Bottom-up revenue forecast from pipeline
Build a probability-weighted forecast from CRM pipeline, segmented by ACV band and stage, with a sanity check vs. top-down plan.
Copy and customize
You are a senior FP&A Analyst producing the following deliverable: bottom-up revenue forecast from pipeline.
Context
- Workflow: Forecasting
- Inputs available: {paste the data here}
- Period: {month / quarter}
- Audience: {who reads this}
What to produce
1. The headline takeaway in one sentence.
2. The three things that materially moved the result, with quantified contribution.
3. The one risk or anomaly worth flagging.
4. A short forward-looking note: what to watch next period.
Guardrails
- Use only the numbers provided; do not invent values.
- Cite a row reference for every claim.
- Flag anything you cannot reconcile rather than smoothing it over.
Run it in four steps
- Export the CRM pipeline with stage, close date, ACV, and segment, plus stage-level close rates from the last four quarters.
- Paste it into
{paste the data here}and set the forecast period in{month / quarter}. - Run it to produce a probability-weighted forecast by segment and stage.
- Reconcile against your top-down plan; a large gap usually means stale close rates or an inconsistent ACV definition, not a real miss.
When to reach for this prompt
Run at the start of each quarter and again at mid-quarter as a re-forecast. Use the output as one input alongside top-down — never as the standalone forecast.
What you can expect back
Q3 Pipeline-weighted forecast
| ACV band | Stage 4 wt. | Stage 5 wt. | Total weighted |
|---|---|---|---|
| <$25K | $480K | $320K | $800K |
| $25–100K | $1.1M | $1.4M | $2.5M |
| $100K+ | $2.8M | $3.6M | $6.4M |
| TOTAL | $9.7M |
Top-down plan: $10.2M. Gap: -$500K (-5%) — within tolerance.
This prompt has real limitations you should understand.
Stage-weighted forecasts assume sales-rep discipline in stage progression. If your CRM hygiene is poor, this prompt will compound the noise. Validate against actual close rates from the last four quarters before trusting the weights.
Stage discipline varies by rep
A stage-4 deal from one rep is a stage-3 deal from another, depending on local hygiene habits. The prompt cannot see this, and will apply the same weight to both — overweighting the optimistic rep's pipeline.
Close-rate windows are short
Stage-level historical close rates over four quarters look stable until the business moves up-market or down-market. The weights will be off by a full quarter of motion, and the forecast will lag reality.
Pull-ins and slips are invisible
A deal expected to close in Q3 that pulls in to Q2 (or vice versa) is impossible to model from stage data alone. The prompt will count the same deal in the wrong quarter — and the top-down/bottom-up gap will look real when it is just timing.
What your data needs to look like
- CRM pipeline with stage, close date, ACV, and segment
- Stage-level historical close rates (last four quarters)
- A clean ACV definition (TCV / contract length, or first-year ARR)
- A top-down plan to compare against
See how FinanceOS handles this prompt on real financial data.
Book a 20-minute walkthrough. We’ll run this exact prompt against a sample dataset reconciled through FinanceOS, and show you what changes when the data underneath is right.
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