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recipe-ga4-funnel-bq
Recipe: GA4 funnel analysis via BigQuery — session_start → page_view → add_to_cart → checkout → purchase.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
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Recipe: GA4 funnel analysis via BigQuery — session_start → page_view → add_to_cart → checkout → purchase.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Google Tag Manager: Accounts, containers, tags, triggers, variables, and versions.
BigQuery: Run pre-built query templates against GMP export datasets.
BigQuery: Query GMP export data (GA4, Ads, GSC) with pre-built templates and custom SQL.
Google Ads: Campaign performance reports with date ranges and status filters.
Google Ads: Campaigns, ad groups, keywords, search terms, and raw GAQL queries.
GSC: Search analytics reports with dimensions, filters, and date ranges.
| name | recipe-ga4-funnel-bq |
| version | 1.0.0 |
| description | Recipe: GA4 funnel analysis via BigQuery — session_start → page_view → add_to_cart → checkout → purchase. |
| metadata | {"openclaw":{"category":"analytics","requires":{"bins":["gmp"]}}} |
Analyze the e-commerce conversion funnel using GA4 BigQuery export data. Shows drop-off at each step: session_start → page_view → add_to_cart → begin_checkout → purchase.
gmp auth login (bigquery.readonly scope)gmp bq explore --project YOUR_PROJECT --dataset analytics_YOUR_PROPERTY_ID -f table
gmp bq query -t ga4-funnel --project YOUR_PROJECT --dataset analytics_YOUR_PROPERTY_ID -r 30d -f table
The output shows unique session counts at each funnel step:
| step | users |
|---|---|
| session_start | 50000 |
| page_view | 48000 |
| add_to_cart | 5000 |
| begin_checkout | 2000 |
| purchase | 800 |
Calculate drop-off rates between steps to identify friction points.
For custom events, use gmp bq custom:
gmp bq custom --project YOUR_PROJECT -q "
SELECT event_name, COUNT(DISTINCT user_pseudo_id) AS users
FROM \`YOUR_PROJECT.analytics_PROPERTY_ID.events_*\`
WHERE _TABLE_SUFFIX BETWEEN '20240101' AND '20240131'
AND event_name IN ('session_start', 'sign_up', 'first_purchase', 'subscription')
GROUP BY event_name
" -f table
device.category to the GROUP BYgeo.country to the GROUP BYga4-attribution template alongside to understand which channels drive conversions