Predicting Student Dropout - Academic Retention Analytics
Early identification of students at risk of dropping out before new semester
📊 Key Performance Metrics
84%
Dropout Prediction Accuracy
17%
Students At Risk
€4,200
Average Revenue Loss per Dropout
💡 Strategic Insights
1
Early Warning System
84% accuracy in predicting student dropout risk before semester begins
2
Risk Population
17% of student population identified as high-risk for academic dropout
3
Financial Impact
€4,200 average revenue loss per dropout justifies intervention investments
📈 Data Visualization Summary
🎓 Accuracy: 84% | At-risk: 17% | Loss: €4,200 per student
🎯 Strategic Action Plan
🚀 Primary Focus: Leverage the key insights from this comprehensive analysis to drive strategic decision-making and optimize business performance across all identified areas.
📈 Implementation Priority: Focus resources on the highest-impact metrics and findings identified in this dashboard to maximize return on investment and accelerate growth.
📊 Performance Monitoring: Establish robust KPI tracking systems based on these analytical findings to ensure continuous improvement and maintain competitive advantage.
🔄 Continuous Optimization: Regularly review and update strategies based on ongoing data collection to maintain relevance and effectiveness of implemented solutions.