How to build Google Ads dashboards in Metabase

Google Ads is Google's advertising platform for search, display, YouTube, and Performance Max campaigns. Metabase is where you turn that marketing data into shared, trustworthy dashboards. This guide covers two complementary paths: a lightweight MCP + CLI route that pulls live data with the Google Ads MCP server and loads a CSV into Metabase with the Metabase CLI, and a durable pipeline route that syncs Google Ads daily stats into a database so you can build dashboards anyone can read.

How do you connect Google Ads to Metabase?

Most teams combine both routes: use MCP and CLI uploads for a fast first pass, then move recurring marketing reporting to a warehouse-backed model.

What can you analyze from Google Ads data in Metabase?

• Spend, clicks, and conversions by campaign — built from campaign daily stats and the related campaigns, ad groups, ads data your sync exposes.
• ROAS by campaign and channel — built from campaign daily stats and the related campaigns, ad groups, ads data your sync exposes.
• Search-term performance and wasted spend — built from campaign daily stats and the related campaigns, ad groups, ads data your sync exposes.
• Budget pacing against monthly targets — built from campaign daily stats and the related campaigns, ad groups, ads data your sync exposes.
• Quality-score and CTR trends — built from campaign daily stats and the related campaigns, ad groups, ads data your sync exposes.

Which Google Ads dashboards should you build in Metabase?

How do you use the Google Ads MCP server with the Metabase CLI?

Pair the Google Ads MCP server with the Metabase CLI for fast, hands-on analysis. MCP is useful for scoped lookups and summarized exports; the Metabase CLI's upload command loads CSV data into Metabase and creates a ready-to-query table and model.

Example workflow

• Ask the MCP server for last month's campaign daily stats with spend, clicks, and conversions by campaign.
• Export the result as CSV, keeping stable IDs, channels, campaigns, and dates.
• Run mb upload csv to load it into Metabase as a table and model, then build questions and dashboards on top.

Be honest about the limits

• MCP lookups are excellent for exploration, not scheduled reporting.
• A CSV upload is a snapshot; refresh it with mb upload replace or move to the pipeline for real history.
• Daily stat rows per campaign are required for pacing and trend cards — a one-off export only supports snapshots.
• mb upload csv needs an uploads database configured under Admin → Settings → Uploads.

How do you set up Google Ads MCP and the Metabase CLI?

{
  "mcpServers": {
    "google-ads": {
      "command": "pipx",
      "args": [
        "run", "--spec",
        "git+https://github.com/googleads/google-ads-mcp.git",
        "google-ads-mcp"
      ]
    }
  }
}

Google's server is read-only and runs GAQL queries — it needs the same developer token and Google Cloud project as the raw API. Hosted community alternatives (for example Pipeboard's google-ads.mcp.pipeboard.co) trade that setup for a third-party OAuth flow.

# Install the Metabase CLI
npm install -g @metabase/cli

# Log in (opens your browser; requires Metabase v62+)
mb auth login --url https://your-metabase.example.com

# Load a campaign-daily-stats export — creates a table AND a model
mb upload csv --file google-ads-campaign-daily-stats.csv --collection root

# Refresh that same table later from a new export
mb upload replace <table-id> --file google-ads-campaign-daily-stats.csv

Can you generate a Google Ads dashboard with AI?

Yes. Use the prompt below with any assistant that can run the Google Ads MCP server and the Metabase CLI. It works end to end: if Google Ads tables already exist in Metabase it analyzes those; otherwise it pulls scoped, summarized data over MCP, loads it with mb upload csv, then builds the dashboard and caveats any metric that needs missing history.

Create a polished Metabase dashboard for Google Ads advertising analytics.
Work end to end: get the data into Metabase if it isn't there yet, then build.

Goal: Help marketing and growth leaders understand spend, ROAS, CTR, CPC, budget pacing, and acquisition cost by campaign and channel from Google Ads data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for google-ads tables and
  models). If durable Google Ads data is already present — synced from a warehouse
  or uploaded earlier — use it and skip to Step 2.
- If nothing is there, pull a scoped, summarized export with the Google Ads MCP server:
  campaign daily stats, plus campaigns, ad groups, ads.
  Prefer daily aggregates over raw events. Write each result to a CSV,
  then load it with the Metabase CLI — run "mb upload csv --file <export>.csv" so
  each upload creates a table and a ready-to-query model. Use "mb upload replace
  <table-id> --file <export>.csv" to refresh an existing table instead of creating
  duplicates.

Step 2 — Inspect before querying:
Do not assume exact table or column names. Inspect available fields, channels,
campaigns, dates, and whether daily history exists before creating trend or
pacing cards.

Important:
- Build on whatever data is present; don't claim Metabase connects natively to
  Google Ads — it reads a database or CLI-uploaded tables.
- Never try to load raw event or click streams into Metabase; use daily
  aggregates, campaign-grain stats, and entity tables.
- Only compute rates (CTR, conversion rate, ROAS, CAC) when both numerator and
  denominator exist — and state the attribution model when reporting conversions.
- Exclude test campaigns and internal traffic from headline cards, and keep
  currency consistent when spend spans accounts.
- A single CSV is a point-in-time snapshot: only build trend cards if there is a
  usable date column or multiple periods have been uploaded.

Dashboard title: Google Ads Advertising Overview

Sections:
1. Executive summary: Spend this month; Conversions; ROAS; CAC; Budget pacing
   vs. plan.
2. Campaigns: Spend, clicks, conversions, and ROAS by campaign by week.
3. Efficiency: CTR, CPC, and cost-per-conversion trends; wasted-spend table.
4. Pacing: Month-to-date spend vs. budget; projected end-of-month spend.
5. Acquisition: CAC and new customers by channel, joined to revenue where synced.

Filters: Date range, Channel, Campaign, Country, Device, Segment.

Output: Build the dashboard if you have permission; otherwise provide the exact
questions, SQL, model definitions, and layout. Include caveats for any metric
that cannot be calculated from the available data.

How do you sync Google Ads data into a database or warehouse?

For dashboards that need history and reliability, land Google Ads daily stats and entities in a database first, then connect Metabase to that database.

Connector options

• Managed ETL — use a connector when one covers the objects you need.
• Custom pipeline — use the Google Ads API (GAQL) for control over grain, fields, and refresh cadence.
• MCP + CSV — use this for quick exploration and one-off slices.

Sync campaigns, ad groups, and daily stats with the dlt google_ads verified source, the Airbyte Google Ads source, or Fivetran's Google Ads connector — remember costs arrive as cost_micros (divide by 1,000,000).

Notes

• Decide the rollup grain first (daily per campaign/channel is the workhorse) — it drives warehouse cost and every trend card.
• Land raw entity tables first, then build clean Metabase models on top.
• Normalize channel, campaign, ad-group, date, spend, impressions, clicks, and conversions fields.

How should you model Google Ads data in Metabase?

Core tables

Modeling advice

• Build a clean ad_performance_daily model with common columns across tools, so multi-channel dashboards don't fork definitions.
• Separate entity tables (campaigns, audiences, pages) from daily time-series rollups.
• Exclude test campaigns and internal traffic from headline metrics; keep channel and campaign as explicit columns.
• Use stable IDs for campaign, channel, and user joins; display names change.

Which Google Ads metrics should you track in Metabase?

What SQL powers Google Ads dashboards in Metabase?

These assume a cleaned analytical model in a warehouse (PostgreSQL dialect). Adjust table and column names to match your pipeline.

SELECT
  campaign_name,
  date_trunc('week', stat_date) AS week,
  ROUND(SUM(spend), 2) AS spend,
  SUM(conversions) AS conversions,
  ROUND(SUM(conversion_value) / NULLIF(SUM(spend), 0), 2) AS roas
FROM ad_performance_daily
GROUP BY 1, 2
ORDER BY 2, spend DESC;

SELECT
  channel,
  date_trunc('month', stat_date) AS month,
  ROUND(100.0 * SUM(clicks) / NULLIF(SUM(impressions), 0), 2) AS ctr_pct,
  ROUND(SUM(spend) / NULLIF(SUM(clicks), 0), 2) AS cpc,
  ROUND(SUM(spend) / NULLIF(SUM(conversions), 0), 2) AS cost_per_conversion
FROM ad_performance_daily
GROUP BY 1, 2
ORDER BY 1, 2;

WITH monthly_spend AS (
  SELECT
    channel,
    date_trunc('month', stat_date) AS month,
    SUM(spend) AS spend
  FROM ad_performance_daily
  GROUP BY 1, 2
),
monthly_customers AS (
  SELECT
    acquisition_channel AS channel,
    date_trunc('month', acquired_at) AS month,
    COUNT(*) AS new_customers
  FROM customers
  GROUP BY 1, 2
)
SELECT
  s.channel,
  s.month,
  ROUND(s.spend, 2) AS spend,
  COALESCE(c.new_customers, 0) AS new_customers,
  ROUND(s.spend / NULLIF(c.new_customers, 0), 2) AS cac
FROM monthly_spend s
LEFT JOIN monthly_customers c
  ON c.channel = s.channel AND c.month = s.month
ORDER BY 1, 2;

What are common mistakes when analyzing Google Ads in Metabase?

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