Anomaly Detection

Pulse continuously monitors every interaction and automatically flags the moment performance deviates from its established baseline. You don't need to set manual thresholds for anomaly detection — Pulse learns normal behavior and surfaces regressions automatically.

How It Works

For each interaction, Pulse maintains a rolling baseline of:

• Apdex score
• Error rate
• Completion rate
• Latency percentiles (P50, P95, P99)

When any of these metrics deviate significantly from the baseline, Pulse flags an anomaly and identifies the affected dimensions — which devices, OS versions, regions, or app versions are impacted.

Viewing Anomalies

Anomalies appear in multiple places:

• Home dashboard — The most recent and highest-impact anomalies are surfaced front and center
• Interaction detail page — Each interaction shows its own anomaly timeline
• Alerts feed — If alerting is configured, anomalies trigger notifications

Each anomaly card shows:

Field	Description
Interaction	Which user journey is affected
Metric	Which signal degraded (Apdex, error rate, latency, etc.)
Severity	Based on the magnitude of deviation and number of affected users
Affected Segment	The specific dimension combination where the regression is concentrated
Time Detected	When Pulse first flagged the anomaly

Investigating an Anomaly

Click on any anomaly to drill into the full investigation view:

1. Timeline — See exactly when the regression started and whether it's ongoing or resolved
2. Dimension Breakdown — Which devices, OS versions, regions, or app versions are affected? Pulse highlights the dimensions with the highest deviation.
3. Comparison — Side-by-side view of the affected segment vs. the unaffected baseline
4. Root Cause Candidates — Pulse surfaces likely causes: a recent release, an API change, or a device-specific condition
5. Session Replay Links — Jump directly to replays of affected sessions to see the issue firsthand

Multi-Dimensional Detection

Pulse doesn't just detect anomalies at the aggregate level. It analyzes across combinations of dimensions simultaneously:

• A checkout interaction might look healthy overall, but degraded for users on Android 14 + Jio network + v4.2.1
• A login flow might spike in error rate only in a specific region due to a network-level issue

This cross-dimensional detection is what separates Pulse from tools that only alert on top-line metrics.

Anomaly States

State	Meaning
Active	Regression is ongoing; metric has not returned to baseline
Resolved	Metric has recovered to within normal range
Acknowledged	A team member has marked the anomaly as being investigated

Next Steps

• Alerting — Get notified when anomalies are detected
• Session Replay — View affected user sessions
• PulseAI — Ask "Why did this interaction degrade?" and get an automated root cause analysis