From identifying rising talent to reducing turnover, predictive analytics help make employee recognition programs more proactive.
By Jesse Harriott
HR leaders know it takes work to attract and maintain top talent. Locating, hiring, and retaining the best people for each open role calls for an ongoing give-and-take between employer and employee—and data is a must to master the inner workings of these dynamics. To engage quality talent and embark on the only long-term path to greater profits, organizations need to refine their talent strategy and the analytics that make it possible.
According to a Society for Human Resource Management (SHRM) report, the average cost to hire ranges from $4,400 up to nearly $15,000, depending on the position’s seniority. That number includes advertising the role, interviewing, and background checks, but not the lost value the previous employee brought to the bottom line or the costs of onboarding.
Artificial intelligence (AI) models that leverage predictive analytics help mitigate these and other costs by providing vital data and information that can help leaders proactively retain new hires, objectively identify rising talent, and predict how likely it is that an employee will leave an organization. The data itself can originate from an employee recognition and continuous performance management platform, which—in addition to increasing overall employee engagement by creating human moments that matter with gratitude-based awards—helps executives see which people leaders, managers, and individual contributors are doing excellent work and building connections with their colleagues.
Retention Through Recognition
Predictive analytics rooted in peer-to-peer employee recognition can help increase engagement among new hires and mitigate turnover within the first 90 to 120 days of employment. This early period is when new employees sometimes struggle to align their values with the organization’s and learn where and how they fit in. By tying values to recognition rewards, such as awards for excellence shown in teamwork or customer service, new employees can more easily integrate into the organization because they have concrete examples that model great behavior.
An analysis of reward activity shows that when people receive seven to 10 values-based awards per year (a rate of about one every 45 days) the likelihood of turnover can be cut in half. Recognizing new hires just a few times at the beginning of their tenure shows them they are valued, increases engagement and alignment, and helps prevent that costly early turnover.
Data from these recognition programs can also show which values employees frequently use to recognize each other and which values they align with most—and least. It shows where leadership must clarify values so they’re easier for employees to understand and associate with day-to-day actions.
Identifying Rising Talent
Peer-to-peer recognition is entirely democratic and provides a continuous feedback loop for people, leaders, and executives. According to Officevibe’s recent The Global State of Employee Engagement study, 62 percent of employees wish they received more feedback from their colleagues. Many voices provide a more rounded view of each employee and can highlight a work culture’s unsung heroes.
Recognition also strengthens ties across team members and departments. Data from these programs illuminates how large employees’ networks are, who has cross-departmental and cross-functional networks, and who holds highly central roles regardless of title. It allows leaders to look at patterns in the language of employees’ recognition moments to identify powerful words such as “indispensable” and “unmatched” that align with the organization’s core values or top leadership qualities.
Some employees receive a lot of recognition, but not from influential people such as managers or C-level executives. People who have smaller internal networks but receive recognition moments containing high-powered words are most likely hidden rising talent. Do not overlook the potential of these dedicated future leaders.
At most organizations, some departments run more smoothly than others. Some may have more experienced teams and others may have more engaged employees. There are, of course, warning signs of turnover, such as few opportunities for advancement and below-average pay and benefits. But generally, it’s difficult to predict which departments, teams, or individual employees are most at risk of high turnover. Even when there are warning signs, they often come too late for leaders to make a significant change before the turnover occurs.
A recognition program that leverages AI-enabled data analysis can assign each individual employee a score—red, yellow, or green—that correlates to how likely that person is to leave based on their connections and activity within the employee recognition program. Each manager can see where their groups fall on the scale; teams with more “green” employees are happier and less likely to experience turnover, whereas teams with more “red” employees are at a higher risk of voluntary turnover.
The potential for applying these predictive analytics within organizations is nearly unlimited. In addition to the cost savings, unexpected resignations can leave even the most accomplished leaders without vital team members. Predictive analytics offer insight that leaders need to make changes before experiencing abrupt departures and help create a work environment in which people feel loyal, connected to company values and a shared purpose, and at home with their colleagues. The savings—on both the financial and human levels—are enormous.
Jesse Harriott is the global head of analytics at Workhuman and the executive director of the Workhuman Analytics and Research Institute.