- Audience Manager tutorials
- Intro to Audience Manager
- Industry news and trends
- Setup and admin
- Web implementation
- Integrating offline data
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- Audience Marketplace
- Build and manage audiences
- Data explorer
- Use the Signals Dashboard to quickly view top available signals
- Use Data Explorer to search for used and unused key/value pairs
- Save searches in Data Explorer for convenient re-use
- Use Data Explorer to create retroactive traits and segments
- Use Data Explorer to validate trait creation for on-boarded data
- Use Data Explorer to work with signals coming from Adobe Analytics
- Traits and segments
- Plan trait creation from Analytics data
- Create a trait taxonomy
- Create a rule-based trait
- Create onboarded traits
- Create a conversion trait
- Choose a data source when creating traits
- Configure trait expiration with the Time to Live (TTL) setting
- Use code view to create and edit traits
- Introduction to Bulk Management (BAAAM)
- Create and manage traits in bulk with BAAAM
- The basics of creating segments
- Create a segment taxonomy
- Practical segment definition and creation
- Create segments from multiple data sources
- Choose a data source when creating a segment
- Enhance your segments with trait recommendations
- Trait recommendation enhancements in segmentation
- Use Recency and Frequency in segments
- Use code view when building segments
- Import segments from Adobe Analytics into Audience Manager
- Build a segment to suppress ads to customers
- Map Audience Manager segments to destinations
- Map segments to a device-based destination
- Understand numbers in the Trait graph
- Understand numbers in the Segment graph
- Customer tips - trait and segment best practices
- Algorithmic models
- Profile merge
- Profile Merge Rules
- Overview of Profile Merge Rules
- Profile merge rule examples and use cases
- Choose the right profile merge rule
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- Overview of Profile Merge Rule enhancements
- Profile Merge Rule enhancements - prerequisites
- How Profile Merge Rule enhancements impact segmentation, activation, and reporting
- Interpret audience identity reporting
- Understand cross-device metrics in Audience Manager
- Customer tips - getting the most out of Profile Merge Rules
- Data explorer
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- Destinations basics
- Audience Lab
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- People-based Destinations videos and tutorials
- People-based Destinations data flow
- Create a data source for People-based Destinations
- Understand Hashed PII data ingestion for People-based Destinations
- Ingest Hashed PII for People-based Destinations
- Use setCustomerIDs() to ingest and hash PII as a declared ID
- Configure Profile Merge Rules for People-based Destinations
- Create segments for People-based Destinations
- Create and Configure People-based Destinations
- Understand and configure the LinkedIn People-based destination
- Understand and configure the Google customer-match People-based destination
- Reports
- Other integrations
- Integrate with Real-time Customer Data Platform
- Ingest data using the Adobe Audience Manager data connector
- Understand first-party data ingestion in Real-time CDP
- Understand Web data ingestion in Real-time CDP
- Understand schemas and XDM in Real-time CDP
- Understand traits in Real-time CDP
- Understand signals in Real-time CDP
- Understand segments in Real-time CDP
- Understand destinations in Real-time CDP
- Understand access control in Real-time CDP
- Understand data governance in Real-time CDP
- Integrating with Ad Cloud
- Integrate with Real-time Customer Data Platform
- API
Documentation
Use Data Explorer to search for used and unused key/value pairs in AAM
In day-to-day work in Audience Manager, you might need to quickly find out if a trait has been created from a specific key/value pair. This video shows you how to find out with Data Explorer.
In order to find out if a signal has been created into a trait, you no longer have to download a BAAAM file and also the Unused Signals report. Now you can simply search for that key/value pair in Data Explorer, AKA the Signals App, in AAM, and find key/value pairs that are in traits and those that aren’t, all in the same list. Great time saver!
For more information about Data Explorer, visit Data Explorer - Overview, Benefits, and Use Cases).
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