- Overview
- Intro to Audience Manager
- Setup and Admin
- Web Implementation
- Integrating Offline Data
- Mobile Implementation
- Audience Marketplace
- Build and Manage Audiences
- Data Explorer
- Using the Signals Dashboard to Quickly View Top Available Signals
- Using Data Explorer to Search for Used and Unused Key/Value Pairs
- Saving Searches in Data Explorer for Convenience in Re-use
- Using Data Explorer to Create Retroactive Traits and Segments
- Using Data Explorer to Validate Trait Creation for Your Onboarded Data
- Using Data Explorer to Work with Signals Coming from Adobe Analytics
- Traits and Segments
- Planning Trait Creation from Analytics Data
- Creating a Trait Taxonomy
- Creating Rule-based Traits
- Creating Onboarded Traits
- Creating Conversion Traits
- Choosing a Data Source when Creating Traits
- Configuring Trait Expiration with the Time to Live (TTL) Setting
- Using Code View to Create and Edit Traits
- Introduction to Bulk Management (BAAAM)
- Creating and Managing Traits in Bulk with BAAAM
- The Basics of Creating Segments
- Creating a Segment Taxonomy
- Practical Segment Definition and Creation
- Creating Segments from Multiple Data Sources
- Choosing a Data Source when Creating a Segment
- Enhancing your Segments with Trait Recommendations
- Trait Recommendation Enhancements in the Segment Builder
- Using Recency and Frequency in Segments
- Using Code View when Building Segments
- Import Segments from Adobe Analytics into Audience Manager
- Building a Segment to Suppress Ads to Customers
- Mapping Audience Manager Segments to Destinations
- Mapping Segments to a Device-based Destination
- Understanding Numbers in the Trait Graph
- Understanding Numbers in the Segment Graph
- Customer Tips - Traits and Segments Best Practices
- Algorithmic Models
- Understanding Predictive Audiences
- Configure and Report on Predictive Audiences
- Creating a First Party Look-Alike Model
- Excluding Traits in Algorithmic (Look-Alike) Models
- Increase ROAS by Using Algorithmic (Look-Alike) Models
- Using Look-Alike Models to Extend Sold Out Inventory from Your 1st Party Data
- Profile Merge
- Profile Merge Rules
- Overview of Profile Merge Rules
- Profile Merge Rule Examples and Use Cases
- Choosing the Right Profile Merge Rule
- Creating and Using Simple Profile Merge Rules
- Using Profile Merge Rules to Personalize in an Authenticated State
- Using Profile Merge Rules for Device Extension and Increased Reach
- Overview of Profile Merge Rule Enhancements
- Profile Merge Rule Enhancements - Pre-Requisites
- How Profile Merge Rule Enhancements Impact Segmentation, Activation, and Reporting
- Interpret Audience Identity Reporting
- Understanding Cross-Device Metrics in Audience Manager
- Customer Tips - Getting the most out of Profile Merge Rules
- Data Explorer
- Data Activation
- Destinations Basics
- Audience Lab
- People-Based Destinations
- People-Based Destinations Videos and Tutorials
- People-Based Destinations Data Flow
- Creating a Data Source for People-Based Destinations
- Understanding Hashed PII Data Ingestion for People-Based Destinations
- Ingesting Hashed PII for People-Based Destinations
- Using setCustomerIDs() to Ingest and Hash PII as a Declared ID
- Configuring Profile Merge Rules for People-Based Destinations
- Creating Segments for People-Based Destinations
- Create and Configure People-based Destinations
- Understanding and Configuring the LinkedIn People-Based Destination
- Understanding and Configuring the Google Customer Match People-Based Destination
- Reports
- Other Integrations
- API
Documentation
Using Data Explorer to Create Retroactive Traits and Segments
This video shows how to create retroactive traits, i.e. traits that backfill data into the trait from before it was created. This is a great breakthrough in your trait creation!
Up until now, when you created traits, they only collected data moving forward. This meant that we had to create a lot of traits “just in case” they received traffic/data into them. Now you can wait to see which key/value pairs receive data, and then create a trait and backfill the data into that trait. This saves you time and energy, and also helps you keep your trait taxonomy tight, instead of having a bunch of traits that don’t have any data in them.
For more information about Data Explorer, please visit the documentation.
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