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Prepare data for AID

Audience: Data and analytics engineers should collaborate closely with business stakeholders to identify the data required for AI Decisioning use cases.
Prerequisite: Configuration →


By the end of this article, you’ll be able to:

  • Define the parent, related, and event models AID needs
  • Identify which tables to include and how they should be structured
  • Build goal-aligned audiences in Customer Studio

Overview

Before creating agents, you’ll need to prepare the data that AID will learn from. This setup ensures that your AI agents have the full picture: who users are, what they’ve done, and how they’ve responded to marketing. That data is then used to both personalize decisions and measure impact. This includes:

  • Defining a global user base (parent model)
  • Enriching it with related data to give user-level context (related models)
  • Creating event models that reflect the outcomes you want to optimize.
  • Building an audience to feed into each agent so it knows which users to target.

AID uses your data to:

  • Engineer user features: Create an understanding of each individual user and detect patterns in behavior across characteristics, traits, events, and responses
  • Train and evaluate models: Identify which behaviors lead to outcomes like purchases or sign-ups

Learn more about what we do with this data: Security →

Once your data is structured correctly, you can reuse these models across all of your lifecycle motions that are powered by AID.

1. Define your parent model

Purpose: Establish the global user base eligible for AID agents

The parent model is your master list of users. Every AID agent will reference an audience that is created from this parent model when defining who is eligible for a set of messages. It should include all users who may become eligible for AID, even if not immediately targeted.

You’ll configure this model in Customer Studio > Schema.

Requirements:

  • One row per user
  • A unique identifier (e.g. user_id) as each user’s primary key
  • Basic user data that can include:
    • Personal: First Name, Last Name, Full Name, Gender, Date of Birth, Age, Email
    • Location & timezone: City, State/Province, Country, Timezone (UTC Offset or IANA Timezone String)
    • Account details: Signup Date, Last Login Date, Account Status, Language preferences, Device type, Operating system
    • Demographics: Income Bracket, Education Level, Occupation, Marital Status, Number of Dependents

Example model:

Parent Model

Learn more: Parent models →

Purpose: Enrich users with attributes that help the model learn

Related models enrich the parent model with extra context the AI can learn from when making predictions and include additional user information like voluntary information, opt-in status, or persona data.

These tables must be joined to the parent model using a foreign key, typically user_id.

Table types to include

You’ll need the following tables that contain information about:

Required:

  • Consent flags for email, SMS, push, in-app

Recommended:

  • User context data from rewards program membership and participation, purchase history and frequency, customer support interactions, etc.
  • Persona voluntary data from quizzes, surveys (e.g. user goals, intent), etc.
AID requires up-to-date marketing consent to ensure compliance and prevent false negatives in model feedback.

Learn more: Related models →

3. Create event models

Purpose: Supply the actions and outcomes AID will learn from

Event models capture the actions users take—purchases, clicks, visits, and more. These power both model training and optimization. AID requires raw, unfiltered event logs that contain:

  • User behavior on-site or in-app
  • Engagement with marketing content
  • Transactional conversion events

At minimum, your model should be structured as one event instance. For instance, if the goal is to increase subscription orders, each row in the table connected to the event model should reflect one distinct order (one row per user), not a line item in a distinct order (multiple rows per one order per user).

Models must include:

  • user_id
  • timestamp
  • event_name of the campaign goal/outcome (e.g. order_completed)
  • Relevant outcome metadata (e.g. purchase value, subscription plan details, etc.)

Table types to include

TypeExamplesWhy it's important
Conversion eventsorder_completed, signupDefine AI Decisioning goals
Engagement signalspage_view, checkout_startedPredict user intent
Campaign eventsemail_clicked, push_unsubscribedTrain on content efficacy and responses

Learn more: Event models →

4. Build audiences in Customer Studio

Purpose: Define who each agent will target

Audiences are created using the models above. Each AID agent targets one audience, which determines who is eligible to receive messages—and who gets held out for lift analysis. Your starting audience should be as broad as possible — if there are certain pockets within that audience that need additional guardrails for consent or compliance, you will be able to configure those filters within the agent at a per-message level.

>Hightouch recommends audiences of at least 500K users in order to build enough training data to optimize for reinforcement learning.

Your audience should:

  • Include all users AID is allowed to message
  • Reflect your lifecycle goal (e.g. win-back, onboarding)
  • Mirror consent and eligibility filters between Hightouch and your ESP

Example: Win-back campaign audience

  • Users who haven’t purchased in the past 12 months
  • AND opened a message in the past 6 months
  • AND have opted into at least one channel

Learn more: Create Audiences →

Summary: What data you’ll need

PurposeTable TypeExamples
Define total user baseParent modelusers
Add traits & permissionsRelated modelsuser_profiles, personas, opt_ins
Supply behaviorsEvent modelsorders, signups, page_views
Target usersAudienceBuilt from the above in Customer Studio

What's next

Configure a destination for AID (SFMC, Braze, Iterable)

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Last updated: Jun 11, 2025

On this page
  • Overview
  • 1. Define your parent model
  • 2. Add related models
  • Table types to include
  • 3. Create event models
  • Table types to include
  • 4. Build audiences in Customer Studio
  • Summary: What data you’ll need
  • What's next

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