Using Predictive Traits updates

Author: pwseg

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

@@ -60,7 +60,7 @@ You can create Predictions outside of these suggestions, but your results may va
 
 ### Likelihood to Purchase
 
-Likelihood to Purchase is identical to Custom Predictive Goals, but Segment prefills the **`Order Completed`** event, assuming it's tracked in your Segment instance. 
+Likelihood to Purchase is identical to Custom Predictive Goals, but Segment prefills the `Order Completed` event, assuming it's tracked in your Segment instance. 
 
 If you don’t track `Order Completed`, choose a target event that represents a customer making a purchase.
 

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

@@ -15,13 +15,13 @@ Once you've selected a cohort, you can use Predictive Traits in concert with oth
 
 ### Prediction tab
 
-Once Segment has generated your prediction, you can access it in your Trait's **Prediction** tab. The Prediction tab gives you actionable insight into your Predictive Trait. 
+Once Segment has generated your Prediction, you can access it in your Trait's **Prediction** tab. The Prediction tab gives you actionable insight into your Predictive Trait. 
 
 ![The Explore your prediction section of the Computed Trait Prediction tab](../../images/explore_prediction.png)
 
-The **Explore your prediction** section of the Prediction tab visualizes prediction data and lets you create Audiences for targeting. An interactive chart displays a percentile cohort score that indicates the likelihood of users in each group to convert on your chosen goal. You can choose the top 20%, bottom 80%, or create custom ranges for specific use cases.
+The **Explore your prediction** section of the Prediction tab visualizes Prediction data and lets you create Audiences to target. An interactive chart displays a percentile cohort score that indicates the likelihood of users in each group to convert on your chosen goal. You can choose the top 20%, bottom 80%, or create custom ranges for specific use cases.
 
-You can then create an Audience from the group you've selected, letting you send efficient, targeted marketing campaigns within Journeys. You can also send your prediction data to downstream Destinations.
+You can then create an Audience from the group you've selected, letting you send efficient, targeted marketing campaigns within Journeys. You can also send your Prediction data to downstream Destinations.
 
 #### Model statistics
 
@@ -33,8 +33,8 @@ The Understand your prediction dashboard displays the following model metrics:
 
 - **AUC**, or Area under [the ROC curve](https://en.wikipedia.org/wiki/Receiver_operating_characteristic){:target="_blank"}; AUC lands between 0 and 1, where 1 is a perfect future prediction, and 0 represents the opposite. Higher AUC indicates better predictions. 
 - **Lift Quality**, which measures the effectiveness of a predictive model. Segment calculates lift quality as the ratio between the results obtained with and without the predictive model. Higher lift quality indicates better predictions.
-- **Log Loss**; the more a predicted probability diverges from the actual value, the higher the log-loss value will be. Lower log loss indicates better predictions.
-- **Top contributing events**; this graph visually describes the events factored into the model, as well as the associated weights used to create the prediction.
+- **Log Loss**; the more a predicted probability diverges from the actual value, the higher the log-loss value will be. Lower log loss indicates better Predictions.
+- **Top contributing events**; this graph visually describes the events factored into the model, as well as the associated weights used to create the Prediction.
 
 ## Predictive Traits use cases
 
@@ -45,13 +45,13 @@ Predictions offer more value in some situations than others. This sections cover
 - **Improve ad targeting**; build targeted audience segments based on predictive behavior. 
 - **Optimize campaign performance**; reduce customer acquisition costs (CAC), and improve customer lifetime value (LTV) by building campaigns that target customers most likely to purchase or perform another desired action.
 - **Power more personalization**; With Predictive Traits, you can deliver the right message at the right time. You can create targeted customer Journeys with personalized offers and recommendations that boost conversion and promote upsell and cross sell.
-- **Win back unengaged customers**; Predictive Traits let you identify unengaged customers and create personalized winback campaigns to reengage them.
+- **Win back unengaged customers**; Predictive Traits let you identify unengaged customers you can reengage and create personalized winback campaigns to reengage them.
 
 ### Data science use cases
 
 - **Model improvement**; You can extract Predictive Traits from Segment and use them to improve proprietary machine learning models.
 - **Testing experiences**; data teams can validate and strengthen existing machine learning models by testing proprietary models against Segment's out-of-the-box models.
-- **Save time on predictive modeling**; data science teams can use Segment's predictive models, freeing up time to building other in-house models like inventory management, fraud alerting, and so on.
+- **Save time on predictive modeling**; data science teams can use Segment's predictive models, freeing up time to building other in-house models like inventory management and fraud alerting.
 
 ### When to use a prediction
 
@@ -71,24 +71,24 @@ Predictions may not be as beneficial in the following situations:
 ## Frequently asked questions
 
 {% faq %}
-{% faqitem What type of machine learning model is used? %}
+{% faqitem What type of machine learning model do you use? %}
 Segment uses a binary classification model that uses decision trees.
 {% endfaqitem %}
 
 {% faqitem What level of confidence can I have in my predictions? %}
-Once Segment creates your prediction, you can check the model statistics page, where Segments shows you how the model was created. Segment also maintains automated systems that monitor model performance and will alert you if your model is not predictive.
+Once Segment creates your Prediction, you can check the model statistics page, where Segments shows you how the model was created. Segment also maintains automated systems that monitor model performance and will alert you if your model is not predictive.
 {% endfaqitem %}
 
 {% faqitem How long do Predictive Traits take to create?  %}
-Trait creation depends on the amount of data, but Segment expects predictions to be completed in around 24 hours. For larger customers, however, this could take 48 hours. Predictive Traits shows a status of `In Progress` while computing; Segment updates this status when customers are scored.
+Trait creation depends on the amount of data, but Segment expects Predictions to be completed in around 24 hours. For larger customers, however, this could take 48 hours. Predictive Traits shows a status of `In Progress` while computing; Segment updates this status when customers are scored.
 {% endfaqitem %}
 
 {% faqitem What are AUC, log loss, and lift quality? %}
 These data science statistics measure the effectiveness of Segment's predictions when tested against historical data. For more information, refer to [ROC Curve and AUC](https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc){:target="_blank"}, [The Lift Curve in Machine Learning](https://howtolearnmachinelearning.com/articles/the-lift-curve-in-machine-learning/){:target="_blank"}, and [Intuition behind log-loss score](https://towardsdatascience.com/intuition-behind-log-loss-score-4e0c9979680a){:target="_blank"}.
 {% endfaqitem %}
 
 {% faqitem What is the Prediction Quality Score? %}
-The Prediction Quality Score factors AUC, log loss, and lift quality to determine whether Segment recommends using the prediction. A model can have a score of Poor, Fair, Good, or Excellent.
+The Prediction Quality Score factors AUC, log loss, and lift quality to determine whether Segment recommends using the Prediction. A model can have a score of Poor, Fair, Good, or Excellent.
 {% endfaqitem %}
 
 {% faqitem How do you store trait values? %}
@@ -106,4 +106,4 @@ Every seven days.
 {% faqitem How many Predictive Traits can I have? %}
 You can have 10 active Predictive Traits.
 {% endfaqitem %}
-{% endfaq %}
+{% endfaq %}