Forecast a defensible range, not a hero number.
“Our forecast keeps coming in as one big number nobody believes. How do I show a range instead?”
Category definitions, coverage math, historical conversion, and a low / likely / high range vs quota.
What this skill does
Reads your open pipeline and builds a forecast that survives scrutiny. It pins what each category actually means, computes coverage against the number you have to hit, applies your own historical conversion instead of a hopeful guess, and reports a range with a low, likely, and high case rather than one number that is wrong to the dollar. The output tells you not just what you will close but how much to trust it.
Inputs
- Works today with: a paste or CSV of open deals with amount, stage, forecast category, close date, and owner, plus your quota or target for the period.
- More powerful connected to a CRM: it reads the live pipeline and category assignments automatically, across every rep.
- Sharper with a data warehouse: it pulls historical close rates by category and stage so the model runs on your numbers, not a benchmark.
- Sharper with a BI tool: pushes the range and coverage into a dashboard leadership can watch move.
How it works
- 1Category definitions
State what each category means as an entry rule, not a vibe. Commit is deals meeting the commit criteria. Best Case is upside that could close with things breaking right. Pipeline is everything else still live. Omitted is out. Every deal lands in exactly one, and the rule is printed.
- 2Coverage math
Divide open pipeline for the period by the target to get coverage. Report it overall and per rep. A team needing the number with 1.5x coverage is in a different reality than one with 4x, and the forecast should say which. State the coverage ratio, do not bury it.
- 3Historical conversion
Apply your own trailing close rate by category and stage, not a benchmark and not 100 percent of commit. If commit historically closes at 85 percent, the model says so. Where history is too thin to trust, widen the range instead of pretending precision.
- 4The range, not the number
Report three cases. Low is roughly commit at its historical rate. Likely blends commit and a slice of best case at their rates. High is commit plus best case landing well. A single number is a false promise. A range with its assumptions printed is a forecast.
- 5Gap to target
Set the range against the target and name the gap or the cushion. If the likely case lands short, say by how much and what category would have to overperform to close it. That is the sentence leadership actually needs.
Output format
FORECAST · current quarter · target $2.40M · coverage 2.8x
Category Open $ Hist. close Weighted
Commit $1.10M 85% $0.94M
Best Case $0.90M 42% $0.38M
Pipeline $1.80M 14% $0.25M
Range for the quarter:
Low $0.98M commit at rate, best case misses
Likely $1.55M commit + partial best case at historical rates
High $2.05M commit + best case land well
Gap: likely case is $0.85M short of target. Best Case would need to nearly double its close rate to close it.
Illustrative figures. Your run reports your real numbers.
Tips
- Every category prints its entry rule. No undefined commit.
- Conversion rates are historical and sourced, never assumed at 100 percent.
- The forecast is always a range. Any single number is labeled as one case within it.
- Thin history widens the range. The model never fakes precision it has not earned.
Turn a messy CRM into a ranked cleanup list.
Finds the hygiene problems that break reporting first: missing fields, stale records, duplicates, and bad stages, before the cosmetic ones.
Build funnel metrics the meeting will actually trust.
Conversion by stage, velocity, and win rate with every definition pinned, and the leak named.