How to build Zoho CRM sales dashboards in Metabase

{
  "mcpServers": {
    "zoho-crm": {
      "url": "https://<your-zoho-mcp-server-url>/mcp"
    }
  }
}

# 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 Zoho CRM CSV export — creates a table AND a model
mb upload csv --file zoho-deals.csv --collection root

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

Create a polished Metabase dashboard for Zoho CRM 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 Zoho CRM data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for Zoho CRM tables and
  models). If durable Zoho 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 Zoho CRM MCP servers (use the read-only
  data-insights server where supported): deals, contacts, accounts, activities,
  and stages. 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:
Zoho CSV exports are usually flat and pre-aggregated (one row per deal, with
columns like stage, amount, and closing date), while warehouse tables are raw and
normalized across modules (deals, stage history, contacts, activities). Inspect
the actual tables and column names first; do not assume exact names or that a
stage-history table exists.

Important:
- Build on whatever data is present; don't claim Metabase connects natively to
  Zoho CRM — it reads a database or CLI-uploaded tables.
- Define "won" and "closed" once (stage = Closed Won vs. Closed Won/Closed 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.
- Only compute sales cycle length, time-in-stage, or stage conversion when stage
  history exists; otherwise use a caveat.
- Zoho supports multi-currency — 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: Zoho CRM 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; deals past close
   date.
3. Conversion: Stage-to-stage conversion; win rate by pipeline and source; lost
   reasons.
4. Velocity: Median cycle length; time-in-stage; deal velocity; aging.
5. Activity: Calls/tasks/meetings by rep; activities per open deal; overdue
   activities.

Filters: Pipeline, Stage, Owner, Lead source, Date range.

Reuse the models Metabase auto-created from uploaded CSVs, or (for a warehouse)
create reusable models: modeled_zoho_deals, modeled_zoho_stage_history,
modeled_zoho_contacts, modeled_zoho_accounts, and modeled_zoho_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
Zoho CRM's own Analytics. 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.closing_date) AS month,
  COUNT(*) FILTER (WHERE d.stage = 'Closed Won')                          AS won,
  COUNT(*) FILTER (WHERE d.stage IN ('Closed Won', 'Closed Lost'))        AS closed,
  ROUND(
    100.0 * COUNT(*) FILTER (WHERE d.stage = 'Closed Won')
      / NULLIF(COUNT(*) FILTER (WHERE d.stage IN ('Closed Won', 'Closed Lost')), 0),
    1
  ) AS win_rate_pct
FROM modeled_zoho_deals d
WHERE d.stage IN ('Closed Won', 'Closed Lost')
  AND d.closing_date >= CURRENT_DATE - INTERVAL '12 months'
GROUP BY 1
ORDER BY 1;

The current funnel — open deals and value by stage.

SELECT
  d.stage,
  COUNT(*)               AS open_deals,
  ROUND(SUM(d.amount), 2) AS open_value
FROM modeled_zoho_deals d
WHERE d.stage NOT IN ('Closed Won', 'Closed Lost')
GROUP BY d.stage
ORDER BY open_value DESC;

Days from created to won; medians beat averages here.

-- Median days from created to won, by month closed
SELECT
  date_trunc('month', d.closing_date) AS month,
  percentile_cont(0.5) WITHIN GROUP (
    ORDER BY EXTRACT(EPOCH FROM (d.closing_date::timestamp - d.created_time)) / 86400.0
  ) AS median_cycle_days,
  percentile_cont(0.9) WITHIN GROUP (
    ORDER BY EXTRACT(EPOCH FROM (d.closing_date::timestamp - d.created_time)) / 86400.0
  ) AS p90_cycle_days
FROM modeled_zoho_deals d
WHERE d.stage = 'Closed Won'
GROUP BY 1
ORDER BY 1;