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Copy file name to clipboardExpand all lines: src/engage/audiences/predictive-traits/index.md
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@@ -3,17 +3,17 @@ title: Predictive Traits
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plan: engage-foundations
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---
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Predictions, Segment's artificial intelligence and machine learning feature, lets you predict the likelihood that users will perform any event tracked in Segment.
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Predictive Traits, Segment's artificial intelligence and machine learning feature, lets you predict the likelihood that users will perform any event tracked in Segment.
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With Predictions, you can identify users with, for example, a high propensity to purchase, refer a friend, or use a promo code. Predictions also lets you predict a user's lifetime value (LTV).
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With Predictive Traits, you can identify users with, for example, a high propensity to purchase, refer a friend, or use a promo code. Predictive Traits also lets you predict a user's lifetime value (LTV).
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Segment saves Predictions to user profiles, letting you build Audiences, trigger Journeys, and send data to downstream Destinations.
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Segment saves Predictive Traits to user profiles, letting you build Audiences, trigger Journeys, and send data to downstream Destinations.
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On this page, you'll learn how to build a Predictive Trait.
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## Access and build a Prediction
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## Access and build a Predictive Trait
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To create a Prediction, you'll first request demo access, then build a Predictive Trait.
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To create a Predictive Trait, you'll first request access, then build a Predictive Trait.
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2. Select **Request Demo** to access Predictive Traits.
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2. Select **Request Access** to access Predictive Traits.
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### Build a Predictive Trait
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Once your Workspace is enabled for Predictive Traits, follow these steps to build a Predictive Trait:
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3. In the Trait Builder, select **Predictive Traits**, choose the Trait you want to create, then click **Next**.
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- Choose **Custom Predictive Goal**, **Likelihood to Purchase**, or **Predicted Lifetime Value**.
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- Choose **Custom Predictive Goal**, **Likelihood to Purchase**, **Predicted Lifetime Value**, or **Likelihood to Churn**.
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4. (For custom Predictive Goals) Add a condition(s) and event to predict, then select **Calculate**. If you're satisfied with the available data, select **Next**.
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5. (Optional) Connect a Destination, then select **Next**.
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6. Add a name and description for the Trait, then select **Create Trait**.
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## Choosing a Predictive Trait
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Segment offers four Predictive Traits: Custom Predictive Goals, Likelihood to Purchase, and Predicted LTV.
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Segment offers four Predictive Traits: Custom Predictive Goals, Likelihood to Purchase, Predicted LTV, and Likelihood to Churn.
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### Custom Predictive Goals
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#### Target event
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The target event is the Segment event that you want to predict a user's likelihood to perform. Predictions work better when many customers have performed the event.
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The target event is the Segment event that you want to predict. In creating a Prediction, Segment determines the likelihood of the user performing the target event. Predictions work better when many customers have performed the event.
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#### Data requirements
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Segment doesn't enforce data requirements for predictions. In machine learning, however, data quality and quantity are critical. Segment recommends that you make predictions for at least 50,000 users and choose a target event that at least 5,000 users have performed in the last 30 days.
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Segment doesn't enforce data requirements for Predictions. In machine learning, however, data quality and quantity are critical. Segment recommends that you make Predictions for at least 50,000 users and choose a target event that at least 5,000 users have performed in the last 30 days.
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You can create predictions outside of these suggestions, but your results may vary.
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You can create Predictions outside of these suggestions, but your results may vary.
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### Likelihood to Purchase
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Likelihood to Purchase is identical to Custom Predictive Goals, but Segment prefills the **Order Completed** event, assuming it's tracked in your Segment instance.
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Likelihood to Purchase is identical to Custom Predictive Goals, but Segment prefills the **`Order Completed`** event, assuming it's tracked in your Segment instance.
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If you don’t track Order Completed, choose a target event that represents a customer making a purchase.
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If you don’t track `Order Completed`, choose a target event that represents a customer making a purchase.
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### Predicted Lifetime Value
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Predicted Lifetime Value predicts a customer's future spend over the next 90 days. To create this prediction, select a purchase event, revenue property, and the currency (which defaults to USD). The following table contains details for each property:
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Predicted Lifetime Value predicts a customer's future spend over the next 90 days. To create this Prediction, select a purchase event, revenue property, and the currency (which defaults to USD). The following table contains details for each property:
| Purchase event | Choose a target event that represents a customer making a purchase. For most companies, this is usually `Order Completed`. |
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| Purchase amount | Select the purchase event property that represents the total amount. For most companies, this is the `Revenue` property. |
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| Currency | Segment defaults all currencies to USD. |
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### Likelihood to Churn
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Likelihood to Churn proactively identifies customers who are likely to stop using your product. Segment builds this trait by determining whether or not a customer will perform a certain action.
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Likelihood to Churn proactively identifies customers likely to stop using your product. Segment builds this Prediction by determining whether or not a customer will perform a certain action.
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To use Likelihood to Churn, you'll need to specify a customer event, a future time frame for which you want the prediction to occur, and if you want to know whether the customer will or won't perform the event.
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For example, suppose you wanted to predict whether or not a customer would view a page on your site over the next three months. You would select `not perform`, `Page Viewed`, and at least one time within `90 days`.
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For example, suppose you wanted to predict whether or not a customer would view a page on your site over the next three months. You would select `not perform`, `Page Viewed`, and `at least 1 time within 90 days`.
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Segment would then build the trait from this criteria and create specific percentile cohorts. You can then use these cohorts to target customers with retention flows, promo codes, or one-off email and SMS campaigns.
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Segment would then build the Prediction from this criteria and create specific percentile cohorts. You can then use these cohorts to target customers with retention flows, promo codes, or one-off email and SMS campaigns.
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