How to build App Store analytics dashboards in Metabase

App Store Connect is Apple's developer console for the App Store — app submission, pricing, TestFlight, and the analytics and sales reports that record every impression, download, purchase, and proceed. 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 App Store Connect Analytics Reports API and loads a CSV into Metabase with the Metabase CLI, and a durable pipeline route that lands App Store Connect data in a database so you can build dashboards anyone can read.

How do you connect App Store Connect to Metabase?

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

The async Analytics Reports API is the whole difficulty of this integration, and it's why a warehouse pays off fast. You don't GET a number: you file a report request, wait a day or two for the first generation, list instances, then download gzipped tab-delimited segments. Automate it once, keep appending, and never think about it again.

What can you analyze from App Store Connect data in Metabase?

Each of these is built from daily store analytics rows joined to the related downloads and redownloads by source type, product page views and impressions, purchases and proceeds data your export exposes:

• Impressions to product page views to installs funnel
• Store conversion rate by country and source
• Proceeds after Apple's commission
• Subscription starts, renewals, and cancellations
• Crash rate against install growth

Which App Store dashboards should you build in Metabase?

How do you use the App Store Connect Analytics Reports API with the Metabase CLI?

Pair the App Store Connect Analytics Reports 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 daily store analytics rows, one row per day per country with impressions, page views, installs, and proceeds.
• 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.
• Store analytics lag by days and are only retained for a bounded window, so start accumulating daily rows before you need the history.
• mb upload csv needs an uploads database configured under Admin → Settings → Uploads.

How do you set up the App Store Connect API and the Metabase CLI?

# Analytics Reports are ASYNCHRONOUS — four hops, not one GET.
# $JWT is an ES256 token signed with your .p8 key (key ID + issuer ID).
BASE=https://api.appstoreconnect.apple.com/v1
AUTH="Authorization: Bearer $JWT"

# 1. Request reports once (ONGOING refreshes daily; first run takes 1-2 days).
curl -s -X POST "$BASE/analyticsReportRequests" -H "$AUTH" \
  -H 'Content-Type: application/json' \
  -d '{"data":{"type":"analyticsReportRequests",
       "attributes":{"accessType":"ONGOING","name":"metabase-export"},
       "relationships":{"app":{"data":
         {"type":"apps","id":"'"$APP_ID"'"}}}}}'

# 2. List the reports generated for that request.
curl -s "$BASE/analyticsReportRequests/$REQUEST_ID/reports" -H "$AUTH"

# 3. List instances of one report (DAILY / WEEKLY / MONTHLY).
curl -s -G "$BASE/analyticsReports/$REPORT_ID/instances" -H "$AUTH" \
  --data-urlencode "filter[granularity]=DAILY"

# 4. Download the segments — gzipped, tab-delimited, sometimes several.
curl -s "$BASE/analyticsReportInstances/$INSTANCE_ID/segments" -H "$AUTH" \
  | jq -r '.data[].attributes.url' \
  | xargs -n1 curl -sL | gunzip > app-store-daily-metrics.tsv

Apple ships no official MCP server (verified July 2026). Community servers exist, but the most widely cited one — JoshuaRileyDev/app-store-connect-mcp-server — is archived and has had no release since September 2025; don't build on it. Maintained alternatives such as SardorbekR/appstore-connect-mcp (npm asc-mcp) and TrialAndErrorAI/appstore-connect-mcp do cover the Analytics Reports flow, but they're community-maintained and one npm name collides with an unrelated repo — vet before you trust. The scripted export above is the dependable route.

# 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 daily-store-analytics-rows export — creates a table AND a model
mb upload csv --file app-store-connect-daily-store-analytics-rows.csv --collection root

# Refresh that same table later from a new export
mb upload replace <table-id> --file app-store-connect-daily-store-analytics-rows.csv

Can you generate a App Store Connect 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 App Store Connect 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 App Store Connect app store analytics.
Work end to end: get the data into Metabase if it isn't there yet, then build.

Goal: Help the team understand the impressions-to-installs funnel, store conversion rate, proceeds after commission, and subscription movement from App Store Connect data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for app-store-connect tables and
  models). If durable App Store Connect 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 App Store Connect Analytics Reports API:
  daily store analytics rows, plus downloads and redownloads by source type, product page views and impressions, purchases and proceeds.
  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
  App Store Connect — 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.
- Store analytics lag by days and are only retained for a bounded window, so start accumulating daily rows before you need the history.
- If a metric can't be computed from the data present, say so on the card
  instead of approximating it.

Dashboard title: App Store Connect App Store Overview

Sections:
1. Executive summary: Installs; Store conversion rate; Proceeds;
   Effective commission rate; Crash rate.
2. Funnel: Impressions → page views → installs; conversion by country.
3. Proceeds: Proceeds by month; commission rate; revenue by product; refunds.
4. Subscriptions: New vs. cancelled; renewal rate by cohort; trial conversion.
5. Quality: Crash rate vs. installs; rating trend; ratings by version.

Filters: Date range, App, Country or territory, Device, Source type, Product.

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 App Store Connect data into a database or warehouse?

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

Fivetran's official Apple App Store connector is the low-effort path, and the <code>fivetran/dbt_apple_store</code> package models the raw reports into overview, territory, device, source-type, and subscription reports on top. Airbyte's <code>source-appstore</code> was deprecated in 2024 — don't start there. Rolling your own means implementing the four-hop async flow, storing request IDs, and polling regularly.

Notes

• Decide the grain first (one row per day per app per country per dimension, appended daily) — it drives every trend card.
• Land raw tables first, then build clean Metabase models on top.
• Normalize report-date, store, app-id, country, impressions, page-views, installs, and proceeds fields.

How should you model App Store Connect data in Metabase?

Core tables

Modeling advice

• Build a clean app_store_daily_metrics 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.
• Keep first-time downloads and redownloads in separate columns. Apple counts both, and conflating them inflates acquisition and hides retention problems.
• Do not join Analytics Reports revenue to Sales and Trends or Finance report revenue as if they were one source — they are different pipelines with different numbers. Pick one for the "revenue" card and label it.
• Standard reports omit Campaign, Source Info, and Page Title; only Detailed reports carry them. If a conversion-by-campaign model comes back empty, check which report tier you requested.
• Store amounts in minor units or with an explicit currency column, and convert once in the model layer rather than per card.

Which App Store metrics should you track in Metabase?

What SQL powers App Store 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', report_date)::date AS month,
  country,
  SUM(impressions) AS impressions,
  SUM(product_page_views) AS page_views,
  SUM(first_time_downloads) AS installs,
  ROUND(
    100.0 * SUM(product_page_views) / NULLIF(SUM(impressions), 0), 2
  ) AS impression_to_view_pct,
  ROUND(
    100.0 * SUM(first_time_downloads)
    / NULLIF(SUM(product_page_views), 0), 2
  ) AS store_conversion_pct
FROM app_store_daily_metrics
WHERE store = 'apple'
  -- Recent days keep getting restated; hold the window back a little.
  AND report_date < CURRENT_DATE - INTERVAL '3 days'
GROUP BY month, country
HAVING SUM(product_page_views) > 500
ORDER BY month DESC, installs DESC;

SELECT
  date_trunc('month', report_date)::date AS month,
  ROUND(SUM(sales_usd), 2) AS gross_sales,
  ROUND(SUM(proceeds_usd), 2) AS proceeds,
  -- Blended across 15% and 30% tiers, so it rarely matches either.
  ROUND(
    100.0 * (SUM(sales_usd) - SUM(proceeds_usd))
    / NULLIF(SUM(sales_usd), 0), 2
  ) AS effective_commission_pct
FROM app_store_daily_metrics
WHERE store = 'apple'
GROUP BY month
ORDER BY month DESC;

SELECT
  date_trunc('month', report_date)::date AS month,
  product_id,
  SUM(new_subscriptions) AS new_subs,
  SUM(cancellations) AS cancelled,
  SUM(new_subscriptions) - SUM(cancellations) AS net_change,
  ROUND(
    100.0 * SUM(cancellations)
    / NULLIF(AVG(active_subscriptions), 0), 2
  ) AS monthly_churn_pct,
  ROUND(
    100.0 * SUM(new_subscriptions)
    / NULLIF(SUM(trial_starts), 0), 1
  ) AS trial_to_paid_pct
FROM app_store_subscriptions
GROUP BY month, product_id
ORDER BY month DESC, net_change DESC NULLS LAST;

What are common mistakes when analyzing App Store Connect in Metabase?

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