Forecasting
Predict revenue with confidence. Methods, categories, and improving accuracy over time.
⏱️ TL;DR: Use forecast categories (Commit, Best Case, Pipeline) based on stage and confidence. Combine bottom-up (rep rollup) with top-down (historical) approaches. Track forecast accuracy and adjust.
Why Forecasting Matters
Accurate forecasts enable:
- Resource planning: Hiring, capacity, inventory
- Cash flow: Financial planning and investment
- Goal setting: Realistic quotas and targets
- Board reporting: Credibility with stakeholders
Forecast Categories
Segment your pipeline by likelihood to close:
| Category | Definition | Close Rate |
|---|---|---|
| Closed | Won deals | 100% |
| Commit | High confidence, verbal yes | 90%+ |
| Best Case | Good momentum, likely to close | 50-80% |
| Pipeline | Active deals, uncertain | 10-40% |
| Upside | Long shot or new deals | 5-20% |
💡 Rep vs Manager Forecast
Have both rep and manager submit forecasts. The delta reveals coaching opportunities and sandbagging.
Forecasting Methods
1. Bottom-Up (Rep Rollup)
Each rep forecasts their deals. Roll up to team forecast.
- Pros: Deal-level visibility, rep accountability
- Cons: Bias (optimistic or sandbag), inconsistent
2. Top-Down (Historical)
Use historical conversion rates × current pipeline.
- Pros: Objective, based on data
- Cons: Ignores deal-specific context
3. Weighted Pipeline
Sum of (Deal Value × Stage Probability).
- Pros: Simple, automated
- Cons: Assumes stage = probability (often wrong)
4. AI-Powered
Machine learning on historical data + deal signals.
- Pros: Pattern recognition, less bias
- Cons: Requires clean data, black box
📊 Best Practice
Combine methods. Use rep forecasts for near-term (this month), historical for longer-term (quarter).
Stage-Based Probabilities
Assign probabilities to each stage based on historical win rates:
| Stage | Typical Probability |
|---|---|
| Discovery | 10% |
| Qualification | 20% |
| Demo/Proposal | 40% |
| Negotiation | 60% |
| Verbal Commit | 80% |
| Contract Sent | 90% |
Calibrate based on YOUR historical data, not benchmarks.
Improving Forecast Accuracy
Track Accuracy Over Time
Measure: (Forecast - Actual) / Forecast
- Good: Within ±10%
- Acceptable: Within ±20%
- Needs work: >20% variance
Common Accuracy Issues
- Sandbagging: Reps under-forecast to beat targets
- Happy ears: Over-optimism on deal confidence
- Slipped deals: Close dates constantly pushed
- Missing pipeline: Late-stage adds not forecast
Improvement Tactics
- Weekly forecast reviews with deal inspection
- Compare rep forecasts to historical win rates
- Track close date accuracy separately
- Use multi-threading to reduce single-point failure
Forecasting Checklist
- ☑️ Forecast categories defined
- ☑️ Stage probabilities calibrated
- ☑️ Weekly forecast submission process
- ☑️ Manager review cadence set
- ☑️ Accuracy tracking in place
- ☑️ Historical data being collected