How to build Mixmax sales-engagement dashboards in Metabase

{
  "mcpServers": {
    "mixmax": {
      "url": "https://mcp.pipedream.net/v2"
    }
  }
}

# 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 Mixmax CSV export — creates a table AND a model
mb upload csv --file mixmax-sequences.csv --collection root

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

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

Goal: Help sales leaders understand the outbound funnel, sequence performance,
email deliverability, and sender activity from Mixmax data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for Mixmax tables and
  models). If durable Mixmax 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 Mixmax MCP server (the managed Pipedream
  connector, scoped tightly): sequences, recipients, message events, and meetings.
  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:
Mixmax CSV exports are usually flat and pre-aggregated (one row per sequence or
recipient, with columns like delivered, open rate, reply rate, and meetings).
Warehouse tables are raw and event-grained (a messages table with sent, delivered,
opened, clicked, replied, bounced, unsubscribed events). 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
  Mixmax — it reads a database or CLI-uploaded tables.
- If the data already provides rates, chart them directly; only recompute open,
  click, and reply rates over the delivered (not sent) base when raw counts are
  available, and track bounce rate separately.
- Count a "meeting booked" from a consistent signal (Mixmax scheduling event) and
  state which one.
- Report rates with denominators visible; small sequences are noisy.
- 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: Mixmax Sales Engagement Overview

Sections:
1. Executive summary (KPI cards): Recipients enrolled; Reply rate; Meetings booked;
   Bounce rate; Unsubscribe rate; Open rate.
2. Funnel: Enrolled → opened → replied → meeting.
3. Sequences: Open, click, and reply rate by sequence; reply rate by stage.
4. Deliverability: Bounce rate by sender; delivered vs. sent; unsubscribe rate.
5. Activity: Messages sent by sender; recipients touched; meetings scheduled.

Filters: Sequence, Sender, Date range.

Reuse the models Metabase auto-created from uploaded CSVs, or (for a warehouse)
create reusable models: modeled_mixmax_sequences, modeled_mixmax_recipients,
modeled_mixmax_messages, and modeled_mixmax_meetings.

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. Reconcile totals against
Mixmax's own analytics. Keep it practical, dense, and executive-readable.

Both based on delivered messages, so bounces don't distort them.

-- Open and reply rate by sequence (delivered as the base)
SELECT
  s.name AS sequence,
  COUNT(*) FILTER (WHERE m.status <> 'bounced')            AS delivered,
  ROUND(100.0 * COUNT(*) FILTER (WHERE m.opened_at IS NOT NULL)
    / NULLIF(COUNT(*) FILTER (WHERE m.status <> 'bounced'), 0), 1) AS open_rate_pct,
  ROUND(100.0 * COUNT(*) FILTER (WHERE m.replied_at IS NOT NULL)
    / NULLIF(COUNT(*) FILTER (WHERE m.status <> 'bounced'), 0), 1) AS reply_rate_pct
FROM modeled_mixmax_messages m
JOIN modeled_mixmax_sequences s ON s.id = m.sequence_id
WHERE m.sent_at >= CURRENT_DATE - INTERVAL '90 days'
GROUP BY s.name
ORDER BY delivered DESC;

Distinct recipients with a booked meeting over the last 90 days.

-- Meetings booked by sender over the last 90 days
SELECT
  mt.sender AS sender,
  COUNT(DISTINCT mt.recipient_id) AS meetings_booked
FROM modeled_mixmax_meetings mt
WHERE mt.booked_at >= CURRENT_DATE - INTERVAL '90 days'
GROUP BY mt.sender
ORDER BY meetings_booked DESC;

A deliverability guardrail; watch senders creeping up.

-- Bounce rate by sender
SELECT
  m.sender,
  COUNT(*)                                              AS sent,
  COUNT(*) FILTER (WHERE m.status = 'bounced')          AS bounced,
  ROUND(100.0 * COUNT(*) FILTER (WHERE m.status = 'bounced')
    / NULLIF(COUNT(*), 0), 2)                           AS bounce_rate_pct
FROM modeled_mixmax_messages m
WHERE m.sent_at >= CURRENT_DATE - INTERVAL '90 days'
GROUP BY m.sender
ORDER BY bounce_rate_pct DESC;