Predictive traits

​

Before you begin

• 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 future action — or the value that action will produce. For example:

• Purchase propensity: 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
• Churn prediction: probability that a user will stop performing a key event (such as a purchase or login) within a given timeframe
• Predicted value: the revenue a user is expected to generate within a given timeframe

Predictive traits are refreshed automatically on a schedule so your campaigns always use the latest predictions. The predictive traits suite also includes catalog recommendations for personalized item-level recommendations and lookalike audiences for finding users similar to your best segments.

​

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 use for training and scoring. To configure predictive inputs:

1. Go to Customer Studio > Schema.
2. Select your parent model (for example, Users).
3. Open the Predictive Inputs tab.
4. Under User Properties, select the columns from this model that can help identify patterns — for example, age, country, device, plan_tier.
5. Under Key events, add the event models that are relevant to the prediction target. Events must have a primary key configured to be available for selection.

Once saved, these inputs are reusable across multiple predictive traits, catalog recommendations, and lookalike audiences.

​

Create a predictive trait

1. Go to Customer Studio > Traits.
2. Select Create > New trait.
3. In the Method step, select the Model you want to score (your parent model, for example Users), then choose Prediction as the calculation method. Click Continue.

4. In the Calculation step, under What action do you want to predict?, select the event to forecast (for example, Purchases). Only events configured as predictive inputs, with a primary key, appear here.

5. Under What kind of prediction is this?, choose the type of score. The timeframe control below adapts to the type you pick.

   Prediction type	What it scores	Timeframe
   Likelihood of event	How likely each user is to perform the event	Event performed within the next N days
   Likelihood of churn	How likely each user is to stop performing the event	Churned after no events for N days
   Value of event	A numeric value from the event, such as total revenue	Value predicted over the next N days

   For Value of event, also choose the aggregation and numeric property to predict — for example, the sum of PURCHASE_VALUE.
6. (Optional) Under Who should this prediction apply to?, add filters to score only certain users — for example, people who have already signed up or started checkout.
7. Under How often should scores be updated?, set the refresh cadence, then click Continue. More frequent updates keep scores fresh but use more warehouse resources.
8. In the Finalize step, name the trait, optionally add a description, review the calculation summary, and click Create trait.

Hightouch trains a model on your data and generates a score for each user. Training runs in three phases — prepare data, train model, run inference — and typically takes several hours, depending on warehouse size and data volume.

​

View and use predictive traits

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

After training finishes, open the Prediction analysis tab on the trait to see how well the model performs. It reports the number of users scored and the last run date, and every metric is measured on a held-out sample the model never saw during training.

​

Cumulative gains

The cumulative gains curve ranks every user by how likely they are to convert, then plots the share of all converters you reach as you target more of that ranked list. The steeper the curve climbs above the diagonal — which represents random targeting — the more converters you reach by targeting a small, high-scoring slice of the audience.

​

Precision vs. random

This compares how concentrated the model’s high scores are among users who actually converted against random ranking, and reports the lift — how many times better than random the model is at pushing real converters toward the top. Higher lift means a sharper model.

​

Predicted vs. actual conversion rate

Each pair of bars represents one tenth of the audience, ordered by model score. Comparing the predicted conversion rate against the rate that actually happened shows whether the scores are well calibrated: the two bars in each decile should sit close together.

​

Top feature importance

This ranks the user properties and events that contribute most to the model’s scores, each as a share of total importance. Use it to confirm the model is learning from signals you’d expect.

​

Use predictive traits in audiences

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

1. Go to Customer Studio > Audiences and click Add audience.
2. In the audience definition, select a trait.
3. Set the score range or percentile range you want to include (for example, 80–100%).
4. (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)

​

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

​

Troubleshooting

If a predictive trait run fails, Hightouch assigns an error code that identifies the cause. The table below covers all predictive trait error codes. For a complete reference of all Hightouch error codes, see Error codes.

​

Next steps

• Catalog recommendations to rank the top items from a catalog for each user.
• Lookalike audiences to find users who resemble a high-value segment without defining an outcome event.
• Traits to see how predictive traits sit alongside the other trait types.
• Build an audience to filter on a predictive score in the visual builder.
• Experiments to measure the lift a predictive audience actually drives.