Monitor Website Traffic Alerts and Anomalies

Monitor Website Traffic Alerts and Anomalies

By Michael Thompson

March 16, 2025 at 12:54 AM

Traffic alerts provide analytical insights about significant changes in your site's traffic patterns. These alerts monitor traffic levels and identify trends across marketing channels, annotating the traffic analysis graph with explanatory notes.

How Traffic Alerts Work

Traffic alerts use machine learning-based anomaly detection to identify statistically significant traffic changes. The system analyzes traffic patterns by comparing current volume against the past 28 days of data. Changes that fall outside 99% of the normal traffic distribution are flagged as anomalies.

The system requires at least 28 days of traffic data to establish baseline patterns and effectively detect abnormal changes. As more data is collected, the machine learning technology continuously improves its analysis accuracy.

Finding and Understanding Anomalies

Anomalies appear as flashing exclamation marks on the traffic analysis line graph. To view them:

  • Navigate to Analytics > Traffic
  • Select your desired date range
  • Click the alert icon for detailed information

Anomaly Details Include:

  • Date of occurrence
  • Traffic volume during the anomaly
  • Top affected pages (up to 2) and their traffic share
  • Primary channels causing the change

Making Use of Anomaly Data

Use anomaly detection to:

  • Identify traffic drivers
  • Understand which sources impact your traffic
  • Make data-driven adjustments to improve site performance
  • Test different strategies for various marketing channels

For example, if you notice decreased Facebook traffic, you might experiment with different social sharing approaches to determine what works best for your audience.

Providing Feedback

To help improve the tool's accuracy:

  • Look for the "Was this helpful?" message
  • Click thumbs up or down
  • Add optional detailed feedback
  • Submit your response

All feedback goes directly to the development team to enhance the feature's effectiveness.

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