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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Alexa
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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
Introduction
Outbound Integrations
Firehose Java SDK
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Data Hosting Locations
Compose ID
Glossary
Migrate from Segment to mParticle
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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
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Data Retention
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Analytics Free Trial
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Apply All for Filter Where Clauses
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Analyses Introduction
Getting Started
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For Clauses
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Save Your Segmentation Analysis
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Explore Users from Segmentation
Getting Started with Funnels
Group By Settings
Conversion Window
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Date Range and Time Settings
Visualization Options
Interpreting a Funnel Analysis
Group By
Filters
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Analyze as Cohort from Funnel
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Manage Analyses in Dashboards
Dashboards––Getting Started
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IDSync Overview
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Default IDSync Configuration
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Aliasing
Overview
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Blocked Data Backfill Guide
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Create Predictive Attributes
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Introduction
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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
When viewing the results from a Funnel analysis, an option appears to view users from a particular step in the Funnel path as a Cohort.
It’s important to note that the query results may differ between the Funnel and Cohort queries. This is because the underlying logic between the two tools is different. Additionally, the Funnel tool may have certain settings that are not available in Cohort, and vice versa.
For example, a Funnel analysis may have a conversion limit applied to define the time window in which steps in the query must be performed. The Cohort tool does not have any sort of conversion limit. Instead, users are grouped into Cohorts based on the time interval in which they performed the Target Behavior. Due to this, users may be uncounted in the Funnel query, but counted in the Cohort query.
Take an example where three events are being measured: Blog View, Site Visit and Subscribe. The Funnel analysis is set with a conversion limit of one day, and the subsequent Cohort analysis is grouped daily.
If a user performs:
Jan 1 - Blog View
Jan 3, 1pm - Site Visit
Jan 3, 5pm - Subscribe
In this scenario, the Funnel results will not show this user’s activity, because the event Site Visit was not performed within one day of event Blog View, and thus the Funnel was not completed. However, a Cohort query with the same steps would include this user in Day 0, because the final step in “Define your cohort” (Site Visit) occurred within 24 hours of “Target behavior” (Subscribe).
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