Post-Analysis: Metrics Recommendation

To identify the right KPIs and analytics events, you can analyze your source code to automatically extract user-facing features from routes and API endpoints, mapping them to relevant metrics across usage, conversion, engagement, churn, and error categories.

You can generate analytics event tracking code by analyzing your codebase to detect existing libraries like Firebase Analytics or Mixpanel, then outputting specific tracking code snippets or pseudocode tailored to those platforms for your identified user actions.

A North Star Metric (NSM) represents the core value your product delivers to customers. You can prototype a dashboard structure by analyzing code to propose an NSM alongside key panels, suggested visualizations, and specific performance thresholds.

Yes, you can recommend metrics for specific features like a user authentication flow by analyzing the relevant source code to suggest targeted KPIs like Sign-up Conversion Rate and Time to First Login, along with tracking events for each step.

Yes, it works without external dependencies by analyzing your source code to map user actions to metric categories, providing pseudocode for event tracking when specific analytics libraries are not detected in your environment.

The best way to map API endpoints to engagement and churn metrics is to analyze source code routes and user actions, automatically linking each endpoint's functionality to relevant KPI categories for comprehensive data tracking.