Commit Accuracy

What is Commit Accuracy?

Commit accuracy is a sales forecasting metric that measures the percentage of deals sales reps or managers confidently "committed" to close within a forecast period that actually closed as predicted, calculated as (Actual Committed Deals Won / Total Committed Deals) × 100. Unlike broader forecast accuracy that includes all pipeline stages, commit accuracy focuses specifically on the highest-confidence segment—deals marked as "Commit" in CRM forecast categories—representing opportunities sales leadership stakes their credibility on closing. This metric serves as the primary reliability indicator for revenue leadership, board reporting, and operational planning because commit-stage forecasts directly inform hiring decisions, quota setting, and investor guidance.

In most B2B sales organizations, the forecast hierarchy includes multiple confidence tiers: Pipeline (early stage, low probability), Best Case (possible but uncertain), Commit (high confidence, publicly forecasted), and Closed (already won). The Commit category represents deals where sales reps assert they will close within the current period—typically requiring documented evidence like signed contracts pending legal review, verbal confirmation from economic buyers, or advanced procurement stages. Commit accuracy measures whether these high-confidence assertions materialize, serving as a credibility and judgment assessment for sales professionals.

High commit accuracy (85-95%+) indicates reliable forecasting judgment, trustworthy pipeline visibility, and disciplined deal qualification that enables confident resource planning. Low commit accuracy (<75%) signals sandbagging (over-conservative commits to guarantee attainment), poor qualification (deals falling out despite confidence), or external factors disrupting late-stage deals. According to Gartner research, companies achieving 90%+ commit accuracy reduce planning variance by 40% and make more aggressive growth investments with lower risk compared to organizations with volatile commit reliability.

Key Takeaways

• High-Confidence Forecast Reliability: Measures whether deals sales teams publicly commit to close actually close, serving as primary credibility metric for revenue leadership and board reporting

• Judgment Assessment Tool: Reveals individual rep and manager forecasting judgment quality—consistent 90%+ accuracy indicates disciplined qualification; volatile accuracy suggests poor deal assessment

• Operational Planning Foundation: Enables confident hiring, expense, and investment decisions based on committed revenue that leadership can reliably expect to materialize within forecast periods

• Sandbagging Detection: Very high accuracy (98-100%) combined with consistent upside surprises indicates over-conservative commits (sandbagging) rather than exceptional forecasting skill

• Stage Gate Validation: Tests whether qualification criteria and stage progression discipline actually ensure only qualified deals reach commit status, surfacing CRM hygiene and process adherence issues

How It Works

Commit accuracy measurement follows a systematic process that defines commit criteria, tracks committed deals, and calculates reliability over time:

Commit Category Definition

Organizations first establish clear criteria for what qualifies a deal for "Commit" forecast category—typically the most stringent requirements in the forecast hierarchy:

Common Commit Criteria:
- Deal in final negotiation stage (90%+ probability)
- Written proposal accepted or contract sent
- Economic buyer confirmed and engaged
- Budget confirmed and allocated
- Legal/procurement review initiated
- Decision timeline within current forecast period
- All technical/business objections resolved
- No identified blockers or competitive threats

Sales reps can only categorize deals as Commit when these criteria are met—ideally validated through required CRM fields forcing documentation of procurement status, contract stage, and buyer confirmation. Managers review and approve commit categorization during forecast calls, ensuring only genuinely late-stage, high-confidence deals receive commit designation.

Period-End Snapshot and Tracking

At the start of each forecast period (typically month or quarter), systems capture a snapshot of all deals marked as Commit—this becomes the committed forecast baseline. Throughout the period, any deal added to Commit gets timestamped to distinguish between initial commits and late additions (which may receive separate accuracy tracking).

Snapshot Capture:
- Beginning of period: Record all deals in Commit category with expected close dates in current period
- Deal attributes: Opportunity ID, value, rep, manager, close date, probability, stage
- Baseline committed amount: Sum of all committed deal values
- Deal count: Number of opportunities in commit category

This snapshot provides the denominator for accuracy calculations and prevents gaming through retroactive categorization changes. Some organizations lock forecast categories after period start to ensure measurement integrity.

Period-End Reconciliation

When the forecast period ends, revenue operations reconciles committed deals against actual outcomes:

Outcome Classification:
- Won as Committed: Deal closed within forecast period at committed value (or within acceptable variance)
- Won but Slipped: Deal closed but after forecast period ended (missed timeline)
- Pushed: Deal didn't close but remains active, moved to future period
- Lost: Deal closed-lost or dead, competitive displacement or no decision
- Partial Win: Deal closed at significantly reduced value (>25% lower than committed)

Commit accuracy calculation focuses on Won as Committed outcomes. Organizations debate whether slipped deals (won but late) should count as partial credit—most use strict measurement where only on-time closes count as accurate commits, maintaining discipline around timeline forecasting.

Accuracy Calculation and Trending

The core commit accuracy formula compares actual closes against committed forecasts:

Deal-Based Accuracy: (Number of Committed Deals Won / Total Committed Deals) × 100
Value-Based Accuracy: (Value of Committed Deals Won / Total Committed Value) × 100

Many organizations track both metrics because they reveal different insights. A rep might achieve 80% deal-based accuracy (8 of 10 deals closed) but only 65% value-based accuracy if their largest committed deals slipped—highlighting that they committed their biggest, riskiest opportunities too aggressively.

Individual and Team Benchmarking

Commit accuracy gets tracked at multiple organizational levels:

Individual Rep Level: Each seller's personal commit accuracy over rolling periods
Manager/Team Level: Aggregated accuracy for all reps reporting to a manager
Regional/Segment Level: Accuracy by geographic region, customer segment, or product line
Company-Wide: Overall organizational commit accuracy for executive and board reporting

Trending analysis identifies whether accuracy improves or degrades over time, seasonal patterns (Q4 commits less accurate than Q2?), and outliers requiring attention (specific reps consistently low/high accuracy).

Key Features

• Binary Outcome Measurement: Clear success/failure definition (deal closed or didn't close) eliminates ambiguity in accuracy calculation, unlike probabilistic pipeline forecasting with subjective confidence ranges

• Confidence Tier Isolation: Separates highest-confidence commits from lower-confidence pipeline categories, focusing measurement on deals where accuracy matters most for planning

• Temporal Precision: Requires not just deal outcome accuracy (won/lost) but timeline accuracy (closed within forecast period), maintaining discipline around when revenue materializes

• Accountability Attribution: Tracks accuracy at individual rep level enabling performance management, coaching, and compensation adjustments based on forecasting reliability

• Leading Indicator Value: Acts as early warning system—degrading commit accuracy signals qualification breakdown, competitive pressure, or market conditions requiring immediate attention

Use Cases

Sales Rep Performance Management and Coaching

A SaaS company tracks commit accuracy for each sales representative as part of quarterly performance reviews, using accuracy trends to identify coaching opportunities and promotion readiness.

Tracking Implementation:
- Calculate rolling 3-month commit accuracy for each rep
- Track deal-based accuracy (deals won/committed) and value-based accuracy (revenue won/committed)
- Segment by accuracy ranges: Elite (90%+), Strong (80-89%), Developing (70-79%), Needs Improvement (<70%)
- Analyze miss patterns: Most commits lost, pushed, or slipped to next period?

Findings Across Rep Population:
- Elite Rep (Sarah, 94% accuracy): Commits conservatively but reliably, rarely adds deals to commit mid-period, thorough qualification before commit categorization
- Sandbagging Rep (Michael, 98% accuracy + consistent upside): Never misses commits but consistently closes 20-30% more than committed, indicating over-conservative forecasting
- Struggling Rep (James, 62% accuracy): Frequently commits deals prematurely, 40% of commits push to next quarter, poor late-stage qualification
- Volatile Rep (Jessica, 78% accuracy with high variance): Some quarters 95%, others 60%, inconsistent judgment

Coaching Interventions:
- Sarah: Encourage slightly more aggressive commits to capture upside (possible for VP promotion)
- Michael: Address sandbagging through commit floor requirements (must commit at least 80% of likely closes)
- James: Mandatory deal validation checklist before commit categorization, manager pre-approval required
- Jessica: Implement structured qualification framework (MEDDIC/SPICED) to improve consistency

Results: After 6 months of targeted coaching, average team commit accuracy improved from 76% to 87%, enabling more aggressive operational planning and reducing upside surprise volatility from 35% to 12%.

Board and Investor Reporting Credibility

A growth-stage B2B company preparing for Series B fundraising needs to demonstrate predictable revenue execution to investors, using commit accuracy as proof of operational maturity and forecast reliability.

Reporting Framework:
- Track quarterly commit accuracy for 8 consecutive quarters
- Present 5-quarter rolling average commit accuracy trend
- Show commit value as percentage of total bookings (commit contribution rate)
- Demonstrate improving accuracy over time as company matured

Historical Performance:
- Q1-Q2 2023: 68% and 71% commit accuracy (early-stage volatility)
- Q3-Q4 2023: 79% and 82% commit accuracy (improving discipline)
- Q1-Q2 2024: 88% and 91% commit accuracy (mature forecasting)
- Q3-Q4 2024: 89% and 93% commit accuracy (sustained excellence)

Investor Presentation Narrative:
- "Our commit accuracy has improved from 68% to 93% over 8 quarters, demonstrating operational maturity"
- "93% commit accuracy means we can confidently guide revenue within ±5% vs. ±20% for companies with poor forecast discipline"
- "High commit accuracy enables aggressive hiring and go-to-market investment with lower risk"
- "Commit category represents 75-80% of quarterly bookings—not sandbagging, capturing genuine pipeline visibility"

Investor Response:
Strong commit accuracy trend was cited in 3 of 5 investor term sheets as evidence of "exceptional operational rigor and revenue predictability relative to peers at similar stage."

Results: Company secured Series B at 25% higher valuation than projected due in part to demonstrated forecast reliability. Post-funding, maintained 90%+ commit accuracy for 6 consecutive quarters, validating investor confidence and enabling confident $12M hiring investment.

Forecast Category Calibration and CRM Hygiene

A mid-market B2B company struggles with inconsistent commit categorization—some reps commit deals too early, others too conservatively—undermining forecast reliability. They implement commit accuracy tracking to calibrate category usage and enforce discipline.

Analysis Approach:
- Analyze historical deal progression: How long do deals stay in each stage before close?
- Calculate "false commit" rate: Percentage of committed deals that push or lose
- Identify commit timing patterns: When do accurate commits typically enter commit category?
- Compare high-accuracy vs. low-accuracy rep commit behaviors

Pattern Discovery:
- High-accuracy reps commit deals average 18 days before close date
- Low-accuracy reps commit deals 45+ days before close date
- Deals committed >30 days out have 58% accuracy; deals committed <20 days out have 92% accuracy
- Most accurate commits require signed contract pending legal review (specific criterion)

Calibration Changes:
- New Commit Criteria: Deal must be within 21 days of forecast period end OR have signed contract pending legal/procurement
- Manager Approval: All commits >$50K require manager review and approval in weekly forecast calls
- CRM Validation: Required fields enforced before commit categorization allowed (contract stage, procurement status, economic buyer confirmation)
- Accuracy Scorecards: Individual rep commit accuracy displayed in CRM dashboards, discussed in 1-on-1s

Results: Average commit accuracy improved from 74% to 88% within two quarters. False commit rate (deals pushing/losing) dropped from 26% to 12%. Most importantly, commit category now represents genuinely late-stage, high-confidence deals leadership can bank on for planning, reducing planning variance from ±18% to ±7%.

Implementation Example

Below is a commit accuracy tracking framework showing monthly forecasting performance for a sales team:

Monthly Commit Accuracy Scorecard

Interpretation:
- Strong overall accuracy: 87-88% deal-based, 91-94% value-based (above 85% target)
- Value accuracy consistently higher than deal accuracy (larger deals more reliable)
- February and May underperformance (80-81% accuracy) worth investigating
- June strength (90% deal, 95% value) suggests end-of-quarter discipline
- Consistent performance Q1→Q2 indicates sustainable forecasting discipline

Individual Rep Commit Accuracy Dashboard

Coaching Actions:
- Sarah & Michael: Investigate sandbagging (100% accuracy + consistent upside closes not committed)
- David & Jennifer: Maintain current qualification rigor (elite/strong performance)
- Rachel: Continue improving (positive trend, approaching elite threshold)
- James: Immediate intervention required (below 70% accuracy, declining trend)

Commit Accuracy Miss Analysis

Q2 Committed Deals That Didn't Close (N=6)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
<p>Miss Reason Breakdown:<br>┌─────────────────────────────────────────────────┐<br>│ Pushed to Q3 (Still Active)        │ 3 deals │<br>│   - Procurement delays              │ 2 deals │<br>│   - Budget approval pending         │ 1 deal  │<br>│                                     │         │<br>│ Closed-Lost                         │ 2 deals │<br>│   - Competitive displacement        │ 1 deal  │<br>│   - No decision / status quo        │ 1 deal  │<br>│                                     │         │<br>│ Deals Slipped to Q3 but Won         │ 1 deal  │<br>│   - Won in July (late by 2 weeks)   │ 1 deal  │<br>└─────────────────────────────────────────────────┘</p>
<p>Preventable vs. External:</p>
<ul>
<li>
<p>Preventable (4): Could have been caught earlier with better qualification</p>
<ul>
<li>2 procurement delays (known risk not documented in commit justification)</li>
<li>1 competitive threat (emerged late but competitor was in deal earlier)</li>
<li>1 budget approval (CFO approval assumed but not confirmed)</li>
</ul>
</li>
<li>
<p>External (2): Legitimately unpredictable circumstances</p>
<ul>
<li>1 economic buyer sudden departure (company restructuring)</li>
<li>1 regulatory change forcing project pause</li>
</ul>
</li>
</ul>
<p>Action Items:</p>

Commit Accuracy Forecast Model

Planning Recommendation: Budget against conservative scenario ($1,785K), plan for base case ($1,890K), celebrate if optimistic materializes ($1,974K). Historical 90% accuracy justifies base case planning with conservative expense management.

Related Terms

• Commit Forecast: The subset of pipeline deals sales teams confidently commit to close within the forecast period

• Sales Qualified Lead: Early-stage qualification whose quality impacts eventual commit accuracy for progressed deals

• Lead Scoring: Upstream qualification methodology that influences which deals reach commit-stage with high accuracy

• Revenue Intelligence: Broader analytics discipline incorporating commit accuracy as key forecasting reliability metric

• Churn Prediction: Similar outcome prediction methodology applied to customer retention versus deal close prediction

Frequently Asked Questions

What is a good commit accuracy percentage?

Quick Answer: Elite B2B sales organizations achieve 85-95% commit accuracy; 90%+ indicates exceptional forecasting discipline while <80% signals qualification or sandbagging issues.

Industry benchmarks vary by sales cycle and deal complexity, but most B2B SaaS companies target 85-90% commit accuracy. Enterprise sales with 6-12 month cycles and complex procurement typically achieve 80-85% due to longer exposure to external factors. High-velocity inside sales with shorter cycles often hit 90-95% because deals commit closer to close date with less risk exposure. Accuracy above 95% combined with consistent upside surprises suggests sandbagging (over-conservative commits). Accuracy below 75% indicates poor qualification, premature commit categorization, or external market challenges disrupting late-stage deals.

How is commit accuracy different from forecast accuracy?

Quick Answer: Commit accuracy measures only highest-confidence "Commit" category deals sales teams publicly stake credibility on, while forecast accuracy measures all pipeline stages weighted by probability.

Forecast accuracy encompasses the entire pipeline—early-stage deals (Pipeline), medium-confidence opportunities (Best Case), and high-confidence deals (Commit)—typically weighted by stage probability to project total bookings. Commit accuracy isolates only the Commit category, measuring whether deals sales leadership publicly committed to close actually closed. Commit accuracy is more stringent and operationally critical because it tests judgment on deals explicitly forecasted as "will close," directly informing resource planning and investor guidance. An organization might have 70% overall forecast accuracy but 90% commit accuracy—the latter matters more for operational reliability because it represents deals leadership explicitly promised to deliver.

Should deals that slip to next period but eventually close count as accurate?

Quick Answer: No—most organizations use strict measurement where only deals closing within the forecast period count as accurate, maintaining discipline around timeline forecasting.

Best practice: use strict measurement where slipped deals (won but after period end) count as inaccurate commits. This maintains discipline around both outcome (won/lost) and timing (closed when committed). Revenue operations needs predictability not just on what will close but when it will close—hiring, expense, and investment decisions depend on quarterly/monthly timing. Some organizations calculate separate "outcome accuracy" (eventually won, regardless of timing) and "timing accuracy" (won within forecast period), but for operational planning purposes, timing accuracy is what matters. Loose measurement allowing slipped deals to count as accurate encourages sloppy commit management where reps commit deals without confidence on timing.

How do we address reps who consistently sandbag (100% accuracy but hold back commits)?

Sandbagging—committing only ultra-conservative deals to guarantee 100% accuracy while holding back likely closes—undermines forecast visibility and planning. Address through: (1) Commit floor requirements: Reps must commit minimum percentage of their likely closes (e.g., 75-80% of qualified pipeline), (2) Upside measurement: Track consistent upside surprises (closes not committed) as negative metric indicating poor forecast visibility, (3) Manager calibration: Require managers to challenge under-commits in forecast reviews, (4) Compensation alignment: Tie forecast accuracy bonuses to 85-92% accuracy range (not 100%), rewarding appropriate confidence versus excessive conservatism. Goal is reliable visibility, not perfect accuracy—prefer 90% accuracy with full pipeline visibility over 100% accuracy with persistent 30% upside surprises.

Should we track commit accuracy monthly or quarterly?

Track both but weight quarterly accuracy more heavily for performance assessment because monthly samples are too small for reliable evaluation. Individual reps might commit only 3-5 deals monthly—one unexpected loss drops monthly accuracy from 100% to 80%, not necessarily indicating poor judgment. Quarterly tracking (12-20 committed deals) provides statistically meaningful sample sizes for evaluating forecasting capability. Use monthly tracking for early warning (trending down?) and operational planning, but base coaching decisions, performance reviews, and compensation on quarterly or rolling 3-month accuracy. For enterprise sales with very low deal volumes, consider rolling 6-month accuracy to ensure sufficient sample sizes.

Conclusion

Commit accuracy represents a foundational sales forecasting metric that measures organizational discipline, individual judgment quality, and operational predictability. By tracking whether deals sales teams confidently committed to close actually closed within forecast periods, revenue leaders assess forecast reliability, make confident planning decisions, and identify coaching opportunities for improving qualification rigor. High commit accuracy (85-95%) enables aggressive growth investments, reduces planning volatility, and builds credibility with boards and investors by demonstrating operational maturity.

For sales leadership, commit accuracy serves as the primary tool for evaluating individual rep and manager forecasting capability, distinguishing between sellers who truly understand their pipeline versus those who commit deals prematurely or sandbag to guarantee attainment. Revenue operations teams use commit accuracy trends as early warning indicators—degrading accuracy signals qualification breakdown, competitive pressure, or market dynamics requiring immediate attention before they materially impact bookings. Finance and executive leadership rely on high commit accuracy to make confident expense, hiring, and investment decisions knowing committed revenue will materialize with 90%+ reliability.

As B2B sales organizations mature and investor expectations for predictable growth intensify, commit accuracy becomes non-negotiable for demonstrating operational excellence. Companies achieving sustained 90%+ commit accuracy command valuation premiums, attract growth capital more easily, and execute aggressive expansion plans with lower risk because their revenue forecasts translate reliably into actual bookings. The discipline required to maintain high commit accuracy—rigorous qualification criteria, manager validation, CRM hygiene, and honest pipeline assessment—creates operational infrastructure that scales as organizations grow.

Related concepts worth exploring include Commit Forecast for understanding what qualifies deals for commit status and Revenue Intelligence for comprehensive forecasting analytics incorporating accuracy metrics.

Last Updated: January 18, 2026