Measure and improve my sales forecast accuracy
An analysis of how accurate past forecasts were, where bias comes from, and the process changes that tighten the number.
The prompt
Help me measure how accurate our sales forecasts have been and how to improve them.
Forecast history: [for each of the last 4-8 periods: forecast at week 1, mid-period, and final week (commit and best case) vs actual closed]
By rep or team, if available: [SAME, split]
Forecast method: [e.g. rep roll-up, stage-weighted, manager judgment, tool or AI prediction]
Forecast categories and definitions: [paste]
Known issues: [e.g. late-quarter surprises, sandbagging, happy ears]
Steps:
1. Calculate accuracy for each period and checkpoint: % error and direction (over or under). Show how accuracy improves through the period.
2. Identify bias patterns: consistently high or low, by rep, team, segment, or deal size. Note where a few large deals explain most of the error.
3. Compare commit-category conversion (how much commit actually closed) and best-case conversion to set realistic weightings.
4. Find process causes: unclear category definitions, close dates set by our quarter, large deals not inspected, missing deal-level evidence.
5. Recommend changes and a target accuracy range for each checkpoint.
Return:
- An accuracy table: Period | Checkpoint | Forecast | Actual | Error % | Direction.
- Bias findings by rep or team, stated factually.
- Historical conversion rates by forecast category.
- 5 process changes ranked by impact, with how to measure each.
Work only from the numbers I provide and flag small sample sizes.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Data & Analysis prompts
What to fill in
- SAME, split
- paste
- e.g. late-quarter surprises, sandbagging, happy ears
How this prompt is built
This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.
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