Behind the scenes: A customer experience powered by Adobe Experience Platform
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In the previous video we saw how an example brand, Luma, was able to create a rich, rewarding and relevant customer experience. This video looks at how Adobe Experience Platform is used to accomplish this journey.
Transcript
In the previous video, we saw how Luma was able to create a rich, rewarding and relevant journey for a customer named Sarah. This video will look at how Adobe Experience Platform is used to accomplish this journey. Experience Platform brings multiple data streams together to fill real time customer profiles, to deliver experiences powered by continuous intelligence. In this example, we’re using data from multiple sources, first from Luma’s website and mobile app captured by Adobe Analytics and available in Experience Platform. Next, Luma’s Studio management system which sends periodic updates through batch ingestion. Finally, Luma’s loyalty system that connects in real time through streaming ingestion. We’ll examine how this works by looking at the data flow for Luma’s loyalty system. The structure of Luma’s loyalty data is expressed by a schema using building blocks from Experience data model. This schema describes the logical meaning of the data. It also contains information for the identities used in this data set used to build the identity graph. In this case, the loyalty ID is used as a primary identifier while an email address and phone number are used as secondary identifiers. Data sets are at the heart of Adobe Experience Platform. They represent the streams of data coming into and going out of Experience Platform. Batches represent individual data processing jobs. Here, new data points of loyalty data are streaming into Experience Platform. The data is stored in the Experience Platform data lake and can be used to build a real time customer profile. Machine learning analysis and cross channel queries can be run on top of the data. Data coming from different sources can be seen as fragments of real time customer profiles. These fragments represent a consumer and his or her corresponding behavior across channels such as web, mobile, retail and in Sarah’s case, lessons that she attended. When Sarah adds a product to the shopping cart, that behavior is captured by Adobe Analytics and added into a real time customer profile in Experience Platform. In the background, an identity graph is built using identity information available in the data. In Sarah’s case, she has several individual identities that are linked deterministically, her Experience cloud IDs representing her web visits and mobile device, Luma membership ID, phone number and email. These identities are tied together in the identity graph. Let’s look at how real time customer profiles are used. Remember, when Sarah registered with a Luma Studio host, she presented the barcode on her mobile device representing her membership ID matching the data in the CRM system. The studio system integrated with Experience Platform performs a lookup for her customer profile using her membership ID. Sarah’s real time customer profile is created on the fly by using the identity graph to merge together individual profile fragments from various data sources. As a result, the studio host can see Sarah’s recent activities and the fact that she has shown interest in clothing. Segments can be used to personalize content. In this case, Luma has defined a segment, online shoppers as membership ID needs to exist with at least one shopping cart operation. When Sarah added a sports shirt to her shopping cart, she was automatically placed in the segment online shoppers. This allows Luma to create and deliver personalized content at scale based on segments, for example, all customer profiles belonging to a segment can be exported and used in an email campaign. All these operations are strictly governed with privacy and preferences in mind. Numerous consumer signals are hidden in the data Luma brings together in Experience Platform. Query service and data science workspace provide tooling to uncover these hidden signals. Based on Sarah’s past visits to the Luma studio, a model in Data Science Workspace added these recommendations to Sarah’s real time customer profile. Using query service, Luma can generate insights into customer behavior and perform cross channel analysis and reporting.
Sarah’s personalized experiences were possible through the power of Adobe Experience Platform. Data Management, data governance, real time customer profiles and continuous intelligence together provide a unique advantage for Luma and a better experience for their customers. -
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
- Import sample data to Experience Platform
- Administration
- AI Assistant
- APIs
- 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 and Data Distiller
- 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