Data Subject Request API Version 1 and 2
Data Subject Request API Version 3
Platform API Overview
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Getting Started
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Upgrade to Version 2 of the SDK
Getting Started
Identity
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
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Rules Developer Guide
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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
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Apply All for Filter Where Clauses
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Understanding the Screen View Event
Analyses Introduction
Getting Started
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For Clauses
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Group By
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Manage Analyses in Dashboards
Dashboards––Getting Started
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IDSync Overview
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Aliasing
Overview
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Create Predictive Attributes
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Predictive Audiences Overview
Using Predictive Audiences
Introduction
Profiles
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Data Subject Requests
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CSV File Reference
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Video Index
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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
Snowflake (Snowplow Schema)
Snowplow Schema Overview
Snowflake (Define Your Own Schema)
Aliasing
Cohort analyses give you the ability to toggle between different modes of analysis that measure user behavior in different ways.
To toggle between the First Time and Recurring cohort analysis modes, click on Recurring in the Query Builder:
In queries that use a generational breakout, such as hour, day, week, or month of the event, the setting for Only Complete/Include Incomplete controls how the cohort visualization accounts for periods with incomplete data.
If the query is set to Only Complete, only cells where all users have had a chance to complete the target behavior in the analysis will be shown.
For example, consider a query showing users who first performed the event Blog View and returned to perform the event Subscribed, grouped by Week of Blog View.
With the visualization set to show Only Complete, results from this week will not be displayed. This is because users who entered the analysis by Blog View in the week have not yet had a full week to be able to complete Subscribed and show up in the completed analysis.
If the visualization is set to Include Incomplete, an extra series of data will be displayed in the cohort. These numbers will typically be lower than the completed figures, because there has not yet been enough time to capture the full extent of users who are completing the parameters of the query.
By utilizing the Only Complete/Include Incomplete setting, you can customize your results so that you’re viewing data as it comes in, or so that you filter incomplete data from your analysis.
The Non-Cumulative and Cumulative cohort analysis modes determine how your cohort data is analyzed over time. To toggle between the non-cumulative and cumulative cohort analysis modes, click on Non-Cumulative in the menu bar, just below the Query Builder.
Filter a query to include or exclude users who meet a certain condition. You can filter on event properties, user properties, or user segments. There is no limit to the number of filters you can apply to a single query row.
In the following example, PetBox wants to exclude any users who downloaded their app on Android. Therefore, the user selects the filter where function, and creates a filter where device type is not equal to Android.
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