Pipeline forecasting. How the number actually gets built.

The short answer. A weighted number, checked against a human judgement call.

Pipeline forecasting is the method a sales team uses to turn its list of open deals into a single, usable revenue prediction for a coming week, month or quarter. Most founder-led teams start with neither a method nor a number, just a rep's sense of what's "looking good," which is a feeling rather than a forecast. The fix isn't complicated software. It's picking one of a small number of established methods and applying it consistently, so the number means the same thing every time someone asks for it.

The most common method is weighted pipeline: multiply each open deal's value by the win probability assigned to its current stage, then sum the results across the pipeline. A discovery-stage deal worth 50,000 pounds with a 10 percent historical win rate contributes 5,000 pounds to the forecast. A proposal-stage deal of the same value with a 60 percent win rate contributes 30,000 pounds. Add every open deal's weighted contribution together and the total is the pipeline forecast, a genuinely different exercise from simply totalling every open deal at full value and hoping most of it lands, which is what an unweighted pipeline report actually shows.

This is a different question to forecast accuracy, covered separately in our guide to improving sales forecast accuracy. Accuracy asks how close a forecast, however it was built, came to what actually closed. Pipeline forecasting is the earlier question: what method builds the number in the first place, before there's anything to measure the accuracy of.

How it works in practice. Three inputs, combined rather than chosen between.

A workable pipeline forecast rests on three inputs used together, not any one of them alone.

Stage-weighted value. Every pipeline stage carries a win probability based on historical closed deals, discovery might sit around 10 percent, proposal around 40 percent, verbal commitment around 75 percent, and every open deal's value is multiplied by its stage's rate. This produces the mechanical, unemotional half of the number.

Forecast categories. Alongside the weighted figure, reps categorise deals by judgement: commit, for deals they're confident will close as currently scoped and priced; best case, for deals that could close if a specific condition lands, budget approval, a competing vendor dropping out; and pipeline, for everything else genuinely still open. This catches context a stage probability can't, like a champion going quiet or a competitor entering late.

Historical run rate. Over time, comparing what the weighted figure and the commit category predicted against what actually closed builds a correction factor. If commit deals have closed at 85 percent over the last four quarters rather than the 100 percent reps implicitly assume when they call something a commit, that gap is worth knowing and adjusting for.

None of the three is sufficient alone. Weighted pipeline without categories misses context; categories without a weighted figure behind them are just opinion; and a run rate with nothing to correct is meaningless. Used together, each one checks the blind spot of the other two.

Most CRMs will calculate the weighted figure automatically once stage probabilities are set, which is useful but easy to over-trust. The software will multiply a deal's value by whatever probability sits against its stage without asking whether that probability still reflects reality, and it has no way of knowing that a rep quietly moved a stalled deal back a stage last week rather than mark it lost. The mechanical part of pipeline forecasting is the easy part. Keeping the inputs honest is the actual job.

What good looks like. A number that survives being questioned.

A good pipeline forecast can be defended line by line. Ask why a specific deal is weighted the way it is, and the answer is its stage and that stage's historical win rate, not a rep's gut feeling relayed secondhand through a sales manager. Ask why the commit number differs from the fully weighted pipeline figure, and the gap is explainable: commit strips out deals the rep isn't confident enough to stand behind, even if they're technically still open and weighted at a reasonable stage probability.

An eight-person B2B services team I advised had been forecasting off a single number each rep gave verbally in the Monday call, no stages, no weighting, just a figure that felt right at the time. We rebuilt it around four pipeline stages with win rates set from the previous year's closed deals, rough at first because a year of data from an eight-person team is a small sample, and layered the three-category commit, best case, pipeline judgement on top. The first month's weighted forecast and the verbal-guess forecast landed within a few thousand pounds of each other by coincidence. By the third month, as the weighted method incorporated genuinely closed data rather than the founder's memory of how deals "usually" go, the two numbers diverged by nearly 20 percent, and the weighted figure was the one that held up against what actually closed that quarter.

Gartner's research into sales forecasting has found that fewer than half of sales leaders express high confidence in their own organisation's forecast, which tracks with what I see in founder-led teams specifically: the number exists, gets reported up, and is quietly distrusted by the person reporting it. A weighted method doesn't remove all doubt. It gives the doubt something specific to point at, a stage probability that might be wrong, a category a rep might have called too optimistically, rather than a vague unease about the whole figure.

Good forecasting also separates the number from the narrative around it. A weighted pipeline figure of 340,000 pounds against a 400,000 pound target isn't a verdict on the quarter by itself. It's a starting point for a specific conversation: which deals make up the gap, what stage are they at, and what, concretely, needs to happen to move them, rather than a general instruction to the team to "push harder." Teams that use pipeline forecasting well treat the number as a diagnostic tool pointed at named deals, not a scoreboard to react to emotionally.

Pitfalls to avoid. Where pipeline forecasting quietly breaks down.

The first pitfall is setting stage probabilities once and never revisiting them. A win rate calculated from a dozen early deals in year one is not the same win rate that applies once the sales process, the average deal size or the target market has shifted. Stale probabilities make a weighted forecast confidently wrong rather than roughly right.

The second is treating the forecast category as fixed once a rep sets it. A deal called "commit" three weeks ago deserves the same scrutiny this week as it did when it was first labelled, not a free pass because it's already been through the conversation once. Deals should move down a category as readily as up one.

The third is confusing a weighted total with a promise. A weighted pipeline figure is a probability-adjusted expectation, not a guarantee, and treating it as a hard commitment to the business, a board or a hiring plan misreads what the method is actually built to do. It narrows the range of likely outcomes. It doesn't remove the range.

The fourth, and the one I flag most often, is building the weighted method and then never comparing it against what closed. A forecast method with no feedback loop back to actual results can drift for years without anyone noticing, because nobody's checking whether the stage probabilities still mean what they meant when they were first set. My rule with every client building this out: pull the actual close rate by stage every quarter, not annually, and adjust the weighting the moment it disagrees with reality, not the moment it becomes convenient to look at.

The fifth is applying one set of stage probabilities across deals that don't actually behave the same way. A small, fast-moving deal and a large, multi-stakeholder enterprise deal rarely share a real win rate at the same nominal stage, even when the CRM's pipeline view lists them side by side. Where deal size or buyer type varies enough to matter, it's worth splitting the weighting by segment rather than forcing every deal through one probability curve that fits none of them precisely.

A weighted forecast is also only as trustworthy as the pipeline hygiene underneath it. A deal sitting untouched in a stage for two months past its expected cycle time is still being counted at that stage's win rate, quietly overstating the forecast until someone moves it or marks it lost. The method described here assumes the pipeline itself is being kept current; where that's not yet true, that's the first problem worth fixing.