Wanted: A Data-Based Method for Retaining Talent

As the prospect of an economic slowdown looms large,¹ companies around the world are struggling to do more with less. Many are cutting costs, with some looking to shift employee responsibilities, reduce overall headcount and scale back rewards.

These moves will put even greater pressure on top performers at a time when worker burnout has reached the level of an international crisis: A survey of more than 10,000 employees in the U.S., Australia, France, Germany, Japan and the UK showed that feelings of burnout had risen 8% from May to October 2022.² Complicating matters is “quiet quitting,” the phenomenon in which workers show up at work and do the minimum required — but not much else.³

40% of workers in a global study report feelings of job burnout.

Source: Future Forum Pulse Report Fall 2022

As job markets begin to swell with the recently laid off, a growing number of burned-out workers may choose quiet quitting over seeking more engaging employment elsewhere. That’s especially the case if their employers are unable to demonstrate how effort and impact correspond to differentiated opportunities and rewards for top performers.

How can employers mitigate the detrimental effects on productivity and morale? By identifying those top performers, understanding what motivates them and applying a data-based strategy aimed at retention.

HR Data Science is evolving

For years, management has used conventional methods like employee surveys, performance reviews and exit interviews to keep a pulse on employee sentiment and understand shifts in morale. Recent advances in data science and machine learning offer far more sophistication; new applications help leaders understand and better manage employee perceptions of their jobs and related levels of satisfaction.

Today, state-of-the-art predictive analytics target the main factors of engagement and retention, from the individual employee to the department level. The goal is to leverage data to refine the employee value proposition (i.e., the unique set of benefits, opportunities and experiences an employee receives in return for the skills and capabilities they bring to a company) and align it more closely with worker expectations.

By identifying exact elements that contribute to an employee’s desire to stay or go, business leaders and their HR partners can identify those individuals who are more likely to remain and they can unlock powerful tools to keep them engaged.

Data sets used in these new predictive analytics models start with traditional HR attributes like age, role, survey and focus group data, employee job history, salary, and number of direct reports. Once data is collected, a predictive engine creates tailored models for each individual by combining baseline industry and functional role data with historical company data.

Employers in virtually any industry, business unit or regional market can take advantage of predictive analytics to better understand who among their top talent is most likely to stay long term and what factors most appeal to them. From there, employers can refine retention incentives based on modifications to work, rewards, recognition programs, career training, paths to career progression and the workplace.

Practical Application

Consider how predictive analytics might resolve this hypothetical scenario: A U.S. manufacturing firm with plants across the country falls behind on fulfilling orders due to persistent retention and recruitment issues that leave it understaffed. Conventional wisdom holds that the best way to attract more talent is to offer higher wages, but predictive analytics models suggest that direct remuneration isn’t always the primary factor for reducing turnover in the manufacturing sector. Addressing local factors like the distance an employee must travel from home to work can often be more effective.

To address this situation, a plant’s leadership team might consider offering additional transportation benefits or organized carpools to differentiate the firm’s value proposition from those of its competitors. Not only do these actions signal that leadership is willing to implement remedial strategies to benefit employees, but they also demonstrate that senior leaders listen, sympathize and respond to employee concerns.

Get the Word Out

Predictive analytics can’t solve all hiring and engagement issues, but they can put companies in a better position to retain their key employees and motivate them to perform at the highest level. Impacts on productivity and innovation — as well as overall recruitment and onboarding budgets — are compelling, and these benefits will only become more important in a difficult economic environment. Ultimately, this represents a smart way to adapt in quickly evolving markets.