codexkit-sales-forecast-analyzer

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

• Forecast method is stated and fits the available data.
• Actuals, pipeline, and assumptions are clearly separated.
• Scenario assumptions are visible and testable.
• Data quality gaps are not hidden.
• Recommendations are operational, not just numerical.

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

• Forecast number and range are defensible from inputs.
• Assumptions are explicit.
• Risks and confidence are visible.
• A sales leader can act on next steps.

Changelog

• v1.0.0 - Initial release