performance_decay

To detect content decay, you analyze traffic curves from Google Search Console or Google Analytics 4 datasets to classify performance trends into winning, losing, or crashing categories. This process applies mathematical signal processing to distinguish normal noise from actual growth or decay patterns at scale.

The best way to classify web page traffic trends is using a source-agnostic engine that reads the shape of traffic curves like a human. It automatically labels pages as Winning, New, Healthy, Volatile, Losing, Crashing, or Crashed based on curve shape rather than simple percentage changes.

Yes, you need numpy and matplotlib to analyze performance trends. Numpy is required for the mathematical signal processing used to evaluate the traffic curves, and matplotlib is necessary for the visual rendering of those performance trends.

You can identify which specific topics are driving traffic changes by using hierarchical drill-down. This allows you to navigate from high-level performance metrics, such as language, down to specific topics or platforms to pinpoint the exact levers driving traffic changes.

Your landing page traffic curve shows as volatile instead of losing because the analysis engine distinguishes between normal noise and actual growth or decay. A volatile classification indicates fluctuating traffic patterns that do not exhibit the sustained downward trend required for a losing or crashing label.