01
Edge Ingestion
Module 1
Ingest customer service audio transcripts, network outage logs, and monthly bill changes.
Use Case 17 · Telecom & Infrastructure
Edge-Scrubbed Customer Churn Risk Diagnostic
Multi-variable churn prediction using edge-anonymized call records and billing sentiment.
Customer need & pain point
The problem this replaces.
- Inability to analyze unstructured customer support complaints due to strict telecom data privacy laws.
Step-by-step execution
How the engine executes it.
02
PII Scrubbing
Module 1
Anonymize all phone numbers and personal identities at local edge servers.
03
Sentiment Signal Processing
Module 3
Analyze frustration markers and service complaint frequency using local sentiment models.
04
Agentic AI
Module 4
Automatically trigger retention offers (e.g., free data upgrades) via automated SMS gateway.
Quantifiable ROI & impact
What changes commercially.
- 19.3% reduction in quarterly subscriber churn
- 100% compliance with privacy regulations.
The “aha” moment
“Traffelo AI flags churn risk from call sentiment while keeping customer voice data fully private.”