---
title: "How Anomaly Detection Works"
description: "How LENS detects anomalies in your analytics data using dynamic statistical thresholds and seasonality awareness."
canonical_url: "https://docs.sealmetrics.com/lens/anomaly-detection"
lang: "en"
date_generated: "2026-08-11T17:34:37.681Z"
source_hash: "385525ea2579a72988721ccbd1a516c8e15af15843b83291e692e2b17d5022c7"
content_type: "documentation"
owner: "docs"
llm_priority: "useful"
source_file: "lens/anomaly-detection/index.mdx"
publisher: "Sealmetrics"
---

# How Anomaly Detection Works

Canonical page: https://docs.sealmetrics.com/lens/anomaly-detection

**Caution:**
Automated anomaly detection is **built but not active**. No detection rule currently runs on customer accounts and no automated alert is generated. This page documents the intended behaviour; the execution cadences below describe how rules will run once enabled. For what LENS does today, see [LENS AI](/lens).

LENS is designed to monitor your analytics data and detect unusual patterns that could indicate problems or opportunities, using a fixed library of detection rules — see the [rules catalog](/lens/anomaly-detection/rule-types).

## Detection methods

### 1. Statistical comparison against a baseline

Each rule compares a current period against a historical baseline and measures the percentage change. If the change exceeds the rule's threshold, an anomaly is flagged.

```
Current value vs. baseline
─────────────────────────────────────
If |change| > threshold → Anomaly detected

Example:
- Baseline conversion rate: 2.5%
- Current conversion rate: 1.6%
- Change: -36%
- Result: candidate anomaly (then confidence-checked)
```

Thresholds are **not** a single fixed number. They are adjusted dynamically based on how much data is available and on seasonality — see [Configuring Thresholds](/lens/anomaly-detection/thresholds) for the full mechanism.

### 2. Confidence weighting

Before raising an alert, LENS calculates a **confidence level** (`high`, `medium`, or `low`) from the sample size and the number of days of data. With less data, the threshold automatically becomes more conservative, so a bigger change is required before LENS alerts. This keeps low-traffic periods from generating noise.

### 3. Seasonality awareness

LENS can account for expected, recurring variations:

- **Year-over-year comparison** — when an account has a seasonal pattern that warrants it (and historical data exists), the baseline is shifted to the same period last year instead of the recent past.
- **Expected change suppression** — if a change matches what's expected for the current season, it is treated as normal rather than as an anomaly.
- **Threshold relaxation** — during known seasonal periods, thresholds are loosened so ordinary seasonal swings don't trigger false alerts.

### 4. Comparative analysis

Some rules compare related metrics to find inconsistencies, for example:

- Traffic up but conversions flat → potential traffic-quality issue
- High traffic on a page or source but below-average conversion rate
- Mobile converting worse than desktop

## Execution models

Once enabled, rules will run under one of two models:

| Type | Cadence | Examples |
|------|---------|----------|
| **Reactive** | Daily / weekly / monthly batches over historical data | `traffic_drop`, `conversion_drop`, `device_gap` |
| **Proactive** | Near real-time checks during the day | `intraday_conversion_drop`, `source_sudden_drop`, `landing_page_failure` |

## How a rule is evaluated

```
┌─────────────────────┐
│ Collect period data │
└──────────┬──────────┘
           ▼
┌─────────────────────┐
│ Compute current vs  │
│ baseline (YoY if    │
│ seasonality wants)  │
└──────────┬──────────┘
           ▼
┌─────────────────────┐
│ Adjust threshold by │
│ confidence + season │
└──────────┬──────────┘
           ▼
┌─────────────────────┐
│ Change beyond       │── No ──► No action
│ adjusted threshold? │
└──────────┬──────────┘
           │ Yes
           ▼
┌─────────────────────┐
│ Expected for the    │── Yes ─► Suppressed
│ season?             │
└──────────┬──────────┘
           │ No
           ▼
┌─────────────────────┐
│ Create insight /    │
│ notify (email)      │
└─────────────────────┘
```

## Data requirements

LENS becomes more confident as data accumulates. The internal confidence thresholds are:

| Signal | Medium confidence | High confidence |
|--------|-------------------|-----------------|
| Days of data | 7+ days | 14+ days |
| Traffic sample (events) | 100+ | 1,000+ |
| Conversions | 20+ | 100+ |

Below the medium thresholds, detection still runs but with more conservative (harder-to-trigger) thresholds, and insights are labeled **low confidence**.

## Notifications

When LENS surfaces an insight, it can notify you by **email** (key metrics, trends, and recommendations delivered to your inbox). Email is currently the only delivery channel for LENS notifications. Make sure email notifications are enabled in your account settings.

## Handling expected changes

LENS reduces false positives automatically through seasonality awareness and confidence weighting. If a recurring seasonal swing keeps surfacing, configure a seasonality pattern so LENS can adjust its baseline (year-over-year) and relax thresholds during that period.

## Next steps

- [View the planned rule library →](/lens/anomaly-detection/rule-types)
- [Configure thresholds →](/lens/anomaly-detection/thresholds)