How to build Overloop sales dashboards in Metabase

{
  "mcpServers": {
    "overloop": {
      "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 an Overloop CSV export — creates a table AND a model
mb upload csv --file overloop-deals.csv --collection root

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

Create a polished Metabase dashboard for Overloop 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 outbound campaign performance from Overloop data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for Overloop tables and
  models). If durable Overloop 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 Overloop MCP server (managed via
  Pipedream): prospects, companies, deals, campaigns, and email/conversation
  events. 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:
Overloop CSV exports are usually flat and pre-aggregated (one row per deal or
campaign, with columns like stage, status, value, sends, opens, and replies).
Warehouse tables are raw and event-grained (email events with sent_at,
opened_at, replied_at, and status). Inspect the actual tables and column names
first; do not assume exact names or that stage-change history exists.

Important:
- Build on whatever data is present; don't claim Metabase connects natively to
  Overloop — it reads a database or CLI-uploaded tables.
- Define "won" and "closed" once (deal status) and reuse them.
- For win rate, state the denominator explicitly (won / closed vs. won / created)
  and hold the cohort fixed.
- For outbound rates, define the denominators (delivered vs. sent) and keep bounces
  out of open/reply denominators.
- Report sales cycle length and deal size as medians (p50) and p90, never plain
  averages — both are right-skewed.
- Only build a card if its underlying column/metric exists in the data; if deal
  stage-change history is missing, skip sales cycle length, time-in-stage, and
  stage conversion and use a caveat instead.
- Convert all amounts to a single reporting currency; caveat any mix.
- 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: Overloop Sales Overview

Sections:
1. Executive summary (KPI cards): Open pipeline; Win rate (last 90 days); Reply
   rate; Meetings booked; Median sales cycle length; Average and median deal size.
2. Pipeline: Open deals by stage; value by owner; stale deals.
3. Outbound: Emails sent by campaign; open and reply rate; bounce rate; meetings
   booked.
4. Conversion: Stage-to-stage conversion; win rate by campaign; lost reasons.
5. Velocity: Median cycle length; time-in-stage; deal velocity; aging.

Filters: Campaign, Stage, Owner, Date range.

Reuse the models Metabase auto-created from uploaded CSVs, or (for a warehouse)
create reusable models: modeled_overloop_deals, modeled_overloop_stage_history,
modeled_overloop_prospects, modeled_overloop_campaigns, and
modeled_overloop_emails.

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
Overloop's own reporting. Keep it practical, dense, and executive-readable.

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_overloop_deals d
WHERE d.status IN ('won', 'lost')
  AND d.closed_at >= CURRENT_DATE - INTERVAL '12 months'
GROUP BY 1
ORDER BY 1;

Replies as a share of delivered emails, by campaign.

-- Outbound performance by campaign over the last 90 days
SELECT
  c.name AS campaign,
  COUNT(*) FILTER (WHERE e.status <> 'bounced')            AS delivered,
  COUNT(*) FILTER (WHERE e.opened_at IS NOT NULL)          AS opened,
  COUNT(*) FILTER (WHERE e.replied_at IS NOT NULL)         AS replied,
  ROUND(
    100.0 * COUNT(*) FILTER (WHERE e.replied_at IS NOT NULL)
      / NULLIF(COUNT(*) FILTER (WHERE e.status <> 'bounced'), 0),
    1
  ) AS reply_rate_pct
FROM modeled_overloop_emails e
JOIN modeled_overloop_campaigns c ON c.id = e.campaign_id
WHERE e.sent_at >= CURRENT_DATE - INTERVAL '90 days'
GROUP BY c.name
ORDER BY delivered DESC;

The current funnel — open deals and value by stage.

SELECT
  d.stage,
  COUNT(*)                AS open_deals,
  ROUND(SUM(d.value), 2)  AS pipeline_value
FROM modeled_overloop_deals d
WHERE d.status = 'open'
GROUP BY d.stage
ORDER BY pipeline_value DESC;