How to build Teachable revenue dashboards in Metabase

Teachable is a course and coaching platform for creators — course hosting, pricing plans, affiliates, and checkout, with a transactions API that records every charge, fee, and refund. Metabase is where you turn that data into shared, trustworthy dashboards. This guide covers two complementary paths: a lightweight API + CLI route that pulls live data via the Teachable Transactions API and loads a CSV into Metabase with the Metabase CLI, and a durable pipeline route that lands Teachable data in a database so you can build dashboards anyone can read.

How do you connect Teachable to Metabase?

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

One detail that trips up almost every first attempt: Teachable's API expects a header literally named apiKey, not Authorization: Bearer. If you're getting 401s with a key you know is valid, that's why.

What can you analyze from Teachable data in Metabase?

Each of these is built from transactions joined to the related enrollments and course progress, pricing plans, affiliates and authors data your export exposes:

• Net revenue after affiliate and author fees
• Revenue by course and pricing plan
• Refunds and chargeback exposure
• Affiliate program performance
• Enrollment and completion by course

Which Teachable dashboards should you build in Metabase?

How do you use the Teachable Transactions API with the Metabase CLI?

Pair the Teachable Transactions API with the Metabase CLI for fast, hands-on analysis. A short scripted export covers the pulls an MCP server would otherwise make; the Metabase CLI's upload command loads CSV data into Metabase and creates a ready-to-query table and model.

Example workflow

• Ask the API for the last 90 days of transactions, 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

• Scripted API pulls 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 the Teachable API and the Metabase CLI?

# Note the header name: apiKey, NOT "Authorization: Bearer".
# Third-party guides get this wrong constantly.
BASE=https://developers.teachable.com/v1

# Page through transactions (per maxes out at 20)
for page in $(seq 1 50); do
  curl -s -G "$BASE/transactions" \
    -H "apiKey: $TEACHABLE_API_KEY" \
    --data-urlencode "page=$page" \
    --data-urlencode "per=20"
  sleep 1   # stay under 100 requests/minute per school
done | jq -s 'map(.transactions[]?)' > teachable-transactions.json

# Flatten to CSV for the Metabase CLI
jq -r '.[] | [
  .id, .purchased_at, .user_id, .pricing_plan_id,
  .charge, .final_price, .currency, .tax_charge, .revenue,
  .status, .affiliate_fees, .author_fees,
  .refunded_at, .amount_refunded, (.has_chargeback | tostring)
] | @csv' teachable-transactions.json > teachable-creator-orders.csv

Teachable has an official MCP server in early access, but it isn't something you can just install: it ships as a local bundle behind an email waitlist, runs on macOS with Claude only, requires a Growth plan or above, and authenticates with the same school API key as the REST API. Until it opens up, the scripted export above is the route that works for everyone — and it's the one an assistant can run for you today.

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

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

Can you generate a Teachable dashboard with AI?

Yes. Use the prompt below with any assistant that can run shell commands (for the API export) and the Metabase CLI. It works end to end: if Teachable 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 Teachable 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 Teachable data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for teachable tables and
  models). If durable Teachable 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 by scripting the Teachable Transactions API:
  transactions, plus enrollments and course progress, pricing plans, affiliates and authors.
  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
  Teachable — 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: Teachable 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, Pricing plan, Affiliate, Currency, Status.

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 Teachable data into a database or warehouse?

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

No managed connector exists for Teachable on Airbyte, Fivetran, or dlt (verified July 2026 — a dltHub page for Teachable is an auto-generated REST-config stub, not a verified source). Schedule your own paged pull against <code>/transactions</code> and <code>/courses/{id}/enrollments</code>. Two limits shape the job: 100 requests per minute per school, and a maximum page size of 20 — so backfills take a while. New transactions can also lag by up to two minutes, which matters only if you're chasing same-minute freshness.

Notes

• Decide the grain first (one row per transaction, rolled up to month for revenue reporting) — 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 Teachable 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.
• Teachable already computes a revenue field net of fees — carry it, but also carry the components (charge, affiliate_fees, author_fees, tax_charge) so you can explain any number back to first principles.
• status is paid or null; treat null as unsettled rather than as zero revenue, and exclude it from recognized revenue instead of summing it as nothing.
• Chargebacks carry both has_chargeback and chargeback_fee — the fee is a real cost that never shows up in a naive gross-minus-refunds calculation.
• Store amounts in minor units or with an explicit currency column, and convert once in the model layer rather than per card.

Which Teachable metrics should you track in Metabase?

What SQL powers Teachable dashboards in Metabase?

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

SELECT
  date_trunc('month', purchased_at)::date AS month,
  ROUND(SUM(charge), 2) AS gross_charges,
  ROUND(SUM(affiliate_fees), 2) AS affiliate_fees,
  ROUND(SUM(author_fees), 2) AS author_fees,
  ROUND(SUM(tax_charge), 2) AS tax,
  ROUND(SUM(COALESCE(amount_refunded, 0)), 2) AS refunded,
  -- Chargeback fees are a real cost that gross-minus-refunds misses.
  ROUND(SUM(COALESCE(chargeback_fee, 0)), 2) AS chargeback_fees,
  ROUND(
    SUM(charge) - SUM(affiliate_fees) - SUM(author_fees)
    - SUM(tax_charge) - SUM(COALESCE(amount_refunded, 0))
    - SUM(COALESCE(chargeback_fee, 0)), 2
  ) AS net_revenue
FROM creator_orders
WHERE status = 'paid'
GROUP BY month
ORDER BY month DESC;

SELECT
  FLOOR(
    EXTRACT(EPOCH FROM (refunded_at - purchased_at)) / (7 * 86400)
  )::int AS weeks_after_purchase,
  COUNT(*) AS refunds,
  ROUND(SUM(amount_refunded), 2) AS refunded_amount,
  ROUND(
    100.0 * COUNT(*) / NULLIF(
      (SELECT COUNT(*) FROM creator_orders WHERE status = 'paid'), 0
    ), 2
  ) AS pct_of_all_sales
FROM creator_orders
WHERE refunded_at IS NOT NULL
GROUP BY weeks_after_purchase
ORDER BY weeks_after_purchase;

SELECT
  date_trunc('month', purchased_at)::date AS month,
  CASE WHEN affiliate_fees > 0 THEN 'affiliate' ELSE 'direct' END
    AS channel,
  COUNT(*) AS transactions,
  ROUND(SUM(charge), 2) AS gross,
  ROUND(SUM(affiliate_fees), 2) AS commission,
  ROUND(
    100.0 * SUM(affiliate_fees) / NULLIF(SUM(charge), 0), 1
  ) AS commission_rate_pct,
  ROUND(AVG(charge), 2) AS avg_transaction
FROM creator_orders
WHERE status = 'paid'
GROUP BY month, channel
ORDER BY month DESC, gross DESC;

What are common mistakes when analyzing Teachable in Metabase?

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