How to build Google Play analytics dashboards in Metabase

Google Play Console is Google's developer console for Android — releases, store listings, pricing, and the statistics and financial reports that record installs, store conversion, ratings, crashes, and earnings. 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 Play Developer Reporting API (vitals) and loads a CSV into Metabase with the Metabase CLI, and a durable pipeline route that lands Google Play Console data in a database so you can build dashboards anyone can read.

How do you connect Google Play Console to Metabase?

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

Play splits analytics across two unrelated systems, and knowing which is which saves a wasted afternoon. The Play Developer Reporting API covers vitals only — crash rate, ANR rate, slow starts — with no installs and no revenue. Everything you'd put on a growth or revenue dashboard comes from the monthly CSV reports in a Cloud Storage bucket. There is no single API that returns Play revenue.

What can you analyze from Google Play Console data in Metabase?

Each of these is built from daily store report rows joined to the related store listing visitors and acquisitions, installs and uninstalls by dimension, earnings and refunds data your export exposes:

• Store listing visitors to acquisitions conversion
• Installs and uninstalls by country and device
• Earnings after Google's commission
• Subscription performance by product
• Crash and ANR rate against install growth

Which Google Play dashboards should you build in Metabase?

How do you use the Play Developer Reporting API (vitals) with the Metabase CLI?

Pair the Play Developer Reporting API (vitals) 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 report 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 Google Play Console API and the Metabase CLI?

# Play's statistics and financial reports are monthly CSVs in a
# Cloud Storage bucket. Find the URI in Play Console > Download reports.
BUCKET="gs://pubsite_prod_rev_${DEVELOPER_ID}"
PKG="com.example.app"
MONTH=$(date -u +%Y%m)

# Installs (overview + per-dimension files land side by side)
gcloud storage cp \
  "${BUCKET}/stats/installs/installs_${PKG}_${MONTH}_country.csv" .

# Store listing conversion (visitors, acquisitions, conversion rate)
gcloud storage cp \
  "${BUCKET}/stats/store_performance/store_performance_${PKG}_${MONTH}_country.csv" .

# Earnings (zipped; includes refunds as negative rows)
gcloud storage cp "${BUCKET}/earnings/earnings_${MONTH}.zip" . \
  && unzip -o "earnings_${MONTH}.zip"

# Then load into Metabase:
#   mb upload csv --file installs_${PKG}_${MONTH}_country.csv

Google ships no official MCP server for Play (verified July 2026). The community servers that exist wrap the Play Developer API — releases, listings, reviews, purchase status — and none of them expose the bulk statistics or earnings CSVs, which is where installs, conversion, and revenue actually live. So an MCP route wouldn't get you this data even if you trusted one. The Cloud Storage export above is the real path.

# 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-report-rows export — creates a table AND a model
mb upload csv --file google-play-daily-store-report-rows.csv --collection root

# Refresh that same table later from a new export
mb upload replace <table-id> --file google-play-daily-store-report-rows.csv

Can you generate a Google Play Console 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 Google Play Console 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 Google Play Console 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 Google Play Console data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for google-play tables and
  models). If durable Google Play Console 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 Play Developer Reporting API (vitals):
  daily store report rows, plus store listing visitors and acquisitions, installs and uninstalls by dimension, earnings and 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 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
  Google Play Console — 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: Google Play Console 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, Traffic source, Device, App version.

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 Google Play Console data into a database or warehouse?

For dashboards that need history and reliability, land Google Play Console 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 Play Developer Reporting API (vitals) for control over grain, fields, and refresh cadence.
• API + CSV — use this for quick exploration and one-off slices.

Fivetran's official Google Play connector reads the Cloud Storage bucket for you and is the least-effort durable path, with <code>fivetran/dbt_google_play</code> modeling the reports and <code>fivetran/dbt_app_reporting</code> unioning Play with the App Store into cross-store models. Airbyte has no official Play source. BigQuery Data Transfer Service also offers a Play connector if your warehouse is BigQuery. Rolling your own means a scheduled <code>gcloud storage</code> copy plus a loader — and note that <code>gsutil</code> is deprecated in favor of <code>gcloud storage</code>, though most older tutorials still use it.

Notes

• Decide the grain first (one row per day per package per dimension value) — 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 Google Play Console 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.
• Pick one install definition and document it. Play reports daily_device_installs, daily_user_installs, install_events, active_device_installs and more — they mean different things, and choosing the wrong one is the classic reason Play and Apple numbers "don't match".
• Earnings are reported in merchant currency with a currency_conversion_rate column; normalize to one reporting currency in the model rather than per card.
• Refunds arrive as negative earnings rows carrying a refund_type — keep them, and report gross and net separately instead of silently netting.
• Per-dimension files are separate: there's a country file and a device file, but no country-by-device file. Don't promise a cross-tab the source can't produce.
• Store amounts in minor units or with an explicit currency column, and convert once in the model layer rather than per card.

Which Google Play metrics should you track in Metabase?

What SQL powers Google Play 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(store_listing_visitors) AS visitors,
  SUM(store_listing_acquisitions) AS acquisitions,
  ROUND(
    100.0 * SUM(store_listing_acquisitions)
    / NULLIF(SUM(store_listing_visitors), 0), 2
  ) AS store_conversion_pct
FROM app_store_daily_metrics
WHERE store = 'google_play'
GROUP BY month, country
HAVING SUM(store_listing_visitors) > 500
ORDER BY month DESC, acquisitions DESC;

SELECT
  date_trunc('week', report_date)::date AS week,
  SUM(daily_device_installs) AS installs,
  SUM(daily_device_uninstalls) AS uninstalls,
  SUM(daily_device_installs) - SUM(daily_device_uninstalls) AS net_installs,
  ROUND(
    100.0 * SUM(daily_device_uninstalls)
    / NULLIF(SUM(daily_device_installs), 0), 1
  ) AS uninstall_ratio_pct
FROM app_store_daily_metrics
WHERE store = 'google_play'
  AND report_date >= CURRENT_DATE - INTERVAL '180 days'
GROUP BY week
ORDER BY week DESC;

SELECT
  date_trunc('month', report_date)::date AS month,
  store,
  SUM(COALESCE(first_time_downloads, daily_device_installs)) AS installs,
  ROUND(SUM(proceeds_usd), 2) AS proceeds,
  ROUND(
    SUM(proceeds_usd)
    / NULLIF(SUM(COALESCE(first_time_downloads,
                          daily_device_installs)), 0), 3
  ) AS proceeds_per_install
FROM app_store_daily_metrics
GROUP BY month, store
ORDER BY month DESC, store;

What are common mistakes when analyzing Google Play Console in Metabase?

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