Migrating to the Amazon Pinpoint API - Amazon Mobile Analytics
Migrating Off of the Mobile Analytics Querying API

Migrating to the Amazon Pinpoint API

After April 30, 2018, the Mobile Analytics REST API will automatically redirect requests to the Amazon Pinpoint API. Events that your application reports to Mobile Analytics are automatically sent to Amazon Pinpoint.

Migrating Off of the Mobile Analytics Querying API

After April 30, 2018, the Mobile Analytics Querying API will no longer be supported. Amazon Pinpoint offers an event streams feature that lets you stream events data in real time to Amazon Kinesis Data Streams or Amazon Data Firehose. Through a Firehose delivery stream, this data can be delivered to Amazon Redshift, Amazon S3, or Amazon OpenSearch Service. You can then access the data and use it to calculate the KPIs that are provided by the Querying API.

KPI Metrics Based on Event Data

Use the following table for guidance when you're calculating KPI metrics that are based on the event data that's streamed by Amazon Pinpoint.

Metric Mobile Analytics KPI How to calculate with Amazon Pinpoint events
Lifetime user count

/kpis/new-users/lifetime-count

Obtain the total unique number of client_id values.

Daily active users (DAU)

/kpis/dau/count

For each day, obtain the count of events that have an event_type of _session.start and a unique value for client_id.

Monthly active users (MAU)

/kpis/mau/count

For each 30-day interval, obtain the count of events that have an event_type of _session.start and a unique value for client_id.
New users

/kpis/new-users/count

For each day, obtain the count of events that have an event_type of _session.start and a value for client_id that wasn't reported previously.
Session count

/kpis/sessions/count

For each day, obtain the count of events that have an event_type of _session.start.
Daily revenue

/kpis/daily-revenue/sum

For each day, obtain the events that have an event_type of _monetization.purchase, and compute the sum of the values that are reported for the _item_price attribute.

Paying daily active users (PDAU)

/kpis/pdau/count

For each day, calculate the DAU metric, but include only those devices that have made one or more purchases in the past. A purchase is indicated by an event_type of _monetization.purchase.
Paying monthly active users (PMAU)

/kpis/pmau/count

For each day, calculate the MAU metric, but include only those devices that have made one or more purchases in the past. A purchase is indicated by an event_type of _monetization.purchase.
Custom events count

/events/custom-event-name/count

For each day, obtain the count of events that have an event_type that matches the name you that you assigned to the custom event. Calculate max, min, and sum values by parsing the values that are assigned to metrics and custom attributes.

Week 1, week 2, and week 3 retention

/kpis/week-1-retention/count

/kpis/week-2-retention/count

/kpis/week-3-retention/count

To calculate the retention for a specific day:

  1. Obtain the original active users for that day. Active users are indicated by events that have an event_type of _session.start and a unique value for client_id.

  2. Obtain the subsequent active users from the weeks that followed the day that you're measuring.

  3. Compare the original active users with the subsequent active users. The retention equals the number of subsequent active users that match one of the original active users. Identify matches by comparing client_id values.

Day 1, day 3, day 5, and day 7 retention

/kpis/day-1-retention/count

/kpis/day-3-retention/count

/kpis/day-5-retention/count

/kpis/day-7-retention/count

To calculate the retention for a specific day:

  1. Obtain the original active users for that day. Active users are indicated by events that have an event_type of _session.start and a unique value for client_id.

  2. Obtain the subsequent active users from the days that followed the day that you're measuring.

  3. Compare the original active users with the subsequent active users. The retention equals the number of subsequent active users that match one of the original active users. Identify matches by comparing client_id values.

Example Events

The following examples demonstrate the JSON attributes that you can parse when you query the data store that holds your event data.

Example Session Start Event
{
  "application_key": "a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6",
  "account_id": "111122223333",
  "event_type": "_session.start",
  "timestamp": 1517537724812,
  "arrival_timestamp": 1517537778048,
  "unique_id": "A1B2C3D4-E5F6-G7H8-I9J0-K1L2M3N4O5P6",
  "cognito_id": "us-east-1:a1b2c3d4-e5f6-g7h8-i9j0-k1l2m3n4o5p6",
  "cognito_identity_pool_id": "us-east-1:a1b2c3d4-e5f6-g7h8-i9j0-k1l2m3n4o5p6",
  "platform": "iOS",
  "model": "iPhone",
  "platform_version": "11.2.2",
  "make": "apple",
  "locale": "en_US",
  "sdk_version": "2.4.16",
  "sdk_name": "aws-sdk-iOS",
  "app_package_name": "com.example.package",
  "app_version_name": "11.02.0",
  "app_version_code": "28198.0",
  "user_agent": "aws-sdk-iOS/2.4.16 iOS/11.2.2 en_US",
  "app_title": "ExampleApp",
  "attributes": {
    "_clientContext": <client context>,
    "_session.id": "70cf4faf-BAAABA8A-20180202-021524806",
    "_session.startTime": "1517537724811"
  },
  "metrics": {}
}
Example Monetization Event
{
  "application_key": "a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6",
  "account_id": "111122223333",
  "event_type": "_monetization.purchase",
  "timestamp": 1517537662978,
  "arrival_timestamp": 1517537778020,
  "unique_id": "A1B2C3D4-E5F6-G7H8-I9J0-K1L2M3N4O5P6",
  "cognito_id": "us-east-1:a1b2c3d4-e5f6-g7h8-i9j0-k1l2m3n4o5p6",
  "cognito_identity_pool_id": "us-east-1:a1b2c3d4-e5f6-g7h8-i9j0-k1l2m3n4o5p6",
  "platform": "iOS",
  "model": "iPhone",
  "platform_version": "11.2.5",
  "make": "apple",
  "locale": "zh_CN",
  "sdk_version": "2.6.5",
  "sdk_name": "aws-sdk-iOS",
  "app_package_name": "com.example.package",
  "app_version_name": "5.03",
  "app_version_code": "5.03.1",
  "user_agent": "aws-sdk-iOS/2.6.5 iOS/11.2.5 zh_CN",
  "app_title": "ExampleApp",
  "attributes": {
    "_currency": "CNY",
    "_product_id": "product_id",
    "_transaction_id": "123456789012345",
    "_clientContext": <client context>,
    "_session.duration": "152021",
    "_session.id": "e79da94c-9BD1DF63-20180202-021345277",
    "_session.startTime": "1517537625277",
    "_item_price_formatted": "¥128.00"
  },
  "metrics": {
    "_item_price": 76.5811965811966,
    "_quantity": 1.0
  }
}

In these examples, <client context> is the x-amz-client-context request header that you provide when you submit a PutEvents request to the Mobile Analytics REST API. For more information, see PutEvents.