How to build Close sales dashboards in Metabase

{
  "mcpServers": {
    "close": {
      "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 Close CSV export — creates a table AND a model
mb upload csv --file close-opportunities.csv --collection root

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

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

Goal: Help sales leaders understand pipeline, win rate, conversion, sales cycle,
and outreach activity from Close CRM data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for Close tables and
  models). If durable Close 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 Close MCP server (via the managed
  Pipedream connector): leads, opportunities, pipelines/statuses, activities, and
  users. 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:
Close CSV exports are usually flat and pre-aggregated (one row per opportunity,
lead, or activity, with columns like status, value, and dates). Warehouse tables
are raw and can carry status-change history for velocity metrics. Note that Close
opportunity value is stored in cents. Inspect the actual tables and column names
first; do not assume exact names or that status history exists.

Important:
- Build on whatever data is present; don't claim Metabase connects natively to
  Close — it reads a database or CLI-uploaded tables.
- Only recompute rates over the correct base (e.g. win rate) when raw counts are
  available; if the data already provides a rate, chart it directly.
- Define "won" and "closed" once (status_type = won vs. won/lost) and reuse them.
- For win rate, state the denominator explicitly (won / closed vs. won / created)
  and hold the cohort fixed.
- Report sales cycle length and deal size as medians (p50) and p90, never plain
  averages — both are right-skewed.
- If opportunity status-change history is missing, do not calculate sales cycle
  length, time-in-status, or status conversion. Use a caveat instead.
- Convert all amounts to a single reporting currency; caveat any mix.
- 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: Close Sales Overview

Sections:
1. Executive summary (KPI cards): Open pipeline; Coverage vs. target; Win rate
   (last 90 days); Median sales cycle length; Average and median deal size;
   Opportunities closing this period.
2. Pipeline: Active opportunities by status; value by user and pipeline; stale
   opportunities.
3. Conversion: Status-to-status conversion; win rate by pipeline and source; lost
   reasons.
4. Velocity: Median cycle length; time-in-status; deal velocity; aging.
5. Activity: Calls/emails/SMS by rep; activities per opportunity; connect rate.

Filters: Pipeline, Status, User, Lead source, Date range.

Reuse the models Metabase auto-created from uploaded CSVs, or (for a warehouse)
create reusable models: modeled_close_opportunities, modeled_close_status_history,
modeled_close_leads, modeled_close_contacts, and modeled_close_activities.

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
Close's own reporting. Keep it practical, dense, and executive-readable. Avoid
vanity metrics.

Won as a share of closed opportunities over the last 12 months.

SELECT
  date_trunc('month', o.closed_at) AS month,
  COUNT(*) FILTER (WHERE o.status_type = 'won')                  AS won,
  COUNT(*) FILTER (WHERE o.status_type IN ('won', 'lost'))       AS closed,
  ROUND(
    100.0 * COUNT(*) FILTER (WHERE o.status_type = 'won')
      / NULLIF(COUNT(*) FILTER (WHERE o.status_type IN ('won', 'lost')), 0),
    1
  ) AS win_rate_pct
FROM modeled_close_opportunities o
WHERE o.status_type IN ('won', 'lost')
  AND o.closed_at >= CURRENT_DATE - INTERVAL '12 months'
GROUP BY 1
ORDER BY 1;

The current funnel — active opportunities and value by status.

SELECT
  o.status_label,
  COUNT(*)                     AS active_opps,
  ROUND(SUM(o.value / 100.0), 2) AS active_value
FROM modeled_close_opportunities o
WHERE o.status_type = 'active'
GROUP BY o.status_label
ORDER BY active_value DESC;

Days from created to won; medians beat averages here.

-- Median days from created to won, by month closed
SELECT
  date_trunc('month', o.closed_at) AS month,
  percentile_cont(0.5) WITHIN GROUP (
    ORDER BY EXTRACT(EPOCH FROM (o.closed_at - o.date_created)) / 86400.0
  ) AS median_cycle_days,
  percentile_cont(0.9) WITHIN GROUP (
    ORDER BY EXTRACT(EPOCH FROM (o.closed_at - o.date_created)) / 86400.0
  ) AS p90_cycle_days
FROM modeled_close_opportunities o
WHERE o.status_type = 'won'
GROUP BY 1
ORDER BY 1;