Merge pull request #7175 from segmentio/recommended-items

Recommended Items GA [DOC-1007]

Author: pwseg

src/_data/sidenav/main.yml

@@ -389,6 +389,8 @@ sections:
       title: Custom Traits
     - path: '/unify/traits/sql-traits'
       title:  SQL Traits
+    - path: '/unify/traits/recommended-items'
+      title: Recommended Items
   - path: /unify/profile-api
     title: Profile API
   - path: /unify/debugger

src/unify/Traits/computed-traits.md

@@ -25,6 +25,8 @@ Segment currently supports the following types of computed traits:
   - [Last](#last)
   - [Unique List](#unique-list)
   - [Unique List Count](#unique-list-count)
+  - [Predictions](/docs/unify/traits/predictions/)
+  - [Recommended Items](/docs/unify/traits/recommended-items/)
 - [Conditions](#conditions)
 - [Connecting your Computed Trait to a Destination](#connecting-your-computed-trait-to-a-destination)
 - [Editing Realtime Traits](#editing-realtime-traits)

src/unify/Traits/recommended-items.md

@@ -0,0 +1,76 @@
+---
+title: Recommended Items
+plan: unify-plus
+---
+
+With Recommended Items, you can add personalized item recommendations as a [computed trait](/docs/unify/traits/computed-traits/) to each user profile.
+
+Based on a user's past interactions, this trait generates a list of up to 5 items, like products, articles, or songs, that each user is most likely to engage with. 
+
+Segment designed Recommended Items for cases where you want to personalize experiences, like email content, in-app recommendations, or website suggestions, to fit each user's unique preferences.
+
+On this page, you’ll learn how Recommended Items works, how to create a Recommended Item trait, and best practices to get the most out of your recommendations.
+
+![The Select Computed Trait screen in the Segment UI, showing options like Predictions, Recommendation (selected), Event counter, Aggregation, and Most frequent. The Recommendation option description reads "Recommend personalized products" and includes additional details about Cross Sell, Personalization, and Next Best Action use cases.](../images/recommendation_items.png).
+
+## How Recommended Items works
+
+Recommended Items uses your interaction events (like `order_completed`, `product_added`, and `product_searched`) along with event metadata to generate personalized recommendations for each user. Here’s an overview of the process:
+
+1. **Data collection**: Segment captures user interactions from your chosen events.
+2. **Pattern analysis**: Machine learning models analyze these interactions to recognize patterns and user preferences.
+3. **Item ranking**: Based on this analysis, Segment generates an ordered list of recommended items for each user, ranked from most to least likely to engage.
+4. **Profile storage**: Segment then saves these recommendations as an array on each eligible user profile.
+
+Once Segment attaches the recommendation array to a profile, you can use it to:
+
+- Personalize experiences with the [Profile API](/docs/unify/profile-api/)
+- Send Recommended Items traits to downstream destinations
+- Build further segments based on Recommended Items
+- Trigger customized campaigns and experiences tailored to individual users
+
+## Create a Recommended Items trait
+
+> info "Before you begin"
+> Before you create Recommended Item traits, you'll first need to set up a Recommendation Catalog. The catalog setup process involves mapping your interaction events and providing product metadata to support recommendations. If you haven't yet set up your Recommendation Catalog, follow the steps in the [Product Based Audiences documentation](/docs/engage/audiences/product-based-audiences/#set-up-your-recommendation-catalog).
+
+To create a Recommended Item trait:
+
+1. In your Segment workspace, navigate to **Unify > Traits > + Create computed trait**.
+2. In the **New Computed Trait** builder, click **Recommendation**, then click **Next**.
+3. In **Select users**, click **+ Add condition** to choose the users who should receive recommendations.
+    - You can create recommendations for up to 2 million *non-anonymous* customers.
+4. In **Define recommended items**, choose the item type you want to recommend. 
+    - This is based on your product catalog.
+5. Choose how many item types you want to return onto each profile. 
+    - You can select up to 5 item types.
+6. Click **Calculate** to get a preview of the number of users who will receive your recommendations, then click **Next**.
+7. (*Optional*) Select destinations you want to sync the trait to, then click **Next**.
+8. Give your trait a name, then click **Create Trait**.
+
+Segment begins creating your new trait. This process could take up to 48 hours.
+
+## Example use case: personalized album recommendations
+
+Suppose you’re managing a music streaming app and want to give each user personalized music recommendations based on their listening habits. 
+
+Here's how you could configure this trait:
+
+| Step                 | Configuration                                                                                                                                                                   |
+| -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Select users         | Use an audience based on up to 2 million active, non-anonymous listeners who played at least one song in the past month.                                                        |
+| Item type            | Select **Albums** as the item type to recommend. Because you have an extensive catalog of music, this lets each listener receive recommendations tailored to their interests.   |
+| Number of item types | You decide to return a maximum of 5 albums for each profile, keeping the recommendations relevant and concise.                                                                  |
+| Calculate            | Clicking **Calculate** gives you an overview of how many users will receive the album recommendations. Use it to ensure your conditions and catalog mapping meet your criteria. |
+| Sync to destinations | This optional step lets you sync the trait to third-party destinations to deliver album recommendations over email, in-app messaging, or push notifications.                    |
+| Trait naming         | Name your trait `Personalized Album Recommendations`, making it easy to identify for future campaigns.                                                                          |
+
+By setting up a trait like this, each user profile now includes personalized recommendations that reflect individual tastes. You can use these recommendations across a range of touchpoints, like in-app sections, personalized email content, or targeted messaging, to create a more engaging and customized user experience.
+
+## Best practices
+
+Keep the following in mind as you work with Recommended Items:
+
+- **Limit recommendations to key items**: Start with 5-7 items per profile. This keeps recommendations concise and tailored to each user's preferences.
+- **Consider audience size**: Larger audiences can dilute engagement rates for each recommended item. Focusing on the top 20% of users keeps recommendations relevant and impactful.
+- **Give the system time to build the trait**: Recommended Item traits can take up to 48 hours to build, depending on data volume and complexity. Segment recommends waiting until 48 hours have passed before using the trait in campaigns.