The challenge
Marketing performance was difficult to monitor in real time due to the volume and variability of call data.
This resulted in:
- Limited visibility into sudden drops or spikes in performance
- Delayed identification of campaign or channel issues
- Difficulty detecting low-quality or fraudulent leads
- Reliance on manual review and delayed reporting
There was a need for a system that could automatically detect anomalies and surface meaningful signals across large datasets.
What we built
We designed and deployed an anomaly detection system focused on identifying unusual patterns in call and campaign performance data.
The solution included:
- Data pipelines aggregating call data, campaign inputs, and performance metrics
- Machine learning models trained to detect deviations from expected behavior
- Real-time monitoring and alerting mechanisms
- Integration with reporting systems to provide actionable insights
The system was designed to operate continuously and adapt to changing campaign patterns.
