Data Subject Request API Version 1 and 2
Data Subject Request API Version 3
Platform API Overview
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ComposeID
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IDSync
AMP SDK
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Android
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Kits
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Upgrade to Version 7
Getting Started
Identity
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Getting Started
Opt Out
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Event Tracking
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Identity
Location Tracking
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Getting Started
Identity
Initialization
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Location Tracking
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Kits
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Preventing Blocked HTTP Traffic with CNAME
Facebook Instant Articles
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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
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Python SDK
Ruby SDK
Java SDK
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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
The Overview Map
Introduction
Data Retention
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Data Filter
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mParticle Users and Roles
Analytics Free Trial
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Usage metering for value-based pricing (VBP)
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Sync and Activate Analytics User Segments in mParticle
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Events
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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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Calculator
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Frequency in Segmentation
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Did [not] Perform Clauses
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Save Your Segmentation Analysis
Export Results in Segmentation
Explore Users from Segmentation
Getting Started with Funnels
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Interpreting a Funnel Analysis
Group By
Filters
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Analyze as Cohort from Funnel
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Export Results from a Funnel
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Manage Analyses in Dashboards
Dashboards––Getting Started
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User Aliasing
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Analytics for Marketers
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Compare Conversion Across Acquisition Sources
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Optimize User Flow with A/B Testing
User Segments
IDSync Overview
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Components of IDSync
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Default IDSync Configuration
Profile Conversion Strategy
Profile Link Strategy
Profile Isolation Strategy
Best Match Strategy
Aliasing
Overview
Create and Manage Group Definitions
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Using Predictive Audiences
Predictive Attributes Overview
Create Predictive Attributes
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Use Predictive Attributes in Campaigns
Introduction
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Data Subject Requests
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Import Data with CSV Files
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
Snowplow Schema Overview
Snowflake (Snowplow Schema)
Snowflake (Define Your Own Schema)
Aliasing
PetBox is a fictional eCommerce company that sells monthly subscription boxes containing animal care products. In the accompanying PetBox app, subscribers can track and customize their monthly boxes, view their box history, and purchase specific products they liked from their boxes. In addition, non-subscribers have access to features such as PetCam, which allows them to watch their cats through connected webcams.
The product team at PetBox wants to increase the user activity this month and they think that PetCam can help with that goal. They will analyze their users and re-engage those users who have not used the PetCam since it was launched.
Understand whether users have tried out the PetCam.
Create a user segment for all users who opened the PetBox app but have not opened PetCam since it was launched in Segmentation. Change Total count of to Users who performed. Select the Open App event from the data dropdown and select Open PetCam from the Did [not] Perform dropdown.
Since we are saving a user segment out of this query, we also want to open up the date range to 03/01/xxxx to Today so we can capture all of the users who have opened the app since the feature was released. This is your final query:
Note: The date ranges used have a static start date of 03/01/xxxx and a relative end date of Today. Using a dynamic date range enables us to examine only those who have not used the filters feature even at a later date but remember to save it as a user segment with a daily update cadence.
Below is the Create a User Segment modal. Name the segment and provide a description for future ease of use.
Congratulations on creating the user segment. Let’s put it to good use.
Analyze the ratio of users who have opened PetCam against those who have not opened PetCam using the calculator tool. First, create two queries: one filters for events performed by users within this segment and one filters for events performed by users who are not in this segment.
Now, we can use the calculator tool to calculate our ratio.
You can export your users to engage with them outside of Analytics in one of two ways:
Download a CSV and upload it to your marketing tools.
This tutorial shows just a few of the ways you can use Analytics to analyze your data and achieve actionable insights from it. If you have any questions or comments, please reach out to support@mparticle.com.
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