Predictive traits


Audience	Marketers and analytics/data engineers
Prerequisites	A defined schema with at least one parent model (for example, `Users`). Event data that reflects the outcome you want to predict (for example, purchases, cart completions). (Optional) Connected destinations for syncing audiences.

Overview

When defining audiences, you sometimes want to act on signals about what customers are likely to do next, not just what they have already done.

Predictive traits extend Customer Studio traits with AI-powered scores that estimate the likelihood of a user performing a future action.

For example:

Purchase propensity (any purchase): probability that a user will make a purchase within the next 7 days
Cart completion propensity: probability that a user who started checkout will complete their purchase

Predictive traits are refreshed automatically on a schedule so your campaigns always use the latest predictions.

Predictive traits are built on the same machine learning models used in Hightouch AI Decisioning. They are automatically trained on your historical event data and do not require custom ML expertise.

Configure predictive inputs

Before you create a predictive trait, you must define predictive inputs in your schema. Predictive inputs are the user properties and event data that models will use for training and scoring.

User properties: Stable attributes such as plan_type, region, signup_source, or account_age
Key events: Behavioral actions such as Viewed Product, Started Checkout, or Clicked Email

Once saved, these inputs are reusable and can be applied to multiple predictive traits.

Create a predictive trait

To create a predictive trait:

Go to Customer Studio → Traits.
Click Create → New Trait.
Configure method:
- Select a parent model (for example, Users).
- Under Calculation method, select Prediction.
- Click continue.

Configure calculation:
- Select the predicted outcome event (e.g., purchases at least once within 30 days)
- Choose eligible users (all users or a filtered subset).
- Set how often scores should be updated (daily is recommended).
- Choose eligible users (all users or a filtered subset).
- Set how often scores should be updated (daily is recommended).
- Click continue.

Finalize:
- Name your trait.
- Optionally, enter a description.
- Review the summary and click Save.

The system trains a model using your data and generates predicted scores for each user. Training typically takes several hours but will vary based on warehouse size and the amount of data being processed.

View and use predictive traits

After training completes, predictive traits appear in the Traits list with type Predictive.

You can:

Preview results in the trait details page
Use in audiences by filtering on score thresholds or percentile ranges
Sync predictions to destinations such as ad platforms, ESPs, or CRMs for activation

Analyze predictions

The Prediction analysis tab shows model performance and conversion rates by percentile.

For example, in a purchase propensity model:

The top 20% of users may convert at a significantly higher rate than average
The bottom 80% may convert at a much lower baseline

This helps you evaluate model lift and choose effective thresholds for targeting.

Use percentile thresholds to build high-value segments. For example, create an audience of “Top 20% purchase propensity” users and sync them to ad channels for efficient spend.

Use predictive traits in audiences

You can use predictive traits when defining audiences the same way you use other traits.

Go to Customer Studio → Audiences and click Add audience.
In the audience definition, select a trait.
Set the score range or percentile range you want to include (for example, 80–100%).
(Optional) Combine the predictive filter with other attributes, such as country = US or plan_type = paid.

Predictive traits can be layered with demographic or behavioral conditions, giving you highly targeted segments. For example:

High propensity to purchase + specific geography (US, Canada)
Cart completion propensity + recent site activity (visited in the last 7 days)

Predictive traits update on a schedule. Between updates, some people may complete the action you’re predicting (like making a purchase) but their score won’t change until the next update. To keep your audience focused on people who haven’t yet acted, add a filter to exclude people who have already performed the predicted event (e.g, Purchases = 0 or Purchases (in last 7 days) = 0). This helps make sure your campaign only includes users who are still likely to take action.

Sync predictive audiences

Once you define an audience with predictive traits, you can sync it to any connected destination the same way you sync other audiences.

For example, you might sync:

High purchase propensity users to Google Ads or Meta Ads for paid acquisition
High cart completion propensity users to an ESP for checkout reminder emails

When syncing, predictive scores are included alongside user attributes so downstream tools can use them directly.

Sync configuration with predictive scores

After syncs run, you can monitor performance and health in the Syncs > Overview tab.

You don’t need to configure anything special for predictive traits when syncing--they behave like any other trait and can be mapped directly to destination fields.