How to build Pipedrive sales dashboards in Metabase

{
  "mcpServers": {
    "pipedrive": {
      "command": "npx",
      "args": ["-y", "@ckalima/pipedrive-mcp-server"],
      "env": {
        "PIPEDRIVE_API_KEY": "your-40-character-api-key"
      }
    }
  }
}

# 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 Pipedrive CSV export — creates a table AND a model
mb upload csv --file pipedrive-deals.csv --collection root

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

Create a polished Metabase dashboard for Pipedrive 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 rep activity from Pipedrive CRM data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for Pipedrive tables and
  models). If durable Pipedrive 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 Pipedrive MCP server (scoped, read-only
  where supported): deals, pipelines/stages, persons, organizations, and
  activities. 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:
Pipedrive CSV exports are usually flat and pre-aggregated (one row per deal,
person, or organization, with columns like stage, value, status, and add time).
Warehouse tables are raw and include deal flow / changes for velocity metrics.
Inspect the actual tables and column names first; do not assume exact names or
that deal-flow history exists.

Important:
- Build on whatever data is present; don't claim Metabase connects natively to
  Pipedrive — 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 = 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 deal flow/stage-change history is missing, do not calculate sales cycle
  length, time-in-stage, or stage 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: Pipedrive 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;
   Deals closing this period.
2. Pipeline: Open deals by stage; value by owner and pipeline; rotting deals.
3. Conversion: Stage-to-stage conversion; win rate by pipeline and source; loss
   reasons.
4. Velocity: Median cycle length; time-in-stage; deal velocity; aging.
5. Activity: Activities by type and rep; activities per open deal; overdue
   activities.

Filters: Pipeline, Stage, Owner, Source, Date range.

Reuse the models Metabase auto-created from uploaded CSVs, or (for a warehouse)
create reusable models: modeled_pipedrive_deals,
modeled_pipedrive_stage_history, modeled_pipedrive_persons,
modeled_pipedrive_organizations, and modeled_pipedrive_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
Pipedrive's Insights. Keep it practical, dense, and executive-readable. Avoid
vanity metrics.

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

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

The current funnel — deals and value by stage, in order.

SELECT
  s.name         AS stage_name,
  s.order_nr,
  COUNT(*)       AS open_deals,
  ROUND(SUM(d.value), 2) AS open_value
FROM modeled_pipedrive_deals d
JOIN modeled_pipedrive_stages s ON s.id = d.stage_id
WHERE d.status = 'open'
GROUP BY s.name, s.order_nr
ORDER BY s.order_nr;

Days from add to won; medians beat averages here.

-- Median days from add to won, by month closed
SELECT
  date_trunc('month', d.closed_at) AS month,
  percentile_cont(0.5) WITHIN GROUP (
    ORDER BY EXTRACT(EPOCH FROM (d.closed_at - d.add_time)) / 86400.0
  ) AS median_cycle_days,
  percentile_cont(0.9) WITHIN GROUP (
    ORDER BY EXTRACT(EPOCH FROM (d.closed_at - d.add_time)) / 86400.0
  ) AS p90_cycle_days
FROM modeled_pipedrive_deals d
WHERE d.status = 'won'
GROUP BY 1
ORDER BY 1;