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Dashboard

What goes in a sales forecast dashboard in Metabase?

A sales forecast dashboard turns your open pipeline into an expected number for the period — weighted vs. unweighted, commit vs. best-case — and checks it against target and past accuracy. Build it from CRM data synced into a database — see HubSpot or Salesforce for the connection.

For: Sales leaders, RevOps, finance. Refresh: daily, with periodic snapshots for accuracy. Source: modeled deals with stage, amount, expected close, and stage probabilities.

What does a sales forecast dashboard look like?

Here's the layout this guide builds, at the grain of one quarter: the expected number and its coverage of the gap at the top, then commit against best case and quota, then where the open pipeline actually sits by rep, segment, and stage. Read it in the weekly forecast call.

Sales forecast dashboard in Metabase showing commit vs. best case, quota attainment, pipeline by stage, and by rep.
An example sales forecast dashboard in Metabase, built from HubSpot or Salesforce data. Figures are illustrative.

Which cards belong on a forecast dashboard?

Headline KPIs

  • Weighted pipeline (expected revenue) for the period
  • Unweighted open pipeline
  • Pipeline coverage vs. target
  • Attainment to date (closed won ÷ target)

Forecast detail

  • Commit vs. best-case vs. target
  • Weighted pipeline by owner and by segment
  • Pipeline by expected close month
  • Forecast accuracy vs. prior periods

What data does a forecast dashboard need?

  • A modeled deals table with stage, amount, and expected close date.
  • Stage win probabilities (from the CRM or modeled from history).
  • Periodic snapshots of the pipeline for forecast accuracy — you can't reconstruct a past forecast from current state alone.
  • A target/quota per period, owner, or segment.

How do you build a forecast dashboard?

  1. Sync your CRM into a database (HubSpot or Salesforce).
  2. Weight open deals by stage probability; compare to unweighted pipeline.
  3. Snapshot the pipeline on a schedule so you can measure forecast accuracy.
  4. Add filters for owner, segment, and expected-close period.

Example card SQL

Weighted vs. unweighted pipeline by ownerPostgreSQL
-- Weighted vs. unweighted open pipeline for the current quarter,
-- weighting each open deal by its stage probability.
SELECT
  d.owner,
  ROUND(SUM(d.amount), 2)                                 AS unweighted_pipeline,
  ROUND(SUM(d.amount * s.win_probability), 2)             AS weighted_pipeline
FROM deals d
JOIN stages s ON s.name = d.stage
WHERE d.stage NOT IN ('won', 'lost')
  AND d.expected_close_at >= date_trunc('quarter', CURRENT_DATE)
  AND d.expected_close_at <  date_trunc('quarter', CURRENT_DATE) + INTERVAL '3 months'
GROUP BY d.owner
ORDER BY weighted_pipeline DESC;

Integrations

HubSpot + MetabaseSalesforce + Metabase

Analytics

Sales analyticsOverviewSales & CRM overviewOverview

Dashboards

Win rate dashboardLead funnel dashboard

Metrics

Forecast accuracyPipeline coverageWin rate

FAQ

What's the difference between weighted and unweighted pipeline?
Unweighted pipeline is the full open amount; weighted pipeline multiplies each deal by its stage win probability to give an expected value. Weighted is closer to a forecast, but it's only as good as the probabilities — calibrate them from historical stage conversion rather than trusting CRM defaults.
Why do I need pipeline snapshots for forecast accuracy?
Because forecast accuracy compares what you predicted at a point in time to what actually closed — and current-state CRM data has already changed. Snapshotting the pipeline on a schedule (e.g. start of each period) is the only way to reconstruct and grade past forecasts.
How does pipeline coverage fit in?
Pipeline coverage is open pipeline ÷ the target it has to cover. It's the quick health check that sits next to the forecast: low coverage means even a good win rate won't hit the number, so it's an early signal to generate more pipeline.