| name | codexkit-sales-forecast-analyzer |
| description | Analyze sales pipeline, historical revenue, conversion rates, and assumptions to produce forecast scenarios. Use for sales reviews, RevOps planning, and founder revenue forecasting. |
| version | 1.0.0 |
| category | data |
Sales Forecast Analyzer
When to Use
- Forecasting revenue from historical sales or active pipeline.
- Preparing weekly, monthly, or quarterly sales reviews.
- Comparing committed, best-case, and upside scenarios.
- Explaining forecast movement to founders, finance, RevOps, or sales leadership.
Procedure
Step 1 - Normalize Inputs
Separate actuals, pipeline, assumptions, and qualitative signals. Do not mix closed revenue with open pipeline.
Step 2 - Segment The Pipeline
Group opportunities by stage, close date, owner, segment, product, and confidence where available.
Step 3 - Apply Forecast Logic
Choose the simplest defensible method:
- historical trend for stable recurring sales
- stage-weighted pipeline for active opportunities
- rep commit for manager-reviewed forecast
- scenario range when inputs are uncertain
Step 4 - Explain Drivers
Identify the movement drivers:
- new pipeline
- slipped deals
- closed-won / closed-lost
- expansion / contraction
- conversion rate change
- average deal size change
Step 5 - Produce Scenarios
Provide Base, Upside, and Downside scenarios with assumptions and confidence. Flag data quality gaps.
Inputs
| Input | Required | Format |
|---|
| Historical sales | Recommended | Period, revenue, bookings, units |
| Pipeline | Recommended | Deal, amount, stage, probability, close date |
| Sales cycle assumptions | Optional | Win rate, stage duration, seasonality |
| Forecast horizon | Yes | Month, quarter, year |
| Business context | Optional | Promotions, market changes, hiring, capacity |
Output
## Sales Forecast - [Period]
### Executive Summary
[Forecast number, confidence, main movement drivers]
### Scenario Forecast
| Scenario | Forecast | Assumptions | Confidence |
|----------|----------|-------------|------------|
### Pipeline Movement
| Driver | Impact | Notes |
|--------|--------|-------|
### Risks And Watch Items
- [Risk] - [mitigation]
### Data Quality Notes
- [Missing fields, stale opportunities, probability caveats]
Quality Criteria
Verification (4C)
| Check | Question |
|---|
| Correctness | Are formulas, stage weights, win rates, dates, and totals calculated consistently? |
| Completeness | Are actuals, open pipeline, assumptions, scenarios, and risks all covered? |
| Context-fit | Does the method match the sales motion, cycle length, and data maturity? |
| Consequence | What decision could be distorted if this forecast is overconfident? |
Edge Cases
- Sparse history - Use scenario ranges and clearly mark confidence as low.
- Stale pipeline - Flag opportunities with old next steps or close dates before including them.
- Enterprise deal concentration - Show forecast with and without the largest deals.
- Seasonal business - Avoid straight-line forecasts unless seasonality is explicitly addressed.
Examples
Prompt: "Use this opportunity export to forecast Q3 bookings. Show commit, base, and upside scenarios and explain slipped-deal risk."
Good pattern: "Base forecast is $1.2M because 64% of weighted pipeline is in late-stage deals with close dates before quarter end. Confidence is medium because 3 of 8 largest deals have stale next steps."
Definition of Done
Changelog
