What problem does it solve? Sitewide analytics averages blend visitor segments with wildly different conversion behavior, producing misleading metrics and inconclusive A/B test results. This Skill applies the Quadrants of Traffic framework so every metric and test result is evaluated per segment before any conclusion is drawn. ## Core Features & Use Cases - Quadrant-native segmentation: Filters any Heatmap MCP metric by new_user, returning_user, and device to produce First-Time Mobile, First-Time Desktop, Returning Mobile, and Returning Desktop breakdowns. - Split test validation: Re-evaluates flat sitewide test results at the quadrant level to uncover hidden wins or losses, with decision rules for quadrant-specific implementation. - Mobile-first prioritization: Ranks optimization targets by First-Time Mobile session volume and RPS gap, and separates paid acquisition revenue from retention-driven revenue. - Use Case: An A/B test shows a flat sitewide result at 68% confidence. Run the four quadrant cuts and discover a +20% lift for mobile users, then implement the change for mobile only instead of discarding the test. ## Quick Start Ask Claude to break down this month's revenue per session and conversion rate by the four traffic quadrants using the Heatmap MCP.
