Data Subject Request API Version 1 and 2
Data Subject Request API Version 3
Platform API Overview
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Apps
Audiences
Calculated Attributes
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Users
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Warehouse Sync API Overview
Warehouse Sync API Tutorial
Warehouse Sync API Reference
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Warehouse Sync SQL Reference
Warehouse Sync Troubleshooting Guide
ComposeID
Warehouse Sync API v2 Migration
Bulk Profile Deletion API Reference
Calculated Attributes Seeding API
Custom Access Roles API
Data Planning API
Group Identity API Reference
Pixel Service
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Events API
mParticle JSON Schema Reference
IDSync
AMP SDK
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API Reference
Upgrade to Version 5
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Web
Android
iOS
Cordova Plugin
Identity
Initialization
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Kits
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Troubleshooting iOS SDK
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iOS 14 Guide
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iOS 18 FAQ
API Reference
Upgrade to Version 7
Getting Started
Identity
Upload Frequency
Getting Started
Opt Out
Initialize the SDK
Event Tracking
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Identity
Location Tracking
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Getting Started
Identity
Initialization
Configuration
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IDSync
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Location Tracking
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Kits
Application State and Session Management
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Persistence
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Web SDK via Google Tag Manager
Preventing Blocked HTTP Traffic with CNAME
Facebook Instant Articles
Troubleshooting the Web SDK
Browser Compatibility
Linting Data Plans
API Reference
Upgrade to Version 2 of the SDK
Web
Alexa
Overview
Step 1. Create an input
Step 2. Verify your input
Step 3. Set up your output
Step 4. Create a connection
Step 5. Verify your connection
Step 6. Track events
Step 7. Track user data
Step 8. Create a data plan
Step 9. Test your local app
Overview
Step 1. Create an input
Step 2. Verify your input
Step 3. Set up your output
Step 4. Create a connection
Step 5. Verify your connection
Step 6. Track events
Step 7. Track user data
Step 8. Create a data plan
Step 1. Create an input
Step 2. Create an output
Step 3. Verify output
Node SDK
Go SDK
Python SDK
Ruby SDK
Java SDK
Introduction
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Firehose Java SDK
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Compose ID
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Glossary
Migrate from Segment to mParticle
Migrate from Segment to Client-side mParticle
Migrate from Segment to Server-side mParticle
Segment-to-mParticle Migration Reference
Rules Developer Guide
API Credential Management
The Developer's Guided Journey to mParticle
Create an Input
Start capturing data
Connect an Event Output
Create an Audience
Connect an Audience Output
Transform and Enhance Your Data
The new mParticle Experience
The Overview Map
Introduction
Data Retention
Connections
Activity
Live Stream
Data Filter
Rules
Tiered Events
mParticle Users and Roles
Analytics Free Trial
Troubleshooting mParticle
Usage metering for value-based pricing (VBP)
Introduction
Sync and Activate Analytics User Segments in mParticle
User Segment Activation
Welcome Page Announcements
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Analytics Data Manager Overview
Events
Event Properties
User Properties
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UTM Guide
Data Dictionary
Query Builder Overview
Modify Filters With And/Or Clauses
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Query Notes
Filter Where Clauses
Event vs. User Properties
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Apply All for Filter Where Clauses
Date Range and Time Settings Overview
Understanding the Screen View Event
Analyses Introduction
Getting Started
Visualization Options
For Clauses
Date Range and Time Settings
Calculator
Numerical Settings
Assisted Analysis
Properties Explorer
Frequency in Segmentation
Trends in Segmentation
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Cumulative vs. Non-Cumulative Analysis in Segmentation
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Save Your Segmentation Analysis
Export Results in Segmentation
Explore Users from Segmentation
Getting Started with Funnels
Group By Settings
Conversion Window
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Visualization Options
Interpreting a Funnel Analysis
Group By
Filters
Conversion over Time
Conversion Order
Trends
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Analyze as Cohort from Funnel
Save a Funnel Analysis
Explore Users from a Funnel
Export Results from a Funnel
Saved Analyses
Manage Analyses in Dashboards
Dashboards––Getting Started
Manage Dashboards
Organize Dashboards
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Favorites
Time and Interval Settings in Dashboards
Query Notes in Dashboards
User Aliasing
The Demo Environment
Keyboard Shortcuts
Analytics for Marketers
Analytics for Product Managers
Compare Conversion Across Acquisition Sources
Analyze Product Feature Usage
Identify Points of User Friction
Time-based Subscription Analysis
Dashboard Tips and Tricks
Understand Product Stickiness
Optimize User Flow with A/B Testing
User Segments
IDSync Overview
Use Cases for IDSync
Components of IDSync
Store and Organize User Data
Identify Users
Default IDSync Configuration
Profile Conversion Strategy
Profile Link Strategy
Profile Isolation Strategy
Best Match Strategy
Aliasing
Overview
Create and Manage Group Definitions
Introduction
Catalog
Live Stream
Data Plans
Blocked Data Backfill Guide
Predictive Audiences Overview
Using Predictive Audiences
Predictive Attributes Overview
Create Predictive Attributes
Assess and Troubleshoot Predictions
Use Predictive Attributes in Campaigns
Introduction
Profiles
Warehouse Sync
Data Privacy Controls
Data Subject Requests
Default Service Limits
Feeds
Cross-Account Audience Sharing
Approved Sub-Processors
Import Data with CSV Files
CSV File Reference
Glossary
Video Index
Single Sign-On (SSO)
Setup Examples
Introduction
Introduction
Introduction
Rudderstack
Google Tag Manager
Segment
Advanced Data Warehouse Settings
AWS Kinesis (Snowplow)
AWS Redshift (Define Your Own Schema)
AWS S3 Integration (Define Your Own Schema)
AWS S3 (Snowplow Schema)
BigQuery (Snowplow Schema)
BigQuery Firebase Schema
BigQuery (Define Your Own Schema)
GCP BigQuery Export
Snowplow Schema Overview
Snowflake (Snowplow Schema)
Snowflake (Define Your Own Schema)
Aliasing
Group Identity allows you to group together users by a shared user attribute. For example, users who live in the same household can be associated by their household street address, or users who share the same account for a streaming service can be associated by their shared account ID.
A group identity can be given group attributes, which are user attributes you select from your data catalog that are given to all group members.
For example, imagine an online streaming service where each account can have multiple users who all log in using the same account ID, and you want to record on each user’s profile if they are a “premium subscriber”.
First, users who subscribe to the premium service should be given a user attribute called premium_subscriber
set to true
.
Next, create a new group definition using the account ID as the group ID and the user attribute premium_subscriber
as a group attribute. Group attributes are differentiated from normal user attributes by a prefix set to the value of the group ID name.
Every week, mParticle runs a “grouping job” where users who share the same account ID are grouped together. For every instance of a group, the account_id:premium_subscriber
group attribute is given to all group members if, and only if, one other member of the group already has the user attribute premium_subscriber
with the value of true
.
This results in two sets of groups: one set of groups that contain premium subscribers, and one set of groups that contain no premium subscribers.
After users have been added to a group, they will appear in the User Activity View when searching for the group ID or group attribute.
When you create a group definition, you select a user attribute that becomes the group ID. Any user attribute can be used as a group ID, as long as it exists in your data catalog.
Every Monday, on a weekly recurring basis, mParticle processes all profiles and groups together any profiles who share the same value for a user attribute that is a designated group ID.
For example, if you create a group definition with account_id
as the group ID, and three users all have the attribute account_id
with a value of 1234
, then all three users are added to a group that is identified by the group ID account_ID:1234
.
Group IDs must be unique hexadecimal or numerical user attributes within the following constraints:
a-z
and 0-9
When you create a group definition, you are defining a set of criteria that is used to create multiple group instances, one for each value of the group ID. Using the example above, if mParticle ingested data for two more users who each had the attribute account_id
with a value of 5678
, they would be added to a separate group identified by account_id:5678
.
A group attribute is a user attribute given to all members of a group. The value of a group attribute is the same for every profile in the group, but the value of a group attribute is calculated based on one of four possible aggregation functions.
When viewing a user profile, you can tell the difference between group attributes and other user attributes by a prefix equaling name of a group ID.
For example, all user profiles in a group with the group ID account_id:1234
and the group attribute premium_subscriber
will display the new attribute account_id:premium_subscriber
.
This naming convention prevents any conflicts between group attributes and user attributes. For example, a user may have a premium subscriber group attribute, showing that they are in a group with a premium subscriber, even though individually they are not marked as a premium subscriber.
Any user attribute that exists in your data catalog can be used as a group attribute.
The value of a group attribute is calculated by aggregating the values of each original user attribute in the group. The aggregation results from one of four functions that you specify when first creating a group attribute. These four aggregation functions are:
Aggregation logic | Supported data type | Description |
---|---|---|
boolean or |
boolean | If any instance of the attribute equals true , the group attribute is set to true . |
latest |
boolean, string, number, integer | The group attribute is set to the value of the most recently updated attribute in the group. |
sum |
non-negative number or integer | The group attribute is set to the sum of all attributes in the group. |
average |
non-negative number or integer | The group attribute is set to the average of all attributes in the group. |
Continue reading below for detailed descriptions and examples of each aggregation logic option.
Group members only inherit a boolean or
group attribute when another member has the same user attribute with a value of true
.
Only boolean user attributes can be used as boolean or
group attributes.
For example, imagine a group with a source attribute has_dog
. Let’s say this group contains three users and none of them had a dog when they joined the group, so each user’s instance of has_dog
equals false
.
If a new user with the has_dog
attribute set to true
joins the group, then the other members will all have their has_dog group
attribute set to true
the next time the grouping job runs.
The value of a latest
group attribute is set to the most recently updated instance of the user attribute in the group. All members of a group with a latest
group attribute inherit that attribute upon joining the group.
User attributes of any data type can be used as latest
group attributes.
For example, imagine a household group with two members: John and Jane. The group has a latest
group attribute called street_address
which is set to 1234 Main Street
.
Next, a new user, Cindy, joins John and Jane’s household group. Cindy’s profile would inherit the group attribute street_address:1234 Main Street
.
The value of a sum
group attribute is equal to the sum of all instances of the user attribute in the group.
Only non-negative numbers or integers can be used as sum
group attributes.
The value of an average
group attribute is equal to the average of all instances of the user attribute in the group.
Only non-negative numbers or integers can be used as average
group attributes.
In addition to the mParticle UI, you can also create and manage group definitions programmatically using the Group Identity API. For more information, see the Group Identity API reference.
Group ID limits:
a-z
and 0-9
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