Add Likelihood to Churn info

Author: pwseg

src/engage/audiences/predictive-traits/index.md

@@ -3,9 +3,6 @@ title: Predictive Traits
 plan: engage-foundations
 ---
 
-> info ""
-> Predictive Traits is in public beta.
-
 Predictive Traits, Segment's artificial intelligence and machine learning feature, lets you predict the likelihood that users will perform any event tracked in Segment. 
 
 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).
@@ -38,11 +35,11 @@ Once your Workspace is enabled for Predictive Traits, follow these steps to buil
 5. (Optional) Connect a Destination, then select **Next**.
 6. Add a name and description for the Trait, then select **Create Trait**.
 
-In the next section, you'll learn more about the three available Predictive Traits.
+In the next section, you'll learn more about the four available Predictive Traits.
 
 ## Choosing a Predictive Trait
 
-Segment offers three Predictive Traits: Custom Predictive Goals, Likelihood to Purchase, and Predicted LTV.
+Segment offers four Predictive Traits: Custom Predictive Goals, Likelihood to Purchase, and Predicted LTV.
 
 ### Custom Predictive Goals
 
@@ -78,6 +75,16 @@ Predicted Lifetime Value predicts a customer's future spend over the next 90 day
 | Purchase amount | Select the purchase event property that represents the total amount. For most companies, this is the **Revenue** property.   |
 | Currency        | Segment defaults all currencies to USD.                                                                                       |
 
+### Likelihood to Churn
+
+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.
+
+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.
+
+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`. 
+
+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.
+
 #### Data requirements
 
 Predicted LTV has strict data requirements. Segment can only make predictions for customers that have purchased two or more times. Segment also requires a year of purchase data to perform LTV calculations.