How to build AppsFlyer dashboards in Metabase

AppsFlyer is a mobile measurement and attribution platform tracking installs, in-app events, and campaign ROI across ad networks. Metabase is where you turn that marketing data into shared, trustworthy dashboards. This guide covers two complementary paths: a lightweight MCP + CLI route that pulls live data with the AppsFlyer MCP and loads a CSV into Metabase with the Metabase CLI, and a durable pipeline route that syncs AppsFlyer daily stats into a database so you can build dashboards anyone can read.

How do you connect AppsFlyer to Metabase?

Most teams combine both routes: use MCP and CLI uploads for a fast first pass, then move recurring marketing reporting to a warehouse-backed model.

What can you analyze from AppsFlyer data in Metabase?

• Installs by media source and campaign — built from attributed installs and the related in-app events, retargeting conversions, SKAN reports data your sync exposes.
• Cost per install and ROAS by network — built from attributed installs and the related in-app events, retargeting conversions, SKAN reports data your sync exposes.
• Post-install event conversion by cohort — built from attributed installs and the related in-app events, retargeting conversions, SKAN reports data your sync exposes.
• Organic vs. paid install mix — built from attributed installs and the related in-app events, retargeting conversions, SKAN reports data your sync exposes.
• Retention by acquisition source — built from attributed installs and the related in-app events, retargeting conversions, SKAN reports data your sync exposes.

Which AppsFlyer dashboards should you build in Metabase?

How do you use the AppsFlyer MCP with the Metabase CLI?

Pair the AppsFlyer MCP with the Metabase CLI for fast, hands-on analysis. MCP is useful for scoped lookups and summarized exports; the Metabase CLI's upload command loads CSV data into Metabase and creates a ready-to-query table and model.

Example workflow

• Ask the MCP server for daily attributed installs for your core activation events.
• Export the result as CSV, keeping stable IDs, channels, campaigns, and dates.
• 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

• MCP lookups 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.
• Daily event rollups and a stable user ID are required for funnels, retention, and activation trends.
• mb upload csv needs an uploads database configured under Admin → Settings → Uploads.

How do you set up AppsFlyer MCP and the Metabase CLI?

{
  "mcpServers": {
    "appsflyer": {
      "url": "https://mcp.appsflyer.com/auth/mcp"
    }
  }
}

In beta as of mid-2026 — AppsFlyer gates access behind an MCP beta program. It covers what your dashboards can see: campaign performance, SKAN data, and audience lists, following your existing account permissions.

# 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 attributed-installs export — creates a table AND a model
mb upload csv --file appsflyer-attributed-installs.csv --collection root

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

Can you generate a AppsFlyer dashboard with AI?

Yes. Use the prompt below with any assistant that can run the AppsFlyer MCP and the Metabase CLI. It works end to end: if AppsFlyer tables already exist in Metabase it analyzes those; otherwise it pulls scoped, summarized data over MCP, loads it with mb upload csv, then builds the dashboard and caveats any metric that needs missing history.

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

Goal: Help marketing and growth leaders understand activation, retention, engagement, and which acquisition channels send users who stay from AppsFlyer data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for appsflyer tables and
  models). If durable AppsFlyer data is already present — synced from a warehouse
  or uploaded earlier — use it and skip to Step 2.
- If nothing is there, pull a scoped, summarized export with the AppsFlyer MCP:
  attributed installs, plus in-app events, retargeting conversions, SKAN reports.
  Prefer daily aggregates over raw events. 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, channels,
campaigns, dates, and whether daily history exists before creating trend or
pacing cards.

Important:
- Build on whatever data is present; don't claim Metabase connects natively to
  AppsFlyer — it reads a database or CLI-uploaded tables.
- Never try to load raw event or click streams into Metabase; use daily
  aggregates, campaign-grain stats, and entity tables.
- Only compute rates (CTR, conversion rate, ROAS, CAC) when both numerator and
  denominator exist — and state the attribution model when reporting conversions.
- Exclude test campaigns and internal traffic from headline cards, and keep
  currency consistent when spend spans accounts.
- 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: AppsFlyer Product Analytics Overview

Sections:
1. Executive summary: Signups last 30 days; Activation rate; WAU; Day-30
   retention; Churned users.
2. Activation: Signup-to-activation funnel; activation by cohort and channel.
3. Retention: Weekly cohort retention; DAU/WAU/MAU; stickiness.
4. Channels: Activation and retention by acquisition channel; CAC vs. LTV.
5. Growth accounting: New, retained, resurrected, churned by month.

Filters: Date range, Channel, Campaign, Country, Device, Segment.

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 AppsFlyer data into a database or warehouse?

For dashboards that need history and reliability, land AppsFlyer daily stats and entities in a database first, then connect Metabase to that database.

Connector options

• Managed ETL — use a connector when one covers the objects you need.
• Custom pipeline — use the AppsFlyer Pull API for control over grain, fields, and refresh cadence.
• MCP + CSV — use this for quick exploration and one-off slices.

Sync installs and in-app events with the Airbyte AppsFlyer source or Fivetran's connector, or use Data Locker for high-volume delivery straight to BigQuery, Snowflake, or cloud storage — the right path once Pull API row caps start to bite.

Notes

• Decide the rollup grain first (daily per campaign/channel is the workhorse) — it drives warehouse cost and every trend card.
• Land raw entity tables first, then build clean Metabase models on top.
• Normalize user ID, event name, event date, acquisition channel, and plan fields.

How should you model AppsFlyer data in Metabase?

Core tables

Modeling advice

• Build a clean event_rollups model with common columns across tools, so multi-channel dashboards don't fork definitions.
• Separate entity tables (campaigns, audiences, pages) from daily time-series rollups.
• Exclude test campaigns and internal traffic from headline metrics; keep channel and campaign as explicit columns.
• Use stable IDs for campaign, channel, and user joins; display names change.

Which AppsFlyer metrics should you track in Metabase?

What SQL powers AppsFlyer 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('week', u.signup_at) AS signup_week,
  COUNT(*) AS signups,
  COUNT(*) FILTER (WHERE u.activated_at IS NOT NULL
    AND u.activated_at <= u.signup_at + INTERVAL '7 days') AS activated_7d,
  ROUND(
    100.0 * COUNT(*) FILTER (WHERE u.activated_at IS NOT NULL
      AND u.activated_at <= u.signup_at + INTERVAL '7 days')
    / NULLIF(COUNT(*), 0), 1
  ) AS activation_rate_pct
FROM users u
GROUP BY 1
ORDER BY 1;

SELECT
  date_trunc('week', u.signup_at) AS cohort_week,
  COUNT(DISTINCT u.user_id) AS cohort_size,
  COUNT(DISTINCT e.user_id) FILTER (
    WHERE e.event_date BETWEEN u.signup_at + INTERVAL '7 days'
      AND u.signup_at + INTERVAL '14 days'
  ) AS retained_week_2
FROM users u
LEFT JOIN event_rollups_by_user e ON e.user_id = u.user_id
GROUP BY 1
ORDER BY 1;

SELECT
  date_trunc('week', event_date) AS week,
  COUNT(DISTINCT user_id) AS weekly_active_users
FROM event_rollups_by_user
WHERE event_date >= CURRENT_DATE - INTERVAL '6 months'
GROUP BY 1
ORDER BY 1;

What are common mistakes when analyzing AppsFlyer in Metabase?

Related

Related analytics

Related dashboards

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FAQ