Lifecycle Analyzer

Map your users across the lifecycle — new, engaged, at-risk, churned — and understand what moves them from one stage to the next.

When to Use

• "How many users are at risk of churning?"
• "What percentage of new users become active?"
• "Show me the lifecycle breakdown for Q3"
• "What's the most common path from new → churned?"
• "Which lifecycle stage has the biggest drop-off?"
• "Compare lifecycle between enterprise and free users"

Mental Model

Every user is in one of these lifecycle stages:

Workflow

Step 1: Build lifecycle segments

Build one segment per stage:

fullstory:build_segment("users with first_seen in last 7 days") → new_users
fullstory:build_segment("users with 2-5 sessions and first_seen in last 30 days") → activating
fullstory:build_segment("users with 6+ sessions in last 30 days") → active
fullstory:build_segment("users with 6+ sessions last month and <3 sessions this month") → at_risk
fullstory:build_segment("users with last_seen before 30 days ago") → churned

Step 2: Measure lifecycle distribution

For each segment, get the user count:

fullstory:build_metric(query="unique users", output_type="single_number")
→ compute for each segment

Present the distribution:

Lifecycle Distribution (Aug 2026)
New:         2,100 (14%)  🆕
Activating:  3,400 (23%)  🌱
Active:      5,800 (39%)  ✅
At-Risk:     1,900 (13%)  ⚠️
Churned:     1,700 (11%)  💤
Total:      14,900

Step 3: Analyze stage transitions

Track how users move between stages:

New → Activating:

Of 2,100 new users in July:
- 1,400 became activating (67% activation rate)
- 700 churned immediately (33% — these are the "one-timers")

Activating → Active:

Of 3,400 activating users:
- 2,100 became active (62% conversion to active)
- 800 stayed activating (still exploring)
- 500 became at-risk (lost momentum)

Active → At-Risk (churn signal):

1,900 currently at-risk users:
- 1,200 were active and stopped gradually (declining sessions)
- 700 were active and stopped abruptly (possible: achieved their goal, found alternative, or had a bad experience)

Step 4: Investigate transition blockers

For the biggest drop-off (New → Activating at 67%), investigate why users don't return:

fullstory:build_segment("users with 1 session, first_seen in last 30 days")
fullstory:get_sessions(segment_id, limit=10)
→ session-context: "What did this user do in their only session? Did they hit an error, get confused, or just not find what they wanted?"

Step 5: Segment the lifecycle

Break down by user properties:

By plan:
Enterprise: 72% active, 8% at-risk, 4% churned
Free:       28% active, 18% at-risk, 18% churned
→ Free users are 4.5x more likely to churn. The free-to-paid conversion gap is the biggest lifecycle lever.

Step 6: Report

## Lifecycle Analysis — Q3 2026

### Stage Distribution
- 14,900 total users
- Healthy ratio: 53% of users are active or activating ✅
- Watch: 13% at-risk — if half churn, that's 950 users lost this month

### Key Transitions
- New → Activating: 67% (good, but 700 one-timers/month means 8,400/year lost)
- Activating → Active: 62% (the biggest improvement opportunity — focus onboarding here)
- Active → At-Risk: 13% of active users become at-risk each month (stable rate)

### At-Risk Profile
- 1,900 at-risk users
- 63% were previously daily users → rapid disengagement
- Common last action: visited /settings or /billing → may be an account management issue
- 32% show frustration signals (rage clicks, errors) in their last 2 sessions

### Recommendations
1. Improve activation: users who complete onboarding are 3x more likely to become active. Focus on onboarding flow.
2. At-risk intervention: email users who miss 3+ consecutive days (especially daily users). Offer support.
3. Churn investigation: 700 one-timers/month. Run `session-review` on 10 one-timer sessions to understand what they saw.

### Lifecycle Funnel
New (2,100) → Activating (1,400, 67%) → Active (2,100*, 62%) → At-Risk (1,900, 13%) → Churned (1,700)

*2,100 became active this month, plus 3,700 already active = 5,800 total active

Lifecycle Benchmarks

Guidelines

• Lifecycle is a model, not reality. Users don't read your lifecycle definition. Some "churned" users are on vacation. Some "at-risk" users are just busy. Use as a directional tool, not a precise measure.
• The biggest lifecycle lever is usually activation (New → Active). Users who become active in the first 30 days are 3-5x more likely to retain long-term.
• At-risk detection is most useful with an intervention. If you can't act on it (no email, no in-app message), it's just a dashboard widget. Pair with predictive-alerts for proactive outreach.
• Segment lifecycle by acquisition channel. Users from paid ads may have different lifecycle patterns than organic users. Use campaign-tracker to segment.
• Resurrection is underrated. Users who churn and come back are often more loyal than users who never left. Track it.