Merge pull request #5235 from segmentio/predictions-updates

Predictions Rebranding

Author: pwseg

src/_data/sidenav/main.yml

@@ -325,14 +325,14 @@ sections:
   - section_title: Traits
     slug: unify/traits
     section:
-    - section_title: Predictive Traits
-      slug: unify/traits/predictive-traits
+    - section_title: Predictions
+      slug: unify/traits/predictions
       section:
-      - path: '/unify/traits/predictive-traits/'
-        title: Predictive Traits
-      - path: '/unify/traits/predictive-traits/using-predictive-traits'
-        title: Using Predictive Traits
-      - path: '/unify/traits/predictive-traits/suggested-predictive-audiences'
+      - path: '/unify/traits/predictions/'
+        title: Predictions
+      - path: '/unify/traits/predictions/using-predictions'
+        title: Using Predictions
+      - path: '/unify/traits/predictions/suggested-predictive-audiences'
         title: Suggested Predictive Audiences
     - path: '/unify/traits/computed-traits'
       title: Computed Traits

src/engage/index.md

@@ -21,7 +21,7 @@ Add detail to user profiles with new traits and use them to power personalized m
 
 - [**Computed Traits:**](/docs/engage/audiences/computed-traits/) Use the Engage drag-and-drop interface to build per-user (B2C) or per-account (B2B) metrics on user profiles (for example, “lifetime value” or “lead score”).
 - [**SQL Traits:**](/docs/engage/audiences/sql-traits/) Run custom queries on your data warehouse using the Engage SQL editor, and import the results into Segment. With SQL Traits, you can pull rich, uncaptured user data back into Segment.
-- [**Predictive Traits (Beta)**:](/docs/unify/traits/predictive-traits/) Predict the likelihood that users will perform custom events tracked in Segment, like LTV, churn, and purchase.
+- [**Predictions**:](/docs/unify/traits/predictions/) Predict the likelihood that users will perform custom events tracked in Segment, like LTV, churn, and purchase.
 
 #### Build Audiences
 Create lists of users or accounts that match specific criteria. For example, after creating an `inactive accounts` audience that lists paid accounts with no logins in 60 days, you can push the audience to your analytics tools or send an SMS, email, or WhatsApp campaign with Engage Channels. Learn more about [Engage audiences](/docs/engage/audiences/).

src/unify/Traits/predictive-traits/index.md renamed to src/unify/Traits/predictions/index.md

@@ -1,64 +1,64 @@
 ---
-title: Predictive Traits
+title: Predictions
 plan: unify-plus 
 redirect_from:
   - "/engage/audiences/predictive-traits"
 ---
 
-Predictive Traits, Segment's artificial intelligence and machine learning feature, lets you predict the likelihood that users will perform any event tracked in Segment. 
+Predictions, 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).
+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).
 
-Segment saves Predictive Traits to user profiles, letting you build Audiences, trigger Journeys, and send data to downstream Destinations.
+Segment saves predictions to user profiles, letting you build Audiences, trigger Journeys, and send data to downstream destinations.
 
-On this page, you'll learn how to build a Predictive Trait.
+On this page, you'll learn how to build a prediction.
 
-## Access and build a Predictive Trait
+## Access and build a prediction
 
-To create a Predictive Trait, you'll first request access, then build a Predictive Trait.
+To create a prediction, you'll first request access, then build a prediction.
 
 ![The Predictive Trait builder in the Segment UI](../../images/trait_builder.png)
 
-### Request Predictive Traits access
+### Request Predictions access
 
-Follow these steps to access Predictive Trait:
+Follow these steps to access Predictions:
 
 1. Navigate to **Engage > Audiences > Computed traits** or **Unify > Traits**. Select **Create computed trait**.
-2. Select **Request Access** to access Predictive Traits.
+2. Select **Request Access** to access Predictions.
 
-### Build a Predictive Trait
+### Build a prediction
 
-Once your Workspace is enabled for Predictive Traits, follow these steps to build a Predictive Trait:
+Once your Workspace is enabled for Predictions, follow these steps to build a prediction:
 
-3. In the Trait Builder, select **Predictive Traits**, choose the Trait you want to create, then click **Next**.
+3. In the Trait Builder, select **Predictions**, choose the Trait you want to create, then click **Next**.
     - Choose **Custom Predictive Goal**, **Likelihood to Purchase**, **Predicted Lifetime Value**, or **Likelihood to Churn**.
 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**.
 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 four available Predictive Traits.
+In the next section, you'll learn more about the four available predictions.
 
-## Choosing a Predictive Trait
+## Choosing a prediction
 
-Segment offers four Predictive Traits: Custom Predictive Goals, Likelihood to Purchase, Predicted LTV, and Likelihood to Churn.
+Segment offers four predictions: Custom Predictive Goals, Likelihood to Purchase, Predicted LTV, and Likelihood to Churn.
 
 ### Custom Predictive Goals
 
 Custom Predictive Goals require a starting cohort, target event, and quality data.
 
 #### Starting cohort
 
-When you build a Custom Predictive Goal, you'll first need to select a cohort, or a group of users, for which you want to make a prediction. Traits with small cohorts compute faster and tend to be more accurate. If you want to predict for an entire Audience, though, skip cohort selection and move to selecting a target event.
+When you build a Custom Predictive Goal, you'll first need to select a cohort, or a group of users, for which you want to make a prediction. Traits with small cohorts compute faster and tend to be more accurate. If you want to predict for an entire audience, though, skip cohort selection and move to selecting a target event.
 
 #### Target event
 
-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.
+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.
 
 #### Data requirements
 
-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. 
+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. 
 
-You can create Predictions outside of these suggestions, but your results may vary.
+You can create predictions outside of these suggestions, but your results may vary.
 
 ### Likelihood to Purchase
 
@@ -68,7 +68,7 @@ If you don’t track `Order Completed`, choose a target event that represents a
 
 ### Predicted Lifetime Value
 
-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:
+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:
 
 | Property        | Description                                                                                                                |
 | --------------- | -------------------------------------------------------------------------------------------------------------------------- |
@@ -78,18 +78,18 @@ Predicted Lifetime Value predicts a customer's future spend over the next 90 day
 
 ### Likelihood to Churn
 
-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.
+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.
 
 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 1 time within 90 days`. 
 
-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.
+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.
 
 #### 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.
 
 ## Use cases
 
-For use cases and information on how Segment builds Predictive Traits, read [Using Predictive Traits](/docs/unify/traits/predictive-traits/using-predictive-traits/).
+For use cases and information on how Segment builds prediction, read [Using Predictions](/docs/unify/traits/predictions/using-predictions/).

src/unify/Traits/predictive-traits/suggested-predictive-audiences.md renamed to src/unify/Traits/predictions/suggested-predictive-audiences.md

@@ -13,7 +13,7 @@ This page explains what a Suggested Predictive Audience is, how to build a Sugge
 
 A Suggested Predictive Audience is an out-of-the-box Audience template driven by machine learning.
 
-Segment offers [five templates](/docs/unify/traits/predictive-traits/suggested-predictive-audiences/#suggested-predictive-audience-types) that are prebuilt with [Predictive Traits](/docs/unify/traits/predictive-traits) like likelihood to purchase and lifetime predicted value. Selecting a template generates a Predictive Audience whose members you can engage in a number of ways:
+Segment offers [five templates](/docs/unify/traits/predictions/suggested-predictive-audiences/#suggested-predictive-audience-types) that are prebuilt with [Predictions](/docs/unify/traits/predictions) like likelihood to purchase and lifetime predicted value. Selecting a template generates a Predictive Audience whose members you can engage in a number of ways:
 
 - [Send an email or SMS campaign](/docs/engage/campaigns/) with a discount code
 - Promote a new product line with a drip campaign
@@ -55,7 +55,7 @@ Engage offers five Suggested Predictive Audiences. The following table summarize
 
 Choose a **Ready to buy** Predictive Audience to target customers who show a high propensity to make a purchase.
 
-Segment builds this Audience with the [Likelihood to Purchase Predictive Trait](/docs/unify/traits/predictive-traits//#likelihood-to-purchase). Audience members show encouraging engagement and have a likelihood to buy in the top 20th percentile. 
+Segment builds this Audience with the [Likelihood to Purchase prediction](/docs/unify/traits/predictions/#likelihood-to-purchase). Audience members show encouraging engagement and have a likelihood to buy in the top 20th percentile. 
 
 #### Long shots
 
@@ -67,13 +67,13 @@ Segment builds this Audience with the `Order Completed` event and `Likelihood to
 
 Choose a **High lifetime value** Predictive Audience to target customers that show a high predicted lifetime value.
 
-Segment builds this Audience with the [Predicted LTV Predictive Trait](/docs/unify/traits/predictive-traits//#predicted-lifetime-value). Audience members are in the top 10th percentile of predicted lifetime value and Segment expects that they'll spend the most over the next 90 days.
+Segment builds this Audience with the [Predicted LTV prediction](/docs/unify/traits/predictions/#predicted-lifetime-value). Audience members are in the top 10th percentile of predicted lifetime value and Segment expects that they'll spend the most over the next 90 days.
 
 #### Potential VIPs
 
 Choose a **Potential VIPs** Predictive Audience to target customers exhibiting several promising marketing behaviors.
 
-Segment builds this Audience with the `Page Viewed` event and Likelihood to Purchase and Predicted LTV Predictive Traits. Audience members have been active on your site within the last two weeks, have a high predicted lifetime value, and a high propensity to purchase. 
+Segment builds this Audience with the `Page Viewed` event and Likelihood to Purchase and Predicted LTV prediction. Audience members have been active on your site within the last two weeks, have a high predicted lifetime value, and a high propensity to purchase. 
 
 #### Dormant