A sales rep is managing two active deals. Deal A is worth $100,000 and is at final sign-off stage, with a 90% probability to close. Deal B is worth $75,000 and is in early qualification, with a 30% probability to close. Weighted Revenue = ($100,000 × 0.90) + ($75,000 × 0.30) = $90,000 + $22,500 = $112,500. The unweighted pipeline shows $175,000, but the weighted view reveals $112,500 in realistic near-term revenue — a meaningful difference for forecasting.
Open Opportunities (Weighted Revenue)
Last updated: Aug 17, 2026
What is Open Opportunities (Weighted Revenue)?
Open Opportunities (Weighted Revenue) is the expected revenue from active deals multiplied by the probability each deal will close. It gives sales teams a realistic view of pipeline value by weighting each opportunity according to its estimated close probability, producing a more accurate revenue forecast than raw pipeline totals alone.
Alternate names: Weighted Pipeline Revenue, Open Weighted RevenueOpen Opportunities (Weighted Revenue) Formula
ƒ (Expected Revenue 1 * Probability to Close 1) + (Expected Revenue 2 * Probability to Close 2) + (Expected Revenue n * Probability to Close n)
How to calculate Open Opportunities (Weighted Revenue)
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More about Open Opportunities (Weighted Revenue)
Why Open Opportunities (Weighted Revenue) matters
Raw pipeline totals are misleading. A deal in early qualification carries far less revenue certainty than one pending final approval, yet both appear at full value in an unweighted pipeline report. Weighted Revenue corrects for this by reflecting probability at each stage.
Sales leaders use this metric to:
- Forecast revenue more accurately than using total pipeline value alone
- Prioritize deals by focusing attention on high-value, high-probability opportunities
- Identify pipeline gaps before they affect quarterly targets
- Coach sales reps on deals that are stalling or losing momentum
Monitoring this metric regularly surfaces unrealized potential and flags when the pipeline needs to be rebuilt.
How probability weights are assigned
Probability estimates typically come from one of three sources:
| Source | Description |
|---|---|
| CRM stage defaults | Fixed percentages assigned to each pipeline stage (e.g., 20% at qualification, 80% at proposal accepted) |
| Historical win rates | Close rates calculated from past deals at each stage, segmented by deal size, industry, or rep |
| Rep judgement | Adjusted manually based on deal-specific signals such as champion strength, budget confirmation, or competitive pressure |
The most reliable approach combines historical win rates with rep-level overrides. Stage-based defaults alone can be inaccurate if win rates vary significantly by segment or deal size.
Sales-led vs. product-led applications
How you act on this metric depends on your go-to-market model.
Sales-led organizations should focus on improving close rates at each stage. Common levers include sales rep coaching, stronger discovery processes, better competitive positioning, and updated sales materials that address common objections.
Product-led organizations can improve weighted revenue by working earlier in the funnel. Start by evaluating whether the right users are entering the pipeline, then assess product onboarding and activation. If users reach a sales conversation without experiencing core product value, close rates will suffer regardless of sales execution.
Common challenges
Stale probability estimates: If CRM stage probabilities were set years ago and never revisited, they may not reflect current win rates. Recalibrate at least annually using closed-won and closed-lost data.
Inconsistent stage definitions: When different reps advance deals through stages using different criteria, the weighted totals become unreliable. Standardize stage entry and exit criteria across the team.
Overweighting late-stage deals: A pipeline dominated by a few large, high-probability deals creates concentration risk. Track the distribution of weighted revenue across stages and deal sizes.
Gaming the metric: Reps may inflate probability estimates to make their pipeline look stronger. Cross-reference weighted revenue against historical close rates by rep to identify patterns.