Accurately forecasting revenue depends on knowing how likely deals in your pipeline are to close. While most CRMs, including HubSpot, offer stage-by-stage win probability settings, these are often based on assumptions and rarely updated. As a result, sales teams may be relying on outdated or inaccurate data which leads to missed targets, poor forecasting, and missed opportunities for coaching.
Tory Ferrall, Director of Revenue Operations at Databox, shared how she built a dataset-powered reporting system in Databox that calculates actual win probability for each HubSpot deal stage based on historical performance, not guesswork. The result? A dynamic, self-updating view of pipeline health that helps leaders pinpoint exactly where deals are stalling and where to focus coaching efforts.
The challenge: Static deal stage probabilities
In HubSpot, each deal stage has a default win probability. But these are often set once based on intuition rather than data, and are rarely updated. That means sales forecasts can be off, and managers may miss key opportunities to coach their team on weak spots in the pipeline.
Tory’s goal: Replace static, assumption-based probabilities with dynamic, data-driven ones that update automatically.
The solution: Gain more visibility using Datasets
Tory’s system is built in three parts:
Count of Closed Won deals from each stage
Using this metric recipe, Tory set up a metric to track the number of deals that entered a specific stage and eventually closed won.
Count of all closed deals from each stage
Next, she used this recipe to measure how many deals that entered the same stage ended up closing—whether won or lost.
Probability to close calculation
Finally, Tory combined the two metrics above to create a calculated metric (recipe here) that divides Closed Won by All Closed, returning the real win rate for that stage.
Why this works so well in Datasets
With Datasets, Tory can:
Pull in Date Entered Deal Stage to accurately tie deals to the period they hit a specific stage.
Filter by Deal Owner Name for rep-level analysis.
Drill down into row-level data to view the actual deals behind the numbers.
Use this data across multiple dashboards without rebuilding metrics.
The impact: From guessing to coaching with precision
Before, deal stage probabilities were static and often inaccurate. Now, Tory’s dashboard updates in real time, showing:
Which stages have the biggest drop-offs.
How win rates change month-over-month.
How each rep performs at each stage of the funnel.
For example, Tory noticed that the Setup Help stage had a much lower win rate than expected. Filtering by rep revealed that one team member was converting strongly early on but losing deals late in the cycle—pinpointing exactly where coaching was needed.
By moving from static stage probabilities to dataset-powered win rates, Tory now has:
More accurate sales forecasts.
Clearer insights into pipeline health.
A scalable method for ongoing performance tracking.