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cohort-analyzer

Analyze customer cohorts by signup and first-purchase times to produce retention matrices, revenue breakdowns, and LTV projections.

1|1|Updated Apr 12, 2026
Search Tags:#data-analytics#retention#cohort-analysis#lifetime-value#revenue-analysis#cohort-segmentation
One-click install
npx skills add https://github.com/huifer/Shopilot --skill cohort-analyzer-huifer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cohort-analyzer
Source: https://github.com/huifer/Shopilot/tree/main/skills/cohort-analyzer
Command: npx skills add https://github.com/huifer/Shopilot --skill cohort-analyzer-huifer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Cohort-analyzer provides a structured approach to analyze how different customer groups (cohorts) behave over time, enabling data-driven retention, revenue, and lifecycle insights.

Core Features & Use Cases

  • Cohort grouping by signup time, first purchase time, or behavior to measure retention and revenue across time windows (D1/D7/D30/D90, M1/M3/M6/M12).
  • Retention and revenue analysis with cross-cohort comparisons, trend identification, and lagged indicators for early churn and late value.
  • LTV modeling and forecasting to quantify long-term value of each cohort and prioritize retention experiments.

Quick Start

Analyze the latest 12 months of cohorts from your events data to generate retention, revenue, and LTV insights.

Frequently Asked Questions about cohort-analyzer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I analyze customer retention and revenue by signup cohort?▼

Cohort analysis groups customers by signup or first-purchase time to reveal retention and revenue contributions across time windows. It generates retention matrices and LTV projections from your event data to compare cross-cohort behavior.

What is the best way to calculate customer lifetime value for different cohorts?▼

Calculating customer lifetime value by cohort involves modeling revenue contributions from first-purchase event data over daily, weekly, or monthly windows. This approach projects long-term value to prioritize retention experiments for each group.

Can I use cohort analysis for both ecommerce and SaaS products?▼

Yes, cohort analysis applies to both ecommerce and SaaS products. It processes event data using monthly, weekly, or daily cohort windows to compare D1, D7, D30, and D90 retention and revenue trends across different business models.

How do I build a retention matrix from event data?▼

Building a retention matrix from event data requires grouping customers by signup or first-purchase time into daily, weekly, or monthly cohorts. The analysis measures lagged indicators to track D1 through D90 retention rates across each cohort group.

Does cohort analysis require specific input data formats for revenue breakdowns?▼

Cohort analysis requires event data containing customer signup and first-purchase timestamps to generate revenue breakdowns. It processes this input to produce cross-cohort comparisons and LTV forecasts without requiring additional dependencies.

What are the limitations of cohort segmentation for tracking late value?▼

Cohort segmentation limitations include dependency on accurate event timestamps for identifying lagged indicators of late value. Monthly or weekly windows may obscure granular daily retention fluctuations, requiring careful window selection for trend analysis.

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