Weighted Sales Forecast

TL;DR

Weighted sales forecast multiplies each open deal's value by its close probability and sums to produce a probability-weighted revenue estimate. It is more accurate than treating all forecast deals at full value, but only when probabilities come from actual historical conversion rates. A weighted forecast complements a commit forecast — it does not replace it. Updated April 2026.

---

Why Weighted Forecasting Is More Accurate Than Raw Commit

Weighted sales forecast is defined as a forecasting method that multiplies each open deal's value by its close probability, then sums across all deals to produce a probability-weighted revenue estimate. It treats the forecast as an expected value calculation, which is mathematically sound when the probabilities are calibrated to reality.

The advantage over a raw commit forecast is that weighted forecasting naturally accounts for deal risk. A commit forecast treats every commit deal as a 100% close. A weighted forecast applies appropriate probabilities based on deal characteristics and stage. When a commit deal's probability drops from 90% to 70%, the weighted forecast reflects the change immediately. The commit forecast only reflects the change when someone manually downgrades the deal.

How to Calculate a Weighted Sales Forecast

The formula: Weighted Forecast = Σ (Deal Value x Close Probability)

Across every open deal in the forecast window.

A worked example:

Deal	Value	Close Probability	Weighted Value
Deal A	$200K	85%	$170K
Deal B	$150K	70%	$105K
Deal C	$100K	50%	$50K
Deal D	$250K	40%	$100K
Deal E	$75K	25%	$18.8K
Total	$775K	-	$443.8K

The raw pipeline is $775K. The weighted forecast is $443.8K. The difference is the deal risk that the weighted approach makes visible.

Where to Get the Probabilities

Default CRM probabilities are almost always wrong. The classic 10/25/50/75/90 stage probabilities are industry folklore, not data. Your own probabilities come from historical conversion analysis: pull 4-6 quarters of closed-won and closed-lost deals, and calculate what percentage of deals that reached each stage ultimately converted.

Stage	Common Default	What Your Data Might Show
Discovery	10%	6%
Evaluation	25%	18%
Proposal	50%	35%
Negotiation	75%	65%
Verbal commit	90%	82%

Most teams find their real probabilities are lower than the defaults, especially in early stages. This is consistent with the reality that many deals never close — only 45.8% of forecast deals win (CSO Insights). Using the real probabilities produces a more conservative but more accurate weighted forecast.

Recalibrate probabilities quarterly. If win rates change or cycle lengths shift, the old probabilities lose accuracy.

Weighted Forecast and Commit Forecast — Use Both

The cleanest forecasting approach runs a weighted forecast and a commit forecast in parallel. The commit forecast is judgment-based — the sales team's view of specific deals they believe will close. The weighted forecast is data-based — the expected value across the pipeline given historical probabilities.

When the two numbers converge, forecast confidence is high. When they diverge significantly, the divergence is the most useful diagnostic in the forecasting process. A commit forecast well above the weighted forecast suggests optimism in the commit classifications. A commit forecast well below the weighted forecast suggests sandbagging.

The CRO-level forecast reported to the board usually lives somewhere between the two numbers, informed by judgment about the specific deals in play but anchored to the mathematical expected value.

Common Mistakes in Weighted Forecasting

Using CRM default probabilities. The most common mistake. Default probabilities produce weighted forecasts that consistently miss because they are not calibrated to your actual conversion. Replace them with historical data. Applying stage probability without deal-level adjustment. Two deals in the same stage can have very different close probabilities. A proposal-stage deal with declining engagement and a single contact should not carry the same probability as a proposal-stage deal with active executive engagement and a signed mutual action plan. Layer deal-level signals on top of stage probability for more accuracy. See weighted pipeline for this layering approach. Ignoring close-date proximity. A deal closing this week should carry a different probability than a deal with the same stage but a close date in six weeks. The closer a deal is to close, the more stage probability alone understates its true likelihood of closing. Time-to-close should be a factor in the probability calculation. Not recalibrating. Probabilities drift. Market conditions change. Competitive dynamics shift. A weighted forecast running on probabilities that were calibrated eighteen months ago is producing numbers that no longer map to reality. Quarterly recalibration is the minimum standard. For related forecasting concepts, see commit forecast category and sales forecasting. The sales forecasting complete guide covers how to build weighted forecasts into the broader forecasting operating rhythm.