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Event Tracking Recommendations for Productivity App
Generates hypothesis framework for a productivity app with 150 users, providing recommended events to track, expected correlations, and data collection templates before full analysis.
Input
What you provide to the skill
I have a productivity app with 150 users. What events should I track to find my activation metric?
Output
What the skill generates for you
Activation Metric Discovery: Productivity App
Framework for Analysis
To find your activation metric, I need to analyze which events predict retention. Please share your event data as CSV with these columns:
- user_id
- event_name
- event_timestamp
- retained_30d (or retained_60d)
Hypothesis: Likely Activation Candidates for Productivity Apps
Based on common productivity app patterns, here are events you should track:
| Event Category | Event Name | Expected Correlation | Rationale |
|---|---|---|---|
| Core Actions | create_task | High | Primary value delivery - indicates user understood core feature |
| complete_task | Very High | Value realization - feeling of accomplishment drives retention | |
| create_project | High | Organization behavior, commitment signal | |
| Habit Formation | complete_task (5+) | Very High | Repeated value extraction builds habit |
| days_active_first_week | Very High | Frequency matters more than single actions | |
| morning_session | Medium-High | Time-of-day habit formation | |
| Organization | add_tag | Medium | Power user behavior |
| set_due_date | Medium-High | Planning ahead = commitment | |
| create_category | Medium | Customization indicates investment | |
| Collaboration | invite_user | High | Network effects and commitment |
| share_task | Medium-High | Collaborative value beyond solo use | |
| assign_task | Medium-High | Team coordination signal | |
| Retention Drivers | enable_notifications | Medium | Re-engagement mechanism |
| set_recurring_task | High | Long-term planning signal | |
| use_search | Medium | Content accumulation (good problem to have) | |
| Integration | connect_calendar | High | Ecosystem lock-in |
| enable_sync | Medium-High | Cross-device commitment |
Recommended Event Tracking Setup
Minimum viable events to track:
- signup - Baseline
- create_task - Did they understand the product?
- complete_task - Did they get value?
- create_project - Organizational commitment
- invite_user - Network effect
- session_start - Track frequency (days_active calculation)
Additional high-value events:
7. set_due_date - Future planning
8. set_recurring_task - Long-term commitment
9. connect_integration - Calendar, email, etc.
10. enable_notifications - Re-engagement opt-in
What I’ll Calculate (Once You Share Data)
With 150 users, I can provide statistically meaningful analysis:
- Event correlation: For each event, what % of users who did it retained at 30/60 days?
- Threshold analysis: How many task completions predict retention? (1, 3, 5, 10?)
- Timing windows: Does activity in first 3 days matter more than first 7 days?
- Combination patterns: Do certain event sequences predict better? (e.g., create → complete → invite)
- Velocity analysis: Time between signup and first value moment
Expected Primary Metric Candidates
Based on productivity app benchmarks, expect one of these:
Most likely: “Complete 3+ tasks in first 7 days”
- Direct value realization
- Within user control
- Clear, measurable threshold
Secondary candidates:
- “Active on 4+ days in first week” (habit formation)
- “Create project + complete 5+ tasks in first week” (composite)
- “Invite teammate in first 14 days” (if team-focused)
Data Collection Template
Export your analytics as CSV with this structure:
user_id,event_name,event_timestamp,retained_30d
u001,signup,2025-12-01 10:00:00,1
u001,create_task,2025-12-01 10:05:00,1
u001,complete_task,2025-12-01 11:30:00,1
u001,create_task,2025-12-01 14:00:00,1
u001,complete_task,2025-12-01 16:00:00,1
u002,signup,2025-12-01 11:00:00,0
u002,create_task,2025-12-01 11:10:00,0
Required columns:
user_id- Unique user identifierevent_name- Event typeevent_timestamp- When event occurred (ISO 8601 format)retained_30d- Binary: 1 if user was active 30 days later, 0 if churned
For 150 users: Aim to export all events from first 14 days after signup, plus retention status at 30 days.
Alternative: Aggregate Data Format
If event-level data is hard to export, you can provide aggregated metrics:
user_id,tasks_created,tasks_completed,projects_created,invites_sent,days_active_first_week,retained_30d
p001,15,12,3,2,6,1
p002,2,1,0,0,1,0
p003,8,6,1,0,4,1
I’ll identify which aggregated behaviors correlate with retention.
Ready for Analysis
Share your CSV and I’ll run the complete analysis with:
- Correlation rankings for all events/metrics
- Specific threshold recommendations (e.g., “3+ tasks” vs “5+ tasks”)
- Framework evaluation (timing, user control, measurability)
- User activation scores and intervention recommendations
- A/B test plan to validate causation
Note: With 150 users, results will be reliable (100+ is the recommended minimum). Confidence increases with more users, but you have sufficient sample size for actionable insights.
About This Skill
Analyze user event data to identify which behaviors predict retention. Combines statistical correlation analysis with a structured framework for evaluating activation metric candidates.
View Skill DetailsMore Examples
Aggregate Data Analysis: Feature Usage Activation
Analysis of 500 users tracked by features_used, days_active, and shared_content. Identifies 'Use 6+ features in first week' as primary metric with composite scoring formula.
Event-Level Analysis: Task Completion Activation
Full activation metric analysis for 250 users with signup, task_completion, and invite events. Identifies 'Complete 3+ tasks in 7 days' as primary metric with 0.91 correlation.