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

1. Segment every funnel — aggregate conversion rates hide the signal; break by device, source, and customer type
2. CLV must outpace CAC 3:1 — anything lower and the acquisition engine is destroying value, not creating it
3. Sample size before peeking — underpowered A/B tests produce false winners; calculate required n before starting
4. RFM actions must differ per segment — Champions and Lost customers cost the same to email but have opposite expected ROI
5. 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 calculation
• references/rfm-abtesting-metrics.md — RFM dimension scoring, segment actions, A/B test statistical design and reporting, core and operational KPI dashboard