domain-ecommerce:ecommerce-analytics
E-commerce analytics: conversion funnel tracking (browse to purchase), revenue attribution, cohort analysis, customer lifetime value (CLV), RFM segmentation, A/B test analysis (statistical significance), and key metrics (AOV, conversion rate, cart abandonment rate, repeat purchase rate).
SKILL.md
Full skill instructions
E-Commerce Analytics
When to use
- Building or debugging conversion funnel tracking (browse → product view → cart → checkout → purchase)
- Implementing revenue attribution models (last click, first click, linear, data-driven)
- Running cohort retention analysis to compare acquisition cohorts over time
- Calculating or predicting customer lifetime value (CLV) for CAC decisions
- Segmenting customers with RFM (Recency, Frequency, Monetary) scoring
- Designing or analyzing A/B tests with proper statistical rigor
- Setting up a core e-commerce metrics dashboard with anomaly alerting
Core principles
- Segment every funnel — aggregate conversion rates hide the signal; break by device, source, and customer type
- CLV must outpace CAC 3:1 — anything lower and the acquisition engine is destroying value, not creating it
- Sample size before peeking — underpowered A/B tests produce false winners; calculate required n before starting
- RFM actions must differ per segment — Champions and Lost customers cost the same to email but have opposite expected ROI
- Alert on rate drops, not absolute counts — seasonal volume swings mask conversion rate problems in absolute metrics
Reference Files
references/funnel-attribution-cohorts.md— funnel stage definitions, tracking implementation, bottleneck diagnosis, attribution models, cohort analysis, CLV calculationreferences/rfm-abtesting-metrics.md— RFM dimension scoring, segment actions, A/B test statistical design and reporting, core and operational KPI dashboard