How to build Mailchimp dashboards in Metabase

Mailchimp is an email marketing platform for campaigns, audiences, automations, and e-commerce integrations. 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 Mailchimp MCP server (community) and loads a CSV into Metabase with the Metabase CLI, and a durable pipeline route that syncs Mailchimp daily stats into a database so you can build dashboards anyone can read.

How do you connect Mailchimp 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 Mailchimp data in Metabase?

• Campaign opens, clicks, and unsubscribes — built from campaign reports and the related campaigns, audiences (lists), subscribers data your sync exposes.
• List growth and churn by audience — built from campaign reports and the related campaigns, audiences (lists), subscribers data your sync exposes.
• Automation performance by workflow — built from campaign reports and the related campaigns, audiences (lists), subscribers data your sync exposes.
• Revenue attributed per campaign — built from campaign reports and the related campaigns, audiences (lists), subscribers data your sync exposes.
• Engagement segmentation by tag — built from campaign reports and the related campaigns, audiences (lists), subscribers data your sync exposes.

Which Mailchimp dashboards should you build in Metabase?

How do you use the Mailchimp MCP server (community) with the Metabase CLI?

Pair the Mailchimp MCP server (community) 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 recent campaign reports with sends, opens, clicks, and bounces.
• 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.
• Per-campaign send and engagement counts with send dates are required for engagement trends.
• mb upload csv needs an uploads database configured under Admin → Settings → Uploads.

How do you set up Mailchimp MCP and the Metabase CLI?

{
  "mcpServers": {
    "mailchimp": {
      "command": "npx",
      "args": ["@cyanheads/mailchimp-mcp-server"],
      "env": {
        "MAILCHIMP_API_KEY": "your-api-key"
      }
    }
  }
}

Mailchimp's official MCP server covers only the Transactional (Mandrill) API at https://mandrillapp.com/mcp — campaign and audience reporting still goes through community servers against the Marketing API.

# 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-reports export — creates a table AND a model
mb upload csv --file mailchimp-campaign-reports.csv --collection root

# Refresh that same table later from a new export
mb upload replace <table-id> --file mailchimp-campaign-reports.csv

Can you generate a Mailchimp dashboard with AI?

Yes. Use the prompt below with any assistant that can run the Mailchimp MCP server (community) and the Metabase CLI. It works end to end: if Mailchimp 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 Mailchimp email marketing 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 campaign engagement, deliverability, automation performance, and list health from Mailchimp data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for mailchimp tables and
  models). If durable Mailchimp 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 Mailchimp MCP server (community):
  campaign reports, plus campaigns, audiences (lists), subscribers.
  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
  Mailchimp — 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: Mailchimp Email Marketing Overview

Sections:
1. Executive summary: Sends last 30 days; Click-through rate; Bounce rate;
   Unsubscribe rate; Net list growth.
2. Campaigns: Opens, clicks, and unsubscribes by campaign; CTR trend.
3. Deliverability: Delivery and bounce rates by week; spam complaints.
4. Automations: Entries, completions, and drop-off by workflow.
5. List health: Growth, engaged share, and email-attributed revenue.

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 Mailchimp data into a database or warehouse?

For dashboards that need history and reliability, land Mailchimp 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 Mailchimp Marketing API for control over grain, fields, and refresh cadence.
• MCP + CSV — use this for quick exploration and one-off slices.

Sync campaigns, reports, lists, and per-recipient email activity with the Airbyte Mailchimp source or Fivetran's Mailchimp connector — campaign report metrics live under /reports, separate from the campaign objects themselves.

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 campaign, list, send date, sends, opens, clicks, bounces, and unsubscribes fields.

How should you model Mailchimp data in Metabase?

Core tables

Modeling advice

• Build a clean email_campaign_stats 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 Mailchimp metrics should you track in Metabase?

What SQL powers Mailchimp 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,
  send_date,
  sends,
  ROUND(100.0 * unique_clicks / NULLIF(delivered, 0), 2) AS ctr_pct,
  ROUND(100.0 * bounces / NULLIF(sends, 0), 2) AS bounce_rate_pct,
  ROUND(100.0 * unsubscribes / NULLIF(delivered, 0), 3) AS unsub_rate_pct
FROM email_campaign_stats
WHERE send_date >= CURRENT_DATE - INTERVAL '90 days'
ORDER BY send_date DESC;

SELECT
  date_trunc('week', send_date) AS week,
  SUM(sends) AS sends,
  ROUND(100.0 * SUM(delivered) / NULLIF(SUM(sends), 0), 2)
    AS delivery_rate_pct,
  ROUND(100.0 * SUM(bounces) / NULLIF(SUM(sends), 0), 2) AS bounce_rate_pct,
  ROUND(10000.0 * SUM(spam_complaints) / NULLIF(SUM(delivered), 0), 2)
    AS complaints_per_10k
FROM email_campaign_stats
GROUP BY 1
ORDER BY 1;

SELECT
  date_trunc('month', changed_at) AS month,
  COUNT(*) FILTER (WHERE change_type = 'subscribe') AS subscribes,
  COUNT(*) FILTER (WHERE change_type = 'unsubscribe') AS unsubscribes,
  COUNT(*) FILTER (WHERE change_type = 'subscribe')
    - COUNT(*) FILTER (WHERE change_type = 'unsubscribe') AS net_growth
FROM list_membership_changes
GROUP BY 1
ORDER BY 1;

What are common mistakes when analyzing Mailchimp in Metabase?

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FAQ