How to build RevenueCat subscription dashboards in Metabase

# RevenueCat (remote, API v2 token)
claude mcp add --transport http revenuecat https://mcp.revenuecat.ai/mcp \
  --header "Authorization: Bearer <YOUR_RC_V2_API_TOKEN>"

{
  "mcpServers": {
    "revenuecat": {
      "url": "https://mcp.revenuecat.ai/mcp",
      "headers": {
        "Authorization": "Bearer <YOUR_RC_V2_API_TOKEN>"
      }
    }
  }
}

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

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

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

Goal: Help founders and growth leaders understand mobile/web subscription
revenue, trials, conversion, churn, and LTV from RevenueCat data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for RevenueCat tables and
  models). If durable RevenueCat 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 RevenueCat MCP server using a read-focused
  API v2 token: customers/subscribers, subscriptions, entitlements, products, and
  transactions (purchases, renewals, trials, refunds). 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/subscribers, Subscriptions,
Entitlements, Products, Transactions (purchases, renewals, trials, refunds),
Store, and Country. Inspect the actual tables and column names first.

Important:
- Build on whatever data is present; don't claim Metabase connects natively to
  RevenueCat — it reads a database or CLI-uploaded tables.
- Compute MRR from active subscriptions, normalizing every product to a monthly
  amount (annual / 12, weekly, etc.) and using proceeds/price in a single
  reporting currency (RevenueCat provides price_in_usd fields — prefer them).
- Separate voluntary churn from billing (failed-renewal) churn.
- Treat trials and intro offers as non-revenue until they convert.
- Exclude refunds from net revenue and show their impact separately.
- 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: RevenueCat Subscription Overview

Sections:
1. Executive summary (KPI cards): MRR; Active subscriptions; Net new MRR this
   month; Trial-to-paid conversion %; Subscription churn %; Refund rate %.
2. Subscriptions & MRR: MRR movement by month; Active subscriptions by product
   and store.
3. Trials & conversion: Trial starts; Trial-to-paid conversion by product, store,
   and country; Time-to-convert.
4. Churn & retention: Subscription retention by cohort; Voluntary vs. billing
   churn; Grace-period recovery.
5. LTV & cohorts: Cumulative revenue by install cohort; ARPU/ARPPU; Revenue by
   store, country, and platform.

Filters: Product, Store (App Store/Play/Stripe), Country, Platform, Date range.

Reuse the models Metabase auto-created from uploaded CSVs, or (for a warehouse)
create reusable models: modeled_rc_subscriptions, modeled_rc_transactions, and
modeled_rc_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
RevenueCat Charts. Keep it practical, dense, and executive-readable. Avoid
vanity metrics.

Sum a monthly per-subscription MRR model in USD.

-- Requires a monthly MRR model built from RevenueCat subscription data
SELECT
  ROUND(SUM(mrr_usd), 2)        AS mrr_usd,
  ROUND(SUM(mrr_usd) * 12, 2)   AS arr_usd,
  COUNT(*)                      AS active_subscriptions
FROM modeled_rc_mrr
WHERE month = date_trunc('month', CURRENT_DATE);

How well trials convert across your product catalog.

-- Trial-to-paid conversion by product
SELECT
  product_identifier,
  COUNT(*) FILTER (WHERE is_trial_period)                        AS trials_started,
  COUNT(*) FILTER (WHERE converted_to_paid)                      AS converted,
  ROUND(100.0 * COUNT(*) FILTER (WHERE converted_to_paid)
    / NULLIF(COUNT(*) FILTER (WHERE is_trial_period), 0), 1)     AS conversion_pct
FROM revenuecat_transactions
WHERE purchase_at >= CURRENT_DATE - INTERVAL '90 days'
GROUP BY product_identifier
ORDER BY trials_started DESC;

Refunded transactions as a share of all transactions.

SELECT
  date_trunc('month', purchase_at)                    AS month,
  store,
  COUNT(*)                                            AS transactions,
  COUNT(*) FILTER (WHERE is_refunded)                 AS refunds,
  ROUND(100.0 * COUNT(*) FILTER (WHERE is_refunded)
    / NULLIF(COUNT(*), 0), 2)                         AS refund_rate_pct
FROM revenuecat_transactions
GROUP BY 1, store
ORDER BY 1, refund_rate_pct DESC;