Churn Risk Analysis
Identify users who were active but have gone silent.
Overview
Churn Risk Analysis finds users who were active during a lookback period but have not performed any qualifying activity within the inactivity threshold. These users are flagged as "at churn risk". The analysis gives you an actionable count today — not a historical trend.
When to use churn: "Which users haven't logged in for 2 weeks?", "How many active users are at churn risk right now?", "Who do I need to reach out to before they leave?" — any question about currently at-risk users.
When NOT to use churn: If you want historical retention trend curves over cohorts, use
retention. If you want a time-series activity count, use trend.
Configuration Reference
Prop
Type
activeEvent values
| Value | Meaning |
|---|---|
"*" | Any event fired by the user counts as activity |
"session_started" | Only this specific event counts as activity |
["session_started", "api_call"] | Any event in this list counts as activity |
Available Template Variables
| Variable | Description |
|---|---|
{{activeBase}} | Total number of users who were active during the lookback period |
{{atRiskCount}} | Number of users currently at churn risk |
{{atRiskRatePct}} | Percentage of the active base that is at churn risk |
{{churnSummary}} | Segment breakdown of at-risk users (when segments are configured) |
{{windowPeriod}} | Human-readable analysis window, e.g. "last 30 days" |
{{dataAsJson}} | Full structured result as JSON |
{{executedAt}} | ISO 8601 execution timestamp |
Example
apiVersion: journium.app/v0Beta
kind: InsightTracker
metadata:
name: churn-risk-14d
displayName: Churn Risk (14-day Inactivity)
description: Users who were active in the last 90 days but silent for 14+ days
spec:
type: LLM
trigger:
mode: automatic
schedule: daily
window:
period: last_30d
analysis:
type: churn
entity: person_id
activeEvent: session_started
inactivityThreshold: 14d
lookbackPeriod: 90d
segments:
- property: plan
metrics:
- atRiskCount
- atRiskRate
- segmentBreakdown
llm:
promptTemplate: |
Churn risk analysis for {{windowPeriod}}.
Active user base: {{activeBase}}. At-risk users: {{atRiskCount}} ({{atRiskRatePct}}%).
Segment breakdown: {{churnSummary}}
Full data: {{dataAsJson}}
Summarize the churn risk situation and recommend one re-engagement action.
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