Retention Analyzer

Measure user retention — how many users come back, how often, and who's at risk of churning.

When to Use

• "What's our 7-day retention rate?"
• "How many new users from last month are still active?"
• "Are enterprise users more sticky than free users?"
• "Show me users who haven't returned in 30 days"
• "What's the churn rate for users who signed up in Q2?"
• "Compare retention between users who completed onboarding vs those who didn't"

Mental Model

Retention is about returning users. There are three common lenses:

1. N-day retention: What percentage of users who were active on Day 0 came back on Day N? (e.g., Day 1, Day 7, Day 30)
2. Cohort retention: Of users who signed up/started in a period, what percentage are still active N days later?
3. Stickiness: How many days per week/month do active users engage? (DAU/MAU ratio)

Fullstory doesn't have a native retention report, so you build it from segments and metrics.

Workflow

Step 1: Define the retention window

Clarify what the user wants:

• N-day return: "Are users active on Day 7 and Day 30?"
• Ongoing engagement: "How many sessions per week do active users have?"
• Churn detection: "Which users haven't returned in 30+ days?"

Step 2: Build a retention cohort

For N-day retention from signup:

fullstory:build_segment("users with first_seen between July 1 and July 7") → cohort_segment

For ongoing activity:

fullstory:build_segment("users with last_seen in last 7 days") → active_users
fullstory:build_segment("users with last_seen before last 30 days") → churned_users

Step 3: Measure return rate

Build a metric that counts users who were active in both the cohort window AND the return window:

fullstory:build_metric(
  query="users who were active between July 1-7 AND active between Aug 1-7",
  output_type="single_number"
)

Compare to the total cohort size to get the retention rate: "1,200 of 5,000 July Week 1 users were active in August (24% 30-day retention)."

Step 4: Engagement depth

For stickiness (how often active users engage):

fullstory:build_metric(
  query="sessions per user in last 7 days",
  output_type="top_n"
)
→ "45% of active users had 1-2 sessions, 30% had 3-5, 15% had 6-10, 10% had 10+"

Step 5: Segment retention

Using the comparisons skill approach, break retention down by cohort:

By plan:
Enterprise: 52% 30-day retention
Free:        18% 30-day retention

By onboarding:
Completed onboarding: 41% 30-day retention
Skipped onboarding:    9% 30-day retention ← BIG GAP

Step 6: Churn signals

Identify users at risk of churning:

fullstory:build_segment("users with last_seen 14-30 days ago AND total_sessions > 5")
→ these are previously engaged users who've gone quiet

For these users, check:

• Did they hit errors or frustrations in their last sessions?
• Did their session frequency drop before they stopped?
• What was the last thing they did?

Use user-journey to trace a sample of churned users and look for common patterns.

Step 7: Present

Format the retention analysis:

## Retention — July 2024 Cohort (5,000 new users)

### Return Rates
- Day 1: 68% (3,400)
- Day 7: 41% (2,050)
- Day 30: 24% (1,200)

### By Segment (30-day)
- Enterprise: 52% | Free: 18%
- Completed onboarding: 41% | Skipped onboarding: 9%

### Churn Risk
- 890 users haven't returned in 14-30 days (previously active)
- Common last action: viewed /pricing, did not convert

### Recommendation
Onboarding completion is the biggest lever — users who finish onboarding are 4.5x more likely to stick. The pricing page is where users go before churning.

Guidelines

• Fullstory doesn't have a built-in retention report. Be transparent: "I'm building this from segments and metrics — it's accurate but takes a few extra calls. For dedicated retention analytics, consider a tool like Amplitude or Mixpanel."
• Day 0 = user's first day (from first_seen). Day 1 = the next day, Day 7 = a week later, etc.
• For B2B products, retention is typically measured in weeks or months, not days. Adjust the window.
• "Active" usually means "had at least one session in the window." Clarify with the user if they define it differently.
• Segment-level retention differences are often the most actionable insight — "enterprise users retain at 52%, free at 18%" tells you where to focus.