How to build Gainsight customer success dashboards in Metabase

Gainsight is the enterprise customer success platform — companies and relationships, scorecards, CTAs, success plans, and timeline activities, built for CS teams managing renewal risk at scale. Metabase is where you turn that record into shared, trustworthy customer success dashboards. This guide covers two complementary paths: a lightweight MCP + CLI route that pulls live data with the Gainsight CS MCP server and loads a CSV into Metabase with the Metabase CLI, and a durable pipeline route that lands Gainsight data in a database so you can build dashboards anyone can read.

How do you connect Gainsight to Metabase?

Most teams combine both routes: quick answers through MCP and CLI uploads first, then recurring reporting on a warehouse-backed model.

What can you analyze from Gainsight data in Metabase?

These all come from the same core objects — companies and their scorecards, plus the CTAs and playbooks, success plans, timeline activities your sync exposes:

• Account health distribution and risk tiers
• Renewal forecast by quarter with at-risk ARR
• CTA volume, aging, and resolution
• Health score vs. actual product usage
• CS coverage: accounts with no recent touchpoint

Which Gainsight customer success dashboards should you build in Metabase?

How do you use the Gainsight CS MCP server with the Metabase CLI?

Pair the Gainsight CS 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 the latest scorecard snapshot for every company — one row per account per measure, with the owner and ARR attached — not raw timeline activity.
• Export the result as CSV, keeping stable IDs, owners, statuses, and dates — and hashing or dropping any personal contact fields.
• 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.
• Scorecards are current-state: the server returns today's grade, so month-over-month health trends need dated snapshots you persist yourself on a schedule.
• mb upload csv needs an uploads database configured under Admin → Settings → Uploads.

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

{
  "mcpServers": {
    "gainsight": {
      "type": "http",
      "url": "https://your-domain.gainsightcloud.com/v1/ds-mcp/mcp"
    }
  }
}

Gainsight's own server, scoped to your tenant's domain. Setup is admin-led: an admin registers an OAuth app in Gainsight with your MCP client's callback URL, then each user authenticates individually. It reads Companies, Relationships, CTAs, Success Plans, Timeline activities, and Scorecards — scorecards are read-only, and neither deleting records nor updating company fields is supported, which suits analytics fine. Note that Gainsight ships several separate MCP servers: this one is for Gainsight CS, while the Gainsight PX server is a different, per-datacenter endpoint and is still labeled beta.

# 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 accounts export — creates a table AND a model
mb upload csv --file gainsight-accounts.csv --collection root

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

Can you generate a Gainsight dashboard with AI?

Yes. Use the prompt below with any assistant that can run the Gainsight CS MCP server and the Metabase CLI. It works end to end: if Gainsight tables already exist in Metabase it analyzes those; otherwise it pulls scoped, summarized data, loads it with mb upload csv, then builds the dashboard and caveats any metric that needs missing history.

Create a polished Metabase dashboard for Gainsight customer success analytics.
Work end to end: get the data into Metabase if it isn't there yet, then build.

Goal: Help customer success and revenue leaders understand account health and risk tiers, CTA coverage, renewal exposure by quarter, and how health scores compare with real product usage from Gainsight data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for gainsight tables and
  models). If durable Gainsight data is already present — a warehouse sync or
  an earlier upload — use it and skip to Step 2.
- If nothing is there, pull a scoped, summarized export with the Gainsight CS MCP server:
  companies and their scorecards, plus CTAs and playbooks, success plans, timeline activities.
  Prefer one row per account per snapshot date over raw activity
  feeds. 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, owners,
segments, statuses, and date ranges before creating trend cards.

Important:
- Build on whatever data is present; don't claim Metabase connects natively to
  Gainsight — it reads a database or CLI-uploaded tables.
- State which health score a card uses and when it was captured. Vendor scores
  are opaque composites and get redefined without notice.
- A single CSV is a point-in-time snapshot: health scores, phases, and renewal
  dates are current-state fields, so only build trend cards once several dated
  snapshots exist.
- Reconcile ARR against billing data, not against the Gainsight record — CS
  platforms hold a projection of CRM, billing, and product data.
- Only compute retention where subscription or license values exist. Gross
  revenue retention excludes expansion and can never exceed 100%; net revenue
  retention includes it and can.

Dashboard title: Gainsight Customer Success Overview

Sections:
1. Executive summary: Accounts by risk tier; ARR at risk; Open CTAs;
   Accounts with no recent touchpoint; ARR up for renewal this quarter.
2. Health: Risk tier distribution; ARR by tier; tier changes this month.
3. CTAs: Open CTAs by reason and age; resolution time; CTAs per CSM.
4. Renewals: ARR up for renewal by quarter; at-risk ARR in 90 days.
5. Reality check: Health score vs. product usage; quiet-but-healthy accounts.

Filters: Date range, CSM owner, Segment, Risk tier, Lifecycle stage.

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 Gainsight data into a database or warehouse?

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

Connector options

• Managed ETL — use a connector when one covers the objects you need, and read its support level and sync mode before you rely on it.
• Custom pipeline — use the Gainsight CS APIs for control over snapshot grain, fields, and refresh cadence.
• MCP + CSV — use this for quick exploration and one-off slices.

Fivetran's Gainsight Customer Success connector is the durable path, and unlike most connectors in this category it's a full standard connector rather than a Lite one (its Hybrid deployment model requires an Enterprise or Business Critical plan). Airbyte publishes a Gainsight PX source but no CS source. Rolling your own means respecting two very different quotas: the synchronous APIs allow roughly 100 calls per minute and 50,000 per day, but the async and bulk APIs — the ones you'd actually use for warehouse extraction — are limited to 10 calls per hour and 100 per day. Plan for a nightly bulk job, not a chatty incremental sync.

Notes

• Decide the snapshot grain first (one row per account per day, or per account per week) — it drives storage and every trend card.
• Land raw tables first, then build clean Metabase models on top.
• Normalize account-id, csm-owner, segment, snapshot-date, measure, weight, and score fields.

How should you model Gainsight data in Metabase?

Core tables

Modeling advice

• Build a clean cs_accounts model with common columns across CS tools, so multi-source dashboards don't fork definitions.
• Re-derive the health score components in the warehouse and keep the vendor's score beside your own as a separate, labeled column. Vendor scores are opaque composites, and they get redefined without anyone telling the analytics team.
• Snapshot renewal forecasts into a renewal_forecast_snapshots table frozen at period start — accuracy cannot be measured against a forecast that keeps moving.
• Model gross and net revenue retention separately: gross excludes expansion and can never exceed 100%, net includes it and can.
• Reconcile ARR against billing rather than the CS platform. What a CS tool holds is a projection of CRM, billing, and product data, and projections drift.

Which Gainsight customer success metrics should you track in Metabase?

What SQL powers Gainsight customer success dashboards in Metabase?

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

WITH latest AS (
  SELECT
    account_id,
    SUM(score * weight) / NULLIF(SUM(weight), 0) AS weighted_score
  FROM account_health_scores
  WHERE snapshot_date = (SELECT MAX(snapshot_date) FROM account_health_scores)
  GROUP BY account_id
)
SELECT
  CASE
    WHEN l.weighted_score >= 80 THEN 'healthy'
    WHEN l.weighted_score >= 60 THEN 'watch'
    ELSE 'at risk'
  END AS risk_tier,
  COUNT(*) AS accounts,
  ROUND(SUM(a.arr), 0) AS arr
FROM latest l
JOIN cs_accounts a ON a.account_id = l.account_id
GROUP BY 1
ORDER BY MIN(l.weighted_score);

WITH month_end AS (
  SELECT DISTINCT ON (account_id, date_trunc('month', snapshot_date))
    account_id,
    date_trunc('month', snapshot_date) AS month,
    snapshot_date
  FROM account_health_scores
  ORDER BY account_id, 2, snapshot_date DESC
),
scored AS (
  SELECT
    m.account_id,
    m.month,
    SUM(h.score * h.weight) / NULLIF(SUM(h.weight), 0) AS weighted_score
  FROM month_end m
  JOIN account_health_scores h
    ON h.account_id = m.account_id
   AND h.snapshot_date = m.snapshot_date
  GROUP BY 1, 2
),
movement AS (
  SELECT
    account_id,
    month,
    weighted_score,
    LAG(weighted_score) OVER (PARTITION BY account_id ORDER BY month)
      AS prior_score
  FROM scored
)
SELECT
  a.name,
  a.csm_owner,
  a.arr,
  ROUND(mv.prior_score::numeric, 1) AS prior_score,
  ROUND(mv.weighted_score::numeric, 1) AS current_score,
  ROUND((mv.weighted_score - mv.prior_score)::numeric, 1) AS score_change
FROM movement mv
JOIN cs_accounts a ON a.account_id = mv.account_id
WHERE mv.month = date_trunc('month', CURRENT_DATE)
  AND mv.prior_score IS NOT NULL
ORDER BY ABS(mv.weighted_score - mv.prior_score) DESC
LIMIT 25;

SELECT
  date_trunc('quarter', renewal_date) AS renewal_quarter,
  COUNT(*) AS accounts_up_for_renewal,
  ROUND(SUM(arr), 0) AS arr_up_for_renewal,
  ROUND(SUM(arr) FILTER (WHERE lifecycle_stage = 'at risk'), 0) AS at_risk_arr
FROM cs_accounts
WHERE renewal_date >= date_trunc('quarter', CURRENT_DATE)
  AND renewal_date < date_trunc('quarter', CURRENT_DATE) + INTERVAL '12 months'
GROUP BY 1
ORDER BY 1;

What are common mistakes when analyzing Gainsight in Metabase?

Related

Related analytics

Related dashboards

Related integrations

FAQ