Sales forecasting built on your CRM data
We replace spreadsheet forecasts with a system that reads pipeline straight from the CRM and calculates weighted pipeline, forecast categories, coverage, stage conversion and deal velocity.
What we measure
We track the numbers that show whether your pipeline can deliver the target, each one calculated from CRM records rather than copied into a spreadsheet.
- 01
Pipeline coverage
Open pipeline measured against the revenue target for the period
- 02
Weighted pipeline
Each open deal valued at its stage probability, then summed
- 03
Forecast categories
Deals grouped into commit, best case and pipeline, with written definitions for each
- 04
Stage conversion
The share of deals that move from one stage to the next
- 05
Win rate
Closed won measured against all closed deals for the period
- 06
Deal velocity
How long deals take to move through the pipeline, by stage and overall
- 07
Forecast vs actual
What the forecast said against what closed, compared every period
Forecasting methods compared
Each method answers a different question, so most teams run more than one and compare them.
| Method | What it is | When it fits |
|---|---|---|
| Weighted pipeline | Each deal's amount multiplied by the probability of its stage, summed across open pipeline | Fits when stage probabilities reflect how deals actually close and the pipeline is wide enough to smooth out single deals |
| Rep-submitted forecast categories | Reps place each deal in a category such as commit, best case or pipeline, and the categories roll up | Fits when reps are accountable for the deals they commit and each category has a written definition |
| Historical conversion rates | Past conversion rates for each stage are applied to current pipeline to project revenue | Fits when there is enough history at each stage and the sales process is stable |
Pipeline QA
We put automated checks on the pipeline so every forecast is built on records that are current.
- 01
Stale close dates
Deals whose close date has passed are flagged for an update or a close
- 02
Missing amounts
Open deals with no value are raised so they don't quietly distort the forecast
- 03
Deals stuck in a stage
Records that haven't moved in a defined number of days go back to the owner
Example: Revenue Intelligence & Forecasting
This system puts the checks and calculations above into one revenue view.
Revenue Intelligence & Forecasting is designed to combine pipeline quality, marketing attribution, forecasting and customer metrics into a single layer for leadership. It starts with pipeline QA, then calculates weighted pipeline, coverage and forecast vs actual from the same CRM records.
See the full systemSales forecasting FAQ
These are the questions we answer most often before a forecast is built.
What is pipeline coverage?
The ratio of open pipeline to the revenue target for a period.
What is weighted pipeline?
The sum of each deal's amount multiplied by the probability of its stage.
Why do spreadsheet forecasts drift?
They depend on manual copying, so they go stale as soon as the CRM changes.
Start with a GTM Audit.
We review your pipeline data and reporting, then define the forecast your team can defend.