How Reliably Accurate Can a Sales Forecast Be?
Put SalesTech Forecast Accuracy Claims to the Test
Actual SalesTech forecast-accuracy claims. The definitions are unclear. We take a stab at deciphering and stress-testing these claims.
Prioritizing accuracy can be harmful because it shifts attention toward an elusive accuracy goal and away from an achievable one: maximizing sales.
Vendors promise 95% forecast accuracy
Let’s interpret 95% forecast accuracy as sales landing within ±5% of the forecast—and see how reliably even a best-case open-pipeline forecast can meet that standard.
The method we use here will seem foreign to sales leaders because they use different forecast processes. A company might rigorously estimate the probability of every deal, apply historical close rates by stage, incorporate micro-signals, roll up forecast categories, build consensus predictions—or throw darts. We are not replicating those processes here.
We are modeling the underlying reality: there is a probabilistic process going on under the hood that produces a distribution of possible outcomes. No one can consistently predict the future. You can change the forecast and how you arrive at it. But that does not change the underlying distribution.
This analysis asks a more fundamental question: if you knew, before the outcomes were determined, the win probability of every deal and used those probabilities to calculate the expected number of wins, how reliably would actual sales land within ±5% of that forecast?
To isolate that question, we simplify the pipeline by assuming all deals are the same size and have the same known win probability. Enter the number of deals in play and their win probability. The explorer will calculate the expected wins and how frequently the 95% accuracy claim would be wrong.
The explorer also makes several deliberately favorable assumptions: deal outcomes are independent, and the forecast creates no feedback that changes seller behavior or deal outcomes.
Real pipelines regularly violate these assumptions which generally widens the range of possible results. The explorer therefore shows a best-case boundary. The width of these best-case distributions underscores that, for most businesses, 95% accuracy is not realistic.
Nor is it necessarily desirable: chasing accuracy can encourage sales teams to manage toward conservative forecasts rather than maximizing the outcome.
The greater value of a forecast is therefore in informing action that improves sales—not in precisely predicting the result.
Accuracy is not the objective
A sales forecast is not a weather forecast. Sales leaders can change the outcome.
Rather than trying to predict the outcome against difficult odds—or worse, managing the team toward a conservative prediction—use the forecast to determine where attention and resources can produce the greatest return.
When sales teams focused on the deals prioritized by Funnelcast, they generated 60% more sales on average than when they followed their existing priorities.