How Do You Forecast Slippage?

Forecasting slippage means putting a number on how much dated pipeline will leave the period before it closes. The output is an expected slip amount, not a list of suspicious deals. It gets applied to the current quarter the same way a haircut does, and it works because push behavior repeats.

Start from close-date history, not stage percentages

Stage probability tells you what happens to a deal eventually. Slippage is a timing question, so the inputs are timing fields. Pull every opportunity that carried a close date inside a past quarter on day one, then record what happened to it: closed in period, pushed once, pushed repeatedly, or lost. That distribution is your base rate.

ORM's data identifies the close-date change as the strongest slippage signal, and a deal that slips from one quarter into the next is less likely to close even when the rep keeps it in commit. So a second push should carry more weight in the model than the first.

Group deals before you apply a rate

A single company-wide slip rate is too blunt to act on. ORM groups each opportunity with a machine learning model and predicts a close-time curve for each group. Those curves run from 1 to 80 weeks, with most of the expectation falling before week 12 and very few groups extending past 52 weeks. A deal sitting past its group's curve is not slow. It is outside the distribution its peers close in.

Meaningful activity is worth defining before you model it. ORM counts a change in stage, close date, or amount. Everything else is noise that makes a dead deal look alive.

Apply the rate and check it against outcomes

Multiply each deal group's open dated value by the historical share that failed to close in period, then subtract the result from the raw pipeline. Compare that adjusted number against actuals every quarter and let the error correct the model.

The accuracy bar matters here. Building a forecast to roughly 90% accuracy on new and expansion business is achievable with manual effort, though it goes stale as conditions change. ORM targets 95% and holds it from day 1 to day 90 of the quarter without manual adjustments, because the model updates as the quarter progresses.

Slippage forecasting fails the same way every other model fails, by running on assumptions that no longer hold. When a new competitor compresses deal sizes or buying committees slow down, push behavior changes before the average does. For the wider method, see sales forecasting and sales forecasting best practices.