| name | revenue-forecasting |
| description | Revenue forecasting pipeline - bottoms-up pipeline forecast, tops-down model, ensemble blending, scenario analysis, and forecast calibration loop. Use when: revenue forecast, sales forecast, pipeline forecast, bookings forecast, NRR forecast, ARR forecast, forecast calibration, scenario planning, ensemble forecasting, board forecast. |
Revenue Forecasting (FORECAST Framework)
Design a revenue-forecasting pipeline that produces a defensible, calibrated number - not a rep-roll-up that's been over-promised twice. FORECAST blends bottoms-up pipeline math with a tops-down model, runs scenarios, and closes the loop with calibration so the forecast improves quarter over quarter.
Core Principle
A forecast is only as good as its calibration loop. Most forecasts re-anchor every quarter and never learn. FORECAST treats forecasting as an ensemble of models with explicit error tracking, so the system gets more accurate over time.
The FORECAST Framework
| Letter | Stage | The Question |
|---|
| F | Foundations | What's the ARR / bookings definition, period boundary, and currency convention? |
| O | Outlook (Bottoms-Up) | What does pipeline-weighted by stage and rep commit produce? |
| R | Run-Rate Model | What does the time-series / cohort model produce independent of pipeline? |
| E | Ensemble Blend | How are bottoms-up and tops-down blended, and what's the confidence band? |
| C | Calibration | What's the historical forecast error by segment, stage, and rep? |
| A | Adjust | What manual adjustments are in, and which are evidence-based vs hope-based? |
| S | Scenarios | What are the base / upside / downside cases and their drivers? |
| T | Track | How is forecast vs actual tracked, and how does it feed back into the model? |
Bottoms-Up Forecast
| Element | Spec |
|---|
| Stage Conversion | Historical conversion % from each stage to closed-won, refreshed quarterly |
| Time-in-Stage Decay | Probability decay for opportunities aging past expected stage duration |
| Rep Commit Categories | Commit / Best Case / Pipeline / Omitted with named definitions |
| Coverage Multiples | 3x for new logo, 1.2-1.5x for renewal, segment-specific |
| Hygiene Rules | Stale opps demoted, no-next-step opps flagged, close-date discipline |
Tops-Down Run-Rate Model
| Method | Use For |
|---|
| Cohort run-rate | Established motions with stable retention |
| Channel attribution roll-up | Multi-channel motions; identifies channel-level slow-down |
| Seasonality-adjusted trend | Markets with clear quarterly / monthly seasonality |
| Leading-indicator regression | Mature businesses with stable lead → revenue mapping |
Ensemble Blending
Don't pick one model - blend them, weighted by historical accuracy:
| Component | Weight Rationale |
|---|
| Bottoms-up rep commit | Weight up when historical commit accuracy > 90% |
| Bottoms-up stage-weighted | Weight up for new motions or new reps |
| Tops-down run-rate | Weight up for mature, stable segments |
| AI / ML model | Weight up only if it beats the others on out-of-sample tests |
Always produce point estimate + confidence band - never a single number with no error bar.
Scenarios
| Scenario | Construction |
|---|
| Base | Ensemble central estimate |
| Upside | Top quartile of pipeline conversion + favorable mix |
| Downside | Bottom quartile conversion + concentration-risk realization |
| Stress | Material churn / lost-deal / macro event sensitivity |
Each scenario must name the 2-3 drivers that move it, not just shift a number.
Calibration Loop
This is where most forecasting programs fail.
| Step | Action |
|---|
| Track forecast vs actual | By period, segment, stage, rep |
| Decompose error | Conversion error vs timing error vs mix error |
| Update model weights | Reweight ensemble based on out-of-sample accuracy |
| Revise stage conversion | At least quarterly; sooner if material drift |
| Coach rep commit accuracy | Visible scorecards |
Output
Save to outputs/revenue-forecasting-[period]-[YYYY-MM-DD].md
| Artifact | Description |
|---|
| Definitions Sheet | ARR / bookings / period / currency conventions |
| Bottoms-Up Spec | Stage conversion, decay, commit categories, hygiene rules |
| Tops-Down Model | Run-rate / regression / cohort approach with assumptions |
| Ensemble Spec | Component weights with historical-accuracy rationale |
| Scenario Pack | Base / Upside / Downside / Stress with named drivers |
| Calibration Report | Forecast vs actual error decomposition, trend |
| Adjustments Log | Every manual adjustment with rationale and owner |
| Forecast Dashboard | Single source of truth across finance, sales, RevOps |
Process
- Lock the definitions - same ARR, period, and currency rules across teams
- Build the bottoms-up with disciplined hygiene and decay rules
- Build at least one tops-down model as a check
- Blend into an ensemble with weights from historical accuracy
- Run scenarios with named drivers, not just percent shifts
- Close the calibration loop every period - forecast credibility lives or dies here
Tips
- Single-number forecasts hide risk - always publish a confidence band
- Decay stale opportunities ruthlessly - they're the #1 source of forecast misses
- Manual adjustments need evidence - log them or they become hope
- Calibrate per segment / per rep - aggregate accuracy hides poor accuracy underneath
- The forecast is a product - ship versioned releases, not slack messages
Pairs With
- revenue-analytics - Drivers and leading indicators feed the forecast
- renewal-orchestration - Risk score informs renewal-stage probability
- customer-analytics - Cohort retention curves feed run-rate models
- budget-allocator - Forecast scenarios drive reallocation decisions
