Commit Forecast

What is a commit forecast?

A commit forecast (also called a call forecast or judgment-based forecast) is a revenue projection where sales reps and managers explicitly declare which deals they expect to close within the current period. Each deal is placed into a confidence category — typically commit, best case, and upside — based on the seller's assessment of deal progress, buyer signals, and competitive dynamics.

The commit forecast relies on human judgment, not mathematical formulas. A rep who has spoken with the economic buyer, received verbal confirmation, and is awaiting contract signature classifies that deal as "commit." A deal where the champion is enthusiastic but budget approval is pending goes to "best case." A late-stage opportunity that appeared unexpectedly lands in "upside."

For B2B SaaS companies running structured sales forecasting processes, the commit number is what the team puts its reputation behind. It is the number the VP of Sales reports to the CEO. It is the number the CEO reports to the board. When it misses, trust erodes. When it consistently lands within 10% of actuals, the organization can plan with confidence.

The commit forecast contrasts with the weighted forecast, which uses mathematical probabilities. A weighted forecast treats all deals at the same stage identically. The commit forecast captures context that probability cannot: the champion just got promoted, the competitor just raised prices, the prospect's fiscal year ends next week.

Why commit forecast matters for operators

The commit number drives downstream decisions across the company — not just in sales. Finance plans cash flow around it. Marketing allocates pipeline generation budgets based on the gap between commit and target. Customer success teams staff onboarding based on expected new logos.

When commit forecasts are inaccurate, every downstream plan breaks. A finance team expecting $800K in committed bookings that comes in at $520K faces a cash shortfall. A CS team staffed for 15 new onboardings that receives 8 has wasted hiring capacity. The damage compounds month over month.

The problem is usually not bad reps. It's inconsistent commit definitions. At one company, "commit" means "I believe this will close." At another, it means "the contract is being reviewed by legal." Without a shared standard, the commit number aggregates different levels of confidence into a single figure — and the operator cannot tell which commits are real and which are wishful.

Operators who enforce clear commit criteria — the buyer has confirmed budget, timeline, and decision-maker access — see commit accuracy improve from 60-65% to 80-90% within two quarters. The discipline is operational, not motivational.

How commit forecasts work

Commit forecasts follow a structured categorization process. Each deal is classified into one of four categories during the forecast review.

Commit Categories:

1. COMMIT — "This deal will close this period."
   Criteria: Budget confirmed, decision-maker engaged,
   timeline agreed, no unresolved blockers.
   Expected close rate: 85-95%

2. BEST CASE — "This deal should close, but one variable remains."
   Criteria: Strong engagement, budget likely approved,
   one outstanding step (legal review, final sign-off).
   Expected close rate: 50-70%

3. UPSIDE — "This deal could close if things break our way."
   Criteria: Active opportunity, positive signals,
   but meaningful uncertainty on timeline or budget.
   Expected close rate: 20-40%

4. OMIT — "This deal will not close this period."
   Moved to next period or marked at risk.

Example forecast:
- Commit deals: $340,000 (4 deals)
- Best case deals: $185,000 (3 deals)
- Upside deals: $270,000 (5 deals)
- Pipeline not in forecast: $890,000

Forecast scenarios:
- Conservative (commit only): $340,000
- Expected (commit + 60% best case): $451,000
- Optimistic (commit + best case + 30% upside): $606,000

The review cadence: Most B2B SaaS companies review commit forecasts weekly. Reps submit their call before the meeting. Managers challenge classifications. The VP of Sales rolls up the team forecast and reports the commit, best case, and upside numbers to the executive team.

Commit forecast accuracy benchmarks

How commit forecast accuracy varies by team maturity and process rigor. Accuracy measured as actual closed revenue / commit forecast.

Sources: Pavilion COO Survey 2025, Clari Forecast Accuracy Report 2025, industry-observed ranges from operator benchmarks.

Common mistakes in commit forecasting

1. No shared definition of "commit"

If one rep's commit means "I feel good about this" and another's means "contract sent," the aggregated number is meaningless. Define commit criteria explicitly: budget confirmed, decision-maker engaged, timeline agreed, and competitive evaluation complete. Write it down. Enforce it.

2. Sandbagging to look good at quarter-end

Reps learn that missing commit hurts more than beating it. So they move winnable deals to "best case" to create a safety buffer. The commit number becomes artificially low. Managers should look for deals in best case that meet commit criteria — and challenge the classification.

3. Not tracking commit accuracy by rep

Company-wide commit accuracy of 78% might include one rep at 95% and another at 45%. The average hides which reps have calibrated judgment and which don't. Track accuracy per rep. Coach the outliers. Rep-level accuracy is the most diagnostic metric in the forecast.

4. Reviewing commits only at month-end

A commit deal that loses its champion in week 2 should not remain a commit until the month-end review. Weekly forecast reviews catch changes while there's still time to respond. Monthly reviews are post-mortems — by the time you update the forecast, the period is over.

5. Treating the commit number as the full forecast

The commit number is the conservative floor, not the expected outcome. The expected forecast should include commit plus a probability-adjusted portion of best case. Reporting only the commit number to the board understates likely revenue and can lead to overly conservative resource allocation.

How Fairview tracks commit forecast automatically

Fairview's Forecast Confidence Engine ingests deal-level data from your CRM (HubSpot, Salesforce, Pipedrive) and overlays it with commit categories from your forecast calls. The system tracks commit, best case, and upside in a unified view alongside the weighted forecast.

The Pipeline Health Monitor flags commit-classified deals that show risk signals — no buyer activity in 10+ days, close date pushed twice, or no recent email exchange. These "at-risk commits" are surfaced before the forecast review so managers can challenge classifications with data, not intuition.

The Operating Dashboard tracks commit accuracy by rep and by team over time. When a rep's commit accuracy drops below 70% for two consecutive periods, Fairview flags it — giving the manager a coaching opportunity grounded in numbers.

→ See how the Forecast Confidence Engine works

Commit forecast vs weighted forecast

The two most common forecasting methods answer different questions with different strengths.

The strongest forecasting approach blends both methods. Use the weighted forecast as the mathematical baseline. Layer in commit data to capture deal-specific intelligence. When the two numbers diverge materially, the deals in the gap deserve immediate attention.

FAQ

What is a commit forecast in simple terms?

A commit forecast is the revenue your sales team says it will close this period, based on deal-level judgment. Reps classify each deal as commit (will close), best case (should close), or upside (could close). The commit number is the confident floor — the amount the team puts its credibility behind.

What is a good commit accuracy rate?

Mature B2B SaaS sales teams with defined processes hit their commit number 80-95% of the time. Growth-stage teams typically land at 65-80%. Below 65% means the commit criteria are too loose, reps are overconfident, or deal evidence is not being verified before classification (Pavilion COO Survey, 2025).

How does a commit forecast differ from a weighted forecast?

A commit forecast relies on rep judgment about which specific deals will close. A weighted forecast applies mathematical stage probabilities to every deal in the pipeline. Commits capture deal context that math misses. Weighted forecasts remove the human bias that commits carry.

What should the commit criteria be?

At minimum: budget confirmed by the economic buyer, decision-maker actively engaged, timeline agreed for this period, no unresolved competitive evaluation, and contract or proposal in the buyer's hands. If any of these are missing, the deal belongs in best case, not commit.

How often should you review the commit forecast?

Weekly. Reps submit their updated deal classifications before the weekly forecast meeting. Managers challenge commits that lack evidence. The VP of Sales rolls up the team number. Monthly or end-of-quarter reviews are too late — deal status changes faster than monthly cadence can capture.

How do you improve commit forecast accuracy?

Define commit criteria explicitly and enforce them. Track accuracy by rep and coach outliers. Require deal evidence (emails, meeting notes, recorded calls) for every commit classification. Review weekly instead of monthly. Compare commit numbers to weighted forecast data to identify where judgment and probability diverge.

Related terms

• Sales Forecast — The broader category of revenue projection methods, including commit, weighted, and historical
• Forecast Confidence — A score reflecting how likely the current forecast is to be accurate
• Weighted Forecast — A forecast method that multiplies deal values by stage-based close probabilities
• Pipeline Coverage Ratio — Total pipeline value relative to target; measures whether enough pipeline exists
• Forecast Accuracy — The percentage difference between forecasted and actual closed revenue

Fairview is an operating intelligence platform that tracks commit forecasts alongside weighted forecasts and forecast confidence in a single view. Start your free trial →

Siddharth Gangal is the founder of Fairview. He built the dual-forecast view after seeing too many quarterly misses caused by commit numbers that nobody challenged until it was too late.