AppLovin Cohort API integration plan

Last updated 10/03/2026

tip

Note that data generated by third-party data integration counts toward the cluster's data consumption

Summary

Interface overview

Interface	Type	Granularity	Attribution	Cost	Revenue	Impressions	Clicks	Conversions
Cohort API	API	Aggregated data			✅	✅

AppLovin's Cohort API returns day-N-after-install data similar to that of the Retention Analysis model, including monetization, impression, and other data for day N after install. For details, see the Cohort API documentation.

Integration process

1. Log in to the AppLovin dashboard and get the Report Key
2. Log in to the AE backend, go to the Third-party Integration module, add an AppLovin Cohort API plan, and complete the related configuration
3. Check whether the AE system receives the data successfully, and build reports

1. Get the Report Key

To connect AppLovin Cohort API data, you need to get the Report Key first. Log in to the AppLovin dashboard and get the Report Key under Account > Key. Keep this key safe

2. Plan configuration

After you get the Report Key, log in to the AE system and configure the new plan in the Third-party Integration module. The image below shows the configuration page of AppLovin Cohort API. Follow this section to create the plan:

2.1 Authorization information configuration

Click the Configure authorization information button under Authorization Information, and enter the information you obtained during authorization in the pop-up

API Key is the Report Key you just obtained. By default, you cannot enter the API Key directly in the pop-up. Click the edit icon on the right first, and then enter it.

2.2 Sync Schedule

In the Sync Schedule module, you can set the policy for the AE system to pull AppLovin Cohort API data on a schedule. You can choose to pull data for a period of time at a specific time every day or every hour.

2.3 Event Data Configuration

After you turn on the Event Data Configuration switch, all data sent back is written to the event table. We recommend that you enable event data ingestion.

2.4 Configuration

In the Configuration module, you can control the detailed settings of data pulling, including the time aggregation granularity of the data, the metric fields and dimensions to pull, and the event name after ingestion.

The content of the configuration is a JSON, which you can customize as follows:

Module	Name	Description
sink_event	event_mapping	Event name after ingestion; customizable
source	report_types	Report type of the data to pull. Because data differs greatly across report types, you can enter only one report type. If you enter more than one, only the first report type in the list is pulled
	metrics	Metrics in the data; list type. Different report types support different metrics, so pay attention when filling it in
	group_by	Grouping dimensions in the data; list type. Different report types support different group_by values, so pay attention when filling it in

2.4.1 revenue report

• Template

The following is the revenue report template. To pull the revenue report, copy the following JSON, paste it into the Configuration module, and customize it:

{
  "sink_event": {
    "event_mapping": {
      "revenue": "applovin_revenue_cohort_data",
      "session": "applovin_session_cohort_data",
      "imp": "applovin_imp_cohort_data"
    }
  },
  "source": {
    "group_by": [
      "day",
      "application",
      "package_name",
      "platform",
      "country"
    ],
    "report_types": [
      "revenue"
    ],
    "metrics": [
      "pub_revenue_0",
      "iap_pub_revenue_0",
      "ads_pub_revenue_0",
      "reward_pub_revenue_0",
      "inter_pub_revenue_0",
      "banner_pub_revenue_0",
      "mrec_pub_revenue_0",
      "rpi_0",
      "iap_rpi_0",
      "ads_rpi_0",
      "reward_rpi_0",
      "inter_rpi_0",
      "banner_rpi_0",
      "mrec_rpi_0"
    ]
  }
}

• Grouping dimensions

The following are the supported group-by dimensions. To adjust them, modify source.group_by

Field	Description	Sample value
day	Data date	2019/3/25
application	App name	My App
package_name	App package name or Bundle ID	com.my.test.app
platform	App platform. Valid values: android, fireos, ios	android
country	Two-letter Country Code	gb

• Metric fields

The following are the supported metric fields. To adjust them, modify source.metrics. Replace X with the day you need (valid values: 0, 1, 2, 3, 4, 5, 6, 7, 10, 14, 18, 21, 24, 27, 30, 45)

Field	Description	Sample value
pub_revenue_X	Total revenue on day X after install	39.35
iap_pub_revenue_X	In-app purchase revenue on day X after install	21.23
ads_pub_revenue_X	Ad revenue on day X after install	18.12
reward_pub_revenue_X	Rewarded video ad revenue on day X after install	9.12
inter_pub_revenue_X	Interstitial ad revenue on day X after install	6.1
banner_pub_revenue_X	Banner ad revenue on day X after install	2.54
mrec_pub_revenue_X	MREC (medium rectangle) ad revenue on day X after install	0.48
rpi_X	Total revenue per install on day X after install	0.15
iap_rpi_X	In-app purchase revenue per install on day X after install	0.08
ads_rpi_X	Ad revenue per install on day X after install	0.07
reward_rpi_X	Rewarded video ad revenue per install on day X after install	0.05
inter_rpi_X	Interstitial ad revenue per install on day X after install	0.04
banner_rpi_X	Banner ad revenue per install on day X after install	0.03
mrec_rpi_X	MREC (medium rectangle) ad revenue per install on day X after install	0.02

2.4.2 imp report

• Template

The following is the imp report template. To pull the imp report, copy the following JSON, paste it into the Configuration module, and customize it:

{
  "sink_event": {
    "event_mapping": {
      "revenue": "applovin_revenue_cohort_data",
      "session": "applovin_session_cohort_data",
      "imp": "applovin_imp_cohort_data"
    }
  },
  "source": {
    "group_by": [
      "day",
      "application",
      "package_name",
      "platform",
      "country"
    ],
    "report_types": [
      "imp"
    ],
    "metrics": [
      "installs",
      "user_count_0",
      "imp_0",
      "imp_per_user_0",
      "inter_imp_0",
      "inter_imp_per_user_0",
      "reward_imp_0",
      "reward_imp_per_user_0",
      "banner_imp_0",
      "banner_imp_per_user_0",
      "mrec_imp_0",
      "mrec_imp_per_user_0"      
    ]
  }
}

• Grouping dimensions

The following are the supported group-by dimensions. To adjust them, modify source.group_by

Field	Description	Sample value
day	Data date	2019/3/25
application	App name	My App
package_name	App package name or Bundle ID	com.my.test.app
platform	App platform. Valid values: android, fireos, ios	android
country	Two-letter Country Code	gb

• Metric fields

The following are the supported metric fields. To adjust them, modify source.metrics. Replace X with the day you need (valid values: 0, 1, 2, 3, 4, 5, 6, 7, 10, 14, 18, 21, 24, 27, 30, 45)

Field	Description	Sample value
installs	Number of new installs	2842
user_count_X	Total active users on day X after install	612
imp_X	Total ad impressions on day X after install	6120
imp_per_user_X	Average ad impressions per user on day X after install: imp_X / user_count_X	10
inter_imp_X	Interstitial ad impressions on day X after install	1224
inter_imp_per_user_X	Average interstitial ad impressions per user on day X after install: inter_imp_X / user_count_X	2
reward_imp_X	Rewarded video ad impressions on day X after install	1836
reward_imp_per_user_X	Average rewarded video ad impressions per user on day X after install: reward_imp_X / user_count_X	3
banner_imp_X	Banner ad impressions on day X after install	2448
banner_imp_per_user_X	Average Banner ad impressions per user on day X after install: banner_imp_X / user_count_X	4
mrec_imp_X	MREC (medium rectangle) impressions on day X after install	612
mrec_imp_per_user_X	Average MREC (medium rectangle) ad impressions per user on day X after install: mrec_imp_X / user_count_X	1

2.4.3 session report

• Template

The following is the session report template. To pull the session report, copy the following JSON, paste it into the Configuration module, and customize it:

{
  "sink_event": {
    "event_mapping": {
      "revenue": "applovin_revenue_cohort_data",
      "session": "applovin_session_cohort_data",
      "imp": "applovin_imp_cohort_data"
    }
  },
  "source": {
    "group_by": [
      "day",
      "application",
      "package_name",
      "platform",
      "country"
    ],
    "report_types": [
      "session"
    ],
    "metrics": [
      "installs",
      "daily_usage_0",
      "session_count_0",
      "user_count_0",
      "session_length_0",
      "retention_0"
    ]
  }
}

• Grouping dimensions

The following are the supported group-by dimensions. To adjust them, modify source.group_by

Field	Description	Sample value
day	Data date	2019/3/25
application	App name	My App
package_name	App package name or Bundle ID	com.my.test.app
platform	App platform. Valid values: android, fireos, ios	android
country	Two-letter Country Code	gb

• Metric fields

The following are the supported metric fields. To adjust them, modify source.metrics. Replace X with the day you need (valid values: 0, 1, 2, 3, 4, 5, 6, 7, 10, 14, 18, 21, 24, 27, 30, 45)

Field	Description	Sample value
installs	Number of new installs	2842
daily_usage_X	Average online duration per user (seconds) on day X after install	501
session_count_X	Total sessions on day X after install	1240
user_count_X	Total active users on day X after install	401
session_length_X	Average session duration per user (seconds) on day X after install: daily_usage_X / session_count_X	0.404
retention_X	Retention rate on day X after install: user_count_X / installs	0.141

2.5 Data ingestion rules

By default, we write the pulled data to the AE project as events:

• The day field in the data, that is, the date of the data, is set as the #event_time of the aggregated data

• The event names in the data are:

  • applovin_revenue_cohort_data
  • applovin_imp_cohort_data
  • applovin_session_cohort_data

• All other fields are stored

2.6 Standardized fields

The following event properties are standardized:

Original field	Standardized field	Description
package_name	te_ads_object.app_id	App ID
application	te_ads_object.app_name	App name
platform	te_ads_object.platform	Platform, such as Android or iOS
country	te_ads_object.country	Country or region code
Fixed value USD	te_ads_object.currency	Currency of the monetization revenue
installs	te_ads_object.installs	Installs