cohort-churn-analyzer

name	cohort-churn-analyzer
description	Run cohort retention curves and churn-driver analysis on user/subscription data; surface the biggest drop-off weeks and the user traits that predict churn. Use when the user says "run cohort analysis", "why are users churning", "retention curve", or "find the churn drivers".
status	new

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."