How to build Salesloft sales-engagement dashboards in Metabase

{
  "mcpServers": {
    "salesloft": {
      "url": "https://your-salesloft-mcp-endpoint/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 Salesloft CSV export — creates a table AND a model
mb upload csv --file salesloft-cadences.csv --collection root

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

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

Goal: Help sales and enablement leaders understand cadence performance, the
outbound funnel, deliverability, and pipeline impact from Salesloft data — joined
to CRM pipeline where possible.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for Salesloft tables and
  models). If durable Salesloft 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 Salesloft MCP server (the announced
  Clari + Salesloft revenue-intelligence server; scope access accordingly): people,
  cadences, cadence memberships, activities, and meetings. 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:
Salesloft CSV exports are usually flat and pre-aggregated (one row per cadence or
rep, with columns like contacted, reply rate, step completion, and meetings).
Warehouse tables are raw and activity-grained (memberships, activities, and
meetings you join yourself). Inspect the actual tables and column names first; do
not assume exact names or that an activity-level table exists.

Important:
- Build on whatever data is present; don't claim Metabase connects natively to
  Salesloft — it reads a database or CLI-uploaded tables.
- Engagement signals are most useful joined to CRM opportunities — align on a
  deal key so cadence activity maps to real pipeline, when raw counts allow it.
- Define "reply rate" and "meeting" explicitly and hold definitions fixed.
- 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: Salesloft Sales Engagement Overview

Sections:
1. Executive summary (KPI cards): Reply rate; Meetings booked; Active accounts in
   cadence; Step completion rate; Bounce rate; Opportunities sourced.
2. Cadence performance: Reply rate by cadence; meetings by rep; step completion.
3. Funnel: Enrolled → contacted → replied → meeting; conversion by step.
4. Deliverability: Bounce rate; opt-out rate; open-rate trend.
5. Pipeline impact: Opportunities sourced; engagement vs. win rate;
   meetings-to-opportunity rate.

Filters: Rep, Cadence, Segment, Date range.

Reuse the models Metabase auto-created from uploaded CSVs, or (for a warehouse)
create reusable models: modeled_salesloft_people, modeled_salesloft_cadences,
modeled_salesloft_memberships, modeled_salesloft_activities, and
modeled_salesloft_meetings.

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
Salesloft's own analytics. Keep it practical, dense, and executive-readable.

Replied as a share of contacted, over the last 90 days.

-- Reply rate by cadence over the last 90 days
SELECT
  c.name AS cadence,
  COUNT(*)                                              AS contacted,
  COUNT(*) FILTER (WHERE m.replied_at IS NOT NULL)      AS replied,
  ROUND(100.0 * COUNT(*) FILTER (WHERE m.replied_at IS NOT NULL)
    / NULLIF(COUNT(*), 0), 2)                           AS reply_rate_pct
FROM modeled_salesloft_memberships m
JOIN modeled_salesloft_cadences c ON c.id = m.cadence_id
WHERE m.first_contacted_at >= CURRENT_DATE - INTERVAL '90 days'
GROUP BY c.name
ORDER BY reply_rate_pct DESC;

Distinct people with a booked meeting over the last 90 days.

-- Meetings booked by rep over the last 90 days
SELECT
  u.name AS rep,
  COUNT(DISTINCT mt.person_id) AS meetings_booked
FROM modeled_salesloft_meetings mt
JOIN modeled_salesloft_users u ON u.id = mt.user_id
WHERE mt.booked_at >= CURRENT_DATE - INTERVAL '90 days'
GROUP BY u.name
ORDER BY meetings_booked DESC;

Bounced emails as a share of sent, a list-hygiene signal.

-- Bounce rate by sender
SELECT
  u.name AS sender,
  COUNT(*)                                              AS sent,
  COUNT(*) FILTER (WHERE a.status = 'bounced')          AS bounced,
  ROUND(100.0 * COUNT(*) FILTER (WHERE a.status = 'bounced')
    / NULLIF(COUNT(*), 0), 2)                           AS bounce_rate_pct
FROM modeled_salesloft_activities a
JOIN modeled_salesloft_users u ON u.id = a.user_id
WHERE a.type = 'email' AND a.sent_at >= CURRENT_DATE - INTERVAL '90 days'
GROUP BY u.name
ORDER BY bounce_rate_pct DESC;