Activation Analysis
Measure what percentage of new users reach the first value moment.
Overview
Activation Analysis measures the "aha moment" rate — what percentage of users who completed a start event (e.g., signed up) also completed a key value action within a defined time window. It tells you how effective your onboarding is at guiding new users to first value.
When to use activation: "What % of signups create a project within 24 hours?", "Are new users finding value quickly enough?", "What is our activation rate for trial users?" — any question about the rate at which new users reach a meaningful milestone.
When NOT to use activation: If you want historical cohort return-rate curves, use
retention. If you want to identify currently inactive users, use
churn.
Configuration Reference
Prop
Type
Available Template Variables
| Variable | Description |
|---|---|
{{newUsers}} | Number of users who fired startEvent in the window |
{{activated}} | Number of users who also fired ahaEvent within activationWindow |
{{activationRatePct}} | Percentage of new users who activated |
{{avgHoursToAha}} | Average hours from startEvent to ahaEvent for activated users |
{{hasComparison}} | true if compareWindow was set and previous-period data is available |
{{prevActivationRatePct}} | Activation rate in the previous period (when hasComparison is true) |
{{activationDeltaPct}} | Percentage-point change vs. previous period (when hasComparison is true) |
{{windowPeriod}} | Human-readable analysis window, e.g. "last 14 days" |
{{dataAsJson}} | Full structured result as JSON |
{{executedAt}} | ISO 8601 execution timestamp |
Example
apiVersion: journium.app/v0Beta
kind: InsightTracker
metadata:
name: signup-to-project
displayName: Signup → First Project (24h Activation)
description: What % of new signups create a project within 24 hours
spec:
type: LLM
trigger:
mode: automatic
schedule: daily
window:
period: last_14d
analysis:
type: activation
entity: person_id
startEvent: user_signed_up
ahaEvent: project_created
activationWindow: 24h
compareWindow: previous_period
llm:
promptTemplate: |
Analyze activation for {{windowPeriod}}.
{{newUsers}} new users signed up. {{activated}} activated ({{activationRatePct}}%).
Average time to aha moment: {{avgHoursToAha}} hours.
{{#if hasComparison}}
Previous period activation: {{prevActivationRatePct}}% ({{activationDeltaPct}}pp change).
{{/if}}
Full data: {{dataAsJson}}
Identify the biggest friction points preventing activation and suggest one targeted fix.
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