Sales forecasting methods for growing teams

A sales forecast answers the question every leader asks: “how much revenue is actually coming?” It turns a pipeline of open deals into a number you can plan around. There are a few established ways to do it, and the right one depends on how much history and pipeline discipline you have. Here are the main methods.

Weighted pipeline

The most common approach: assign each deal a probability of closing based on its stage, and multiply. A $100,000 deal at a stage with a 40% close rate contributes $40,000 to the forecast. Sum it across the pipeline and you get a weighted, realistic total. Its accuracy depends entirely on honest, consistent stages and probabilities grounded in real history — garbage stages, garbage forecast.

Historical / run-rate

Instead of looking at individual deals, you project forward from past performance — last quarter’s revenue, adjusted for growth and seasonality. Simple and stable, it works well for steady, repeatable businesses, but it’s blind to what’s actually in the pipeline right now, so it misses sudden changes.

Committed / likely / best-case

Rather than one number, this method produces a range:

• Committed — deals already won or as good as certain; the safe floor.
• Likely — the realistic weighted number.
• Best case — if everything open lands; the ceiling.

A range is often more useful than a single figure, because it tells leadership both the floor they can count on and the upside if things go well.

Which to use

Most growing teams benefit from combining them: a weighted pipeline for the “likely” number, a historical run-rate as a sanity check, and the committed/likely/best-case range for planning. The common thread is that all of them need a clean, honestly-staged pipeline underneath — the forecast is only as good as the data feeding it.