Applications built on Experience Platform
Last update: February 14, 2025
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Learn about the applications built natively on top of Adobe Experience Platform: Customer Journey Analytics, Real-Time Customer Data Platform, Journey Optimizer, and Mix Modeler.

Transcript
Let’s talk about the applications built on top of Adobe Experience Platform, otherwise known as platform-based applications. Platform-based applications directly utilize Experience Platform’s key capabilities to address emerging next-gen customer experience use cases. These include Real-Time Customer Data Platform, Adobe Journey Optimizer, Customer Journey Analytics, and Adobe Mix Modeler. Let’s start with Customer Journey Analytics. Customer Journey Analytics, or CJA, is a journey-based analytics application that delivers cross-channel analysis and omnichannel insights within seconds. CJA leverages the rich behavior history of Experience Platform datasets to track and analyze journey events in real-time. Using CJA, marketers can analyze customer behaviors online and offline, and then use these insights to understand conversion patterns, optimize experiences, and predict future needs. Product managers can get a better understanding of product usage through a deeper understanding of customer needs and experiences, and data analysts can run unlimited cross-channel data breakdowns for deep ad hoc analysis, helping to drive improved customer experiences. CJA allows you to manage event and journey-based audiences, create end-to-end visualizations, and even leverage anomaly detection AI to find irregularities and pinpoint factors affecting your business. All in all, CJA is enabling organizations to leverage unique insights to activate and optimize engagement across all of their customer journeys. Next up is Real-Time Customer Data Platform, or Real-Time CDP. Real-Time CDP lets marketers collect data from across systems and unify it into rich customer profiles, ready for activation across any channel. Whether you’re operating from a B2C or B2B model, Real-Time CDP can consolidate first-party and partner data across online and offline channels. This data is transformed into profiles for each of your customers, accounts, or prospects, where they can then be grouped into actionable audiences through powerful segmentation workflows. You can enhance profiles with additional attributes, either through merging it with other audience data using Segment Match, or using Customer AI to enrich with churn and propensity models. You can also leverage machine learning-based lookalike audiences to identify profiles similar to those in your existing audiences without restricting them to exact matches. When you’re ready to activate your profiles and audiences, Real-Time CDP provides a robust catalog of destination connectors and APIs for paid media, email marketing, web personalization use cases, and more. Let’s move on to Adobe Journey Optimizer. With Journey Optimizer, marketers can manage one-to-one personalized journeys and scheduled campaigns for millions of customers from a single application. Journey Optimizer also includes a set of decisioning capabilities for Next Best Offer, Next Best Action, and more. These capabilities can optimize experiences through email, mobile, in-app, push, SMS, direct mail, and web channels at enterprise scale. Whether you want to create one-off marketing messages or build a personalized multi-step omnichannel campaign, Adobe Journey Optimizer allows you to turn vision into reality. Finally, there’s Adobe Mix Modeler. Powered by Adobe Sensei, Mix Modeler uses AI and machine learning frameworks that let marketing leaders measure and forecast the effectiveness of their campaigns across multiple channels. To achieve this, Mix Modeler uses a combination of marketing mix modeling and multi-touch attribution. Marketing mix modeling processes summary-level data to provide aggregate insights at the channel or product level, while multi-touch attribution processes event-level data to focus on the incremental impact of each individual touchpoint in the customer journey. Mix Modeler is able to share the outputs between these features, producing consistent insights from marketing leaders to measure campaign performance across all channels, optimize budget allocation for marketing spend, and to forecast revenue when planning for different scenario spend mixes on future campaigns. With your data already consolidated in platform, these insights can be generated in minutes, so marketing leaders can always act on the latest information with minimal downtime. So that’s a quick overview of the platform-based applications. To learn more about each of these applications, check out Adobe Experience League, where you’ll find additional guides and tutorials. Thanks for watching.
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Experience Platform
- Platform Tutorials
- Introduction to Platform
- A customer experience powered by Experience Platform
- Behind the scenes: A customer experience powered by Experience Platform
- Experience Platform overview
- Key capabilities
- Platform-based applications
- Integrations with Experience Cloud applications
- Key use cases
- Basic architecture
- User interface
- Roles and project phases
- Introduction to Real-Time CDP
- Getting started: Data Architects and Data Engineers
- Authenticate to Experience Platform APIs
- Import sample data to Experience Platform
- Administration
- AI Assistant
- Audiences and Segmentation
- Introduction to Audience Portal and Composition
- Upload audiences
- Overview of Federated Audience Composition
- Connect and configure Federated Audience Composition
- Create a Federated Audience Composition
- Audience rule builder overview
- Create audiences
- Use time constraints
- Create content-based audiences
- Create conversion audiences
- Create audiences from existing audiences
- Create sequential audiences
- Create dynamic audiences
- Create multi-entity audiences
- Create and activate account audiences (B2B)
- Demo of streaming segmentation
- Evaluate batch audiences on demand
- Evaluate an audience rule
- Create a dataset to export data
- Segment Match connection setup
- Segment Match data governance
- Segment Match configuration flow
- Segment Match pre-share insights
- Segment Match receiving data
- Audit logs
- Data Collection
- Collaboration
- Dashboards
- Data Governance
- Data Hygiene
- Data Ingestion
- Overview
- Batch ingestion overview
- Create and populate a dataset
- Delete datasets and batches
- Map a CSV file to XDM
- Sources overview
- Ingest data from Adobe Analytics
- Ingest data from Audience Manager
- Ingest data from cloud storage
- Ingest data from CRM
- Ingest data from databases
- Streaming ingestion overview
- Stream data with HTTP API
- Stream data using Source Connectors
- Web SDK tutorials
- Mobile SDK tutorials
- Data Lifecycle
- Destinations
- Destinations overview
- Connect to destinations
- Create destinations and activate data
- Activate profiles and audiences to a destination
- Export datasets using a cloud storage destination
- Integrate with Google Customer Match
- Configure the Azure Blob destination
- Configure the Marketo destination
- Configure file-based cloud storage or email marketing destinations
- Configure a social destination
- Activate through LiveRamp destinations
- Adobe Target and Custom Personalization
- Activate data to non-Adobe applications webinar
- Identities
- Intelligent Services
- Monitoring
- Partner data support
- Profiles
- Understanding Real-Time Customer Profile
- Profile overview diagram
- Bring data into Profile
- Customize profile view details
- View account profiles
- Create merge policies
- Union schemas overview
- Create a computed attribute
- Pseudonymous profile expirations (TTL)
- Delete profiles
- Update a specific attribute using upsert
- Privacy and Security
- Introduction to Privacy Service
- Identity data in Privacy requests
- Privacy JavaScript library
- Privacy labels in Adobe Analytics
- Getting started with the Privacy Service API
- Privacy Service UI
- Privacy Service API
- Subscribe to Privacy Events
- Set up customer-managed keys
- 10 considerations for Responsible Customer Data Management
- Elevating the Marketer’s Role as a Data Steward
- Queries
- Overview
- Query Service UI
- Query Service API
- Explore Data
- Prepare Data
- Adobe Defined Functions
- Data usage patterns
- Run queries
- Generate datasets from query results
- Tableau
- Analyze and visualize data
- Build dashboards using BI tools
- Recharge your customer data
- Connect clients to Query Service
- Validate data in the datalake
- Schemas
- Overview
- Building blocks
- Plan your data model
- Convert your data model to XDM
- Create schemas
- Create schemas for B2B data
- Create classes
- Create field groups
- Create data types
- Configure relationships between schemas
- Use enumerated fields and suggested values
- Copy schemas between sandboxes
- Update schemas
- Create an ad hoc schema
- Sources
- Use Case Playbooks
- Experience Cloud Integrations
- Industry Trends