Forecast Accuracy Dashboard
Your forecast is only useful if you can trust it.
What changes when you build this
The gaps you're living with today,
and what this tool fixes.
Problems
- Reps submit a forecast number every week, but nobody compares it to what actually closed
- Managers inflate or discount forecasts by "gut feel" because they have no historical accuracy data
- Deal slippage compounds silently — a $50K deal pushed three quarters in a row never gets flagged
- Finance plans headcount and spend around a forecast that's consistently 30-40% off
- RevOps can't tell if forecast misses come from bad pipeline data, optimistic reps, or broken stage definitions
Solutions
- Every forecast snapshot is stored and compared against actual closed revenue automatically
- Rep-level accuracy scores show who consistently hits their call and who doesn't
- Deals that slip past their forecasted close date are flagged with variance history
- Historical accuracy trends expose whether the team is getting better or worse over time
- Drill-downs by deal stage, segment, and rep reveal exactly where and why forecasts break
What the data model looks like
Refine generates this table structure from your
prompt. Edit columns, types, and relationships after.
100%
Mistakes to avoid
These are the failure patterns teams hit most often
when building this.
No historical snapshotsFix: Capture forecast data weekly so you can compare predictions against outcomes — retroactive accuracy tracking is impossible.
No historical snapshots
Fix:Capture forecast data weekly so you can compare predictions against outcomes — retroactive accuracy tracking is impossible.
Treating all misses equallyFix: A rep who's off by 5% every quarter is different from one who's off by 40% once. Track variance patterns, not just totals.
Treating all misses equally
Fix:A rep who's off by 5% every quarter is different from one who's off by 40% once. Track variance patterns, not just totals.
Sandbagging goes undetectedFix: Flag reps who consistently forecast 20%+ below actual. Underforecasting causes just as many planning problems as overforecasting.
Sandbagging goes undetected
Fix:Flag reps who consistently forecast 20%+ below actual. Underforecasting causes just as many planning problems as overforecasting.
No deal-level drill-downFix: Surface which specific deals drove the variance — a forecast miss from one slipped enterprise deal is a different problem than 10 small deals falling out.
No deal-level drill-down
Fix:Surface which specific deals drove the variance — a forecast miss from one slipped enterprise deal is a different problem than 10 small deals falling out.
Static quarterly reviews onlyFix: Run accuracy comparisons at week and month level, not just end of quarter. Earlier signal means earlier correction.
Static quarterly reviews only
Fix:Run accuracy comparisons at week and month level, not just end of quarter. Earlier signal means earlier correction.