Case Study - Tech Company Reduces Turnover by 58%

The Challenge

A fast-growing technology company with 1,200 employees was experiencing unsustainable turnover rates of 28% annually, significantly above the industry average of 13%. The cost of turnover was estimated at €15M per year, including recruitment, onboarding, lost productivity, and knowledge loss.

Key Pain Points

• High turnover (28% annually) costing €15M per year
• Critical talent leaving unexpectedly, disrupting projects
• Limited visibility into flight risk and turnover drivers
• Reactive retention efforts after resignation notices
• Inconsistent manager effectiveness in retaining talent

The Solution

The company implemented BrainPredict People's Turnover Prediction and Prevention suite, leveraging 27 AI models to predict flight risk, identify retention drivers, and recommend proactive interventions.

Phase 1: Flight Risk Prediction (Months 1-2)

Deployed the Turnover Prediction Engine to analyze 1,200 employees and identify those at risk of leaving in the next 6-12 months. The AI analyzed engagement data, performance metrics, compensation, career progression, manager effectiveness, and external market factors.

Phase 2: Root Cause Analysis (Months 2-3)

Used the Retention Driver Analyzer to identify why employees were leaving. The AI revealed that the top drivers were:

• Limited career growth opportunities (35% of turnover)
• Below-market compensation (28% of turnover)
• Poor manager relationships (22% of turnover)
• Work-life balance issues (15% of turnover)

Phase 3: Targeted Interventions (Months 3-6)

Implemented AI-recommended retention strategies for high-risk employees:

• Career development plans for 180 high-potential employees
• Compensation adjustments for 95 underpaid employees
• Manager coaching for 22 managers with high team turnover
• Flexible work arrangements for 140 employees with work-life balance concerns

Phase 4: Continuous Monitoring (Months 6-12)

Established ongoing flight risk monitoring with automated alerts for managers when employees showed signs of disengagement. Implemented quarterly retention reviews to proactively address issues.

The Results

Within 12 months of implementation, the company achieved remarkable results:

Turnover Reduction: 28% → 12% (-58%)

• Annual turnover dropped from 28% to 12%, below industry average
• Critical talent turnover reduced from 18% to 5% (-72%)
• Regrettable turnover (high performers) reduced from 15% to 4% (-73%)

Cost Savings: €8.7M Annually

• €6.2M from reduced recruitment and onboarding costs
• €1.8M from reduced lost productivity during transitions
• €700K from reduced overtime and contractor costs

Operational Improvements

• Flight risk prediction accuracy: 87% (6-12 months in advance)
• Retention intervention success rate: 73% (prevented resignations)
• Employee engagement scores improved from 6.8 to 8.2 (+21%)
• Manager effectiveness scores improved from 7.1 to 8.5 (+20%)

Strategic Benefits

• Proactive retention instead of reactive firefighting
• Data-driven talent decisions replacing manager intuition
• Improved employer brand and talent attraction
• Competitive advantage through talent stability

Key Success Factors

The company's success was driven by several key factors:

• Executive sponsorship from the Chief People Officer
• Manager training on using AI insights and having retention conversations
• Budget allocation for retention interventions (compensation, development, etc.)
• Integration with HRIS, performance management, and engagement survey systems
• Transparent communication about AI use and employee privacy

Lessons Learned

"BrainPredict People transformed our approach to retention. Instead of reacting to resignations, we now predict and prevent them 6-12 months in advance. The ROI was immediate, and our employees appreciate the proactive support." - Chief People Officer

Advice for Others

• Start with high-value employees (top performers, critical roles)
• Train managers on having proactive retention conversations
• Allocate budget for retention interventions before starting
• Be transparent with employees about AI use and privacy
• Act quickly on AI insights - timing is critical for retention