How Predictive Analytics Can Help Companies Hire Faster, Better, Stronger


In a recent survey of more than 1,000 business leaders, CEOs and other C-suite executives, they said “attracting and retaining talent” is their top concern. However, while acknowledging that talent is a critical success factor, few companies claim to be good at building — or keeping — a strong workforce. Why is this?

There may be a dozen plausible explanations for talent shortages, but one of the most important is the consistent failure of companies to take full advantage of the information they have about their own employees and candidates. Predictive analytics and other data-based technologies can help streamline the hiring process while identifying the best possible talent but more importantly a solid cultural fit.

When it comes to technology in hiring, many companies concentrate recruiting efforts on posting job openings on their websites and by leveraging social media. In fact, nearly 85 percent of HR professionals say social media has a role in hiring, usually through LinkedIn, Facebook, Instagram and Monster or other web-based options.

Social media absolutely produces viable candidates, but this form of technology is just touching the surface when it comes to identifying new hires that fit your company culture while having the personal and professional qualifications for a particular position. Then there is the challenge of keeping good people once you have them on board. Final vetting too often boils down to a “gut check” by HR or a tech lead — and while that has proven to work, harnessing the power of the data we already own can lead to much more powerful results.

Why Predictive Analytics?

While the human element should always be at the heart of employee recruitment and retention, managers involved in the hiring process will find that predictive analytics can reduce areas of uncertainty around a candidate’s background as well as assist in identifying personal and professional qualities that will contribute to the company’s mission and profitability.

Simply put, predictive analytics involves the use of historical data to predict future outcomes. You’ve probably seen predictive analytics at work as your Google searches turn up increasingly accurate results that take into account your personal search history. Serious business applications are widespread. Insurance companies, for example, examine historical claims to modify their future exposure to certain risks. Amazon has filed a patent for a service that will use predictive data analysis to ship products before consumers even order them.

From a talent perspective, there is an abundance of historical data about a candidate that can be generated through normal procedures, such as the filling out of a job application. Wells Fargo, the San Francisco-based bank, deploys predictive analytics with a focus on biometric data that can be verified — including a candidate’s job history, tenure at previous employers, career highlights and areas of expertise.

In all, Wells Fargo developed 65 questions for each candidate. Subject-matter experts created the questions and then tested with existing employees representing key demographics, finding correlations between its corporate culture and employee backgrounds. As a result, the bank finds “statistically significant differences” in predictive analytics-vetted employees versus those hired in the market.

With unemployment rates at an historic low, employee retention has remained a key objective for many organizations. Predictive analytics allows an employer to achieve a more in-depth analysis of what may be causing turnover in different parts of the organization. This in turn helps managers adapt quickly to change work conditions to prevent top performer turnover.

Companies also are finding that predictive analytics can boost workforce diversity. Exelon, a Fortune 100 energy company, uses predictive analytics to reduce turnover among women and minorities. Other companies are finding predictive analytics and other AI options as a means to combat workplace discrimination or even to identify bad management practices that contribute to turnover.

It is fair to argue that HR and other hiring managers used job applications to screen candidates long before predictive analytics arrived on the scene. True enough. But with today’s computing capacity, the rise of AI with widespread reach and the aggregation of more data than we ever imagined, predictive analytics can do the job faster, better and even reasonably priced than a traditional labor-intensive hiring process.

The payoff for hiring better, more-committed employees can be significant. Consulting firm McKinsey and Co., citing a survey with more than 600,000 respondents, reports that high performers are a whopping 400 percent more productive than average. The gap rises with a job’s complexity. McKinsey translated the findings this way: “If a competitor used 20 percent more talent in similar efforts (such as a cross-functional initiative), it would beat you to market even if it started a year or two later.”

Predictive analytics go beyond recruiting and retention to help drive process improvement. Data on how well a team is performing, as well as how well your overall process is working, can be used to drive better return on investment. A/B testing data sets, for example, allows employers to see what worked best for certain projects or scenarios which in turn allows for greater optimization of teams, processes and planning.

The fact is that most companies are already in the business of leveraging “big data” and predictive analytics. But too often data management has overlooked HR and talent with its focus on operations and revenue generation. Through predictive analytics and better use of data, HR has an opportunity to expand its role in contributing to enterprise success.

Giancarlo Di Vece is president of Unosquare, a software development outsourcing service. Its U.S. headquarters is in Portland, Oregon. To comment, email


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