web-analytics

You can detect anomalies in GA4 web analytics data using STL-based decomposition to identify unusual patterns and root causes across sources, devices, geographies, and pages. The process extracts daily metrics via MCP or API and outputs analysis files to your workspace.

Markov path analysis models session flows to identify top conversion paths and funnel insights. It processes extracted GA4, Mixpanel, or Amplitude data to reveal how users navigate and convert, writing navigation paths to workspace output files.

Predictive audience scoring works with GA4, Mixpanel, and Amplitude data to calculate propensity to convert and churn. It applies temporal holdout validation to ensure scoring accuracy and outputs audience profiles to your analysis workspace.

Anomaly detection requires pandas, numpy, scipy, statsmodels, and scikit-learn to perform STL-based decomposition and predictive scoring. These dependencies support data normalization, statistical analysis, and machine learning workflows for web metrics.

Automating GA4 data extraction involves using MCP or API calls to pull reports, normalizing them into daily traffic and conversion metrics, and writing outputs to workspace/processed. Script-driven pipelines handle incremental processing for dashboard integration.

STL decomposition separates seasonal trends from residual noise, enabling precise root-cause analysis across dimensions like devices and geographies. Standard thresholds lack this decomposition, missing subtle anomalies in session flow and engagement metrics.