Aggregate Data Analysis: Feature Usage Activation

Activation Metric Discovery Analysis

Dataset Overview

• Total Users: 500
• Overall Retention (30d): 70.0%
• Metrics Tracked: features_used, days_active, shared_content

Correlation Analysis

Recommended Activation Metric

PRIMARY METRIC: Use 6+ features within first week

Why this metric:

1. Highest correlation (0.95) with 30-day retention
2. Clear inflection point: Users with 6+ features show 97% retention vs 18% for <4 features
3. Within user control: Users can explore features if properly guided
4. Leading indicator: Feature exploration happens early and predicts long-term engagement
5. Easy to measure: Simple count of distinct features accessed

Threshold Analysis:

• 6+ features: 97% retention rate (350 users)
• 4-5 features: 65% retention rate (75 users)
• 1-3 features: 18% retention rate (75 users)
• Inflection point: 6 features is where retention jumps significantly

Secondary Activation Signals

1. Days Active (correlation: 0.93)

• Threshold: 5+ days active in first week
• Retention: 98% for users who achieve this
• Note: Highly predictive but requires sustained behavior; harder to influence immediately
• Use case: Combine with primary metric for composite scoring

2. Shared Content (correlation: 0.89)

• Threshold: 3+ pieces of shared content
• Retention: 95% for users who achieve this
• Note: Indicates value realization and network effects
• Use case: Power user indicator; may require having contacts/team

Composite Activation Score

For nuanced user segmentation, combine all three signals:

Activation Score Formula:

Score = (features_used ≥ 6) × 0.50 + 
        (days_active ≥ 5) × 0.30 + 
        (shared_content ≥ 3) × 0.20

User-Level Activation Status (Sample)

*Note: u008 retained despite low score - outlier or delayed activation

Framework Assessment

Evaluating features_used ≥ 6 against activation metric criteria:

OVERALL RECOMMENDATION: PRIMARY ACTIVATION METRIC ✓

Activation Rate Benchmarks

Current Performance:

• 70% of users achieve activation (6+ features in first week)
• 30% fail to activate and show 18% retention

Target Recommendations:

• Immediate goal: Increase activation rate from 70% to 80% (+10pp)
• Stretch goal: 85%+ activation rate
• Focus area: First 3 days - guide users to try 2+ features per day

Expected Impact:

• Moving 10% of users from “not activated” to “activated” would:
  • Increase overall retention from 70% to ~77%
  • Reduce churn by ~25%

Validation Roadmap

CRITICAL: These are correlations, not proven causation. To validate:

Phase 1: Observational Validation (Week 1-2)

1. Track new user cohort (n=200)
2. Measure time-to-6th-feature
3. Confirm correlation holds in fresh data

Phase 2: A/B Test (Week 3-6)

1. Control Group: Standard onboarding
2. Test Group: Enhanced feature discovery
   • In-app tooltips for unused features
   • “Try 6 features” progress bar
   • Feature recommendation engine
3. Primary metric: % achieving 6+ features in week 1
4. Secondary metric: 30-day retention difference

Phase 3: Refinement (Week 7+)

1. If test group shows significantly higher activation AND retention:
   • Roll out enhanced onboarding
   • Optimize for time-to-activation
   • Consider gamification (feature exploration badges)
2. If no retention improvement despite higher feature usage:
   • Investigate: Are users trying wrong features?
   • Consider feature quality/relevance
   • May need segmented activation metrics by user type

Caution: Correlation ≠ Causation

What we know: Users who try 6+ features are much more likely to retain.

What we DON’T know:

• Does feature usage cause retention?
• Or do highly engaged users naturally try more features?
• Could forcing feature exploration backfire if features aren’t relevant?

Action: Validate causation via A/B test before major product changes.

Implementation Checklist

• Track activation metric: Implement “features_used_count_first_week” in analytics
• Create activation dashboard: Monitor daily activation rate
• Design experiment: Plan A/B test for feature discovery interventions
• User segmentation: Tag users by activation status for targeted campaigns
• Onboarding optimization: Guide new users toward 6-feature goal
• Identify feature gaps: Which 6 features drive most value? Prioritize those.
• Monitor activation velocity: How quickly do activated users reach 6 features? (Day 1, 2, 3?)
• Churn prediction: Flag users at day 5 with <4 features for intervention

Next Steps

1. Immediate: Start tracking composite activation score for all new users
2. Week 1: Design and implement feature discovery nudges
3. Week 2: Launch A/B test with 20% of new signups
4. Week 6: Analyze test results; decide on rollout
5. Ongoing: Monitor activation rate as key growth metric

Success Metric: Increase activation rate from 70% to 80% within 60 days, resulting in ~7pp retention improvement.