ecom-analytics

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Analyze e-commerce performance using GA4 metrics, conversion funnel analysis, and key e-commerce KPIs. Use this skill when the user needs to evaluate online store performance, diagnose conversion drop-offs, set up e-commerce tracking, or create performance dashboards — even if they say 'why are sales down', 'optimize our online store', 'set up GA4 for e-commerce', or 'what metrics should we track'.

3installs

Sourceasgard-ai-platform/skills

Added on2026-04-15

NPX Install

npx skill4agent add asgard-ai-platform/skills ecom-analytics

SKILL.md Content

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E-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

markdown

# 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
```