How to build Klaviyo dashboards in Metabase

{
  "mcpServers": {
    "klaviyo": {
      "url": "https://mcp.klaviyo.com/mcp?read-only=true"
    }
  }
}

# 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 Klaviyo CSV export — creates a table AND a model
mb upload csv --file klaviyo-campaigns.csv --collection root

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

Create a polished Metabase dashboard for Klaviyo email/SMS marketing analytics.
Work end to end: get the data into Metabase if it isn't there yet, then build.

Goal: Help marketing leaders understand attributed revenue, campaign and flow
performance, list growth, and deliverability from Klaviyo data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for Klaviyo tables and
  models). If durable Klaviyo data is already present — synced from a warehouse or
  uploaded earlier — use it and skip to Step 2.
- If nothing is there, pull it with the Klaviyo MCP server in read-only mode:
  campaign performance, flow performance, list growth, and profile/segment
  summaries. 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:
Klaviyo CSV exports are usually flat and pre-aggregated (one row per campaign or
flow, with columns like recipients, open rate, click rate, placed-order value,
and revenue per recipient). Warehouse tables are raw and event-grained (filter an
events table by metric: Received/Opened/Clicked Email, Placed Order). Inspect the
actual tables and column names first; do not assume exact names or that an
event-level table exists.

Important:
- Build on whatever data is present; don't claim Metabase connects natively to
  Klaviyo — it reads a database or CLI-uploaded tables.
- If the data already provides rates, chart them directly; only recompute over the
  delivered (not sent) base when raw counts are available.
- Define the attribution window explicitly (e.g. Klaviyo's default) and state it.
- Separate campaign revenue from flow revenue.
- Only build a card if its underlying column/metric exists in the data.
- 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: Klaviyo Marketing Overview

Sections:
1. Executive summary (KPI cards): Attributed revenue last 30 days; Revenue per
   recipient; Open rate; Click rate; Net subscriber growth.
2. Campaigns: Revenue by campaign; Open/click/conversion trend; Revenue per
   recipient.
3. Flows: Revenue by flow; Flow conversion rate; Flow vs campaign split.
4. List growth: Net growth by week; New subscribers by source; Unsubscribe rate.
5. Deliverability: Bounce rate; Spam complaints; Engagement by segment.

Filters: Date range, Channel (email/SMS), Campaign, Flow, Segment.

Reuse the models Metabase auto-created from uploaded CSVs, or (for a warehouse)
create reusable models: modeled_klaviyo_campaigns, modeled_klaviyo_flows,
modeled_klaviyo_events, and modeled_klaviyo_profiles.

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. Keep it practical, dense,
and executive-readable. Avoid vanity metrics.

Placed Order events tied to each campaign over 30 days.

SELECT
  c.name                                          AS campaign,
  COUNT(*) FILTER (WHERE e.metric = 'Placed Order') AS orders,
  SUM(e.value) FILTER (WHERE e.metric = 'Placed Order') AS attributed_revenue
FROM events e
JOIN campaigns c ON c.id = e.campaign_id
WHERE e.timestamp >= CURRENT_DATE - INTERVAL '30 days'
GROUP BY c.name
ORDER BY attributed_revenue DESC NULLS LAST
LIMIT 20;

Engagement computed over the delivered base.

SELECT
  date_trunc('week', e.timestamp) AS week,
  COUNT(*) FILTER (WHERE e.metric = 'Received Email') AS delivered,
  ROUND(100.0 * COUNT(*) FILTER (WHERE e.metric = 'Opened Email')
    / NULLIF(COUNT(*) FILTER (WHERE e.metric = 'Received Email'), 0), 2) AS open_rate,
  ROUND(100.0 * COUNT(*) FILTER (WHERE e.metric = 'Clicked Email')
    / NULLIF(COUNT(*) FILTER (WHERE e.metric = 'Received Email'), 0), 2) AS click_rate
FROM events e
GROUP BY 1
ORDER BY 1;

Profile creation trend over the last 180 days.

SELECT
  date_trunc('week', p.created) AS week,
  COUNT(*)                      AS new_profiles
FROM profiles p
WHERE p.created >= CURRENT_DATE - INTERVAL '180 days'
GROUP BY 1
ORDER BY 1;