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Smart Behavioral Cohorts

Rise automatically segments users based on behavior patterns, not demographics.

Traditional vs Behavioral Cohorts​

Traditional Cohorts (Mixpanel, Amplitude)​

Based on attributes:

  • Plan type (Free, Pro, Enterprise)
  • Signup date
  • Company size
  • Industry

Limitation: Demographics don't explain behavior.

Rise Behavioral Cohorts​

Based on how users actually behave:

  • Exploration patterns
  • Feature usage intensity
  • Learning speed
  • Friction experienced

Advantage: Predict outcomes, personalize interventions.

Auto-Generated Cohorts​

1. By Exploration Style​

Explorers (24% of users)

  • High feature discovery rate
  • Try new things frequently
  • Click around, experiment
  • Low friction tolerance

Task-Oriented (58% of users)

  • Goal-focused
  • Repeat same workflows
  • Ignore new features
  • Want efficiency

Stuck Users (18% of users)

  • High friction scores
  • Repeated confusion loops
  • Low feature adoption
  • Need guidance

2. By Discovery Timing​

Early Adopters (15% of users)

  • Discover features within days
  • High engagement
  • Power user trajectory

Gradual Learners (65% of users)

  • Steady feature discovery
  • Normal engagement pace
  • Typical user journey

Late Discoverers (20% of users)

  • Slow feature discovery
  • May miss key features
  • Risk of churn

3. By Usage Intensity​

Power Users (12% of users)

  • Daily active
  • Use 80%+ of features
  • High automation adoption
  • Expansion candidates

Regular Users (53% of users)

  • Weekly active
  • Use 30-50% of features
  • Core workflows

Casual Users (35% of users)

  • Monthly active
  • Use <30% of features
  • Risk of churn

4. By Context/Experience​

Empty State Dwellers (22% of users)

  • Frequently see empty states
  • Low data volume
  • May not understand how to populate data

Paywall Frustrated (8% of users)

  • Repeatedly hit plan limits
  • High engagement with locked features
  • Prime upgrade candidates

Search-First Users (16% of users)

  • Rely heavily on search
  • May indicate poor navigation
  • Or just preferred workflow

Mobile-Primary (11% of users)

  • Majority usage on mobile
  • Different UX needs
  • May miss desktop-only features

Cohort Characteristics​

Example: Power Users​

Defining Behaviors:

Power Users (n=156, 12% of users):

Feature Usage:
  - Avg features used: 23 / 28 (82%)
  - Daily active: 94%
  - Session length: 42 min avg
  - Keyboard shortcuts: 85% usage

Workflows:
  - Efficiency score: 87% (very efficient)
  - Automation acceptance: 91%
  - Custom configurations: 78%

Engagement:
  - Churn risk: Very low (2%)
  - Expansion opportunity: High (68%)
  - NPS: 72 (promoters)

Insights:

  • Most valuable cohort
  • Rarely churn
  • Ideal for upsell
  • Beta test candidates

Recommended Actions:

  • Offer early access to new features
  • Solicit product feedback
  • Upsell premium tiers
  • Turn into advocates/case studies

Example: Stuck Users​

Defining Behaviors:

Stuck Users (n=234, 18% of users):

Friction Indicators:
  - Friction index: 8.2 (high)
  - Loop patterns: 42% of sessions
  - Dead clicks: 18 per session
  - Backtracking: 56% of workflows

Feature Usage:
  - Avg features used: 4 / 28 (14%)
  - Daily active: 12%
  - Session length: 6 min avg (get frustrated, leave)

Engagement:
  - Churn risk: Very high (68%)
  - Support tickets: 3x average
  - NPS: -12 (detractors)

Insights:

  • High churn risk
  • Need immediate help
  • Frustrated with product
  • CSM intervention needed

Recommended Actions:

  • High-priority Rise Jobs targeting this cohort
  • CSM outreach (proactive)
  • Simplified onboarding flow
  • Guided demos or walkthroughs

Cohort Transitions​

Track how users move between cohorts over time:

Month 1:
  Stuck: 18%
  Regular: 65%
  Power: 12%

Month 2:
  From Stuck → Regular: 42% ✅ (good progress)
  From Stuck → Churned: 38% ❌ (lost them)
  From Regular → Power: 8% ✅ (great activation)
  From Power → Regular: 5% ⚠️ (engagement dropped)

Insight: 42% of stuck users recover → Rise guidance working. But 38% still churn → need faster intervention.

Predictive Cohorts​

Rise predicts future outcomes:

Churn Risk Cohorts​

High Churn Risk (n=89):
  Behaviors:
    - Decreasing session frequency (↓35% last 14 days)
    - Rising friction index (↑65%)
    - Feature usage down (↓40%)

  Predicted churn: 72% within 30 days

  Recommended intervention:
    - Job: "Rediscover Value" (re-engagement flow)
    - CSM outreach
    - Special offer or incentive

Expansion Opportunity Cohorts​

Expansion Ready (n=67):
  Behaviors:
    - Hitting plan limits regularly
    - Exploring premium features
    - High engagement (daily active)

  Predicted upgrade: 68% likely within 60 days

  Recommended intervention:
    - Job: "Unlock Premium Features"
    - Trial premium tier for 14 days
    - Highlight value of upgrade

Cohort-Specific Analytics​

View metrics by cohort:

Feature Usage by Cohort​

Advanced Filters:
  Power Users: 89% adoption
  Regular Users: 34% adoption
  Stuck Users: 3% adoption

Insight: Power users love it. Stuck users don't know it exists.
Action: Create Job targeting Stuck/Regular users.

Workflow Efficiency by Cohort​

Export Data Workflow:
  Power Users: 92% efficiency (know shortcuts)
  Regular Users: 68% efficiency (take longer path)
  Stuck Users: 41% efficiency (lots of backtracking)

Action: Teach regular users the shortcuts power users use.

Personalizing with Cohorts​

Use cohorts to personalize Rise interventions:

Different Guidance Styles​

For Explorers:

Style: Minimal guidance
Approach: "Hey, check out this new feature!"
Frequency: Low (they discover on their own)

For Task-Oriented Users:

Style: Efficiency tips
Approach: "Save time with this shortcut"
Frequency: Moderate, contextual

For Stuck Users:

Style: Step-by-step guidance
Approach: "Let me walk you through this"
Frequency: High, proactive

Cohort-Specific Jobs​

Job: "Master Keyboard Shortcuts"
  Target: Power Users + Explorers (already efficient, want more)

Job: "Discover Core Features"
  Target: Stuck Users + Late Discoverers (need basics)

Job: "Automate Repetitive Tasks"
  Target: Task-Oriented Users (efficiency-minded)

Importing External Cohorts​

Import cohorts from other tools:

From Mixpanel​

Import Mixpanel Cohort: "Trial Users - High Engagement"
  Sync: Daily
  Use in Rise for: Premium feature discovery Jobs

From Amplitude​

Import Amplitude Cohort: "At-Risk Users"
  Sync: Real-time
  Use in Rise for: Re-engagement Jobs

Learn more about integrations →

Creating Custom Cohorts​

Define your own behavioral cohorts:

Custom Cohort: "Report Power Users"

Criteria:
  - Creates 5+ reports per week
  - Uses 3+ report types
  - Active for 30+ days

Purpose: Target for advanced reporting features
Size: 234 users (18%)

Next Steps​