How to build Thinkific revenue dashboards in Metabase

Thinkific is a course platform for creators and businesses — course building, student management, bundles, and checkout, with orders, enrollments, and progress tracked per student. Metabase is where you turn that data into shared, trustworthy dashboards. This guide covers two complementary paths: a lightweight MCP + CLI route that pulls live data with the Thinkific MCP Server and loads a CSV into Metabase with the Metabase CLI, and a durable pipeline route that lands Thinkific data in a database so you can build dashboards anyone can read.

How do you connect Thinkific to Metabase?

Most teams combine both routes: quick answers through MCP and CLI uploads first, then recurring reporting on a warehouse-backed model.

Thinkific is the one tool in this group with a real managed-connector story, which changes the recommendation: use MCP for exploratory questions about students and enrollments, and let Fivetran or Airbyte carry orders into the warehouse for revenue reporting. You don't have to choose.

What can you analyze from Thinkific data in Metabase?

Each of these is built from orders joined to the related enrollments and progress, users and students, products and bundles data your sync exposes:

• Course revenue and refunds by product
• Enrollment volume and source mix
• Course completion and lesson drop-off
• Coupon and promotion effectiveness
• Student activity and re-enrollment

Which Thinkific dashboards should you build in Metabase?

How do you use the Thinkific MCP Server with the Metabase CLI?

Pair the Thinkific 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 the last 90 days of orders, one row per order with product, gross amount, fees, and refund status.
• Export the result as CSV, keeping stable IDs, dates, currency codes, and the fee breakdown intact.
• 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.
• Revenue trends need complete months of order history, and cohort views need each order's enrollment date preserved.
• mb upload csv needs an uploads database configured under Admin → Settings → Uploads.

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

{
  "mcpServers": {
    "thinkific": {
      "url": "https://mcp.thinkific.com/mcp"
    }
  }
}

Read-only by design, which suits analytics — but note the boundary carefully: Thinkific's own FAQ states the MCP server excludes billing, payment, and financial data. It's excellent for students, courses, and enrollments; revenue has to come from the Admin API or a connector. It's in beta, needs a Plus plan and a Site Owner or Admin account, and Claude support is ahead of other clients.

# 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 orders export — creates a table AND a model
mb upload csv --file thinkific-orders.csv --collection root

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

Can you generate a Thinkific dashboard with AI?

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

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

Goal: Help the team understand gross vs. net revenue, product performance, refunds, student completion, and payout reconciliation from Thinkific data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for thinkific tables and
  models). If durable Thinkific data is already present — a warehouse sync, a
  direct database connection, or an earlier upload — use it and skip to Step 2.
- If nothing is there, pull a scoped, summarized export with the Thinkific MCP Server:
  orders, plus enrollments and progress, users and students, products and bundles.
  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, products,
locations or countries, currencies, and date ranges before creating trend cards.

Important:
- Build on whatever data is present; don't claim Metabase connects natively to
  Thinkific — it reads a database or CLI-uploaded tables.
- Separate gross amounts, platform and processing fees, taxes, and refunds into
  distinct columns; never let one card silently mix gross and net.
- 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.
- Revenue trends need complete months of order history, and cohort views need each order's enrollment date preserved.
- If a metric can't be computed from the data present, say so on the card
  instead of approximating it.

Dashboard title: Thinkific Course Revenue Overview

Sections:
1. Executive summary: Net revenue this month; MoM growth; Refund rate;
   Effective fee rate; Active students.
2. Revenue: Gross vs. net by month; revenue by course; new vs. installments.
3. Acquisition: Sales by offer and coupon; AOV by product; new vs. repeat buyers.
4. Engagement: Completion rate by course; lesson drop-off; active students.
5. Payouts: Payouts vs. net earned; fee rate; outstanding plan balance.

Filters: Date range, Course or product, Coupon, Enrollment source.

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 Thinkific data into a database or warehouse?

For dashboards that need history and reliability, land Thinkific data in a database first, then connect Metabase to that database.

Connector options

• Managed connector — use Airbyte, Fivetran, or dlt when one genuinely covers the objects you need.
• Custom pipeline — use the Thinkific Admin API for control over grain, fields, and refresh cadence.
• MCP + CSV — use this for quick exploration and one-off slices.

The strongest connector coverage of any creator platform here. Fivetran ships an official Thinkific connector (Lite type, generally available) with orders, enrollments, users, products, coupons, and promotions — enrollments sync incrementally. Airbyte has a community source with 11 streams, but no incremental sync, so schedule it accordingly. The Admin API itself allows 120 requests per minute and returns a <code>RateLimit-Reset</code> header on 429s.

Notes

• Decide the grain first (one row per order and one per enrollment, joined on user and product) — it drives every trend card.
• Land raw tables first, then build clean Metabase models on top.
• Normalize order-date, product, customer-email, gross-amount, fee-amount, net-amount, and refund-status fields.

How should you model Thinkific data in Metabase?

Core tables

Modeling advice

• Build a clean creator_orders model with the same column names you use for sibling tools, so cross-tool dashboards don't fork definitions.
• Keep gross amount, platform fee, processing fee, tax, and net amount as separate, labeled columns — never let one card silently mix them.
• Keep free and paid enrollments in separate buckets — bundled or comped enrollments almost never complete, and blending them halves an otherwise healthy completion rate.
• Thinkific's External Orders endpoints record purchases made elsewhere and do not appear in the platform's own order reports. Decide explicitly whether they belong in your revenue model, and never let them double-count native orders.
• Store percentage_completed alongside completed_at: one gives you a drop-off curve, the other gives you a binary completion rate, and dashboards need both.
• Store amounts in minor units or with an explicit currency column, and convert once in the model layer rather than per card.

Which Thinkific metrics should you track in Metabase?

What SQL powers Thinkific dashboards in Metabase?

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

SELECT
  e.course_id,
  COUNT(*) AS mature_enrollments,
  COUNT(*) FILTER (WHERE e.completed_at IS NOT NULL) AS completed,
  ROUND(
    100.0 * COUNT(*) FILTER (WHERE e.completed_at IS NOT NULL)
    / NULLIF(COUNT(*), 0), 1
  ) AS completion_rate_pct,
  ROUND(AVG(e.percentage_completed), 1) AS avg_progress_pct
FROM creator_enrollments e
-- 90 days is the cohort maturity window; enrollments newer than that
-- haven't had a fair chance to finish and would drag the rate down.
WHERE e.enrolled_at < CURRENT_DATE - INTERVAL '90 days'
  AND e.is_free_trial = false
GROUP BY e.course_id
ORDER BY completion_rate_pct DESC;

WITH starters AS (
  SELECT course_id, COUNT(DISTINCT user_id) AS students
  FROM creator_lesson_progress
  WHERE lesson_position = 1 AND completed_at IS NOT NULL
  GROUP BY course_id
)
SELECT
  p.course_id,
  p.lesson_position,
  COUNT(DISTINCT p.user_id) FILTER (
    WHERE p.completed_at IS NOT NULL
  ) AS reached,
  ROUND(
    100.0 * COUNT(DISTINCT p.user_id) FILTER (
      WHERE p.completed_at IS NOT NULL
    ) / NULLIF(s.students, 0), 1
  ) AS pct_of_starters
FROM creator_lesson_progress p
JOIN starters s ON s.course_id = p.course_id
GROUP BY p.course_id, p.lesson_position, s.students
ORDER BY p.course_id, p.lesson_position;

SELECT
  date_trunc('month', created_at)::date AS month,
  COALESCE(coupon_code, '(none)') AS coupon,
  COUNT(*) AS orders,
  ROUND(SUM(amount), 2) AS revenue,
  ROUND(AVG(amount), 2) AS avg_order_value,
  ROUND(
    100.0 * COUNT(*) / NULLIF(SUM(COUNT(*)) OVER (
      PARTITION BY date_trunc('month', created_at)
    ), 0), 1
  ) AS share_of_orders_pct
FROM creator_orders
WHERE status = 'complete'
GROUP BY month, coupon
ORDER BY month DESC, revenue DESC;

What are common mistakes when analyzing Thinkific in Metabase?

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