pipeline-health

Solid

Analyze pipeline coverage and forecast accuracy

AI & Automation 448 stars 122 forks Updated today NOASSERTION

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Quality Score: 85/100

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Skill Content

## Purpose Analyze pipeline coverage, conversion rates, velocity, and forecast accuracy to ensure you're on track to hit targets and identify where to focus attention. ## Usage - `/pipeline-health` - Full pipeline analysis - `/pipeline-health [timeframe]` - Focus on specific period (e.g., "this quarter", "Q1") - `/pipeline-health forecast` - Forecast-focused view --- ## Step 1: Gather Pipeline Data Collect deal information from 04-Projects/: ### For Each Deal Extract: - Company name - Deal value/size - Current stage - Entry date to current stage (or last modified date) - Close date (if specified) - Confidence level (if mentioned) ### Read Targets: - Check 01-Quarter_Goals/Quarter_Goals.md or user files for revenue targets - Check for quota information - Typical sales cycle length (or calculate from historical data) --- ## Step 2: Calculate Pipeline Metrics ### Coverage Metrics **Pipeline coverage ratio** = Total pipeline value / Target - 3x coverage = Healthy - 2-3x coverage = Adequate - <2x coverage = At Risk **Weighted pipeline** = Sum of (Deal value × Stage probability) - Discovery: 10% - Demo: 25% - Proposal: 50% - Negotiation: 75% - Contract: 90% ### Velocity Metrics **Average time in stage:** - For each stage, calculate average days deals spend there - Flag deals exceeding average by 50%+ **Average sales cycle:** - From discovery to close - Compare current deals to average ### Conversion Metrics **Stage conversion rates:** - Discovery → Demo: X% - Demo...

Details

Author
davekilleen
Repository
davekilleen/Dex
Created
6 months ago
Last Updated
today
Language
Python
License
NOASSERTION

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