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variance-analysis

variance-analysis

github.com/gtmagentsData & databases43063 installsUpdated 30w ago
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About variance-analysis

variance-analysis is a skill for explaining revenue differences in forecast reviews, board updates, and segment or channel investigations. It connects quantified drivers to root causes and remediation actions, then feeds findings back into forecast assumptions.

Key features

  1. 01Revenue driver classification
  2. 02Consistent attribution formulas
  3. 03Operational root-cause analysis
  4. 04Owner-based remediation plans
  5. 05Forecast feedback loop
  6. 06Variance and action templates

Use cases

  1. 01Explaining forecast variance in reviews
  2. 02Investigating segment or product misses
  3. 03Prioritizing actions tied to revenue drivers
  4. 04Updating assumptions for the next forecast

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/gtmagents/gtm-agents --skill variance-analysis

After installing, type/variance-analysisin Claude Code to confirm it loaded.

Details

From the project README

Revenue Variance Analysis Skill

When to Use

  • Preparing forecast reviews or board updates that require variance explanations.
  • Investigating misses/exceeds across segments, products, or channels.
  • Prioritizing remediation plays tied to specific variance drivers.

Framework

  1. Driver Taxonomy – classify deltas into volume, conversion, price/mix, churn, expansion, currency.
  2. Attribution Logic – define formulas for each driver and maintain consistent baselines.
  3. Root Cause Layer – connect drivers to operational issues (pipeline quality, capacity, enablement, macro).
  4. Action Mapping – translate each root cause into specific plays with owners and expected impact.
  5. Feedback Loop – update forecasting assumptions once variance is understood.

Templates

  • Variance waterfall chart setup instructions.
  • Driver worksheet (metric → delta → driver → root cause → owner → due date).
  • Remediation tracker with status and forecast impact.

Tips

  • Keep a glossary so stakeholders interpret drivers consistently.
  • Combine quantitative attribution with qualitative context from GTM leaders.
  • Feed learnings back to forecast-modeling to tighten assumptions next cycle.

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