What is the Revenue Cohort Analysis skill?

It is a specialized capability for Claude Code designed to help GTM teams slice and analyze business data by specific groups, timeframes, or acquisition sources.

Can I use this for non-revenue data?

While optimized for revenue and pipeline, the underlying framework can be adapted for any time-based behavioral analysis such as user engagement or feature adoption.

Does it provide data visualization guidance?

Yes, the skill includes frameworks for creating waterfall tables, heatmaps, and overlapping growth curves to help highlight performance divergence across segments.

How does it assist with financial forecasting?

By analyzing historical baseline behavior within specific cohorts, it allows you to stress-test and validate the assumptions in your future revenue and growth forecasts.

Which metrics does this skill help track?

The skill focuses on critical revenue KPIs including Net Revenue Retention (NRR), Win Rate, Annual Contract Value (ACV), and payback periods.

Revenue Cohort Analysis

Name: Revenue Cohort Analysis
Author: gtmagents

bygtmagents

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Analíticas y Monitorización

Analyzes revenue, pipeline, and retention data through structured cohort slicing to diagnose business performance and forecast trends.

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The Revenue Cohort Analysis skill provides a systematic framework for Go-To-Market (GTM) teams to track and analyze groups of customers across their lifecycle. It guides Claude through a multi-step process: defining cohort keys based on signup month or lead source, selecting relevant KPIs like NRR and ACV, normalizing for seasonality, and generating diagnostic visualizations like heatmaps. This skill is essential for RevOps professionals and leadership who need to identify conversion drop-offs, compare performance across acquisition channels, or stress-test financial forecasts against historical baseline behavior.

Características Principales

KPI tracking for Win Rate, ACV, NRR, and CAC payback per cohort
Automated narrative generation for identifying performance anomalies
Standardized cohort definition for signups, lead sources, and product tiers
Normalization techniques for seasonality and currency fluctuations
29 GitHub stars
Visualization frameworks for heatmaps and waterfall tables

Casos de Uso

Diagnosing specific conversion bottlenecks within historical booking vintages
Comparing long-term customer retention across different marketing acquisition channels
Validating future sales forecast assumptions using historical baseline behavior

Acerca de

Características Principales

KPI tracking for Win Rate, ACV, NRR, and CAC payback per cohort
Automated narrative generation for identifying performance anomalies
Standardized cohort definition for signups, lead sources, and product tiers
Normalization techniques for seasonality and currency fluctuations
29 GitHub stars
Visualization frameworks for heatmaps and waterfall tables

Casos de Uso

Diagnosing specific conversion bottlenecks within historical booking vintages
Comparing long-term customer retention across different marketing acquisition channels
Validating future sales forecast assumptions using historical baseline behavior