Executive Summary
A Tier-1 telecom operator serving over 45 million subscribers faced increasing network congestion during peak hours. Rising data consumption and 5G rollout complexities led to customer complaints and reduced service quality. Quation implemented an AI-driven telecom network analytics solution that reduced congestion by 37% and improved service reliability across urban clusters.
Business Challenges
- Network congestion during peak traffic hours
- High customer complaints related to call drops and latency
- Limited real-time network visibility
- Inefficient capacity planning
- Rising operational costs
Quation’s AI-Powered Telecom Analytics Solution
Real-Time Traffic Monitoring
Integrated network data from OSS/BSS systems and cell towers into a centralized AI-powered analytics platform.
Predictive Congestion Modeling
Machine learning algorithms analyzed historical traffic patterns and predicted congestion hotspots 24–48 hours in advance.
Automated Capacity Optimization
AI-driven recommendations dynamically optimized bandwidth allocation and resource utilization.
Network Performance Dashboards
Provided NOC teams with real-time KPIs including latency, packet loss, and throughput metrics.
Results Achieved
- 37% Reduction in Network Congestion
- 28% Improvement in Network Throughput
- 22% Decrease in Customer Complaints
- 19% Reduction in Operational Costs
Strategic Impact
With advanced Telecom Data Analytics Solutions, the operator improved network intelligence, enhanced subscriber satisfaction, and optimized infrastructure investments.