How to build Etsy dashboards in Metabase

{
  "mcpServers": {
    "etsy": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://mcp.apideck.com/etsy"
      ]
    }
  }
}

# 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 an Etsy CSV export — creates a table AND a model
mb upload csv --file etsy-orders.csv --collection root

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

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

Goal: Help Etsy sellers understand sales, average order value, listing performance,
fees and profitability, and repeat purchase from Etsy data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for Etsy tables and
  models). If durable Etsy data is already present — synced from a warehouse or
  uploaded earlier — use it and skip to Step 2.
- If nothing is there, pull it with an Etsy MCP server in read-only mode:
  receipts (orders), transactions (line items), listings, buyers, shop sections,
  and payments/fees. 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:
Etsy CSV exports are usually flat and pre-aggregated (one row per listing or day,
with columns like units sold, gross sales, fees, and net proceeds). Warehouse
tables are raw and order-grained (a receipt is one order; transactions are line
items). Inspect the actual tables and column names first; do not assume exact
names. Map the available raw tables into these analytical concepts where possible:
Receipts (orders), Transactions (line items), Listings, Buyers, Shop sections, and
Payments/fees.

Important:
- Build on whatever data is present; don't claim Metabase connects natively to
  Etsy — it reads a database or CLI-uploaded tables.
- Report NET proceeds: subtract Etsy listing, transaction, and payment fees, and refunds.
- Use receipt-level grain for AOV (an Etsy receipt is one order).
- Only build conversion cards if listing view/stats data is present.
- Define a "repeat buyer" once (2+ paid orders) and reuse it everywhere.
- 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: Etsy Shop Overview

Sections:
1. Executive summary (KPI cards): Net proceeds last 30 days; Orders; AOV; Repeat-
   purchase rate; Fees as % of sales.
2. Sales & demand: Net proceeds by day; Orders by day; AOV trend; Revenue by section.
3. Listing performance: Top listings by net proceeds; Units by listing; Conversion.
4. Customers & retention: Repeat-purchase rate by cohort; Orders per buyer.
5. Fees & profitability: Fees % of sales; Fee breakdown; Net margin by listing.

Filters: Date range, Shop section, Listing, Buyer type.

Reuse the models Metabase auto-created from uploaded CSVs, or (for a warehouse)
create reusable models: modeled_etsy_receipts, modeled_etsy_transactions,
modeled_etsy_listings, and modeled_etsy_buyers.

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. Keep it practical, dense,
and executive-readable. Avoid vanity metrics.

Paid receipts and shipping over the last 30 days.

SELECT
  date_trunc('day', r.created_timestamp)              AS day,
  COUNT(*)                                            AS orders,
  SUM(r.total_price)                                  AS gross_sales,
  SUM(COALESCE(r.total_shipping_cost, 0))             AS shipping
FROM receipts r
WHERE r.was_paid = true
  AND r.created_timestamp >= CURRENT_DATE - INTERVAL '30 days'
GROUP BY 1
ORDER BY 1;

AOV at the receipt grain, on paid receipts.

SELECT
  date_trunc('week', r.created_timestamp) AS week,
  COUNT(*)                                AS orders,
  ROUND(SUM(r.total_price) / NULLIF(COUNT(*), 0), 2) AS aov
FROM receipts r
WHERE r.was_paid = true
GROUP BY 1
ORDER BY 1;

Units and gross revenue by listing over 90 days.

SELECT
  l.title                          AS listing,
  SUM(t.quantity)                  AS units,
  SUM(t.price * t.quantity)        AS gross_revenue
FROM transactions t
JOIN receipts r ON r.receipt_id = t.receipt_id
JOIN listings l ON l.listing_id = t.listing_id
WHERE r.was_paid = true
  AND r.created_timestamp >= CURRENT_DATE - INTERVAL '90 days'
GROUP BY l.title
ORDER BY gross_revenue DESC
LIMIT 20;

Share of buyers with two or more paid orders.

WITH buyer_orders AS (
  SELECT buyer_user_id, COUNT(*) AS paid_orders
  FROM receipts
  WHERE was_paid = true
    AND buyer_user_id IS NOT NULL
  GROUP BY buyer_user_id
)
SELECT
  ROUND(100.0 * COUNT(*) FILTER (WHERE paid_orders >= 2)
    / NULLIF(COUNT(*), 0), 2) AS repeat_purchase_rate_pct
FROM buyer_orders;