Enrich Real-time Customer Profile with machine learning insights

Adobe Experience Platform Data Science Workspace provides the tools and resources to create, evaluate, and utilize machine learning models to generate data predictions and insights. When machine learning insights are ingested into a Profile-enabled dataset, that same data is also ingested as Profile records which can then be segmented using Adobe Experience Platform Segmentation Service. As profile and time series data is ingested, Real-time Customer Profile automatically decides to include or exclude that data from segments through an ongoing process called streaming segmentation, before merging it with existing data and updating the union view. As a result, you can instantaneously perform computations and make decisions to deliver enhanced, individualized experiences to customers as they interact with your brand.

This document provides links to tutorials that enable you to enrich Real-time Customer Profile with your machine learning insights.

Getting started

In order to complete the tutorials below, you are required to have a working understanding of ingesting Profile data and creating segments. Before beginning this tutorial, please review the documentation for the following services:

  • Real-time Customer Profile: Provides a complete, unified representation of each individual customer based on aggregated data from multiple sources.
  • Identity Service: Enables Real-time Customer Profile by bridging identities from disparate data sources being ingested into Platform.
  • Experience Data Model (XDM): The standardized framework by which Platform organizes customer experience data.

In addition to the above-mentioned documents, it is highly recommended that you also review the following guides on schemas and the Schema Editor:

  • Basics of schema composition: Describes XDM schemas, building blocks, principles, and best practices for composing schemas to be used in Experience Platform.
  • Schema Editor tutorial: Provides detailed instructions for creating schemas using the Schema Editor within Experience Platform.

Create and configure an output schema and dataset

The first step towards enriching Real-time Customer Profile with scoring insights is knowing what real-world object (such as a person) your data defines. Having an understanding of your data enables you to describe and design a structure to add meaning, much like designing a relational database.

Composing a schema begins by assigning a class. Classes define the behavioral aspects of the data the schema will contain (record or time-series). To start making your own schemas, follow the steps in the tutorial on creating a schema using the Schema Editor. Note that before you can enable a dataset for Profile, you need to configure the dataset’s schema to have a primary identity field and then enable the schema for Profile. When data is ingested into a Profile-enabled dataset, that same data is also ingested as Profile records.

If you prefer to compose a schema using the Schema Registry API instead, start by reading the Schema Registry developer guide before attempting the tutorial on creating a schema using the API.

Once your schema and dataset are prepared, you can generate and ingest scoring data to the dataset by performing scoring runs using an appropriate model.

Create segments using the Segment Builder

After you have generated and ingested your scoring data insights to your Profile-enabled dataset, you can create dynamic segments using the Segment Builder.

The Segment Builder provides a rich workspace that allows you to interact with Profile data elements. The workspace provides intuitive controls for building and editing rules, such as drag-and-drop tiles used to represent data properties. Follow the Segment Builder user guide to learn about:

  • Creating segment definitions using a combination of attributes, events, and existing audiences as building blocks.
  • Using the rule builder canvas and containers to control the order in which segment rules are executed.
  • Viewing estimates of your prospective audience, allowing you to adjust your segment definitions as required.
  • Enabling all segment definitions for scheduled segmentation.
  • Enabling specified segment definitions for streaming segmentation.

Next steps

To learn more about segments and the Segment Builder, read the Segmentation Service overview.

To learn more about Real-time Customer Profile, read the Real-time Customer Profile overview

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