ecommerce-analysis
Skill244 starsChanged 43 days agoMITView on GitHub
Revenue, conversion rate, AOV, and funnel drop-off using GA4 ecommerce events.
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 72
Use separate queries for item-level and session-level analysis
≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Ecommerce Analysis
Revenue, conversion rate, AOV, and funnel drop-off using GA4 ecommerce events.
When to use
- You have purchase, add_to_cart, begin_checkout events in GA4
- You want to measure revenue, conversion rate, or average order value
- You want to find where users drop off in the purchase funnel
Correct metric and dimension names
GA4 ecommerce uses specific API names — do not guess:
| Concept | GA4 API name |
| Revenue | purchaseRevenue |
| Transactions | ecommercePurchases |
| AOV | averagePurchaseRevenue |
| Add to cart | addToCarts |
| Checkout started | checkouts |
| Cart-to-view rate | cartToViewRate |
| Purchase-to-view rate | purchaseToViewRate |
| Item views | itemsViewed |
| Item revenue | itemRevenue |
| Items purchased | itemsPurchased |
| Product name | itemName |
| Product category | itemCategory |
| Transaction ID | transactionId |
Step 1 — Revenue overview
dimensions: ["date"]
metrics: ["ecommercePurchases", "purchaseRevenue", "averagePurchaseRevenue"]
date_range: last 30 days
order_by: date ASCStep 2 — Funnel: views → cart → checkout → purchase
dimensions: ["sessionDefaultChannelGroup"]
metrics: ["itemsViewed", "addToCarts", "checkouts", "ecommercePurchases",
"cartToViewRate", "purchaseToViewRate"]
date_range: last 30 days
order_by: ecommercePurchases DESCDrop-off between stages = 1 - (next_stage / current_stage).
Fix the biggest drop-off first.
Step 3 — Top products
dimensions: ["itemName", "itemCategory"]
metrics: ["itemsViewed", "addToCarts", "itemsPurchased", "itemRevenue"]
date_range: last 30 days
order_by: itemRevenue DESCStep 4 — Revenue by channel
dimensions: ["sessionDefaultChannelGroup"]
metrics: ["ecommercePurchases", "purchaseRevenue", "averagePurchaseRevenue"]
date_range: last 30 days
order_by: purchaseRevenue DESCIncompatible combinations to avoid
itemIdoritemSkucannot be combined with session-level metricsecommercePurchasescannot be combined witheventCountin the same query- Use separate queries for item-level and session-level analysis
What to report
- Revenue trend over the period
- Funnel conversion rates and biggest drop-off point
- Top 10 products by revenue
- Best-performing acquisition channel by revenue and AOV
In this repository21 other files it gives its agents.
- AGENTS.mdAGENTS.md
- docs/llms-full.txtllms.txt
- docs/llms.txtllms.txt
- llms.txtllms.txt
- ga4-analyticsSkill
- ai-referral-analysisSkill
- attribution-scopeSkill
- bot-traffic-detectionSkill
- channel-acquisitionSkill
- common-metric-namesSkill
- compatible-combinationsSkill
- content-performanceSkill
- custom-dimensionsSkill
- date-rangesSkill
- filter-structuresSkill
- ga4-limitationsSkill
- gcp-setupSkill
- geo-device-segmentationSkill
- gcp-setupSkill
- traffic-diagnosisSkill
- ua-to-ga4Skill
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