Sales Pipeline Analysis & Commentary
Analyze sales pipeline data to identify trends, forecast accuracy, deal risks, and generate management-ready pipeline reviews.
Your CRM shows $4M in pipeline but you'll close $1.5M — and you don't know which $1.5M until the quarter ends. Pipeline reviews that just list deals alphabetically don't reveal which deals are real, which are stalled, and where your forecast is lying to you. Analysis turns pipeline data into reliable revenue predictions.
Who it's for: sales directors running weekly pipeline reviews for their regions, revenue operations analysts building pipeline health dashboards, VPs of sales preparing forecast calls with the CRO, sales managers identifying at-risk deals before they slip, CROs presenting pipeline commentary to the board
Example
"Analyze our Q2 pipeline and prepare the forecast review" → Pipeline analysis: deal velocity metrics with stage-by-stage conversion rates, 8 at-risk deals flagged with specific concerns, pipeline coverage ratio by segment, forecast confidence scoring (commit/best case/upside), and a management-ready narrative explaining the pipeline story
CLAUDE.md Template
New here? 3-minute setup guide → | Already set up? Copy the template below.
# Sales Pipeline Analysis & Commentary
## Your Role
You are an expert sales operations analyst. Your job is to analyze pipeline data, assess forecast accuracy, and generate management-ready pipeline reviews.
## Core Principles
- Use historical conversion rates, not rep-submitted probabilities
- 3x coverage is healthy; below 2x is a red flag
- Flag aging and stalled deals proactively
- Track deal push rates as a leading indicator
- Always provide likely, best, and worst case forecasts
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What This Does
Analyzes CRM pipeline data to assess deal health, forecast accuracy, conversion trends, and risk. Generates pipeline review commentary and recommendations for sales leadership.
Quick Start
Step 1: Download the Template
Click Download above to get the CLAUDE.md file.
Step 2: Export Pipeline Data
Pull: deal list with stages, amounts, close dates, and deal age from your CRM.
Step 3: Start Using It
claude
Say: "Analyze our Q4 pipeline. Are we going to hit target? Flag deals at risk and stalled opportunities."
Analysis Output
| Section | Content |
|---|---|
| Pipeline Summary | Total value, coverage ratio, stage distribution |
| Forecast Assessment | Likely, best case, worst case scenarios |
| At-Risk Deals | Stalled, aging, or unlikely to close |
| Conversion Analysis | Stage-to-stage conversion rates and trends |
| Deal Velocity | Average time in each stage vs. benchmark |
| Recommendations | Where to focus sales effort for maximum impact |
Tips
- Pipeline coverage ratio: 3x target is healthy; below 2x is a red flag
- Flag aging deals: Deals stuck longer than average cycle time need intervention
- Weighted vs. unweighted: Forecast using stage-weighted probabilities, not total pipeline
- Track push rates: Deals that keep moving close dates are likely not closing
Commands
"Analyze this pipeline data and assess Q4 forecast"
"Which deals are at risk based on age and stage?"
"Calculate stage-to-stage conversion rates"
"Generate a pipeline review commentary for the VP Sales"
Troubleshooting
Forecast seems too optimistic Ask: "Apply historical conversion rates by stage, not rep-submitted probabilities"
Too many deals flagged as at-risk Set thresholds: "Only flag deals >$50K that are 2x past average cycle time"
Data is messy Say: "Ignore deals last updated more than 30 days ago — they're likely dead"