Imagine if you could leverage your own internal data, along with data available from third-party sources, to predict:
- The applicants for a specific job opening who are most likely to be successful in the role.
- Which senior leaders are most at risk of jumping ship in the near future.
- Which high-potential employees’ performance may worsen.
- The impact that employee demographics will have on turnover over the next several months.
- Which departments are likely to experience higher than average turnover.
All of these things, and more, are now possible. In fact, companies are already leveraging data to produce these kinds of critical insights into future HR trends.
Predictive Analytics in Action
Dave Millner is the founder and consulting partner of HR Curator and co-author of Introduction to People Analytics: A Practical Guide to Data Driven HR (Kogan Page; 2nd edition, 2023). He offers an example of how one company in the U.K. used predictive analytics to improve business performance by analyzing performance management data and performance trends.
Key questions explored were:
- What drives business-critical success metrics?
- What impact do business-critical roles have on those key metrics?
- What is the difference between the top 10 percent of performers and average performers?
The analysis resulted in the identification of personality traits that were more predictive of higher performance. These insights were used to create a refocused recruiting process emphasizing the identified characteristics.
“The key reason for using data, some with AI platforms, is to make better people decisions,” Millner said. “This enables a more objective assessment of talent potential, as well as personalized development, so ultimately the organization can identify methods, processes and techniques enhancing workforce quality, performance and engagement.”
Fueled by increasingly powerful technology like artificial intelligence and machine learning (ML), HR professionals are using predictive analytics in more strategic and integrated ways to glean insights on an ongoing basis.
Taking Deeper Dives with Predictive Analytics
Firstup, a San Francisco-based intelligent communication platform for the workplace, uses its own communication platform to view and gather employee engagement data in real time and then automates and orchestrates personalized communications through an advanced machine learning model.
“While many organizations gather information about their employees’ experience from sources such as [human resource information] systems or annual engagement surveys, these data points are often not sufficient,” said Firstup’s Chief People Officer Sabra Sciolaro.
“Relying solely on infrequent performance reviews or manager check-ins, you miss crucial signals about an employee’s experience at every touchpoint along their journey—both big and small—that truly matter to them.” Having a deeper understanding of key moments that impact the employee experience using AI and ML, she said, allows immediate action to improve these experiences in the moment.
Ed Barry, national director of the HR Benefits Technology Practice at Gallagher, an insurance, risk management and consulting firm with headquarters in Rolling Meadows, Ill., believes that “one of the best uses of predictive analytics is to help employers connect the dots between employee behavior and retention or attrition.” For instance, he said, it’s possible to link different types of data to employee dissatisfaction and low engagement, including time and attendance, pay, commute time, and how employees feel about their jobs.
In addition, Barry said, predictive analytics algorithms can conduct sentiment analysis, scanning work emails for changes in sentiment like increased frustration, anger or boredom. “While it’s easy to dismiss any single factor, when multiple factors are combined, predictive analytics boasts high accuracy rates for predicting the future loss of an employee—giving an employer a chance to intervene and change the predicted course of action, he said. Importantly, though, he noted: “By itself, predictive analytics won’t enhance employee satisfaction.” Once an organization has identified that an employee is at risk, human-directed intervention is required.
Staying on Top of Technology
Technology, of course, plays a critical role in using predictive analytics to gain insights across the employee life cycle. But, while there are many predictive analytics tools on the market, Barry said that “in my experience, many employers are unaware of them.” He suggests that HR leaders attend the SHRM Annual Conference & Expo and other HR tech events to talk with vendors, watch demos and ask questions.
“Before long, every CHRO will have some predictive analytics dashboard because they need to manage the ongoing competition for talent,” Barry said. “The only way to know what’s happening with your talent pool is to pay attention to the signals sent by employees. Predictive analytics can help HR leaders read those signals.”
In the future, Barry said, “the HR tech industry needs to move beyond predictive analytics to prescriptive analytics to help determine the best course of action. This is where the true innovation will come from, tying insights identified by predictive analytics to a response that maximizes outcomes and mitigates risk.”
However, there will always be an important place for humans—especially in HR circles. “Organizations that use predictive analytics software without human involvement risk making ill-informed decisions that can be costly to the organization’s people strategy and brand,” Barry said.
Lin Grensing-Pophal is a freelance writer in Chippewa Falls, Wis.