Analysis Types

Journium trackers use a structured analysis block that selects one of nine structured analysis algorithms, plus Custom analysis. Each type determines:

What queries are compiled against your event data
Which template variables are injected into your llm.promptTemplate
What computed metrics are surfaced in the insight

Choosing the Right Type

Funnel Analysis

Track ordered step completion and identify where users drop off.

Retention Analysis

Cohort return-rate curves over time — D1, D7, D30, and beyond.

Activation Analysis

Measure what percentage of new users reach the first value moment.

Churn Risk Analysis

Identify users who were active but have since gone silent.

Feature Adoption

What fraction of active users have tried a specific feature.

Anomaly Detection

Automated spike and dip detection vs. a statistical baseline.

Conversion Rate

Simple numerator / denominator rate — percentage of group A that does B.

Cohort Analysis

Compare a metric across cohorts defined by join date or a property value.

Trend / Growth

Count events or unique users per time period — daily, weekly, monthly.

Custom Analysis

Pass raw matching events to the LLM — not available in the NL Builder.

Quick Selection Guide

If you want to…	Use type
See where users drop off across an ordered multi-step flow	`funnel`
Track what fraction of cohorts return at D1 / D7 / D30	`retention`
Measure the aha-moment rate for new users	`activation`
Find users who went quiet and are at risk of churning	`churn`
Know what fraction of active users have adopted a feature	`featureAdoption`
Get alerted when a metric spikes or dips anomalously	`anomaly`
Compute a simple event-A-to-event-B conversion rate	`conversion`
Compare engagement or revenue across cohort groups	`cohort`
Plot event counts, DAU, or sums over time	`trend`
Analyze matching events without pre-computed metrics	`custom`

Universal Template Variables

All analysis types inject these variables into llm.promptTemplate:

Variable	Description
`{{windowPeriod}}`	Human-readable description of the analysis window, e.g. `"last 7 days"`
`{{dataAsJson}}`	Full structured analysis result serialised as JSON
`{{executedAt}}`	ISO 8601 timestamp of when this execution started

Each analysis type also provides its own type-specific variables. See the individual type pages for details.