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cohort-analysis skill

/plugins/revenue-analytics/skills/cohort-analysis

This skill helps you perform cohort analysis to diagnose performance, normalize metrics, and visualize insights across channels and products.

npx playbooks add skill gtmagents/gtm-agents --skill cohort-analysis

Review the files below or copy the command above to add this skill to your agents.

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SKILL.md
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---
name: cohort-analysis
description: Standard method for slicing bookings, pipeline, and retention cohorts
  for diagnostics.
---

# Cohort Analysis Framework Skill

## When to Use
- Comparing performance across acquisition channels, segments, or product lines.
- Diagnosing conversion drop-offs within specific booking/vintage cohorts.
- Stress-testing forecast assumptions with historical baseline behavior.

## Framework
1. **Cohort Definition** – choose cohort key (signup month, lead source, product tier, segment).
2. **Metric Stack** – select KPIs (coverage, win rate, ACV, NRR, payback) per cohort.
3. **Normalization** – adjust for seasonality, deal size mix, or currency.
4. **Visualization** – waterfall tables, heatmaps, or overlapping curves to highlight divergence.
5. **Narrative Layer** – annotate drivers, anomalies, and recommended actions.

## Templates
- Cohort definition worksheet (keys, filters, inclusion/exclusion rules).
- Standardized chart pack for leadership readouts.
- Diagnostic checklist for follow-up analyses.

## Tips
- Keep cohorts mutually exclusive to avoid double-counting.
- Pair with `inspect-pipeline-levers` to link cohort insights to pipeline stages.
- Rebaseline quarterly so assumptions stay current.

---

Overview

This skill provides a production-ready cohort analysis framework for slicing bookings, pipeline, and retention cohorts to support diagnostics and decision-making. It standardizes cohort definition, metric selection, normalization, visualization, and narrative annotation to make cohort comparisons repeatable and actionable. The skill is tuned for GTM workflows across sales, marketing, customer success, and revenue operations.

How this skill works

The skill guides you through defining mutually exclusive cohorts (signup month, lead source, product tier, segment) and selecting a metric stack (coverage, win rate, ACV, NRR, payback) for each cohort. It applies normalization steps for seasonality, deal-size mix, and currency, then produces recommended visualizations—waterfall tables, heatmaps, and overlapping curves—to surface divergence. Finally, it produces a narrative layer that annotates drivers, anomalies, and prescriptive next steps.

When to use it

  • Compare acquisition channels, segments, or product lines to prioritize investments.
  • Diagnose conversion drop-offs or retention issues within booking or vintage cohorts.
  • Stress-test forecasting assumptions using historical cohort behavior as a baseline.
  • Prepare leadership readouts with standardized cohort reporting.
  • Rebaseline commercial assumptions each quarter to reflect current trends.

Best practices

  • Define cohorts so they are mutually exclusive to avoid double-counting.
  • Select a consistent metric stack for cross-cohort comparability.
  • Normalize for seasonality, deal-size mix, and currency before comparing.
  • Use a standard chart pack (waterfalls, heatmaps, curves) for repeatable insights.
  • Annotate findings with clear drivers, anomalies, and recommended actions.

Example use cases

  • Compare win rates and ACV by lead source to reallocate marketing spend.
  • Track retention and NRR over vintage cohorts to identify product churn drivers.
  • Analyze payback periods by customer tier to inform pricing and packaging changes.
  • Diagnose where pipeline conversion breaks down for a specific booking month cohort.
  • Produce a quarterly cohort-driven forecast sanity check for revenue ops.

FAQ

How do I choose the best cohort key?

Pick the key that maps to the decision you want to make—signup month for vintage behavior, lead source for marketing attribution, product tier for monetization decisions. Ensure cohorts remain mutually exclusive.

What normalization steps are essential?

Adjust for seasonality, deal-size mix, and currency differences. At a minimum, normalize by deal size distribution and remove cyclical effects that would distort cohort comparisons.