How to build Similarweb dashboards in Metabase

Similarweb is a digital-market-intelligence platform estimating traffic, engagement, and channel mix for any website — yours and your competitors'. Metabase is where you turn that SEO data into shared, trustworthy dashboards. This guide covers two complementary paths: a lightweight MCP + CLI route that pulls live data with the Similarweb MCP server and loads a CSV into Metabase with the Metabase CLI, and a durable pipeline route that syncs Similarweb snapshots into a database so you can build dashboards anyone can read.

How do you connect Similarweb to Metabase?

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

What can you analyze from Similarweb data in Metabase?

• Market traffic share vs. named competitors — built from monthly traffic and engagement estimates and the related marketing channel mix, audience geography, competitor rankings data your sync exposes.
• Visits and engagement benchmarks by month — built from monthly traffic and engagement estimates and the related marketing channel mix, audience geography, competitor rankings data your sync exposes.
• Channel mix: direct, search, social, referral — built from monthly traffic and engagement estimates and the related marketing channel mix, audience geography, competitor rankings data your sync exposes.
• Geography and audience-overlap comparisons — built from monthly traffic and engagement estimates and the related marketing channel mix, audience geography, competitor rankings data your sync exposes.
• Category leaders and fastest movers — built from monthly traffic and engagement estimates and the related marketing channel mix, audience geography, competitor rankings data your sync exposes.

Which Similarweb dashboards should you build in Metabase?

How do you use the Similarweb MCP server with the Metabase CLI?

Pair the Similarweb MCP server 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 monthly traffic and engagement estimates for your domain and two competitors, trailing six months.
• Export the result as CSV, keeping keywords, domains, positions, and snapshot dates as stable columns.
• 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.
• Monthly estimates are re-modeled over time; store each month's pull with a snapshot date so trend lines don't rewrite themselves.
• mb upload csv needs an uploads database configured under Admin → Settings → Uploads.

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

{
  "mcpServers": {
    "similarweb": {
      "url": "https://mcp.similarweb.com",
      "headers": {
        "api-key": "your-similarweb-api-key"
      }
    }
  }
}

Requires a plan with API access (API, Business, or Enterprise). Remember the numbers are modeled estimates from panel and clickstream data — treat them as directional market shares, not analytics-grade counts.

# 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 monthly-traffic-and-engagement-estimates export — creates a table AND a model
mb upload csv --file similarweb-monthly-traffic-and-engagement-estimates.csv --collection root

# Refresh that same table later from a new export
mb upload replace <table-id> --file similarweb-monthly-traffic-and-engagement-estimates.csv

Can you generate a Similarweb dashboard with AI?

Yes. Use the prompt below with any assistant that can run the Similarweb MCP server and the Metabase CLI. It works end to end: if Similarweb 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 Similarweb market intelligence analytics.
Work end to end: get the data into Metabase if it isn't there yet, then build.

Goal: Help SEO and marketing leaders understand market traffic share, channel benchmarks, and competitor engagement over time from Similarweb data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for similarweb tables and
  models). If durable Similarweb 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 Similarweb MCP server:
  monthly traffic and engagement estimates, plus marketing channel mix, audience geography, competitor rankings.
  Prefer dated snapshots and rollups over wide raw pulls — API units cost money.
  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 keywords, domains,
dates, and whether multiple snapshot dates exist before creating trend cards.

Important:
- Build on whatever data is present; don't claim Metabase connects natively to
  Similarweb — it reads a database or CLI-uploaded tables.
- Trend cards need multiple dated snapshots; a single export only supports
  point-in-time cards — label them as such.
- Keep vendor metrics separate from Search Console data: estimated traffic and
  search volume are modeled numbers, clicks and impressions are measured ones.
- State the position convention (best rank per keyword vs. all appearances)
  once and reuse it on every card.
- Segment branded from non-branded keywords before reporting averages.

Dashboard title: Similarweb Market Intelligence Overview

Sections:
1. Executive summary: Your visits; Market share; Growth vs. category;
   Engagement vs. median.
2. Share: Traffic share by competitor by month; fastest movers.
3. Channels: Channel mix comparison; search and referral share.
4. Geography: Country splits and growth markets by competitor.
5. Engagement: Duration, pages per visit, and bounce benchmarks.

Filters: Date range, Domain/Project, Keyword group, Search engine, Location,
Competitor.

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 Similarweb data into a database or warehouse?

For dashboards that need history and reliability, land Similarweb snapshots and rollups 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 Similarweb Web Traffic & Batch APIs for control over grain, fields, and refresh cadence.
• MCP + CSV — use this for quick exploration and one-off slices.

The Batch API is the standout warehouse story in this cluster: it delivers scheduled reports for up to a million domains directly into Snowflake tables (or S3), so competitor traffic lands next to your own data without custom ETL.

Notes

• Decide the snapshot grain first (daily per keyword for rankings, weekly for backlinks is the workhorse) — it drives API-unit spend and every trend card.
• Land raw snapshot tables first, then build clean Metabase models on top.
• Normalize domain, month, channel, country, visits, and share-of-traffic fields.

How should you model Similarweb data in Metabase?

Core tables

Modeling advice

• Build a clean similarweb_traffic_monthly model with common columns across tools, so multi-tool SEO dashboards don't fork definitions.
• Every snapshot table needs an explicit snapshot or crawl date column — trends live or die on it.
• Keep modeled vendor estimates (volume, traffic, difficulty) in separate columns from measured Search Console data.
• Store keyword and domain join keys in a consistent normalized form (lowercase, no protocol, no trailing slash).

Which Similarweb metrics should you track in Metabase?

What SQL powers Similarweb dashboards in Metabase?

These assume a cleaned analytical model in a warehouse (PostgreSQL dialect). Adjust table and column names to match your pipeline.

SELECT
  month,
  domain,
  visits,
  ROUND(100.0 * visits
    / NULLIF(SUM(visits) OVER (PARTITION BY month), 0), 1)
    AS traffic_share_pct
FROM similarweb_traffic_monthly
WHERE domain IN ('yoursite.com', 'competitor-a.com', 'competitor-b.com')
ORDER BY 1, 3 DESC;

SELECT
  domain,
  channel,
  ROUND(AVG(share_of_traffic) * 100, 1) AS avg_share_pct
FROM similarweb_channels_monthly
WHERE month >= date_trunc('month', CURRENT_DATE) - INTERVAL '6 months'
GROUP BY 1, 2
ORDER BY 1, 3 DESC;

SELECT
  domain,
  month,
  visits,
  ROUND(100.0 * (visits - LAG(visits, 12) OVER (
    PARTITION BY domain ORDER BY month
  )) / NULLIF(LAG(visits, 12) OVER (
    PARTITION BY domain ORDER BY month
  ), 0), 1) AS yoy_growth_pct
FROM similarweb_traffic_monthly
ORDER BY 1, 2;

What are common mistakes when analyzing Similarweb in Metabase?

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FAQ