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Revenue

Enterprise

This feature is part of Countly Enterprise. To get access, contact sales or compare versions. Existing customers can reach the support portal with questions.

Overview​

The Revenue feature tracks and analyzes in-app purchase and monetization metrics. Monitor paying users, revenue trends, and purchase patterns across time periods. Segment revenue data by in-app events and calculate paying user metrics automatically.

Key Features​

  • Paying User Tracking: Identify and track users who have made purchases
  • Revenue Metrics: Calculate total revenue, revenue per user, purchase frequency
  • Event-Based Revenue: Associate revenue with specific in-app purchase events
  • Time-Based Analytics: Revenue trends by day, week, month, year
  • User Segmentation: Filter revenue by user properties and cohorts
  • Dashboard Integration: Revenue widgets show key metrics
  • Period Analysis: Compare revenue across custom date ranges
  • Purchase Frequency: Track repeat purchase behavior

Configuration​

Revenue feature configuration includes:

SettingTypeDescription
iap_eventsArrayList of event names representing in-app purchases

The iap_events configuration specifies which events should be counted as purchases. These events typically have sum values representing purchase amounts.

Revenue Tracking​

Revenue is tracked through:

  1. User Properties (stored in app_users collection):

    • tp (total purchases amount): Cumulative revenue from user
    • tpc (total purchase count): Total number of purchases
    • lp (last purchase timestamp): Time of most recent purchase
    • lpa (last purchase amount): Amount of most recent purchase
    • purchased (purchased status): "yes" if user has made purchases

  2. Users Aggregation (stored in users collection):

    • d.p or d.{period}.p: Count of paying users in period
    • Tracked at day, week, month, and year granularities

API Endpoints​

Read Endpoints​

Dashboard Integration​

  • Revenue widgets automatically calculate and display metrics

Export Data​

Revenue data can be exported for external analysis through:

  • Revenue time-series data (paying users, totals)
  • Custom event filtering
  • Period-based analysis

Authentication Requirements​

All revenue endpoints require:

  • Feature Permission: revenue feature enabled for user
  • App Access: User must have read access to the specific app
  • Method: GET or POST (both accepted)

Common Use Cases​

  1. Revenue Dashboard: Display revenue trends and metrics
  2. Period Comparison: Compare revenue across time periods
  3. Event Analysis: Analyze revenue from specific purchase events
  4. Paying User Tracking: Monitor active paying user count
  5. Revenue Forecasting: Predict future revenue based on trends
  6. Custom Reports: Export revenue data for business intelligence

Best Practices​

  • Event Naming: Use clear event names for purchase events (e.g., purchase, in_app_purchase)
  • Amount Tracking: Always include sum value for revenue events
  • Timestamp Recording: Ensure accurate timestamps on purchase events
  • Regular Analysis: Monitor revenue trends regularly for insights
  • Segmentation: Analyze revenue by user properties for better targeting
  • Data Validation: Verify IAP events are being tracked correctly

Performance Considerations​

  • Revenue calculations performed asynchronously
  • Large date ranges may require additional processing time
  • Paying user aggregation cached for dashboard efficiency
  • Custom queries filtered through drill database
Implementation details

Database Collections

  • Collection: countly.app_users{app_id}
    • Stores user purchase data (tp, tpc, lp, lpa, purchased)
  • Collection: countly.users
    • Aggregated paying user counts by time period
  • Collection: countly_drill.drill_events
    • Source for revenue event data
  • Collection: countly.apps
    • Stores IAP event configuration