and skill categories. This is why forward-looking talent acquisition teams are modeling industry employment projections five and 10 years down the road to evaluate whether current talent markets will provide sustainable candidate pipelines. Such predictive analytics can be the foundation of new skills-training programs or even determine the cities in which new businesses open factories, warehouses and offices.
Setting Expectations and Gaining Influence
The same type labor market analysis can help recruiters become a better resource for internal stakeholders.
The right data can simply change the conversation around recruitment. Too often, a breakdown occurs between HR and front-line managers because of an inadequate exchange of information and misaligned expectations.
Reviving Recruiting: A Numbers Game
A summer 2015 CareerBuilder/Harris Poll survey of chief executives at large companies points to an ever-evolving relationship between CEOs and human resources leadership. Nearly half of CEOs said their companies have lost money because of inefficient recruiting policies and 9 in 10 said their HR leaders need to adopt workforce analytics initiatives.A majority of CEOs, however, said that in the post-recession economy, human resources opinions carry greater weight and that there’s still room for HR leaders to grow their influence internally.What are the top ways to carry more weight? Provide actionable data, proactively consult work with other leaders, and be a resource for how everything functions within the organization.—Matt Ferguson
A hospital executive unable to find an experienced computer systems administrator, for instance, could easily place the blame on recruiting efforts when in reality the market conditions make competition for such labor intense. In these types of scenarios, HR should be considered a valued resource — an internal consultant — for hiring managers rather than a source of frustration. Specifically, data analytics can lead to better job descriptions, accurate time-to-hire estimates based on historical trends, and compensation rates that stay steps ahead of the competition.
But even if talent acquisition leaders aren’t sold on the promise of data analytics, company chief executives almost universally are.
A separate summer of 2015 CareerBuilder/Harris Poll survey of CEOs at large organizations confirmed this. Nine in 10 CEOs expect human resources leaders to be proficient in workforce analytics. Only 9 percent said it was “not very important.” More than half (57 percent) said providing actionable human capital data was a key way HR could develop broader influence within the organization.
CEOs, realizing how data analytics transformed other business functions like marketing, sales and customer service, know how the right information can cut through human bias and identify previously unseen solutions.
This doesn’t necessarily mean HR departments need to staff their teams with data scientists and programmers. Technologies and services will in most cases automate the heavy number-crunching. But departments should place an emphasis on bringing in professionals who are comfortable telling stories with data and capable of devising new strategies from the findings.
If there is one aspect of workforce/human capital from which most every organization can benefit, it is the ability to harness the power of the data you already own.
Recently, Johnson & Johnson reversed a three-year policy of favoring experienced candidates over recent college graduates. The company previously assumed recent grads would be harder to retain and not perform as well. After its HR team analyzed data on nearly 47,000 employees, it found that this group actually performed just as well as and stayed longer than hires with a few years of experience, according to The Wall Street Journal.
Data analytics uncovered an organizational bias, and, as a result, the company has a more efficient recruiting process. Similar big data studies have shown recruiting policies that screen out job hoppers or candidates with long employment gaps are also misguided, and only serve to shrink a candidate pool without improving quality.
This type of analysis can be applied to the technical aspects of recruitment itself. Large companies typically use several vendors and sources to attract applicants — from job boards and staffing agencies to internal careers sites and employee referral systems. Over the course of weeks, months and years, countless data points amass in these disconnected systems and, in most cases, go completely unseen.
If all that data were to be aggregated and standardized, a top-down view of the firm’s entire talent acquisition strategy emerges. Which sources are the most effective at delivering qualified candidates and, ultimately, hires? Is the cost per hire proportionately aligned with the time to hire? Are some sources better for acquiring skilled occupations than others?
For enterprise companies hiring thousands of workers annually, the answers to these questions are incredibly valuable. But most don’t have the right people or tools in place to begin the analysis.
Not one of the above examples would mean much if there weren’t some evidence that big data works for its advocates. Fortunately, 60 percent of the hiring managers and recruiters in the CareerBuilder/Harris Poll survey that use data analytics in recruitment said it lowered their cost per hire, and 68 percent said it reduced their time to hire.
And better yet, it is clear that the best applications of big data elevate the prominence of talent acquisition and HR professionals. The ability to diagnose recruitment problems with data-backed insights builds interdepartmental trust and makes the entire pre-hire process easier for all parties.
Data will not take the human out of human resources; rather, it will empower the fields’ practitioners to become highly efficient, effective internal consultants and recruiting experts.
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