How to build Stripe revenue dashboards in Metabase

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

{
  "mcpServers": {
    "stripe": {
      "command": "npx",
      "args": ["-y", "@stripe/mcp", "--tools=all", "--api-key=rk_live_..."]
    }
  }
}

# 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 Stripe CSV export — creates a table AND a model
mb upload csv --file stripe-subscriptions.csv --collection root

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

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

Goal: Help founders and finance leaders understand recurring revenue, churn,
retention, failed payments, and cohort economics from Stripe data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for Stripe tables and
  models). If durable Stripe 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 the Stripe MCP server using a read-only
  restricted key: customers, subscriptions, subscription items, prices, invoices,
  and charges. 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 names. Map the available raw tables into these
analytical concepts where possible: Customers, Subscriptions, Subscription items,
Invoices, Invoice line items, Charges, Payment intents, Prices/Plans, Products,
Refunds, Disputes, and Balance transactions. Inspect the actual tables and column
names first.

Important:
- Build on whatever data is present; don't claim Metabase connects natively to
  Stripe — it reads a database or CLI-uploaded tables.
- Compute MRR from active subscription items, normalizing every plan to a monthly
  amount (divide annual by 12, multiply weekly, etc.) and converting all amounts
  from the smallest currency unit (e.g. cents) to a major unit.
- Report revenue in a single reporting currency; if multiple currencies exist,
  convert with a documented rate or caveat the mix.
- Separate voluntary churn from involuntary (failed-payment) churn.
- Exclude one-time charges, taxes, and refunds from MRR unless explicitly asked.
- Only build a card if its underlying column/metric exists in the data.
- A single CSV is a point-in-time snapshot: MRR movement and cohorts need history,
  so build trend cards only if a warehouse sync or multiple uploads provide it.

Dashboard title: Stripe Revenue Overview

Sections:
1. Executive summary (KPI cards): MRR; ARR; Active subscriptions; Net new MRR this
   month; Gross revenue churn %; Net revenue retention (only if MRR-movement data
   can be derived).
2. MRR movement: New, expansion, contraction, and churned MRR by month (waterfall
   or stacked bar); Net new MRR by month.
3. Churn & retention: Customer churn vs. revenue churn by month; Gross vs. net
   retention; Voluntary vs. involuntary churn.
4. Failed payments & dunning: Failed charges and $ at risk; Recovery rate after
   retries; Declines by reason; Past-due subscriptions by age.
5. Cohorts & LTV: Revenue retention by signup-month cohort; Cumulative LTV by
   cohort; ARPU by plan.

Filters: Plan/Price, Product, Billing interval, Currency, Country, Date range.

Reuse the models Metabase auto-created from uploaded CSVs, or (for a warehouse)
create reusable models: modeled_stripe_customers, modeled_stripe_subscriptions,
modeled_stripe_invoices, modeled_stripe_charges, and modeled_stripe_mrr (a monthly
per-subscription MRR model).

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. Reconcile totals against the
Stripe Dashboard. Keep it practical, dense, and executive-readable. Avoid vanity
metrics.

Normalize active subscription items to a monthly amount and sum.

WITH monthly AS (
  SELECT
    si.subscription_id,
    -- normalize every price to a monthly amount, in major currency units
    SUM(
      CASE p.interval
        WHEN 'year'  THEN p.unit_amount / 100.0 / 12.0
        WHEN 'month' THEN p.unit_amount / 100.0
        WHEN 'week'  THEN p.unit_amount / 100.0 * 52.0 / 12.0
        WHEN 'day'   THEN p.unit_amount / 100.0 * 365.0 / 12.0
      END * si.quantity / NULLIF(p.interval_count, 0)
    ) AS mrr
  FROM subscription_items si
  JOIN prices p        ON p.id = si.price_id
  JOIN subscriptions s ON s.id = si.subscription_id
  WHERE s.status IN ('active', 'past_due')
  GROUP BY si.subscription_id
)
SELECT ROUND(SUM(mrr), 2) AS mrr_now,
       ROUND(SUM(mrr) * 12, 2) AS arr_now
FROM monthly;

Cancellations against subscriptions active at each month's start.

WITH months AS (
  SELECT generate_series(
    date_trunc('month', CURRENT_DATE - INTERVAL '11 months'),
    date_trunc('month', CURRENT_DATE),
    INTERVAL '1 month'
  ) AS month
)
SELECT
  m.month,
  COUNT(*) FILTER (
    WHERE s.created <= m.month
      AND date_trunc('month', s.canceled_at) = m.month
  ) AS churned_subscriptions,
  COUNT(*) FILTER (
    WHERE s.created <= m.month
      AND (s.canceled_at IS NULL OR s.canceled_at > m.month)
  ) AS active_at_month_start
FROM months m
CROSS JOIN subscriptions s
GROUP BY m.month
ORDER BY m.month;

Declined charges by week and reason code — the dunning worklist.

SELECT
  date_trunc('week', to_timestamp(c.created)) AS week,
  COUNT(*)                                     AS failed_charges,
  ROUND(SUM(c.amount) / 100.0, 2)              AS dollars_at_risk,
  c.failure_code
FROM charges c
WHERE c.status = 'failed'
  AND to_timestamp(c.created) >= CURRENT_DATE - INTERVAL '90 days'
GROUP BY 1, c.failure_code
ORDER BY 1, dollars_at_risk DESC;