Weighted Sales Forecast Calculator

Each deal's value is multiplied by the probability attached to its stage, and the results are summed. A $100K deal at 40% contributes $40K. It is more honest than counting the full pipeline and less pessimistic than counting only what is signed — but it is only as good as the probabilities, which is why they are editable here.

Your own history. Take every deal that entered a given stage over the last year and calculate what share of them eventually closed won. That is the probability. Borrowed defaults are fine for the first quarter and misleading after that, because your stages and your buyers are not somebody else's.

You want roughly three times your target in open pipeline to land the target, which follows from a win rate somewhere near a third. The Ebsta and Pavilion benchmarks put average B2B win rate at 21%, so if your rate is lower, your coverage needs to be higher. Coverage below 2× at the start of a quarter is not a forecasting problem, it is a pipeline generation problem, and no amount of deal review will fix it.

The common definition is a stage probability at or above 70%, but the more useful test is behavioural: is there a scheduled next step, has the economic buyer been in a conversation, and has the customer articulated what happens after signature. A deal missing any of those is not a commit however far along the stage list it sits.

Almost always because stages are being advanced on optimism rather than evidence. A deal moves to "proposal" because a proposal was sent, not because the buyer agreed to a decision date. Attach an exit criterion to each stage — something the customer has to do, not something you did — and the slip mostly disappears.

Update deals weekly, forecast monthly, commit quarterly. Weekly forecasting for a business with a two-month cycle is mostly noise, and it trains the team to defend numbers rather than work them.