Supermetrics

분석

OpenClaw의 Neotask이 Supermetrics를 통해 모든 마케팅 데이터를 쿼리합니다 — 마케팅 보고서 생성, 크로스 채널 분석, 예약된 데이터 쿼리를 자동으로 수행합니다.

할 수 있는 것

The Supermetrics integration gives Neotask 7 actions for unified marketing data access through OpenClaw.

Data Source Discovery

List all available data sources in your Supermetrics account — see every platform you have connected and available for querying.

Account Discovery

Find the right advertising or analytics accounts within any connected source. Navigate to the specific account you want to query before pulling data.

Field Discovery

Explore available metrics and dimensions for any data source and account combination. Understand what data is available before building your query.

Data Query

Execute queries against any connected source — pulling metrics, breakdowns, and time-series data with full filter and dimension control.

Async Query Results

Retrieve results from long-running queries that execute asynchronously.

User Information

Get current user context and account details.

Date Utility

Get today's date for building relative time range queries.

모든 액션은 자율적으로 실행되거나 승인을 요청합니다 — 여러분이 결정합니다.

이렇게 물어보세요

  • "Pull last week's Google Ads performance by campaign — show impressions, clicks, conversions, and cost"
  • "What marketing data sources do I have connected in Supermetrics?"
  • "Query Facebook Ads for the last 30 days of spend and ROAS by ad set"
  • "Compare our LinkedIn campaign performance this quarter vs. last quarter"
  • "What fields are available in the Shopify data source?"
  • "Pull a cross-channel summary: total spend and revenue across all paid channels last month"
  • 프로 팁

  • Use field discovery before building complex queries — your agent maps available metrics so you query the right field names the first time
  • Schedule weekly cross-channel performance queries and feed them into automated stakeholder reports
  • Combine Supermetrics with Zoho Analytics or Coupler.io in an app group for a complete collect-analyze-report pipeline
  • Approval gates work well for queries pulling large date ranges — review the scope before executing to avoid credit overuse