What is the Revenue Variance Analysis skill?

It is a specialized capability for Claude Code designed to help GTM teams analyze and explain the difference between revenue forecasts and actual results.

How does this skill classify revenue drivers?

It uses a standardized taxonomy including factors like volume, conversion rates, price/mix, churn, expansion, and currency fluctuations for precise attribution.

Can this skill help with board reporting?

Yes, it provides the structured framework and data-driven explanations required for quarterly forecast reviews and executive board updates.

Does it suggest specific actions for revenue misses?

Yes, the skill includes action mapping that translates identified root causes into specific remediation plays with assigned owners and expected forecast impact.

How does this skill improve future forecasting accuracy?

It incorporates a feedback loop that feeds variance learnings back into your forecast-modeling skill to tighten assumptions for the next cycle.

Revenue Variance Analysis

Name: Revenue Variance Analysis
Author: gtmagents

bygtmagents

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分析与监控

Analyzes and attributes deltas between revenue forecasts and actual results to recommend data-driven remediation strategies.

关于

The Revenue Variance Analysis skill empowers go-to-market teams to bridge the gap between financial projections and real-world performance by systematically identifying the root causes of revenue misses or exceeds. By applying a structured framework that classifies drivers—such as volume, conversion rates, and churn—it provides actionable insights for board updates and forecast reviews. This skill streamlines the transition from raw data to executive strategy by translating complex metric variances into prioritized remediation plays with clear ownership and measurable impact targets.

主要功能

Multi-driver taxonomy for classifying deltas including volume, price/mix, and churn
29 GitHub stars
Root cause analysis mapping operational issues to pipeline and capacity data
Feedback loop integration to refine future forecasting model assumptions
Remediation play generation with assigned owners and impact tracking
Automated attribution logic for maintaining consistent metric baselines

使用场景

Investigating performance discrepancies across specific market segments or product lines
Prioritizing go-to-market remediation efforts based on the highest-impact variance drivers
Preparing detailed variance explanations for quarterly board reviews and executive updates

关于

主要功能

Multi-driver taxonomy for classifying deltas including volume, price/mix, and churn
29 GitHub stars
Root cause analysis mapping operational issues to pipeline and capacity data
Feedback loop integration to refine future forecasting model assumptions
Remediation play generation with assigned owners and impact tracking
Automated attribution logic for maintaining consistent metric baselines

使用场景

Investigating performance discrepancies across specific market segments or product lines
Prioritizing go-to-market remediation efforts based on the highest-impact variance drivers
Preparing detailed variance explanations for quarterly board reviews and executive updates