How to build Kustomer support dashboards in Metabase

Kustomer is a CRM-style support platform that unifies customer conversations from email, chat, social, and voice onto a single timeline. Metabase is where you turn that activity into shared, trustworthy dashboards. Because Metabase reads from SQL databases, the reliable way to connect them is a small pipeline: sync Kustomer into a database or warehouse on a schedule, then point Metabase at it. This guide walks through that path end to end — including a free option with no paid connector.

How do you connect Kustomer to Metabase?

Metabase connects to SQL databases and warehouses — not to SaaS APIs directly, and there's no native Kustomer connector. So connecting Kustomer to Metabase means one thing: run a small pipeline that copies Kustomer data into a database on a schedule, then connect Metabase to that database. Once the data lands, the models, metrics, and SQL later in this guide all work.

The good news: this doesn't require a paid tool. Use a managed connector if you want zero maintenance, or a free, code-based sync you host yourself — both are covered in Build the pipeline below, and in more depth in our guide to building a data pipeline.

What can you analyze from Kustomer data in Metabase?

• Conversation volume — created vs. done by day and channel
• Time to first response — across all channels
• Queue health — where work waits and backlog by queue
• Backlog and aging — open work and how long it's been waiting
• CSAT — satisfaction by channel and over time
• Repeat contacts — customers writing in repeatedly
• Agent and team load — workload distribution and handle time

Which Kustomer dashboards should you build in Metabase?

How do you build the Kustomer → Metabase pipeline?

For dashboards that need history and reliability, land Kustomer data in a database first, then connect Metabase to that database.

Connector options

• dlt (free, code) — write a Python pipeline against the Kustomer REST API; the most reliable route since there's no first-party managed connector.
• Kustomer REST API (free, raw) — the source of truth; paginate conversations, messages, customers, and use search exports for bulk history.
• Managed ETL (paid, verify) — check whether your ETL vendor offers a Kustomer connector; availability varies, so confirm before relying on it.

Notes

• Land raw tables first, then build clean models on top.
• Kustomer is event/timeline-driven — sync messages (not just conversations) to compute response times.
• Custom objects (Klasses) may hold satisfaction or business data; map them explicitly.

Can you generate a Kustomer dashboard with AI?

Yes — and once Kustomer data is synced into a database, this is the fastest way to a strong first draft. First give an AI assistant a way to read your Metabase schema and create questions and dashboards, then paste the prompt below. It builds the dashboard from your database tables and tells the agent to skip metrics the schema can't support instead of faking them.

Two ways to let an assistant query and build in Metabase

Both connect to a Metabase instance that's already pointed at your synced database — the pipeline above moves the data; these just let the assistant read and write Metabase. Pick whichever fits your setup:

Set up the Metabase MCP server

Enable it under Admin → AI → MCP, then point your client at the endpoint:

# Metabase built-in MCP (replace with your instance URL)
claude mcp add --transport http metabase https://your-metabase.example.com/api/metabase-mcp

{
  "mcpServers": {
    "metabase": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "https://your-metabase.example.com/api/metabase-mcp"]
    }
  }
}

Clients with native remote support can use a "url" field instead of the mcp-remote bridge. Confirm the current endpoint in the Metabase MCP docs.

Set up the Metabase CLI

Install it globally, then authenticate once (the binary is mb):

# Install the CLI (the binary is `mb`)
npm install -g @metabase/cli

# Authenticate once — opens your browser on Metabase v62+, or use an API key
mb auth login --url https://your-metabase.example.com
mb auth status

On Metabase v62+ mb auth login opens your browser; older servers fall back to an API key. A terminal-based assistant can then inspect your schema (mb db schemas, mb table get --include fields) and create content (mb card create, mb dashboard create) against the synced tables.

Prompt: build the Kustomer Support Overview dashboard

With MCP or the CLI connected, paste this into your assistant to generate the dashboard:

Create a polished Metabase dashboard for Kustomer support analytics using the
available Kustomer tables in this database.

Goal: Help support leaders understand omnichannel volume, responsiveness, CSAT,
queue health, and agent workload from Kustomer data.

First, inspect the schema and identify the available Kustomer tables. Do not assume
exact table names. Map the available raw tables into these analytical concepts
where possible: Conversations, Messages, Customers, Companies, Users (agents),
Teams, Queues, and Satisfaction / custom objects (Klasses) if present.

Important:
- Build the dashboard from durable database/warehouse tables.
- Use medians (p50) and p90 for response times, never averages.
- Define "first response" as the first outbound agent message, excluding internal
  notes and automated messages.
- Kustomer is timeline/event-driven; if message-level history is missing, do not
  calculate response time. Use a caveat instead.
- Do not claim Metabase connects natively to Kustomer unless that is explicitly
  true in this environment.

Dashboard title: Kustomer Support Overview

Sections:
1. Executive summary (KPI cards): Conversations created last 7 days; Done last 7
   days; Open backlog; Median time to first response; CSAT % (only if satisfaction
   data exists); Volume by channel.
2. Volume & backlog: Created vs done by day; Open by status; Backlog aging;
   Volume by channel.
3. Queues & response time: First response p50/p90 by week; Open by queue; Reopened
   by week (only if history exists).
4. CSAT & quality: CSAT by week; Satisfaction by channel; Volume by tag; Repeat
   contacts.
5. Agent & team: Done by agent; Median handle time by team; Open assigned by agent;
   Volume by queue.

Filters: Channel, Queue, Team, Agent, Tag, Status, Date range.

Before finalizing, create or recommend reusable Metabase models:
modeled_kustomer_conversations, modeled_kustomer_messages,
modeled_kustomer_customers, modeled_kustomer_users, and modeled_kustomer_queues.

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

How should you model Kustomer data in Metabase?

Core tables

Modeling advice

• Define first response from the first outbound, non-note message in a conversation.
• Normalize status (open/snoozed/done) and channel so charts stay stable.
• Roll the timeline up to a conversation grain for most dashboards; keep messages for response-time math.
• Treat tags as a bridge table so a conversation can carry many tags.
• Define "done" once and reuse it everywhere.

Which Kustomer metrics should you track in Metabase?

What SQL powers Kustomer dashboards in Metabase?

These assume the modeled tables above (PostgreSQL dialect). Adjust identifiers to match your warehouse.

SELECT
  date_trunc('day', c.created_at) AS day,
  COUNT(*)                                        AS created,
  COUNT(*) FILTER (WHERE c.status = 'done')       AS done
FROM conversations c
WHERE c.created_at >= CURRENT_DATE - INTERVAL '30 days'
GROUP BY 1
ORDER BY 1;

WITH first_outbound AS (
  SELECT
    m.conversation_id,
    MIN(m.created_at) AS first_reply_at
  FROM messages m
  WHERE m.direction = 'out'
    AND m.is_note = false
  GROUP BY m.conversation_id
)
SELECT
  date_trunc('week', c.created_at) AS week,
  percentile_cont(0.5) WITHIN GROUP (
    ORDER BY EXTRACT(EPOCH FROM (f.first_reply_at - c.created_at)) / 60.0
  ) AS median_first_reply_min
FROM conversations c
JOIN first_outbound f ON f.conversation_id = c.id
GROUP BY 1
ORDER BY 1;

SELECT
  q.name             AS queue,
  COUNT(*)           AS open_conversations
FROM conversations c
JOIN queues q ON q.id = c.queue_id
WHERE c.status <> 'done'
GROUP BY q.name
ORDER BY open_conversations DESC;

SELECT
  c.channel,
  COUNT(*)           AS conversations
FROM conversations c
WHERE c.created_at >= CURRENT_DATE - INTERVAL '30 days'
GROUP BY c.channel
ORDER BY conversations DESC;

What are common mistakes when analyzing Kustomer in Metabase?

Related

Related analytics

Related integrations

FAQ