Anomaly Detection

Never Miss a Traffic Spike or Drop Again

By the time you notice a traffic crash in your analytics, you've already lost days of revenue. PreHog's Anomaly Detection spots unusual patterns the moment they happen.

How PreHog Detects Anomalies

The detection engine learns your normal patterns and flags deviations:

1. Baseline learning — Analyzes your historical data to understand normal patterns, including day-of-week and seasonal variations
2. Real-time monitoring — Continuously checks key metrics against expected ranges
3. Anomaly classification — Categorizes deviations as spikes (positive) or dips (negative) with severity levels
4. Root cause analysis — Investigates what changed when an anomaly is detected
5. Alert generation — Produces actionable alerts with context and recommended responses

Why This Approach Wins

1. Pattern-aware baselines mean Monday traffic dips don't trigger false alarms
2. Root cause analysis tells you why something changed, not just that it changed
3. Adjustable sensitivity lets you tune between "only critical" and "notice everything"

Set up anomaly detection on your most important metrics first — conversions, revenue, and key traffic sources.

By PreHog · February 2026