Reducing Turnover Through Predictive Analytics and Retention Strategies

The Business & The Challenge

A multinational food company, listed in the S&P 100 and S&P 500, with more than 40,000 employees worldwide and over $25 billion in annual revenue, faced a major issue: Struggling to retain blue-collar employees in its U.S. operations.

Key problems included:

• High voluntary turnover in the first 90 days
• Ineffective shift structures and onboarding processes
• No analytical framework to understand or predict employee churn

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Approach & Methodology

Falconi implemented a 3-phaseanalytics-based solution to reduce turnover and improve workforce retention.

1. Problem & Data Discovery

• Gathered employee data from ERP and HR systems
• Conducted correlation analysis between variables like shift patterns, rest     time, and tenure

2. Model Development

• Transformed and unified data tables
• Built statistical models to evaluate risk factors and build predictive insights

3. Model Operations & Execution

• Created "flight risk" dashboards, segmenting employee risk into     high, medium, and low categories
• Implemented targeted retention initiatives

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Insights Uncovered

• Rest time had a strong inverse correlation with voluntary turnover
• Employees with poor rest schedules showed up to 3x higher turnover rates
• 86.8% of employees fell into a “low risk” group, enabling precision targeting of higher-risk cohorts

Results Achieved

Metric                                            4Q 2020         3Q 2021        Improvement

Voluntary Turnover (All)       2.53%             1.10%            -1.4 p.p. / -57%

Turnover in First 90  Days    7.93%              2.27%          -5.6 p.p. / -71%

Absenteeism                             7.20%               5.13%           -2.1 p.p. / -29%

✅ Significant improvement in early-stage retention
✅ Targeted actions reduced absenteeism and improved shift alignment

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Implemented Actions

• Shift model redesign: Changed to A/B/C/D shift rotation structure
• Introduced buddy system onboarding for new hires
• Defined a “no-flight period” allowing new hires time to stabilize
• Improved planning to align commercial demand with plant production, reducing overtime fluctuation

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Conclusion

This project shows how Falconi empowered a global food company to cut early-stage turnover by over70% through data-driven HR strategies. By targeting high-risk employees with specific actions and redefining shift structures, Falconi helped foster a more stable and committed frontline workforce — reducing both cost and operational disruption.

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