How Do I Build Revenue Forecasting Models?
Layer stage, cohort, and time-series methods on one dictionary and clean data—then govern it so leaders trust the number.
By Pedowitz Group RevOps Practice • 200+ GTM transformations
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Executive Summary
Direct answer: Build forecasts in layers: 1) lock definitions and data quality, 2) ship a pipeline-stage probability model, 3) add cohort models for new/expansion/renewal, 4) baseline with time-series, 5) blend models with overrides and governance. Instrument accuracy, bias, and variance; publish one forecast with audit logs and a weekly cadence.
Guiding Principles
Decision Matrix: Model Types & When to Use Them
| Model | Best for | Inputs | Pros | Cons |
|---|---|---|---|---|
| Stage/Probability (roll-up) | Early baseline; CRM-driven sales | Stage, amount, age, owner, close date | Simple, explainable, quick to ship | Sensitive to data hygiene, sandbagging |
| Cohort (new/expansion/renewal) | PLG, CS-led growth, renewals | ARR base, logo cohorts, churn, upsell rates | Separates motions; clearer accountability | Needs product/usage and CS data |
| Time-Series (ARIMA/ETS/ML) | Seasonality, long-range trends | Historical bookings/ARR by period | Captures seasonality; independent baseline | Doesn’t “know” current pipeline |
| Opportunity Scoring (logit/GBM) | Large pipelines; many features | Firmographics, activity, product, pricing | Granular probabilities; bias checks | More data science & monitoring |
| Top-Down Coverage | Early quarters; planning sanity check | Target ÷ weighted pipeline | Simple guardrail | Crude; use as a constraint, not the forecast |
Critical Inputs & Data Health Rules
| Input | Definition | Minimum standard | Owner |
|---|---|---|---|
| Stage criteria | Entry/exit rules per stage | 100% adoption; audited weekly | RevOps Enablement |
| Close date discipline | Quarter-aligned, realistic dates | No stale dates; SLA for updates | Sales Management |
| Amount & product | Line-level with currency & product | Precision to SKU; FX rules | Sales Ops/Finance |
| Renewal base | ARR by logo/cohort/term | 100% reconciled to finance | CS Ops/Finance |
| Activity/intent | Meetings, product usage, intent | Tracked and joined to opps | MOPs/Prod Analytics |
Forecast Metrics & Gates
| Metric | Formula | Target/Range | Stage | Notes |
|---|---|---|---|---|
| Forecast accuracy | |Forecast−Actual| ÷ Actual | Improving trend QoQ | Operate | Report by motion/region |
| Bias | Mean(Actual−Forecast) | Near zero | Operate | Track per leader |
| Coverage | Weighted pipeline ÷ Target | 2.5–4.0× by stage mix | Plan | Use as constraint |
| Velocity | Median days stage-to-stage | Stable or improving | Diagnose | Drive early warnings |
| Data health | % opps meeting hygiene rules | ≥ 95% | Prereq | Required fields, dates, amount |
60–90 Day Forecasting Playbook
| Step | What to do | Output | Owner | Timeframe |
|---|---|---|---|---|
| 1 — Foundations | Publish dictionary; fix hygiene & SLAs | Clean pipeline; renewal base | RevOps + Sales/CS | Weeks 1–2 |
| 2 — Baseline Model | Ship stage/probability roll-up | Forecast v1.0 | Analytics | Weeks 2–3 |
| 3 — Cohort Models | Split new/expansion/renewal | Motion-level forecasts | RevOps + CS Ops | Weeks 3–5 |
| 4 — Time-Series Baseline | Add seasonal baseline & variance | Blended forecast v2.0 | Analytics | Weeks 5–6 |
| 5 — Governance & Cadence | Weekly call; override notes; versioning | Audit-ready forecast | CRO + RevOps | Weeks 6–8 |
Deeper Detail
Blending & overrides: expose model components (stage roll-up, cohort, time-series) and publish the weighting rules. Allow human overrides with mandatory reason codes (risk, deal-specific info). Version every run, store inputs and weights, and reconcile to bookings/ARR monthly with variance attribution (price, volume, mix, FX, timing).
Early warning system: track leading indicators—coverage by stage, velocity, win rate, push rate, renewal risk score, and product usage trends. Trigger alerts when any drift outside bands; feed back into forecast review.
TPG POV: We implement forecasting for HubSpot, Salesforce, Marketo-integrated stacks, and modern data warehouses—setting dictionary, hygiene, and blended models—so finance and GTM finally trust the same number.
Related resources: Marketing Operations • Revenue Operations • Revenue Marketing Index.
Additional Resources
Frequently Asked Questions
Operate on weekly intra-quarter forecasts plus monthly rolling 4-quarter and annual plans. Use time-series for long-range sanity checks.
Separate model output from manager overrides, require reason codes, and report bias by leader over time.
Not to start. Stage roll-ups and cohort models cover most needs; add ML scoring when data volume and value justify it.
Capture inputs in CRM; compute in BI/warehouse or a governed forecasting tool; publish the number back to CRM for visibility.
In 2–3 weeks with clean data and standard stage rules; 6–8 weeks to layer cohorts, time-series, and governance.