Commerce Analytics

You cannot optimize what you mis-measure. Define the KPIs that actually move a store and instrument the standard ecommerce event spec correctly — platform-agnostic, with a validator for GA4 dataLayer events.

Before Starting

Gather: current tracking (GA4/GTM/other), business model (one-time vs subscription), key conversion actions, and goal (define KPIs, design/instrument events, or audit existing tracking).

How This Skill Works

Mode	Trigger	Output
KPIs	kpis	KPI framework + dashboard spec
Events	events	Ecommerce event taxonomy + dataLayer spec
Audit	audit	Tracking gap + data-quality review

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Phase 1 — Core KPIs

Metric	Formula	Watch for
Conversion rate	orders / sessions	Segment by device/channel
AOV	revenue / orders	Moves with promos/bundles
CLV	avg order value × frequency × lifespan	The number that justifies CAC
Cart abandonment	1 − (checkouts / carts)	Diagnoses checkout friction
Repeat rate	repeat customers / total	Retention health
Sell-through	units sold / units received	Feeds inventory-stock forecasts

Rule: Pair every acquisition metric with a margin metric — revenue growth on negative contribution margin is a trap.

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Phase 2 — The Ecommerce Event Spec

The standard GA4 ecommerce funnel events, each carrying currency + an items[] array:

view_item_list → view_item → add_to_cart → begin_checkout → add_shipping_info → add_payment_info → purchase (plus remove_from_cart, refund).

Each items[] entry: item_id, item_name, price, quantity (+ optional item_brand, item_category, item_variant). purchase also needs transaction_id and value.

Validate a captured dataLayer against this spec:

python skills/commerce-analytics/scripts/ga4_ecommerce_validate.py            # demo events
python skills/commerce-analytics/scripts/ga4_ecommerce_validate.py events.json

It reports PASS/FAIL per event with the missing required fields. For the full taxonomy and parameter reference, see references/ga4-ecommerce-events.md.

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Phase 3 — Audit & Data Quality

Common faults: missing transaction_id (duplicate purchases), value excluding/including tax inconsistently, items[] absent on purchase, double-firing (GTM + hardcoded). Cross-check store revenue vs GA4 revenue — variance > 5% means a tracking bug.

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Output Artifacts

Request	Output
KPIs	KPI framework + dashboard layout
Events	Event taxonomy + dataLayer spec
Audit	Gap analysis + data-quality scorecard

Commerce tracking is COMPLIANT when the full funnel fires with valid items[] and currency, purchase carries a unique transaction_id, revenue reconciles within 5%, and KPIs pair growth with margin.

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Related Skills

• analytics-tracking: general GA4/GTM setup this builds on for SaaS-style funnels.
• inventory-stock: sell-through metrics feed restock forecasting.
• commerce-data-model: item taxonomy maps to items[] parameters.
• ab-test-setup / campaign-analytics: experimentation and campaign measurement.