From identifying rising talent to reducing turnover, predictive analyticsÂ help make employee recognition programs more proactive.
By Jesse Harriott
HR leaders know it takes work to attract and maintainÂ top talent. Locating, hiring, and retaining the best peopleÂ for each open role calls for an ongoing give-and-takeÂ between employer and employeeâand data is a must toÂ master the inner workings of these dynamics. To engageÂ quality talent and embark on the only long-term path toÂ greater profits, organizations need to refine their talentÂ strategy and the analytics that make it possible.
According to a Society for Human Resource ManagementÂ (SHRM) report, the average cost to hire ranges fromÂ $4,400 up to nearly $15,000, depending on the positionâsÂ seniority. That number includes advertising the role,Â interviewing, and background checks, but not the lostÂ value the previous employee brought to the bottom lineÂ or the costs of onboarding.
Artificial intelligence (AI) models that leverage predictiveÂ analytics help mitigate these and other costs by providingÂ vital data and information that can help leadersÂ proactively retain new hires, objectively identify risingÂ talent, and predict how likely it is that an employee willÂ leave an organization. The data itself can originate fromÂ an employee recognition and continuous performanceÂ management platform, whichâin addition to increasingÂ overall employee engagement by creating humanÂ moments that matter with gratitude-based awardsâhelps executives see which people leaders, managers,Â and individual contributors are doing excellent work andÂ building connections with their colleagues.
Retention Through Recognition
Predictive analytics rooted in peer-to-peer employeeÂ recognition can help increase engagement among newÂ hires and mitigate turnover within the first 90 to 120Â days of employment. This early period is when newÂ employees sometimes struggle to align their values withÂ the organizationâs and learn where and how they fit in.Â By tying values to recognition rewards, such as awards forÂ excellence shown in teamwork or customer service, newÂ employees can more easily integrate into the organizationÂ because they have concrete examples that model greatÂ behavior.
An analysis of reward activity shows that when peopleÂ receive seven to 10 values-based awards per year (a rateÂ of about one every 45 days) the likelihood of turnover canÂ be cut in half. Recognizing new hires just a few times atÂ the beginning of their tenure shows them they are valued,Â increases engagement and alignment, and helps preventÂ that costly early turnover.
Data from these recognition programs can also showÂ which values employees frequently use to recognize eachÂ other and which values they align with mostâand least.Â It shows where leadership must clarify values so theyâreÂ easier for employees to understand and associate withÂ day-to-day actions.
Identifying Rising Talent
Peer-to-peer recognition is entirely democratic andÂ provides a continuous feedback loop for people, leaders,Â and executives. According to Officevibeâs recent TheÂ Global State of Employee Engagement study, 62 percentÂ of employees wish they received more feedback from theirÂ colleagues. Many voices provide a more rounded view ofÂ each employee and can highlight a work cultureâs unsungÂ heroes.
Recognition also strengthens ties across team membersÂ and departments. Data from these programs illuminatesÂ how large employeesâ networks are, who has cross-departmental and cross-functional networks, and whoÂ holds highly central roles regardless of title. It allowsÂ leaders to look at patterns in the language of employeesâÂ recognition moments to identify powerful words suchÂ as âindispensableâ and âunmatchedâ that align with theÂ organizationâs core values or top leadership qualities.
Some employees receive a lot of recognition, but not fromÂ influential people such as managers or C-level executives.Â People who have smaller internal networks but receiveÂ recognition moments containing high-powered wordsÂ are most likely hidden rising talent. Do not overlook theÂ potential of these dedicated future leaders.
At most organizations, some departments run moreÂ smoothly than others. Some may have more experiencedÂ teams and others may have more engaged employees.Â There are, of course, warning signs of turnover, such asÂ few opportunities for advancement and below-averageÂ pay and benefits. But generally, itâs difficult to predictÂ which departments, teams, or individual employeesÂ are most at risk of high turnover. Even when there areÂ warning signs, they often come too late for leaders toÂ make a significant change before the turnover occurs.
A recognition program that leverages AI-enabled dataÂ analysis can assign each individual employee a scoreâred, yellow, or greenâthat correlates to how likely thatÂ person is to leave based on their connections and activityÂ within the employee recognition program. Each managerÂ can see where their groups fall on the scale; teams withÂ more âgreenâ employees are happier and less likely toÂ experience turnover, whereas teams with more âredâÂ employees are at a higher risk of voluntary turnover.
The potential for applying these predictive analyticsÂ within organizations is nearly unlimited. In addition to theÂ cost savings, unexpected resignations can leave even theÂ most accomplished leaders without vital team members.Â Predictive analytics offer insight that leaders need to makeÂ changes before experiencing abrupt departures and helpÂ create a work environment in which people feel loyal,Â connected to company values and a shared purpose, andÂ at home with their colleagues. The savingsâon both theÂ financial and human levelsâare enormous.
Jesse Harriott is the global head of analytics at Workhuman andÂ the executive director of the Workhuman Analytics and ResearchÂ Institute.