---

name: ops-posthog-cohort-builder
description: Use when building user cohorts in PostHog for segmentation, A/B test targeting, feature rollout audiences, retention analysis, or email campaign segmentation. Defines canonical cohort definitions (activation, retention, persona, tier, feature usage) for a legal AI product, with rules for tenant boundary enforcement and PII handling in cohort queries.
license: MIT
metadata:
id: ops.posthog-cohort-builder
category: ops
priority: P1
intent: [ops, posthog, cohort, segmentation, analytics]
related: [ops-posthog-funnel-debugger, ops-feature-flag-experiment-launcher, ops-churn-risk-detector]
source: Louis — HAQQ Legal AI (github.com/sboghossian/mini-claude-for-legal)
version: "1.0"

Ops — PostHog Cohort Builder

Purpose

Cohorts are the foundation of product analytics in a legal AI platform. Without well-defined, consistently applied cohorts, A/B test results are unreliable, retention curves are noisy, and feature rollouts reach the wrong users. This skill defines the canonical cohort library and the rules for building, maintaining, and querying them safely.

Canonical cohort definitions

Activation cohorts

Cohort name	Criteria	Use case
New users	Account created in last 7 days	Onboarding funnel analysis
Activated users	Completed at least 1 substantive prompt (not just signed up and browsed)	Activation rate measurement
Power users	5+ sessions per week for the last 4 weeks	Feature rollout first audience; NPS benchmark
Churned-activated	Activated but no session in last 30 days	Win-back targeting

A "substantive prompt" is defined as a user message that resulted in a draft, a document analysis, or a research response — not a greeting, a settings change, or a navigation event.

Retention cohorts

Cohort name	Criteria	Use case
Week-1 retained	Signed up >7 days ago; had at least 1 session in last 7 days	Day-7 retention metric
Month-1 retained	Signed up >28 days ago; active in last 28 days	Month-1 retention
Month-3 retained	Signed up >84 days ago; active in last 30 days	Long-term retention
Dormant	No session in last 30 days; was previously active	Re-engagement campaign targeting
Resurrected	Dormant for >30 days; then had a session in last 7 days	Resurrection rate metric

Persona cohorts

Cohort name	Criteria	Notes
Lawyers (private practice)	Email domain matches known law firm pattern or user declared role	Broadest professional cohort
In-house counsel	Corporate domain + role indicator in profile	Often on Business tier
Law students	.edu domain or declared student role	Typically on Free tier
Consumers	Personal email domain (gmail, hotmail, yahoo, etc.) + no professional role indicator	Consumer product experience
Enterprise admin	Account has team members + admin role flag	Relevant for team feature rollouts

Note: persona inference from email domain is approximate. Encourage users to declare their role in onboarding for higher accuracy.

Tier cohorts

Cohort name	Criteria
Free tier	No active Stripe subscription, or on free plan
Starter	Active Stripe subscription on starter plan
Pro	Active Stripe subscription on pro plan
Business	Active Stripe subscription on business plan
Enterprise	Active Stripe subscription on enterprise plan
BYO-key users	Free tier + has configured a custom API key
Trial	Active Stripe trial (trial_end is in the future)

Feature usage cohorts

Cohort name	Criteria	Use case
Heavy doc workspace users	5+ document workspace sessions per week	Target for collaboration feature rollout
Skills observability users	Has opened the skills observability tab at least once	Target for observability feature announcements
Drafting board users	Has had at least 1 drafting board session	Drafting feature experiment audience
Multi-jurisdiction users	Has used more than 2 different jurisdiction contexts	Target for jurisdiction-specific feature rollouts
Deep-research users	Has triggered deep-research skill at least 3 times	Power users; target for research feature improvements

Cohort use cases

• A/B testing: use persona + tier cohorts to ensure experiments are measured on the right population.
• Feature rollouts: start with power users, then activated, then all.
• Push / email campaigns: use dormant or churned-activated for re-engagement; use retained + high-NPS for testimonial asks.
• Retention analysis: run retention curves on the Week-1 and Month-1 retained cohorts segmented by persona and tier.
• Churn deep-dive: compare feature breadth (from feature usage cohorts) between churned and retained users.

Building a cohort in PostHog

1. Navigate to Cohorts → New Cohort.
2. Name the cohort using the canonical name from the table above (consistency is important for cross-team communication).
3. Define the filter criteria using PostHog's behavioral filter builder.
4. Set the cohort to dynamic (recalculates daily) unless you need a static snapshot for a specific experiment.
5. Add a description explaining the criteria in plain English (not just the filter expression).
6. Document the version date if the definition has changed — cohort definitions should be version-tracked.

Critical rules

Tenant boundaries

Cohort queries must always include a tenant filter if the product is multi-tenant. Never build a cohort that inadvertently mixes users from different tenants — this is both a privacy issue and a data quality issue.

PII compliance

• PostHog person properties should not store raw PII (full name, email). Use anonymized IDs.
• If email domain is used as a cohort criterion, store only the domain, not the full address.
• Cohort membership lists exported from PostHog must be handled under the same data handling rules as any other personal data export.

Cohort overlap

When using cohorts for A/B tests, check for overlap between your target cohort and any cohort that is already enrolled in another running experiment. Overlapping experiments produce unreliable results.

Definition documentation

Every cohort must have a written definition (the criteria in plain English, not just the PostHog filter). Store these definitions in the product wiki alongside the cohort's PostHog URL.

Related skills

• [[ops-posthog-funnel-debugger]] — use cohorts to segment funnel analysis
• [[ops-feature-flag-experiment-launcher]] — cohorts define the experiment audience
• [[ops-churn-risk-detector]] — churn model is validated against the dormant and churned-activated cohorts