Operators often rely on monitoring tools to track operational incidents such as network faults, alerts, and overall business performance. However, these approaches are difficult to scale and hard to operate consistently.
To better align operations with business objectives, engineering teams define monitoring KPIs and continuously adjust them as market conditions evolve. In practice, this creates two key challenges:
Monitoring tends to depend on experienced specialists who can set meaningful targets and thresholds.
The process involves significant trial and error. Defining effective KPIs and tuning thresholds often requires multiple iterations, consuming time and increasing the risk of missed issues or excessive, noisy alerts.
In the context of roaming, the cost of delayed detection is particularly high. Congestion, service degradation, and outages across partner networks can quickly impact customer experience, create SLA risks, and reduce retail revenue, often before teams can clearly identify what changed and where.
TOMIA provides AI-enabled monitoring through anomaly detection to shift from reactive tracking to proactive quality management, automating analysis and delivering actionable insights in real-time. The service provides:
Automated analysis across system counters: Instead of relying only on manually defined KPIs, AI continuously analyzes a broad range of counters, making custom KPI creation optional.
Detection of anomalies against historical baselines: Live behavior is compared with historical patterns, with alerts triggered when meaningful deviations occur.
Flexible comparison windows: Teams can assess changes across different timeframes, such as the previous day or the same day in the prior week, to distinguish true anomalies from expected patterns.
Efficient alert management: Alerts can be acknowledged, snoozed, or dismissed, reducing noise and allowing teams to focus on what matters most.
Clear visualization for faster diagnosis: Intuitive graph views highlight traffic variations, making it easier to identify unusual behavior and accelerate root cause analysis.
By proactively identifying congestion, service degradation, and outages across partner networks, anomaly detection strengthens roaming quality management. It enables earlier intervention, helping protect customer experience, safeguard revenue, and maintain SLA compliance.
With AI driven anomaly detection, operators can deliver measurable improvements across operations, quality, and business outcomes:
Earlier detection, faster response: Proactively surfaces anomalies before they escalate into widespread customer impact, enabling quicker intervention.
Reduced reliance on expert-only monitoring: Automation makes monitoring more accessible by reducing the need for complex KPI design and repeated threshold tuning.
Less noise, more action: Structured alert workflows (acknowledge/snooze/ignore) help teams manage volume and focus on priority incidents.
Improved roaming quality and SLA posture: Continuous anomaly detection supports consistent quality management across partner networks, helping protect experience and maintain SLA compliance.
Revenue protection through better quality: By spotting roaming issues sooner (e.g., congestion/outages), operators reduce the risk of poor roaming experiences that can drive churn and revenue loss.
Explore TOMIA’s AI‑driven approach to smarter roaming here.