Deal Slippage

Definition

Deal Slippage tracks the difference between a deal's forecasted close date and its actual close date (or stalled status), quantifying how reliably reps and managers predict deal timing. In B2B SaaS, it's a critical pipeline health metric because slippage erodes forecast confidence, disrupts quota pacing, and signals issues like poor qualification, weak buyer commitment, or execution gaps.

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Sales leaders use slippage to identify risky deals early, coach on realistic forecasting, and improve mutual action plans. High slippage (>30% of deals) typically indicates process or coaching problems.

How to Calculate Deal Slippage

Slippage Rate (%)

Slippage Rate = Number of Deals Missing Forecasted Close Date ÷ Total Forecasted Deals × 100

Average Slippage (Days)

Average Slippage = ∑ (Actual Close Date - Forecasted Close Date) for Slipped Deals ÷ Number of Slipped Deals

Step-by-Step Calculation

1. Identify deals forecasted to close in the period (commit + best case).
2. Count how many missed their forecasted date (still open or closed later).
3. For slipped deals, calculate days between forecast and actual close (or current date if still open).
4. Express as % of total forecasted deals or average days late.

Example

• 10 deals forecasted for Q1 close
• 4 deals closed on time
• 3 deals slipped and closed 15 days late
• 3 deals still open 

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Slippage Rate = 6/10 = 60% 

Average Slippage = 15 days 

Why Deal Slippage Matters

Deal Slippage directly impacts forecast accuracy, cash flow predictability, and sales capacity. Consistent slippage creates…
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• Unreliable forecasts (Q1 "commit" becomes Q2 reality)
• Quota pacing issues (late closes spike EOM)
• Pipeline compression (everything rushes to quarter-end)
• Manager bandwidth waste (chasing "sure things" that slip)
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CROs target slippage <20–25% for healthy orgs. Reducing slippage improves velocity, attainment, and investor confidence.

Industry Benchmarks

Real-World Examples

• A mid-market SaaS team sees 45% Q1 slippage; they implement a "forecast close only if MAP signed" policy, cutting slippage to 22%.
  
  
• An enterprise sales team with 35-day average slippage discovers that 80% of slippage occurs post-proposal; using pricing playbooks and adding executive sponsors reduces it to 18 days.
  
  
• RevOps flags reps with >50% slippage rates; they coach on buyer commitment signals to drop team average from 38% to 26%.

Common Mistakes

• Forecasting "hopeful closes" without buyer commitment (verbal intent ≠ signed MAP).
• No slippage thresholds—all deals treated equal regardless of risk.
• Ignoring stage patterns (slippage spikes negotiation → legal).
• Reps gaming dates (pushing forecast 1 day into next period).
• No post-mortem on slipped deals (same patterns repeat).

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The Fix: Require MAPs for forecast commits, set clear slippage thresholds (close or re-forecast if >14 days late), track by stage/rep, and coach on realistic buyer signals vs. optimism.

Frequently Asked Questions

What causes most deal slippage?

Weak buyer commitment, unclear next steps, single-threaded deals, and "no decision" drift. MAPs and multithreading fix 70% of cases.

Should "no decision" deals count as slippage?

Yes. No decision deals consume forecasting capacity. Mark them as closed-lost, no decision to keep your pipeline clean.

What's acceptable slippage for enterprise deals?

25–35% is common, due to longer cycles and legal review. Target <25% slippage by getting better executive sponsors and using pre-built legal docs.

Does deal slippage impact quota attainment?

High slippage = EOM crunches, burnout, discounting pressure.

Consistent low slippage = smooth pacing, better closes.

Can slippage ever be positive (early closes)?

Yes, but it’s rare. Track "early closes" separately. Positive slippage often signals over-forecasting conservative deals.

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Last Updated: December 18, 2025

‍Reviewed by: Ben Hale