Using workforce analytics to retain frontline employees in limited-service restaurants

Limited-service restaurants face a persistent and costly challenge: high turnover among frontline workers. In the third quarter of 2024, hourly employee turnover at limited-service restaurants was 135%,¹ reflecting ongoing instability in the workforce. Similarly, in the same quarter in 2024, management turnover also increased, with the most recent rate at 55%, up from 45% in 2019.²

This level of churn has wide-ranging consequences. Frequent staff changes disrupt daily operations, lead to morale issues and inconsistent customer service, and drive up costs for recruiting, hiring, and training new employees. A Cornell University School of Hospitality Management study estimated the average total cost of frontline employee turnover to be $5,864.³ If the above statistics were extended to a full year, a hypothetical limited-service restaurant with 50 employees and a 135% turnover rate translates to 67 annual departures and a turnover cost of about $393,000. In a sector known for slim margins and intense competition, these costs can significantly impact overall performance.

To remain competitive, limited-service restaurants have an opportunity to predict, understand, and actively reduce turnover. Imagine if managers could pinpoint which employees are most likely to leave and intervene early. With data analytics and AI, limited-service restaurants can track and interpret workforce trends, uncover the root causes of attrition, and implement targeted retention strategies. This data-driven approach has the potential to boost morale, streamline operations, enhance customer experience, and lower costs in this industry. The combination of these outcomes can result in a future-ready workforce—a restaurant industry imperative identified in Deloitte’s perspective on The future of restaurants and food service: Thriving amid disruption. 

Insights hidden beneath the surface

Limited-service restaurants are well positioned to address worker turnover due to the increasing wealth of employee data available, which can be analyzed to understand the root causes of attrition. Potential data sources include:

Identifying turnover trends

Each of these data points offers a piece of the puzzle. For example, time and attendance records can reveal hidden patterns in which absenteeism, consecutive long shifts, or frequent shift changes lead to turnover. Alternatively, exit interviews and employee surveys provide direct attitudinal and sentiment feedback on why employees may be dissatisfied.

By applying advanced analytics, including machine learning and AI, restaurants can identify patterns and correlations within this data. Analytics platforms can integrate with existing HR, payroll, and scheduling systems to collect, clean, and standardize data, visualizing it in intuitive dashboards. These tools can quickly surface trends, such as spikes in turnover following schedule changes or correlations between exit rates and specific management practices.

Generating insights and outcomes

The true value of data analytics and AI lies in the actionable insights it generates. These insights can inform both corporate strategy and day-to-day store management. Here are some hypothetical examples:

• Employees working consecutive closing and opening shifts are more likely to resign, prompting managers to adjust scheduling practices to prevent burnout.

• Turnover is highest among employees who did not complete a structured onboarding program, highlighting the need for a more robust training and mentorship program for new hires.

• Frontline workers who remain in their roles without receiving upskilling opportunities leave at a higher rate, suggesting the need for an enhanced career development program.

• Certain locations are losing staff due to insufficiently competitive compensation and benefits compared to nearby competitors, suggesting that specific benefits could make roles more attractive at a modest cost.

Data-driven retention strategies can be applied at multiple levels within the organization. At the corporate level, leadership can use aggregated data to shape company-wide policies, such as revising scheduling guidelines or investing in new training programs and AI-enabled scheduling solutions. These shifts can help create a strong employer brand and employee value proposition (EVP).

At the store level, managers can receive tailored recommendations based on their team’s unique turnover drivers and local labor market trends. This enables limited-service restaurants to identify employees and locations with the highest turnover risk, along with the drivers of that risk. Leaders can then tailor retention strategies according to data, perhaps by recognizing top performers, offering more flexible shift swaps, or adjusting compensation packages by individual or by location

Potential benefits of data-driven strategies

Addressing the root causes of turnover benefits both employees and the business. Workers gain a more supportive, predictable, and rewarding work environment, boosting morale and job satisfaction. For the business, reducing turnover means lower hiring and training costs, greater operational consistency, better morale, and a stronger employer brand, leading to improved customer service and financial performance.

By using data to guide their efforts, restaurants can apply targeted retention strategies that are empirically derived and responsive to the real needs of their workforce. This proactive approach helps transform raw information into actionable intelligence, empowering management to make evidence-based decisions about their workforce.

Conclusion

For limited-service restaurants ready to tackle turnover with data analytics, the first steps are to assess the data they collect and determine how it can be integrated and analyzed. Investing in data science capabilities may help each restaurant to differentiate as the employer of choice for frontline worker talent. By harnessing the power of data analytics, they can move from reactive problem-solving to proactive workforce management, creating a win-win-win for employees, managers, and customers alike.