UM-Model 1 identifies true causes of network issues, predicts churn through causal factors, and optimizes infrastructure by understanding the causal relationships between network performance and customer satisfaction.
Why causal reasoning prevents service degradation
Observes: "Network congestion increases during evening hours in downtown area"
Add more bandwidth to downtown towers ✗
Confounding! Congestion caused by single malfunctioning tower routing traffic incorrectly, not capacity shortage. Wastes $2.3M on unnecessary infrastructure.
Builds causal graph: Tower Health → Routing, Routing → Congestion, Time → Usage Patterns
"What if Tower 47 routing was fixed?" Identifies root cause, not symptom
Fix routing configuration on Tower 47. Congestion resolved with $15K fix instead of $2.3M expansion ✓
Explore causal factors in telecom operations
Proven results from network operations
Identified causal failure patterns before outages. Predictive maintenance reduced downtime 42%
Separated service quality issues (causal) from price shopping (correlational). Targeted interventions reduced churn 31%
Identified actual growth drivers vs seasonal spikes. Optimized infrastructure investment saved $6.1M
Traced quality issues to specific network hops, not user devices. Targeted fixes improved satisfaction 12%
Analyze network performance with causal modeling
Adjust parameters and click "Run Analysis" to see network predictions