Cohort Churn Analyzer

Cohort & Churn Analyzer (Retention Squad)

Produces the three artifacts every retention conversation needs: cohort curve, churn driver table, and a "save list" of at-risk users.

Triggers

• "run a cohort analysis"
• "why are users churning"
• "build our retention curve"
• "find the churn drivers"
• "at-risk user list"

Inputs required

1. Event data or subscription export — enough to derive sign-up date, last-active date, churn date, plan
2. Churn definition — days-inactive threshold or explicit cancel event
3. User attributes — plan, acquisition source, company size, any feature-usage flags
4. Time range — default: last 12 cohorts (monthly)

Process

Step 1 — Build the cohort curve

Group users by signup month. For each cohort, compute retention at week 1, 2, 4, 8, 12, 24. Output the classic cohort triangle.

Flag:

• Cliff weeks — where > 15pp retention is lost in a single week
• Flattening point — week at which the curve stabilizes (your "sticky rate")
• Cohort-over-cohort trend — are newer cohorts retaining better or worse

Step 2 — Segment the curve

Repeat Step 1 by:

• Plan
• Acquisition source
• Activation status (activated vs. not — from Aha-Moment Mapper)
• Company size band

Find the segment with the widest spread. That's where the leverage is.

Step 3 — Identify churn drivers

For users who churned, compare their pre-churn behavior to retained users. Run:

• Feature-usage diff (which features do retained users touch that churned don't)
• Time-to-aha comparison (did churners miss activation entirely?)
• Support-ticket volume
• Last NPS score if available

Rank the top 5 driver hypotheses by effect size + reach.

Step 4 — Survival-ish analysis (lightweight)

For each driver, estimate: "if we moved every user from low-usage to high-usage of feature X, what would W12 retention become?" Use a basic uplift estimate; flag confidence honestly.

Step 5 — Build the save list

Produce a ranked list of currently-active users whose behavior matches the "about to churn" pattern. Include:

• User ID, plan, last-active date
• Risk score (0–100)
• Which driver(s) they match
• Recommended intervention (from the Re-engagement Campaigner playbook)

Step 6 — Write the report

Produce:

• Cohort triangle visual
• 2–3 segmented curves showing the widest spreads
• Churn-driver table with effect sizes
• Save list with suggested interventions

Output artifacts

• cohort-analysis.html (interactive chart) via data:create-viz
• churn-drivers.md
• save-list.csv

Handoff

• Save list → Re-engagement Campaigner (interventions) + Feature Adoption Tracker (nudges)
• Driver hypotheses → A/B Test Designer to validate
• Cohort curve → Performance Reporter + Growth Dashboard Builder

Example call

> "Run a cohort analysis on users.csv + events.csv. Churn = no login for 30 days. I want to see if our Pro plan retains better than our Team plan and what features predict retention."

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: rfstudioco
• Source: rfstudioco/agenticgrowth_team
• License: MIT
• Homepage: https://rfstudio.co

Install and usage instructions live in the source repository linked above.