Cohort Intelligence

The Cohort Intelligence engine groups accounts into cohorts by vintage (sign-up quarter), segment, territory, or rep, then computes per-cohort revenue metrics (NRR, GRR, churn, expansion), generates vintage retention curves, and detects anomalies like accelerating churn or health score decline.

Cohort Intelligence consumes data from the Accounts and Renewals tables. It does not modify entity-level scores — it produces aggregate cohort metrics for trend analysis and board reporting.

How It Works

Accounts + Renewals data
  → Cohort definitions (vintage, segment, territory, rep)
  → Cohort snapshots (NRR, GRR, churn, expansion per period)
  → Vintage curves (month-by-month retention from cohort start)
  → Cohort comparison (ranked by NRR with best/worst flags)
  → Anomaly detection (low NRR, accelerating churn, health decline)

Cohort Types

Type	Grouping Field	Example
Vintage	`created_at` quarter	Vintage 2025-Q3
Segment	`segment`	Segment: Enterprise
Territory	`territory`	Territory: Northeast
Rep	`owner_id`	Rep: sarah-jones
Custom	User-defined filter	Custom criteria

Cohort Snapshots

Each cohort receives a periodic snapshot with the following metrics:

Metric	Description
Total ARR	Sum of ARR across all cohort members
Starting ARR	ARR at the beginning of the snapshot period
NRR	(Starting + Expansion - Contraction - Churn) / Starting x 100
GRR	(Starting - Contraction - Churn) / Starting x 100 (capped at 100%)
Churn Count	Number of accounts with CHURNED disposition in the period
Churn ARR	ARR lost to churn
Expansion ARR	ARR gained from expansion
Contraction ARR	ARR lost to downsell
Avg Health Score	Average health score across cohort members
Avg Risk Score	Average risk score across cohort members
Avg Products per Account	Average product count
Avg Adoption %	Average product adoption percentage

Vintage Curves

Vintage curves track month-by-month retention from the cohort’s start date for up to 36 months. Each data point includes:

Field	Description
Months Since Start	0, 1, 2, … up to 36
Retention %	(Starting ARR - Cumulative Churn + Cumulative Expansion) / Starting ARR x 100
Accounts Remaining	Starting account count minus cumulative churned accounts
Cumulative Churn ARR	Running total of ARR lost to churn
Cumulative Expansion ARR	Running total of ARR gained from expansion

Vintage curves can exceed 100% retention when expansion ARR outpaces churn — this is a sign of strong net revenue retention.

Anomaly Detection

The engine flags three anomaly types:

Anomaly	Description
Low NRR	Net revenue retention below healthy thresholds, with escalating severity
Accelerating Churn	Churn count increasing period-over-period, with severity based on acceleration rate
Health Decline	Average health score declining significantly from prior period

Exact anomaly thresholds and severity classifications are configurable per organization and available in the PILLAR Implementation Guide provided to active customers.

Cohort Comparison

Cohorts are ranked by NRR with explicit best/worst indicators:

Indicator	Meaning
`best`	Highest NRR cohort
`worst`	Lowest NRR cohort
`normal`	All others

Data Model

PILLAR stores cohort definitions (grouping criteria and member counts), periodic cohort snapshots (revenue metrics, health scores, churn/expansion data per period), and vintage retention curves (month-by-month retention tracking for up to 36 months).

Detailed data model schemas are available in the PILLAR Implementation Guide provided to active customers.

API Endpoints

GET  /api/cohorts/definitions
POST /api/cohorts/definitions

See the Cohorts API reference for full endpoint documentation.

Access

Available to: CRO/CEO, VP Sales, VP CS, RevOps

​Cohort Intelligence

​How It Works

​Cohort Types

​Cohort Snapshots

​Vintage Curves

​Anomaly Detection

​Cohort Comparison

​Data Model

​API Endpoints

​Access

Cohort Intelligence

How It Works

Cohort Types

Cohort Snapshots

Vintage Curves

Anomaly Detection

Cohort Comparison

Data Model

API Endpoints

Access