How to build Braze dashboards in Metabase

# Pull 30 days of daily campaign stats (use your instance's
# REST endpoint, e.g. rest.iad-01.braze.com)
curl "https://rest.iad-01.braze.com/campaigns/data_series?campaign_id=$CAMPAIGN_ID&length=30" \
  -H "Authorization: Bearer $BRAZE_API_KEY" > braze-campaign.json

# Flatten the JSON series to CSV (jq, or a few lines of Python),
# then load it into Metabase
mb upload csv --file braze-campaign-daily.csv --collection root

# 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 campaign-daily-stats export — creates a table AND a model
mb upload csv --file braze-campaign-daily-stats.csv --collection root

# Refresh that same table later from a new export
mb upload replace <table-id> --file braze-campaign-daily-stats.csv

Create a polished Metabase dashboard for Braze email marketing 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 campaign engagement, deliverability, automation performance, and list health from Braze data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for braze tables and
  models). If durable Braze 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 from the Braze REST API (Export) (write and run a short export script):
  campaign daily stats, plus campaigns, canvases, segments.
  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
  Braze — 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: Braze Email Marketing Overview

Sections:
1. Executive summary: Sends last 30 days; Click-through rate; Bounce rate;
   Unsubscribe rate; Net list growth.
2. Campaigns: Opens, clicks, and unsubscribes by campaign; CTR trend.
3. Deliverability: Delivery and bounce rates by week; spam complaints.
4. Automations: Entries, completions, and drop-off by workflow.
5. List health: Growth, engaged share, and email-attributed revenue.

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.

CTR, bounce, and unsubscribe rates per send.

SELECT
  campaign_name,
  send_date,
  sends,
  ROUND(100.0 * unique_clicks / NULLIF(delivered, 0), 2) AS ctr_pct,
  ROUND(100.0 * bounces / NULLIF(sends, 0), 2) AS bounce_rate_pct,
  ROUND(100.0 * unsubscribes / NULLIF(delivered, 0), 3) AS unsub_rate_pct
FROM email_campaign_stats
WHERE send_date >= CURRENT_DATE - INTERVAL '90 days'
ORDER BY send_date DESC;

Delivery, bounce, and complaint rates over time.

SELECT
  date_trunc('week', send_date) AS week,
  SUM(sends) AS sends,
  ROUND(100.0 * SUM(delivered) / NULLIF(SUM(sends), 0), 2)
    AS delivery_rate_pct,
  ROUND(100.0 * SUM(bounces) / NULLIF(SUM(sends), 0), 2) AS bounce_rate_pct,
  ROUND(10000.0 * SUM(spam_complaints) / NULLIF(SUM(delivered), 0), 2)
    AS complaints_per_10k
FROM email_campaign_stats
GROUP BY 1
ORDER BY 1;

Subscribes vs. unsubscribes from membership changes.

SELECT
  date_trunc('month', changed_at) AS month,
  COUNT(*) FILTER (WHERE change_type = 'subscribe') AS subscribes,
  COUNT(*) FILTER (WHERE change_type = 'unsubscribe') AS unsubscribes,
  COUNT(*) FILTER (WHERE change_type = 'subscribe')
    - COUNT(*) FILTER (WHERE change_type = 'unsubscribe') AS net_growth
FROM list_membership_changes
GROUP BY 1
ORDER BY 1;