24/7 proactive investigation across all signals
Hundreds of detectors continuously watch your email ecosystem. Not static thresholds watching for known problems, but learned baselines that understand what's normal for each sender, ISP, and time of day.
The eyes that never sleep.
Beyond static thresholds
Traditional monitoring watches for known thresholds: bounce rate > 5%, complaint rate > 0.1%. But what's normal for your transactional stream differs from promotional. What's normal on Monday differs from Thursday. What's normal at Gmail differs from Telenet.be.
Engagor learns what's normal for each combination, then watches for deviations from YOUR baseline, not arbitrary industry numbers.
7 specialized detection methods
Each detector type is optimized for different patterns. Together, they catch anomalies that single-method approaches miss.
Spike Detection
Sudden increases in bounce, deferral, or complaint rates. Day-over-day comparison catches single-day spikes that period averages dilute.
17% bounce vs 0.9% baseline = 18x spike
Decay Detection
Gradual engagement decline over time. 7-day vs 7-14 day comparison reveals trends that daily snapshots miss.
Open rate -25% over 14 days
Distribution Shift
Changes in device mix, geo distribution, or ISP share. A 10-point mobile share drop signals rendering or deliverability issues.
Mobile 45% -> 32% (-13 points)
Correlation Detection
Patterns that emerge when crossing dimensions. Finds relationships between seemingly unrelated metrics.
Complaints correlate with Tue volume
Threshold Breach
Absolute thresholds for critical metrics. Complaint rate >0.10% triggers regardless of baseline.
Complaint 0.12% > 0.10% threshold
Baseline Deviation
Learned baselines per identity, per ISP. What's normal for promo differs from transactional.
2x above YOUR baseline
Seasonality Anomaly
Accounts for day-of-week and time-of-day patterns. Tuesday 10am has a different baseline than Sunday 6pm.
Abnormal for Tuesday 2pm UTC
Continuous monitoring loop
Collect
Pull metrics from all connected ESPs and MTA logs. Aggregate by ISP, device, geo, and identity dimensions.
Baseline
Calculate learned baselines per combination. Account for seasonality and historical patterns.
Scan
Run all 7 detector types against current data. Compare to baselines and thresholds simultaneously.
Detect
Flag anomalies with severity rating and confidence score. Deduplicate to prevent alert storms.
Generate
Create AI Kanban case with full context, analysis, and recommended actions.
What we watch
Deliverability
- Bounce rate (hard/soft)
- Deferral patterns
- Complaint rates
- ISP-level delivery
Engagement
- Open rates
- Click-through rates
- Unsubscribe rates
- Read time trends
Volume
- Send volume by identity
- Queue depth
- Throughput rates
- Delivery latency
Dimensions
- ISP breakdown
- Device distribution
- Geographic spread
- Identity performance
Ready for 24/7 autonomous monitoring?
See how continuous monitoring can transform your email operations from reactive to proactive.