Most people learning how to create a sales forecast start in the wrong place. They open a spreadsheet, list the open deals, multiply by a gut-feel close percentage, and call it a forecast. The number looks legitimate. It is rarely correct, and it never tells anyone what to do about the deals that are quietly slipping.
A forecast is assembled in sequence, and each step depends on the one before it. Skip the input step and the math is precise nonsense. Skip the calibration step and you are forecasting against probabilities that belong to a different company. This guide walks the full process and threads one example all the way through it. I am going to build a forecast in front of you, by hand, from a six-deal pipeline, so you can see exactly where the number comes from and where it goes soft.
The numbers below are illustrative, not benchmarks. Use the method, not the figures. Your probabilities come from your own history, which is the entire point of step one.
The sample pipeline we will forecast
Here is a fictional company, Veltra, a B2B SaaS team selling a mid-market product. Six open deals, end of the second month in the quarter. This is the pipeline we carry through all five steps.
| Deal | Segment | Stage | Value (illustrative) | Days in stage |
|---|---|---|---|---|
| A | Mid-market | Negotiation | $60,000 | 9 |
| B | Mid-market | Proposal | $45,000 | 31 |
| C | Enterprise | Proposal | $120,000 | 14 |
| D | Mid-market | Discovery | $30,000 | 6 |
| E | Enterprise | Negotiation | $90,000 | 41 |
| F | Mid-market | Discovery | $25,000 | 5 |
Step 1: Gather your inputs
Every forecast is bounded by the data you start with. Before any math, pull four things together.
- Open pipeline. Every active deal with value, stage, owner, and expected close date. That is the table above. - Historical close rates by stage. What share of deals at each stage actually closed, drawn from at least 12 months of your own won and lost data. For Veltra: Discovery closes at 15%, Proposal at 35%, Negotiation at 60%. Those are their rates, back-tested from their own history, which is what makes them usable. - Deal-level activity. Stakeholders engaged, meetings held, last contact, and days in current stage. The last column in the table is not decoration. It is the signal that separates a live deal from a corpse that nobody has buried. - The target. Veltra's quota for the quarter is $200,000, and they have already closed $120,000. So the open pipeline has to produce $80,000 to make the number.
If your stage definitions are inconsistent, or every closed-lost reason is "timing" because reps click the first dropdown, fix that before you go further. This is the unglamorous step nobody photographs, and it decides whether everything downstream is real. For the wider picture of methods and models this feeds, see the complete guide to sales forecasting.
Step 2: Pick a method
The right method depends on how much data you have, not on what sounds impressive in a board deck.
- Stage-weighting multiplies each deal by the historical close rate for its stage. Needs a defined process and roughly 12 months of data. It backs most CRM forecasting features and it is where Veltra should live. - Opportunity scoring adds deal-level nuance, rating each deal on engagement, stakeholders, budget, and timeline. It earns its keep on enterprise deals that vary widely from each other. - Regression and machine learning find which variables actually predict close. These need a few hundred closed deals and someone whose job is the model.
Here is the contrarian part, and I will defend it: most teams are running a method one or two tiers too advanced for the data they have, and it makes their forecast worse, not better. A 40-deal pipeline fed into a machine-learning model produces confident output from almost no signal, and confident-and-wrong is more expensive than simple-and-roughly-right. Veltra has six open deals in this quarter and a few hundred in their history. Stage-weighting is not the beginner option they will outgrow. It is the correct option. The complete forecasting guide breaks down all six methods and where each one breaks.
Step 3: Weight the pipeline with the Two-Pass Pipeline Weighting method
This is the core calculation, and I run it in two passes. Call it Two-Pass Pipeline Weighting. The first pass applies stage probability. The second pass applies a time-in-stage haircut. Almost every spreadsheet forecast does the first and forgets the second, which is exactly why almost every spreadsheet forecast runs hot.
Pass one: stage probability. Multiply each deal by its calibrated stage rate.| Deal | Value | Stage rate | Pass-one weighted |
|---|---|---|---|
| A | $60,000 | 60% | $36,000 |
| B | $45,000 | 35% | $15,750 |
| C | $120,000 | 35% | $42,000 |
| D | $30,000 | 15% | $4,500 |
| E | $90,000 | 60% | $54,000 |
| F | $25,000 | 15% | $3,750 |
| Deal | Pass-one weighted | Haircut | Pass-two weighted |
|---|---|---|---|
| A | $36,000 | none | $36,000 |
| B | $15,750 | 50% (31 days in Proposal) | $7,875 |
| C | $42,000 | none | $42,000 |
| D | $4,500 | none | $4,500 |
| E | $54,000 | 50% (41 days in Negotiation) | $27,000 |
| F | $3,750 | none | $3,750 |
There is a second payoff. Deals B and E did not just lose weight. They got named. A 41-day-old enterprise negotiation worth $90K is not a forecasting line item, it is a fire drill. The haircut surfaced the two deals a human should walk into tomorrow. To see how stage velocity compounds into your number, run yours through the pipeline velocity calculator. If you would rather start from a wired-up layout than a blank sheet, the sales forecast template has the stage-weighted math built in.
Step 4: Pressure-test the number
A bottom-up number on its own is easy to fool yourself with. Check it against a top-down baseline before you commit it.
Take Veltra's last four quarters of revenue, adjust for seasonality, project forward. Say that baseline lands around $215,000 of total quarter revenue. They have closed $120K and the weighted open pipeline adds $121K, for a forecast near $241K. That sits above the top-down baseline, which is a flag worth chasing, not ignoring. Either the pipeline is genuinely stronger this quarter, or it is inflated and pass two did not cut deep enough. When bottom-up and top-down disagree by a wide margin, one of them is lying, and the job is to find out which before the board does.
Then stress the assumptions against the market. Are you penciling in win rates above your trailing average? With median B2B win rates having fallen to 19% (First Page Sage, 2025), a model leaning on a 30% close assumption is quietly optimistic. Are your cycle-time assumptions current? Sales cycles have lengthened 22% since 2022 (Digital Bloom, 2025), so a model calibrated to older velocity reads deals as closing faster than they will, which is precisely the error the time-in-stage haircut is built to catch. This is also why so many teams miss: 87% of enterprises missed revenue targets in 2025 (Clari Labs, 2026), and a forecast that never gets pressure-tested is how the miss stays invisible until it is too late to fix. The forecast accuracy guide covers the formulas that tell you whether your model is actually tracking, and the forecast accuracy scorecard grades where yours stands now.
Step 5: Set a review cadence
A forecast is not a deliverable you produce once and file. It degrades the moment you stop maintaining it.
Lock a weekly rhythm. Each week, compare what the forecast predicted against what actually happened. Which deals closed that the model did not expect? Which slipped that sat in the commit? Why? Run Veltra's six deals next Monday and you would already know whether Deal E moved or sat for another week, and a week of new silence on a $90K negotiation is the difference between a save and a write-off. Layer monthly probability recalibration on top, so this quarter's results sharpen next quarter's rates, and a quarterly check on whether the model's structural assumptions still hold. A forecast reviewed once a quarter is a report. A forecast reviewed weekly is an operating tool. For how this connects to setting the targets up front, see the sales planning framework, and for the wider operating system it sits inside, the revenue operations guide.
Why forecasts run hot: the three usual suspects
When I audit a forecast that keeps missing, the cause is almost always one of three things, in this order of frequency.
| Failure | What it looks like | Fix |
|---|---|---|
| Stage data that is not real | Stage 3 means "champion identified" in the playbook and "good call" in practice | Audit stage compliance quarterly before trusting any probability |
| Borrowed probabilities | CRM defaults or benchmark-report rates applied to your deals | Calibrate against your own won and lost history, like Veltra's 15/35/60 |
| Lagging stands in for leading | Forecasting on closed revenue, ignoring time in stage and activity | Build leading signals into the weighting, which is what pass two does |
The do and do-not checklist
Pin this next to your pipeline. It is the short version of everything above.
Do- Calibrate stage probabilities from your own won and lost deals, then re-pull them every month. - Run both passes. Stage probability first, time-in-stage haircut second, every time. - Forecast a range, not a point. A floor at high confidence, a target at moderate, a stretch that depends on named deals breaking right. - Pressure-test bottom-up against a top-down baseline and chase any gap wider than you can explain. - Use the haircut to surface deals for action, not just to lower the total. The deals that lost the most weight are your week's work. - Review weekly. One hour. Non-negotiable.
Do not- Do not ship the CRM default percentages. They describe an average company, never yours. - Do not present the raw roll-up. The $370K number and the $156K pass-one number are both traps. - Do not reach for machine learning on 40 deals. Match the method to the data you actually have. - Do not let "timing" be every closed-lost reason. Garbage in that field poisons next quarter's rates. - Do not confuse a single number with a forecast. A point estimate carries no honesty about what has to go right. - Do not file it and walk away. A forecast you do not inspect is a guess with a timestamp.
A forecast built this way does more than tell you where you stand. It tells you which two deals to walk into on Monday. For Veltra, the model did not just say $121K, it pointed at a stale $45K proposal and a stalled $90K negotiation and said those, now. That is the line ORM builds toward: turning a weighted pipeline into a prescription for which deals to move and what moves them, while the quarter is still yours to change.
Frequently Asked Questions
How do you create a sales forecast?
Build it in five steps: gather your inputs (open pipeline, historical close rates by stage, deal activity, target), pick a method that fits your data maturity, weight each deal by its calibrated stage probability, pressure-test the bottom-up number against a top-down baseline, then set a weekly review cadence. The fastest way to learn the sequence is to run a handful of real deals through it once, by hand, before you automate anything.
What data do you need to build a sales forecast?
Four inputs: current open pipeline with stage and expected close date, historical close rates by stage and segment drawn from your own won and lost deals, deal-level activity data like stakeholder count and time in stage, and the quota you are forecasting against. Without close rates calibrated from your own history, any method produces a number that looks precise and is not accurate.
What is the easiest way to create a sales forecast?
Stage-weighting. Multiply each open deal by the historical close rate for its current stage, then sum the results. It needs a defined sales process and about 12 months of stage conversion data, but no data science. Start there, then add a time-in-stage haircut for deals sitting past the median before you reach for anything more advanced.
How do you forecast a weighted pipeline?
Assign each stage a close probability from your own conversion history, not the CRM defaults. Multiply every open deal by its stage probability, then lower the probability on any deal sitting in-stage longer than your median. Sum the adjusted values. That adjusted sum, not the raw roll-up, is your weighted pipeline forecast.
How often should you update a sales forecast?
Weekly. A deal that goes quiet in the first week of a quarter is recoverable if you see it; by month-end the lagging numbers have already decided the period for you. Weekly inspection is the cheapest accuracy upgrade most teams are not using, and it costs an hour.
Why is my sales forecast inaccurate?
Usually one of three things: CRM stage probabilities that do not match your real conversion rates, a roll-up that inherits rep optimism instead of checking it, or leading signals like time in stage being ignored in favor of closed-revenue totals that arrive too late to act on. Dirty stage data is the biggest single driver, because every downstream calculation inherits the error.
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