ecom-analytics
About ecom-analytics
An e-commerce analytics skill for store teams assessing performance across traffic, conversion, revenue, and retention. It covers GA4 event tracking and uses funnel stages to help diagnose changes in sales rather than treating them as a single symptom.
Key features
- 01Measure acquisition through retention
- 02Review GA4 e-commerce events
- 03Locate funnel drop-offs
- 04Assess revenue, AOV, and product mix
- 05Compare retention across cohorts
Use cases
- 01Diagnose a decline in store revenue
- 02Find where shoppers leave the purchase funnel
- 03Review channel traffic and conversion quality
- 04Track repeat purchases and customer lifetime value
Install
Copy the command and run it in a terminal.
npx skills add https:/ / github.com/ asgard-ai-platform/ skills --skill ecom-analyticsAfter installing, type/ecom-analyticsin Claude Code to confirm it loaded.
Bundled files
References and scripts installed along with SKILL.md.
examples/sample_scenario.mdreferences/ecom-benchmarks.mdga4-setup.mdDetails
From the project READMEE-Commerce Analytics
Overview
E-commerce analytics measures online store performance across traffic, conversion, and revenue dimensions. This skill covers GA4 e-commerce tracking setup, funnel analysis, and key metric interpretation to diagnose why a store is or isn't performing.
Framework
IRON LAW: Diagnose by Funnel Stage, Not by Symptom
"Sales are down" is a symptom, not a diagnosis. Decompose into funnel stages:
Traffic × Conversion Rate × AOV = Revenue
If revenue drops 20%, is it because traffic dropped (acquisition problem),
conversion dropped (UX/pricing problem), or AOV dropped (product mix problem)?
Each requires a completely different fix.
E-Commerce Funnel & Key Metrics
| Stage | Metrics | What It Tells You |
|---|---|---|
| Acquisition | Sessions, Users, Traffic sources, CPC, CAC | Are you attracting enough visitors? From where? At what cost? |
| Engagement | Pages/session, Time on site, Bounce rate, Product views | Are visitors interested? Are they browsing? |
| Conversion | Add-to-cart rate, Checkout initiation rate, Purchase conversion rate | Where in the funnel are they dropping off? |
| Revenue | Revenue, AOV, Items per order, Revenue per session | How much are they spending? Is the mix healthy? |
| Retention | Repeat purchase rate, Purchase frequency, Customer lifetime value | Are they coming back? |
GA4 E-Commerce Events
| Event | Trigger | Key Parameters |
|---|---|---|
view_item | Product page view | item_id, item_name, price, category |
add_to_cart | Add to cart click | items array, value, currency |
begin_checkout | Checkout started | items, value, coupon |
add_payment_info | Payment entered | payment_type |
purchase | Order completed | transaction_id, value, tax, shipping, items |
Diagnosis Framework
Phase 1: Traffic Check
- Is total traffic up/down/flat vs prior period?
- Which channels changed? (organic, paid, social, direct, referral)
- Is traffic quality declining? (bounce rate, pages/session by source)
Phase 2: Conversion Check
- Where is the biggest funnel drop-off?
- Compare: View → Add to cart → Checkout → Purchase
- Industry benchmark conversion rates: 1-3% overall, 5-10% add-to-cart
Phase 3: Revenue Check
- AOV trend: rising (upselling working) or falling (discounting eroding value)?
- Product mix: is revenue shifting to lower-margin products?
- Revenue per session: the master metric (traffic quality × conversion × AOV)
Phase 4: Retention Check
- Repeat purchase rate by cohort
- Time between first and second purchase
- LTV trend by acquisition channel
Output Format
# E-Commerce Performance Report: {Store}
## Summary Dashboard
| Metric | Current | Prior Period | Change | Status |
|--------|---------|-------------|--------|--------|
| Sessions | {N} | {N} | {%} | 🟢/🟡/🔴 |
| Conversion Rate | {%} | {%} | {%} | 🟢/🟡/🔴 |
| AOV | ${X} | ${X} | {%} | 🟢/🟡/🔴 |
| Revenue | ${X} | ${X} | {%} | 🟢/🟡/🔴 |
## Funnel Analysis
| Stage | Volume | Rate | Drop-off | Benchmark |
|-------|--------|------|----------|-----------|
| Sessions | {N} | 100% | — | — |
| Product Views | {N} | {%} | {%} | — |
| Add to Cart | {N} | {%} | {%} | 5-10% |
| Checkout | {N} | {%} | {%} | 40-60% of ATC |
| Purchase | {N} | {%} | {%} | 1-3% overall |
## Diagnosis
- Primary issue: {funnel stage} — {specific problem}
- Root cause: {analysis}
## Recommendations
1. {action targeting the diagnosed stage}
Gotchas
- Conversion rate is meaningless without traffic quality context: A 5% conversion rate from email (high-intent) and 0.5% from display ads (low-intent) are both normal. Don't compare across channels.
- GA4 sessions ≠ Universal Analytics sessions: GA4 uses event-based model. Session timeout and attribution rules differ. Expect 5-15% discrepancy during migration.
- Mobile conversion is always lower: Mobile: 1-2%, Desktop: 3-5% is typical. Don't mix them in one number — analyze separately.
- Seasonality matters: Compare same period YoY, not just MoM. E-commerce has strong seasonal patterns (11.11, Christmas, Chinese New Year).
- Revenue ≠ profit: A 20% revenue increase from aggressive discounting may reduce profit. Track margin alongside revenue.
References
- For GA4 setup guide, see
references/ga4-setup.md - For e-commerce benchmark data by industry, see
references/ecom-benchmarks.md