Analytics Tools

Product analytics tools for data-driven feature development

MCP Analytics Tools

The Appizer MCP server provides powerful analytics tools designed for product managers to make data-driven decisions about feature development, user engagement, and retention strategies.

Overview

These tools enable you to:

  • Analyze user retention and cohort behavior
  • Build conversion funnels to identify drop-off points
  • Track feature adoption rates
  • Calculate engagement metrics (DAU, WAU, MAU)
  • Understand event trends and patterns
  • Identify where users spend the most time

Query Events (Enhanced)

Query events with advanced filtering, date ranges, and aggregations.

Example Questions

Basic Queries:

  • "Show me all purchase_completed events in the last 30 days"
  • "What events happened between January 1 and January 15?"
  • "Find all events for user ID abc123"

Time-Based Analysis:

  • "Group events by day for the last week"
  • "Show me hourly event counts for yesterday"
  • "What's the weekly trend of app_open events?"

Property Filtering:

  • "Find all purchase events where amount is greater than 100"
  • "Show me video_watched events for premium users"
  • "What events have platform set to 'ios'?"

Advanced Queries:

  • "Show me all checkout_started events in December, grouped by day"
  • "Find events where properties contain category='electronics' in the last 90 days"
  • "List all error events sorted by oldest first"

Parameters

{
  "app_id": "your-app-id",
  "event_name": "purchase_completed",
  "start_date": "2026-01-01T00:00:00Z",
  "end_date": "2026-01-31T23:59:59Z",
  "group_by": "day",
  "properties_filter": { "amount": 100 },
  "order_by": "created_at",
  "order_direction": "desc",
  "limit": 100,
  "offset": 0
}

Analyze Retention

Calculate user retention rates for cohort analysis.

Example Questions

Basic Retention:

  • "What's the 7-day retention rate for users who signed up in January?"
  • "Show me retention rates for the last 3 months"
  • "What percentage of users come back after 30 days?"

Cohort Analysis:

  • "Compare weekly cohort retention for Q4 2025"
  • "Show me daily cohort retention for the last month"
  • "What's the retention trend for monthly cohorts?"

Event-Based Retention:

  • "Calculate retention for users who completed onboarding"
  • "What's the retention rate for users who made a purchase?"
  • "Show retention for users who enabled notifications"

Segmented Retention:

  • "Compare retention between iOS and Android users"
  • "What's the retention rate for premium vs free users?"
  • "Show retention for users from different acquisition channels"

Use Cases

  1. Product Health Monitoring: Track if product changes improve retention
  2. Onboarding Optimization: Measure impact of onboarding improvements
  3. Feature Impact: See how new features affect user retention
  4. Cohort Comparison: Identify which user groups have best retention
  5. Churn Prediction: Spot declining retention trends early

Parameters

{
  "app_id": "your-app-id",
  "cohort_period": "week",
  "retention_periods": [1, 7, 14, 30],
  "start_date": "2026-01-01T00:00:00Z",
  "end_date": "2026-01-31T23:59:59Z",
  "cohort_event": "signup_completed",
  "retention_event": "app_opened"
}

Analyze Funnel

Build conversion funnels from event sequences to identify drop-off points.

Example Questions

Conversion Analysis:

  • "What's the conversion rate from signup to first purchase?"
  • "Show me the onboarding funnel for the last month"
  • "What percentage of users complete the checkout process?"

Drop-off Identification:

  • "Where do most users drop off in the signup flow?"
  • "What step in the checkout process has the biggest drop-off?"
  • "Which onboarding step loses the most users?"

Time Analysis:

  • "How long does it take users to complete the purchase funnel?"
  • "What's the average time between signup and first purchase?"
  • "How quickly do users move through onboarding?"

Segmented Funnels:

  • "Compare funnel performance between mobile and web users"
  • "What's the conversion rate for iOS vs Android?"
  • "Show me the funnel for users from paid ads"

Use Cases

  1. Conversion Optimization: Identify and fix bottlenecks in user flows
  2. A/B Testing: Compare funnel performance across variants
  3. Onboarding Analysis: Optimize user activation flows
  4. Checkout Optimization: Reduce cart abandonment
  5. Feature Adoption: Track multi-step feature usage

Parameters

{
  "app_id": "your-app-id",
  "events": ["signup_started", "email_verified", "profile_completed", "first_purchase"],
  "start_date": "2026-01-01T00:00:00Z",
  "end_date": "2026-01-31T23:59:59Z",
  "time_window": "7 days",
  "strict_order": true
}

Get Event Analytics

Comprehensive analytics for specific events including trends, geographic breakdowns, device analytics, peak times, and combined dimensional analysis.

Example Questions

Trend Analysis:

  • "Show me purchase trends for the last 3 months"
  • "Is the video_watched event growing or declining?"
  • "What's the trend for error events over time?"

Geographic Analysis:

  • "Show me purchase trends by country for the last 3 months"
  • "What countries have the most user engagement?"
  • "Break down signups by geographic region"
  • "Compare event frequency across different cities"

Device & Platform Analysis:

  • "What are the peak hours for app_open events broken down by device type?"
  • "Show me browser usage trends over time"
  • "Compare mobile vs desktop engagement"
  • "What operating systems are most popular?"

Combined Analytics:

  • "Break down video_watched events by browser and OS combination"
  • "Show me country-device type combinations for purchases"
  • "What regions prefer which browsers?"
  • "Compare engagement across device types by country"

Peak Time Analysis:

  • "What are the peak hours for app_open events?"
  • "When do most purchases happen?"
  • "What day of the week has the most activity?"

Property Breakdown:

  • "Break down video_watched events by content category"
  • "What's the distribution of purchase amounts?"
  • "Show me error events grouped by error type"

Platform Analysis:

  • "What's the average purchase amount by platform?"
  • "Compare event frequency across iOS and Android"
  • "Show me feature usage by user plan type"

Use Cases

  1. Feature Monitoring: Track usage of specific features over time
  2. Performance Analysis: Monitor error rates and patterns
  3. Content Strategy: Understand what content users engage with
  4. Capacity Planning: Identify peak usage times for infrastructure
  5. Revenue Analysis: Track purchase patterns and amounts
  6. Geographic Strategy: Understand regional performance and optimize localization
  7. Device Optimization: Analyze device-specific usage patterns for better UX
  8. Marketing Campaigns: Measure campaign effectiveness by geographic region
  9. Platform Prioritization: Focus development on most-used platforms/browsers
  10. User Experience Analysis: Identify device-specific issues and opportunities

Parameters

{
  "app_id": "your-app-id",
  "event_name": "purchase_completed",
  "start_date": "2026-01-01T00:00:00Z",
  "end_date": "2026-03-31T23:59:59Z",
  "group_by": "week",
  "property_breakdown": "platform",
  "geographic_breakdown": true,
  "device_breakdown": true,
  "combined_breakdowns": ["country_device_type", "browser_os"],
  "percentiles": [50, 90, 95, 99]
}

Enhanced Parameters:

ParameterTypeDefaultDescription
geographic_breakdownbooleantrueInclude country, region, and city breakdowns
device_breakdownbooleantrueInclude device type, browser, and OS breakdowns
combined_breakdownsarray[]Combined breakdowns (country_device_type, browser_os, region_browser)

Enhanced group_by Options:

  • Time-based: hour, day, week, month
  • Geographic: country, region, city
  • Device: device_type, browser, os
  • Combined: country_device_type, browser_os, region_browser

Enhanced Response Structure

{
  "event_name": "purchase_completed",
  "total_occurrences": 5000,
  "unique_users": 1200,
  "unique_ips": 950,
  "unique_user_agents": 1100,
  
  "geographic_breakdown": {
    "country": {
      "US": { "count": 2000, "percentage": 0.40 },
      "UK": { "count": 800, "percentage": 0.16 },
      "Unknown": { "count": 200, "percentage": 0.04 }
    },
    "region": {
      "California": { "count": 600, "percentage": 0.12 },
      "Unknown": { "count": 300, "percentage": 0.06 }
    },
    "city": {
      "San Francisco": { "count": 200, "percentage": 0.04 },
      "Unknown": { "count": 400, "percentage": 0.08 }
    }
  },
  
  "device_breakdown": {
    "device_type": {
      "desktop": { "count": 3000, "percentage": 0.60 },
      "mobile": { "count": 1800, "percentage": 0.36 },
      "Unknown": { "count": 200, "percentage": 0.04 }
    },
    "browser": {
      "Chrome": { "count": 2500, "percentage": 0.50 },
      "Safari": { "count": 1500, "percentage": 0.30 },
      "Unknown": { "count": 1000, "percentage": 0.20 }
    },
    "os": {
      "Windows": { "count": 2000, "percentage": 0.40 },
      "macOS": { "count": 1200, "percentage": 0.24 },
      "Unknown": { "count": 1800, "percentage": 0.36 }
    }
  },
  
  "combined_breakdowns": {
    "country_device_type": {
      "US_desktop": { "count": 1200, "percentage": 0.24 },
      "US_mobile": { "count": 600, "percentage": 0.12 },
      "Unknown_Unknown": { "count": 100, "percentage": 0.02 }
    },
    "browser_os": {
      "Chrome_Windows": { "count": 1000, "percentage": 0.20 },
      "Safari_macOS": { "count": 600, "percentage": 0.12 }
    }
  },
  
  "time_series": [...],
  "peak_times": {...},
  "trend": {...}
}

Get Engagement Metrics

Calculate user engagement scores including DAU, WAU, MAU, stickiness, and user segments.

Example Questions

Core Metrics:

  • "What's our DAU/MAU ratio for January?"
  • "How many daily active users do we have?"
  • "What's our stickiness score?"

User Segmentation:

  • "How many power users do we have?"
  • "What percentage of users are at risk of churning?"
  • "Show me the distribution of casual vs power users"

Session Analysis:

  • "What's the average session length?"
  • "How many sessions does the average user have?"
  • "What's the average number of events per session?"

Trend Analysis:

  • "Is engagement growing or declining week over week?"
  • "Show me engagement metrics for premium users"
  • "Compare engagement between different user segments"

Use Cases

  1. Product Health: Monitor overall app health and engagement
  2. Growth Tracking: Measure user base growth and activity
  3. User Segmentation: Identify power users and at-risk users
  4. Retention Strategy: Target re-engagement campaigns
  5. Feature Planning: Understand usage patterns for roadmap decisions

Parameters

{
  "app_id": "your-app-id",
  "start_date": "2026-01-01T00:00:00Z",
  "end_date": "2026-01-31T23:59:59Z",
  "engagement_definition": "3+ events per day",
  "user_segment": "plan_type"
}

Key Metrics Explained

DAU (Daily Active Users): Unique users who performed at least one event in a day

WAU (Weekly Active Users): Unique users active in the last 7 days

MAU (Monthly Active Users): Unique users active in the last 30 days

Stickiness (DAU/MAU): Percentage of monthly users who are active daily. Higher is better.

  • 20%+ is excellent for most apps
  • 10-20% is good
  • less than 10% indicates low engagement

Power Users: Top 10% most active users by event count

At-Risk Users: Users who haven't been active in 14+ days

Analyze Feature Adoption

Track adoption of specific features/events over time.

Example Questions

Adoption Rates:

  • "What's the adoption rate for our new sharing feature?"
  • "How many users have tried dark mode?"
  • "What percentage of users use the premium features?"

Time to Adoption:

  • "How long does it take users to try the premium features?"
  • "What's the average time from signup to first share?"
  • "How quickly do users discover new features?"

Repeat Usage:

  • "What percentage of users who try the feature use it again?"
  • "How many one-time vs repeat users do we have?"
  • "What's the retention rate for feature users?"

Segment Comparison:

  • "Compare feature adoption between iOS and Android"
  • "Which user segment adopts new features fastest?"
  • "Do premium users adopt features faster than free users?"

Use Cases

  1. Feature Validation: Determine if new features are being adopted
  2. Onboarding Optimization: Improve feature discovery
  3. Product Roadmap: Prioritize features based on adoption
  4. User Education: Identify features that need better promotion
  5. A/B Testing: Compare adoption across different rollout strategies

Parameters

{
  "app_id": "your-app-id",
  "feature_events": ["dark_mode_enabled", "dark_mode_settings_viewed"],
  "start_date": "2026-01-01T00:00:00Z",
  "end_date": "2026-01-31T23:59:59Z",
  "user_segment": "platform",
  "cohort_comparison": true
}

Common Product Manager Workflows

1. Weekly Product Review

"What's our DAU/MAU ratio for the last week?"
"Show me the top 10 events by volume"
"What's the retention rate for last week's cohort?"
"Are there any declining trends in key events?"

2. Feature Launch Analysis

"What's the adoption rate for [new feature] since launch?"
"How long does it take users to discover [new feature]?"
"Show me the funnel from feature discovery to first use"
"Compare adoption between different user segments"

3. Onboarding Optimization

"Show me the signup to activation funnel"
"What's the drop-off rate at each onboarding step?"
"What's the 7-day retention for users who completed onboarding?"
"How long does the average user take to complete onboarding?"

4. Retention Deep Dive

"Show me monthly cohort retention for the last 6 months"
"What events correlate with better retention?"
"Compare retention between power users and casual users"
"What's the retention rate for users who use [key feature]?"

5. Conversion Analysis

"What's the free to paid conversion rate?"
"Show me the purchase funnel from browse to checkout"
"Where do users drop off in the payment flow?"
"What's the average time from signup to first purchase?"

6. Engagement Monitoring

"What are the peak usage hours for our app?"
"Show me session length trends over time"
"How many power users do we have this month?"
"What percentage of users are at risk of churning?"

Best Practices

Date Ranges

  • Use specific date ranges for accurate analysis
  • Limit queries to 1 year maximum for performance
  • Use ISO 8601 format: 2026-01-01T00:00:00Z

Event Selection

  • Focus on key business events (purchases, signups, feature usage)
  • Use consistent event naming conventions
  • Track both success and failure events

Cohort Analysis

  • Weekly cohorts are good for most apps
  • Daily cohorts for high-frequency apps
  • Monthly cohorts for long sales cycles

Funnel Design

  • Keep funnels to 3-5 steps for clarity
  • Use strict_order for sequential flows
  • Set appropriate time_windows (7 days for most funnels)

Segmentation

  • Segment by platform, plan type, acquisition channel
  • Compare segments to identify opportunities
  • Focus on actionable segments

Performance Tips

  1. Use date ranges: Always specify start_date and end_date to limit data scanned
  2. Limit results: Use appropriate limit values (default 100, max 1000)
  3. Cache results: Results are cached for 15 minutes for repeated queries
  4. Aggregate when possible: Use group_by for time-series analysis
  5. Sample large datasets: For exploratory analysis, use smaller date ranges first

Next Steps

Support

Need help with analytics queries? Contact us at support@appizer.com or check our GitHub discussions.

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