Add your deals below to build a weighted sales forecast. The template calculates expected revenue based on stage probability and shows your total pipeline, weighted forecast, and coverage ratio against target.
| Deal Name | Account | Stage | Amount ($) | Close Date | Prob. | Weighted |
|---|
What goes in a sales forecast template
A sales forecast template needs five things for each deal: the account name, the deal amount, the current stage, the expected close date, and a probability weight. Everything else is noise.
The probability weight is where most templates fall apart. Teams either use Salesforce default probabilities (which almost never match actual conversion rates) or let reps assign their own gut-feel percentages. Both approaches produce forecasts that look precise but are not accurate.
A better approach: calculate your actual stage-to-close conversion rates from the last four quarters of closed deals. Use those as your probability weights. This is what the template above does by default, with editable probabilities so you can match them to your real conversion data.
How to structure a B2B SaaS sales forecast
B2B SaaS deals follow a pattern. They enter the pipeline, move through stages, and either close or die. The forecast should reflect this reality, not obscure it.
Pipeline by stage. Group deals by their current stage. This shows concentration risk. If 60% of your pipeline is in early stages (discovery, qualification) and you need to close $500K this quarter, your coverage is probably insufficient even if the total number looks healthy.
Weighted by conversion probability. A $100K deal at 20% probability contributes $20K to the weighted forecast. A $50K deal at 80% contributes $40K. The weighted forecast is more honest than the raw pipeline number because it accounts for the likelihood each deal actually closes.
Time-phased. Deals closing in 30 days are different from deals closing in 90 days. The forecast should show expected revenue by month or by quarter so you can see when cash hits, not just if it hits.
Coverage ratio. Your weighted forecast divided by your target gives your coverage ratio. Most B2B SaaS companies need 3-4x pipeline coverage to hit target, though the exact number depends on your stage conversion rates. Below 3x is a red flag. Above 5x usually means your qualification criteria are too loose.
Common sales forecast template mistakes
Using generic stage probabilities. Salesforce defaults (10%, 25%, 50%, 75%, 90%) are not based on your data. A company with a 35% win rate has very different stage probabilities than one with a 15% win rate. Always use your own historical conversion data.
Counting dead deals. Deals that have not been updated in 30+ days are probably dead. But they sit in the pipeline inflating coverage ratios. Add a "days since last activity" column and flag anything over 21 days for review.
Single-point estimates. A deal is not $100K or $0. It might close at $80K or $120K depending on negotiation. The best sales forecast templates include best case and worst case columns alongside the expected amount.
No commit category. "Commit" means the rep is willing to bet their commission check that this deal closes this quarter. It is a higher bar than "best case" and gives sales leadership a more reliable number for the immediate quarter. Your template should separate commit from best case from pipeline.
Static snapshots. A template you update once a week is a reporting tool, not a forecasting tool. The real value comes from tracking how deals move between snapshots. Which deals slipped? Which ones accelerated? This week-over-week movement is the best leading indicator of forecast accuracy.
When a template is not enough
Templates work for teams with 20-50 deals in the pipeline. They give you structure, force discipline around stage definitions, and produce a weighted forecast that is better than gut feel.
They stop working when your pipeline grows beyond 100 deals, when you need segment-level forecasting, or when your board expects accuracy better than plus-or-minus 20%. At that point, you need models built on your historical conversion data, not just current-quarter probabilities.
ORM builds and maintains those models for B2B SaaS companies. The template above is a good starting point. But if you need 85-95% forecast accuracy, the model needs to account for deal velocity, rep performance variance, seasonal patterns, and segment-specific conversion rates. That is beyond what any spreadsheet template can do.
Want accuracy beyond spreadsheets?
This template tracks your pipeline. ORM builds custom forecast models that tell you what is going to close, when, and what to do about the gaps. Schedule a demo to see the difference.
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