How to build Customer.io dashboards in Metabase

Customer.io is a customer engagement platform for lifecycle email, push, SMS, and in-app messages driven by behavioral data. 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 Customer.io MCP server and loads a CSV into Metabase with the Metabase CLI, and a durable pipeline route that syncs Customer.io daily stats into a database so you can build dashboards anyone can read.

How do you connect Customer.io 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 Customer.io data in Metabase?

• Campaign and broadcast engagement — built from campaign metrics and the related campaigns, broadcasts, journeys data your sync exposes.
• Journey performance and conversion — built from campaign metrics and the related campaigns, broadcasts, journeys data your sync exposes.
• Deliverability by message type — built from campaign metrics and the related campaigns, broadcasts, journeys data your sync exposes.
• Segment growth over time — built from campaign metrics and the related campaigns, broadcasts, journeys data your sync exposes.
• Message-attributed conversions — built from campaign metrics and the related campaigns, broadcasts, journeys data your sync exposes.

Which Customer.io dashboards should you build in Metabase?

How do you use the Customer.io MCP server with the Metabase CLI?

Pair the Customer.io 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 recent campaign metrics 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 Customer.io MCP and the Metabase CLI?

{
  "mcpServers": {
    "customer-io": {
      "url": "https://mcp.customer.io/mcp"
    }
  }
}

EU-region workspaces use https://mcp-eu.customer.io/mcp. Sensitive attributes stay blocked unless both the account setting and the user's Sensitive Data View permission allow them, and read, write, and delete surfaces are separate tools — a sane default posture for exploratory analysis.

# 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-metrics export — creates a table AND a model
mb upload csv --file customer-io-campaign-metrics.csv --collection root

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

Can you generate a Customer.io dashboard with AI?

Yes. Use the prompt below with any assistant that can run the Customer.io MCP server and the Metabase CLI. It works end to end: if Customer.io 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 Customer.io 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 Customer.io data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for customer-io tables and
  models). If durable Customer.io 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 Customer.io MCP server:
  campaign metrics, plus campaigns, broadcasts, journeys.
  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
  Customer.io — 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: Customer.io 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 Customer.io data into a database or warehouse?

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

For durable reporting, route Customer.io Data Pipelines to a warehouse destination, or use Fivetran's Customer.io connector — the Airbyte source is community-maintained and covers only campaigns and newsletters, too thin for full journey reporting.

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 Customer.io 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 Customer.io metrics should you track in Metabase?

What SQL powers Customer.io 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 Customer.io in Metabase?

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FAQ