LoopKit.ai Documentation

AI Insight Examples ​

Real examples of the AI-powered insights LoopKit generates from your event data.

Overview ​

LoopKit's AI analyzes your event patterns and automatically generates actionable insights every week. Here are real examples of insights our customers receive, organized by insight type and business impact.

User Behavior Insights ​

Onboarding Optimization ​

🎯 Insight: "Users who complete the 'data import' step within 2 hours of signup are 5.2x more likely to become paid customers. Currently, 34% of users complete this step within 2 hours, while 66% take longer or never complete it."

📊 Supporting Data:

  • 2-hour completion group: 78% conversion rate
  • Longer completion group: 15% conversion rate
  • Never complete group: 3% conversion rate

💡 Recommendation: Add in-app prompts and email reminders for users who haven't imported data within 1 hour of signup.

Feature Discovery Patterns ​

🎯 Insight: "The 'API integration' feature has a discovery rate of only 23%, but users who find it show 92% higher engagement scores. Most users discover it through documentation (45%) rather than in-app navigation (18%)."

📊 Supporting Data:

  • Total feature discovery: 23%
  • Discovery via documentation: 45%
  • Discovery via navigation: 18%
  • Discovery via search: 37%
  • Engagement lift: +92%

💡 Recommendation: Add API integration prompts to the main dashboard and improve navigation visibility.

Session Behavior Analysis ​

🎯 Insight: "Users with sessions longer than 15 minutes are 3.1x more likely to upgrade within 30 days. These sessions typically involve using 3+ features, with 'report generation' being the strongest upgrade predictor."

📊 Supporting Data:

  • Long session upgrade rate: 31%
  • Short session upgrade rate: 10%
  • Average features used in long sessions: 3.8
  • Top upgrade predictor: report_generated (odds ratio: 4.2)

💡 Recommendation: Guide users toward multi-feature workflows, especially including report generation.

Conversion & Revenue Insights ​

Trial-to-Paid Patterns ​

🎯 Insight: "Users who invite team members during their trial convert at 67% vs. 18% for solo users. However, only 12% of trial users currently invite teammates, representing a significant opportunity."

📊 Supporting Data:

  • Team trial conversion: 67%
  • Solo trial conversion: 18%
  • Current team invitation rate: 12%
  • Potential revenue impact: +$47K monthly

💡 Recommendation: Add team invitation prompts on day 3 and day 7 of trials, with collaboration benefit messaging.

Pricing & Plan Insights ​

🎯 Insight: "Enterprise plan users generate 12x more API calls than Pro users but are only charged 3x the price. API usage could be a stronger pricing dimension than current seat-based model."

📊 Supporting Data:

  • Enterprise avg API calls/month: 120,000
  • Pro avg API calls/month: 10,000
  • Current price ratio: 3x
  • Usage ratio: 12x

💡 Recommendation: Consider usage-based pricing tiers or API call limits per plan.

Upgrade Trigger Analysis ​

🎯 Insight: "Plan upgrades are most likely to occur on Tuesdays (31% of upgrades) after users hit feature limits on Monday. The 'export limit reached' event has the highest upgrade conversion rate at 23%."

📊 Supporting Data:

  • Tuesday upgrade concentration: 31%
  • Monday limit hits: 68% of upgrade cohort
  • Export limit → upgrade rate: 23%
  • Storage limit → upgrade rate: 15%
  • User limit → upgrade rate: 19%

💡 Recommendation: Optimize upgrade prompts for Tuesday delivery and focus on export limit messaging.

Feature Performance Insights ​

Feature Adoption Patterns ​

🎯 Insight: "The new 'advanced filtering' feature has 89% satisfaction but only 34% adoption. Users who discover it through the tutorial have 3x higher usage than those who find it organically."

📊 Supporting Data:

  • Feature satisfaction: 89%
  • Feature adoption: 34%
  • Tutorial discovery usage: 4.2 times/week
  • Organic discovery usage: 1.4 times/week

💡 Recommendation: Add advanced filtering to the mandatory tutorial flow and create in-app tooltips.

Feature Correlation Analysis ​

🎯 Insight: "Users who use both 'custom dashboards' and 'scheduled reports' have 4.7x lower churn rates. These features are currently used together by only 8% of customers."

📊 Supporting Data:

  • Combined feature churn rate: 2.3%
  • Average churn rate: 10.8%
  • Current combined usage: 8%
  • Individual dashboard usage: 45%
  • Individual report usage: 23%

💡 Recommendation: Create workflows that connect dashboard creation to report scheduling.

User Segmentation Insights ​

Geographic Performance ​

🎯 Insight: "European users have 43% higher lifetime value than US users, primarily due to lower churn (4.2% vs 7.8% monthly). European users also use collaboration features 2.3x more frequently."

📊 Supporting Data:

  • EU LTV: $1,247
  • US LTV: $872
  • EU monthly churn: 4.2%
  • US monthly churn: 7.8%
  • EU collaboration usage: 2.3x higher

💡 Recommendation: Promote collaboration features more heavily to US users and study EU retention practices.

Device & Platform Insights ​

🎯 Insight: "Mobile users represent 28% of traffic but only 11% of conversions. However, mobile users who complete onboarding have comparable engagement to desktop users, suggesting mobile UX barriers in early funnel."

📊 Supporting Data:

  • Mobile traffic: 28%
  • Mobile conversions: 11%
  • Mobile post-onboarding engagement: 94% of desktop
  • Mobile onboarding completion: 34%
  • Desktop onboarding completion: 67%

💡 Recommendation: Optimize mobile onboarding flow, particularly the signup and initial setup steps.

Predictive Insights ​

Churn Prediction ​

🎯 Insight: "Users with declining login frequency (50%+ drop over 2 weeks) have an 83% probability of churning within 30 days. This pattern currently affects 127 users and predicts $31K in at-risk MRR."

📊 Supporting Data:

  • Prediction accuracy: 83%
  • At-risk users: 127
  • At-risk MRR: $31,000
  • Average churn prevention cost: $45/user

💡 Recommendation: Implement automated re-engagement campaigns for users showing this pattern.

Growth Opportunity Prediction ​

🎯 Insight: "Companies with 10-25 employees who use the 'team analytics' feature are 7.2x more likely to upgrade to Enterprise within 60 days. 34 current customers match this profile."

📊 Supporting Data:

  • Upgrade probability: 7.2x baseline
  • Qualifying customers: 34
  • Potential Enterprise upgrades: 23
  • Revenue opportunity: $18,400/month

💡 Recommendation: Create targeted Enterprise sales outreach for this segment with team analytics success stories.

Time-Based Insights ​

Seasonal Patterns ​

🎯 Insight: "B2B customer activity drops 34% during the last week of December but surges 67% in the first week of January. Plan this capacity difference and consider year-end campaign timing."

📊 Supporting Data:

  • Late December activity: -34%
  • Early January activity: +67%
  • Peak usage day: January 3rd
  • Lowest usage day: December 29th

💡 Recommendation: Schedule maintenance for late December and prepare infrastructure for January surge.

Weekly Usage Patterns ​

🎯 Insight: "Report generation peaks on Monday mornings (47% of weekly volume) as teams review weekend activity. API usage is highest on Wednesdays, suggesting mid-week analysis workflows."

📊 Supporting Data:

  • Monday morning reports: 47% of weekly volume
  • Peak API day: Wednesday (23% above average)
  • Lowest activity: Friday afternoon
  • Weekend usage: 8% of weekday levels

💡 Recommendation: Optimize system performance for Monday mornings and consider automated weekend data processing.

Integration & Technical Insights ​

API Usage Patterns ​

🎯 Insight: "Customers using webhooks have 2.8x higher engagement than those using polling APIs. Webhook users also upgrade plans 45% faster, suggesting real-time data access drives value realization."

📊 Supporting Data:

  • Webhook engagement: 2.8x higher
  • Webhook upgrade speed: 45% faster
  • Current webhook adoption: 23%
  • Webhook setup completion: 78%

💡 Recommendation: Promote webhook setup in onboarding and create webhook-first documentation.

Data Export Behavior ​

🎯 Insight: "Users who export data in the first 7 days have 3.4x higher retention at 30 days. CSV exports correlate with plan upgrades, while PDF exports correlate with churn."

📊 Supporting Data:

  • Early export retention: 3.4x higher
  • CSV export → upgrade correlation: +0.73
  • PDF export → churn correlation: +0.45
  • Export adoption: 41%

💡 Recommendation: Encourage CSV exports in onboarding and investigate PDF export use cases to address underlying issues.

How These Insights Drive Action ​

Implementation Examples ​

Insight: "Feature discovery through tutorials drives 3x higher usage"

Actions Taken:

  • Added feature to mandatory tutorial
  • Created interactive tooltips
  • Built progressive disclosure UI

Results:

  • Feature adoption: 34% → 67%
  • Feature satisfaction maintained: 89%
  • Overall engagement: +23%

Insight: "Team invitation during trial increases conversion 3.7x"

Actions Taken:

  • Added day-3 team invitation prompt
  • Created collaboration benefit messaging
  • Simplified invitation flow

Results:

  • Team invitation rate: 12% → 34%
  • Trial conversion: 18% → 29%
  • MRR growth: +$47K monthly

Getting Better Insights ​

Send Rich Context ​

javascript
// ❌ Basic event
LoopKit.track('feature_used');

// ✅ Rich context for better insights
LoopKit.track('report_generated', {
  report_type: 'user_analytics',
  data_range: '30d',
  export_format: 'csv',
  generation_time: 2.3,
  user_plan: 'pro',
  team_size: 12,
  first_time_user: false,
});

Track Complete Journeys ​

javascript
// Track the full user journey
LoopKit.track('feature_discovery_started');
LoopKit.track('tutorial_step_completed', { step: 'setup' });
LoopKit.track('feature_first_use', { success: true });
LoopKit.track('feature_mastery_achieved', { days_to_mastery: 5 });

Include Business Context ​

javascript
// Add business dimensions
LoopKit.track('subscription_upgraded', {
  from_plan: 'pro',
  to_plan: 'enterprise',
  revenue_impact: 500, // Monthly increase
  team_size: 25, // Company context
  industry: 'fintech', // Business context
  days_since_trial: 14, // Time context
});

Next Steps ​

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