How to build Gumroad sales dashboards in Metabase

# Generate an access token on your Gumroad application page.
# /sales pages with an opaque cursor (page_key), not numeric offsets.
BASE=https://api.gumroad.com/v2
NEXT="${BASE}/sales?access_token=${GUMROAD_TOKEN}&after=2026-01-01"

: > gumroad-sales.json
while [ -n "$NEXT" ]; do
  RESP=$(curl -s "$NEXT")
  echo "$RESP" | jq -c '.sales[]' >> gumroad-sales.json
  NEXT=$(echo "$RESP" | jq -r '.next_page_url // empty')
  [ -n "$NEXT" ] && NEXT="https://api.gumroad.com${NEXT}"
done

# Flatten to CSV for the Metabase CLI
jq -r '[
  .id, .created_at, .product_name, .price, .gumroad_fee, .tax_cents,
  .subscription_id, (.is_recurring_billing | tostring),
  (.refunded | tostring), (.chargedback | tostring),
  .variants, .utm_source, .utm_campaign
] | @csv' gumroad-sales.json > gumroad-creator-orders.csv

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

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

Create a polished Metabase dashboard for Gumroad digital product sales 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 sales, product and launch performance, repeat purchase, and effective fee rate from Gumroad data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for gumroad tables and
  models). If durable Gumroad 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 Gumroad Sales API:
  sales, plus products and variants, subscribers and recurring charges, refunds, chargebacks, and disputes.
  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
  Gumroad — 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.
- Trends need complete months of sales history — a single export covers only the window you requested.
- If a metric can't be computed from the data present, say so on the card
  instead of approximating it.

Dashboard title: Gumroad Digital Product Sales Overview

Sections:
1. Executive summary: Net sales this month; MoM growth; Refund rate;
   Effective fee rate; Repeat purchase rate.
2. Sales: Gross vs. net by month; sales by product; daily trend.
3. Launches: First-7-day revenue by product; discount usage; AOV over time.
4. Customers: Repeat purchase rate; purchases per customer; top customers.
5. Take-home: Fee rate by month; net per product; refunds in dollars.

Filters: Date range, Product, Variant, UTM source or campaign, Currency.

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.

What buyers paid, and what actually reached you.

SELECT
  date_trunc('month', created_at)::date AS month,
  COUNT(*) FILTER (WHERE NOT refunded AND NOT chargedback) AS sales,
  ROUND(SUM(price) FILTER (
    WHERE NOT refunded AND NOT chargedback
  ), 2) AS gross_sales,
  ROUND(SUM(gumroad_fee) FILTER (
    WHERE NOT refunded AND NOT chargedback
  ), 2) AS platform_fees,
  ROUND(
    SUM(price - gumroad_fee - COALESCE(tax_cents, 0) / 100.0) FILTER (
      WHERE NOT refunded AND NOT chargedback
    ), 2
  ) AS net_to_creator,
  ROUND(
    100.0 * SUM(gumroad_fee) FILTER (
      WHERE NOT refunded AND NOT chargedback
    ) / NULLIF(SUM(price) FILTER (
      WHERE NOT refunded AND NOT chargedback
    ), 0), 2
  ) AS effective_fee_rate_pct
FROM creator_orders
GROUP BY month
ORDER BY month DESC;

How much of a product's revenue arrives in its opening week.

WITH first_sale AS (
  SELECT product_id, MIN(created_at) AS launched_at
  FROM creator_orders
  GROUP BY product_id
)
SELECT
  o.product_name,
  f.launched_at::date AS launched_on,
  ROUND(SUM(o.price) FILTER (
    WHERE o.created_at < f.launched_at + INTERVAL '7 days'
  ), 2) AS first_7d_revenue,
  ROUND(SUM(o.price), 2) AS lifetime_revenue,
  ROUND(
    100.0 * SUM(o.price) FILTER (
      WHERE o.created_at < f.launched_at + INTERVAL '7 days'
    ) / NULLIF(SUM(o.price), 0), 1
  ) AS launch_share_pct
FROM creator_orders o
JOIN first_sale f ON f.product_id = o.product_id
WHERE NOT o.refunded
GROUP BY o.product_name, f.launched_at
ORDER BY lifetime_revenue DESC;

Checkout-time attribution, which most creator platforms don't capture at all.

SELECT
  date_trunc('month', created_at)::date AS month,
  COALESCE(NULLIF(utm_source, ''), '(direct)') AS source,
  COUNT(*) AS sales,
  ROUND(SUM(price), 2) AS revenue,
  ROUND(AVG(price), 2) AS avg_sale,
  ROUND(
    100.0 * SUM(price) / NULLIF(SUM(SUM(price)) OVER (
      PARTITION BY date_trunc('month', created_at)
    ), 0), 1
  ) AS share_of_revenue_pct
FROM creator_orders
WHERE NOT refunded AND NOT chargedback
GROUP BY month, source
ORDER BY month DESC, revenue DESC;