How to build PostHog dashboards in Metabase

PostHog is an open-source product analytics platform with events, funnels, session replay, feature flags, and experiments. 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 PostHog MCP and loads a CSV into Metabase with the Metabase CLI, and a durable pipeline route that syncs PostHog daily stats into a database so you can build dashboards anyone can read.

How do you connect PostHog 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 PostHog data in Metabase?

• Activation funnel by signup source — built from event rollups and the related events, persons, sessions data your sync exposes.
• Retention cohorts by feature usage — built from event rollups and the related events, persons, sessions data your sync exposes.
• Experiment exposure and conversion — built from event rollups and the related events, persons, sessions data your sync exposes.
• Active users by product area — built from event rollups and the related events, persons, sessions data your sync exposes.
• Account-level engagement for B2B — built from event rollups and the related events, persons, sessions data your sync exposes.

Which PostHog dashboards should you build in Metabase?

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

Pair the PostHog 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 event rollups 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 PostHog MCP and the Metabase CLI?

{
  "mcpServers": {
    "posthog": {
      "url": "https://mcp.posthog.com/mcp",
      "headers": {
        "Authorization": "Bearer your-personal-api-key"
      }
    }
  }
}

The hosted server auto-routes to your US or EU data region, and npx @posthog/wizard mcp add automates client setup. It can run HogQL queries directly — handy for scoped, summarized exports.

# 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 event-rollups export — creates a table AND a model
mb upload csv --file posthog-event-rollups.csv --collection root

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

Can you generate a PostHog dashboard with AI?

Yes. Use the prompt below with any assistant that can run the PostHog MCP and the Metabase CLI. It works end to end: if PostHog 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 PostHog 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 PostHog data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for posthog tables and
  models). If durable PostHog 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 PostHog MCP:
  event rollups, plus events, persons, sessions.
  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
  PostHog — 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: PostHog 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 PostHog data into a database or warehouse?

For dashboards that need history and reliability, land PostHog 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 PostHog batch exports for control over grain, fields, and refresh cadence.
• MCP + CSV — use this for quick exploration and one-off slices.

PostHog Cloud ships managed batch exports to BigQuery, Snowflake, Postgres, Redshift, and S3 — usually the shortest path. The Airbyte PostHog source and Fivetran's connector work too, and self-hosted instances can point Metabase straight at ClickHouse.

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 PostHog 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 PostHog metrics should you track in Metabase?

What SQL powers PostHog 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 PostHog in Metabase?

Related

Related analytics

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FAQ