diff --git a/src/unify/Traits/computed-traits.md b/src/unify/Traits/computed-traits.md
index b5bde17b2e..f56accfa57 100644
--- a/src/unify/Traits/computed-traits.md
+++ b/src/unify/Traits/computed-traits.md
@@ -18,7 +18,7 @@ Computed Traits allow you to quickly create user or account-level calculations t
{% include content/trait-types.md %}
-## Types of Computed Traits
+## Types of computed traits
Segment currently supports the following types of computed traits:
- [Types of Computed Traits](#types-of-computed-traits)
@@ -41,7 +41,7 @@ Segment currently supports the following types of computed traits:
> warning "Event Properties per Computed Trait limit"
> Segment limits the number of Event Properties on each Computed trait to 10,000. If your Computed Trait exceeds this limit, Segment will not persist any new Event Properties and will drop new trait keys and corresponding values.
-### Event Counter
+### Event counter
An Event Counter trait stores a count of an **event** over a period of time. For example, you can create a trait called `number_logins_90_days` based on a `User Logged In` event. You can also use event properties to only specific types of events.
@@ -69,9 +69,9 @@ Account-level use cases
-### Most Frequent
+### Most frequent
-A most frequent user-level computed trait will return the **most common value** for an **event property**. This is helpful to create traits like `preferred_product_viewed` or `most_commonly_viewed_category` that tell you what a user's preferred product, or content category might be. Note that the most frequent computed trait requires the event property to have been tracked at least twice. In the case of a tie, Segment returns the first alphabetical value. For account-level computed traits, you can also return the most frequent **user trait**. This is helpful when you want to determine which user has performed an event the most frequently. For example, you might to return the email of the user in an account most actively viewing your app.
+A most frequent user-level computed trait will return the **most common value** for an **event property**. This is helpful to create traits like `preferred_product_viewed` or `most_commonly_viewed_category` that tell you what a user's preferred product, or content category might be. Note that the most frequent computed trait requires the event property to have been tracked at least twice. In the case of a tie, Segment returns the first alphabetical value. For account-level computed traits, you can also return the most frequent **user trait**. This is helpful when you want to determine which user has performed an event the most frequently. For example, you might to return the email of the user in an account most actively viewing your app.
User-level examples:
- Favorite Blog Post
@@ -113,7 +113,7 @@ Account-level examples:
-### Unique List
+### Unique list
Unique list computed traits will output a **list of unique values** in alphabetical order for an **event property**. This is helpful to understand the different types of products or content that a customer or users in an account have interacted with or purchased. Customers are creating traits like `unique_product_categories_viewed` and sending them to email marketing tools and accessing them through the Profiles API for in-app personalization.
@@ -125,7 +125,7 @@ Example use cases:
-### Unique List Count
+### Unique list count
Unique list count computed traits will output a **count of the unique list of values** for an **event property**. Customers are creating traits like `unique_product_categories_viewed_count` to understand the variety of products that a customer is viewing. At the account-level, customers are creating traits like `unique_visitors_count` to calculate the number of unique visitors by ip address.
@@ -144,7 +144,7 @@ Account-level examples:
All computed trait types support a common "Add Conditions" section. Conditions defined here restrict the messages considered when calculating the final value of the computed trait by looking at a property of the events. For example, you could limits events to only those where "price" is greater than 30.00 or where "page.url" contains "pricing".
-The following operators are available.
+The following operators are available:
- equals
- not equals
- less than
@@ -162,15 +162,15 @@ The following operators are available.
- equals one of
- contains one of
-## Connecting your Computed Trait to a Destination
+## Connecting your computed trait to a destination
Segment sends user-level computed Traits to destinations using the [Identify call](/docs/connections/spec/identify/) for user traits, or using the [Track call](/docs/connections/spec/track/) for event properties. Segment includes the trait value and property in the Identify and Track calls.
For example, the name of a computed trait is added to the user profile as a trait, and the trait's value is set to the value of the computed trait. Segment sends an Identify or Track call when the trait is computed, depending on the destination configuration. If a computed trait counts the number of times a user visits your pricing page, and the user visits your pricing page five times, Segment sends an Identify call with the property `pricing_page_visits: 5`.
-Learn more about [Computed trait generated events here](/docs/engage/using-engage-data/#computed-trait-generated-events). The trait name corresponds to the snake cased name that you see in the trait settings, for example `most_viewed_page_category`. See the [list of Engage-compatible destinations](/docs/engage/using-engage-data/#compatible-engage-destinations)
+See [Computed trait generated events](/docs/engage/using-engage-data/#computed-trait-generated-events) to learn more. The trait name corresponds to the _snake_cased_ name that you see in the trait settings, for example `most_viewed_page_category`. See the [list of Engage-compatible destinations](/docs/engage/using-engage-data/#compatible-engage-destinations).
-For account-level computed traits, you have the option to send either a [Group](/docs/connections/spec/group/) call and/or [Identify](/docs/connections/spec/identify/) call. Group calls will send one event per account, whereas Identify calls will send an Identify call for each user in the account. This means that even if a user hasn't performed an event, Segment will still set the account-level computed trait on that user. Because most marketing tools are still based at the user level, it is often important to map this account-level trait onto each user within an account. See [Account-level Audiences](/docs/engage/audiences/account-audiences) for more information.
+For account-level computed traits, you have the option to send either a [Group](/docs/connections/spec/group/) call and/or an [Identify](/docs/connections/spec/identify/) call. Group calls will send one event per account, whereas Identify calls will send an Identify call for each user in the account. This means that even if a user hasn't performed an event, Segment will still set the account-level computed trait on that user. As most marketing tools are still based at the user level, it is often important to map this account-level trait onto each user within an account. See [Account-level Audiences](/docs/engage/audiences/account-audiences) for more information.
> warning "Computed Traits can only be sent to Event destinations"
> When Engage sends a computed trait to an [Event destinations](/docs/engage/using-engage-data/#event-destinations), it uses an Identify call to send user traits and a Group call to send account-level computed traits.
@@ -183,7 +183,7 @@ After you create a computed trait, use the Overview page to view a compute progr
> info "Viewing compute progress"
> When you create a real-time computed trait, you'll see a progress bar, computed percentage, and status updates. For existing traits that you edit, Segment displays the compute status but not the progress bar or percentage.
-## Editing Realtime Traits
+## Editing realtime traits
Segment supports the editing of real-time Traits, which allows you to make nuanced changes to existing Traits in situations where cloning or building from scratch may not suit your use case.
@@ -200,7 +200,7 @@ Segment then processes your Trait edits. While the edit task runs, the trait rem
> warning ""
> It is not possible to edit a trait to convert it from real-time to batch, or vice-versa. If the computation type needs to be changed, you will need to recreate the trait with the appropriate conditions.
-## Accessing your Computed Traits using the Profiles API
+## Accessing your computed traits using the Profiles API
You can access your computed traits using the Profile API by querying the `/traits` endpoint. For example, you can query for the `emails_opened_last_30_days` with the following GET request:
@@ -231,13 +231,13 @@ By default, the response includes 20 traits. You can return up to 200 traits by
You can read the [full Profile API docs](/docs/unify/profile-api/) to learn more.
-## Deleting Computed Traits
+## Deleting computed traits
When computed traits are deleted, any user that had a value for that trait will now have a custom trait on the Unify profile.
-## Downloading your Computed Trait as a CSV file
+## Downloading your computed trait as a CSV file
-You can download a copy of your trait by visiting the the computed trait overview page.
+You can download a copy of your trait by visiting the computed trait overview page.
Computed Trait CSVs are generated on demand. Before you can download the CSV, you will need to generate it. There are three different options for formatting:
- **Unformatted:** Contains three columns. The first contains the user or account key, the second contains the trait value and the third is a JSON object containing the external IDs. Generating this CSV is by far the fastest of the three options. [Download example unformatted CSV](/docs/engage/files/trait_csv_format_a.csv)
@@ -247,7 +247,7 @@ Computed Trait CSVs are generated on demand. Before you can download the CSV, yo
|  |
Generating a CSV can take a substantial amount of time for large traits (around 30 seconds for a formatted CSV with 1 million rows). For CSVs that are expected to take over 20 seconds, the Segment app displays an estimated generation time. After clicking Generate, it is recommended that you leave the modal and page open while the CSV is created.
- (If the trait recalculates between when you click Generate and when you download the file, you might want to regenerate the file. The CSV is a snapshot from when you clicked Generate, and could be outdated.) |
+ (If the trait recalculates between when you click Generate and when you download the file, regenerate the file. The CSV is a snapshot from when you clicked Generate, and could be outdated.)
diff --git a/src/unify/index.md b/src/unify/index.md
index 5b272c9b79..7dd20494cc 100644
--- a/src/unify/index.md
+++ b/src/unify/index.md
@@ -10,7 +10,7 @@ Use Segment Unify, formerly known as Profiles, for a complete view of your custo
With [Identity Resolution](#identity-resolution), track every interaction across the entire user journey to create unified, real-time customer identities. View user profiles in one place through the [Profile explorer](#profile-explorer) in the Segment app. Use the [Profile API](#profile-api) to programmatically query user profiles, traits, and events.
-You can then use this interaction data with customer engagement tools, such as Engage, to deliver personalized, omnichannel experiences.
+You can then use this interaction data with customer engagement tools, such as [Engage](/docs/engage/), to deliver personalized, omnichannel experiences.
> success ""
> If you need to troubleshoot or learn about your profile data, use [Profiles Insights](/docs/unify/insights/) for a transparent view of your Unify profiles.
diff --git a/src/unify/profiles-sync/tables.md b/src/unify/profiles-sync/tables.md
index 4329c9dcd6..37117b1cb4 100644
--- a/src/unify/profiles-sync/tables.md
+++ b/src/unify/profiles-sync/tables.md
@@ -113,8 +113,8 @@ The `Timestamp` column will be empty for backfilled data because, during backfil
The `id_graph_updates` table maps between the following:
-- `segment_id`: the profile ID that Segment appends to an event or an identifier at the time it was first observed
-- `canonical_segment_id`: the fully-merged segment ID (that is, the profile Segment now understands any events or identifiers to map to)
+- `segment_id`: the profile ID that Segment appends to an event or an identifier at the time it was first observed.
+- `canonical_segment_id`: the fully-merged segment ID (the profile Segment now understands any events or identifiers to map to).
As a result, this table contains information about the creation and merging of profiles, as well as the specific events that triggered those changes.
@@ -130,14 +130,14 @@ Using the events from the profile merge case study, Segment would generate three
-In this example, the table shows `profile_2` mapping to two places: first to itself, then, later, to `profile_1` after the merge occurs.
+In this example, the table shows `profile_2` mapping to two places: first to itself, then to `profile_1` after the merge occurs.
#### Recursive entries
Segment shows the complete history of every profile. If, later, `profile_1` merges into a different `profile_0`, Segment adds recursive entries to show that `profile_1` and `profile_2` both map to `profile_0`. These entries give you a comprehensive history of all profiles that ever existed.
-If you’ll use Profiles Sync to build models, refer to the `id_graph` model, which can help you put together a complete view of a customer.
+If you use Profiles Sync to build models, refer to the `id_graph` model, which can help you put together a complete view of a customer.
### The external_id_mapping_updates table
@@ -196,7 +196,7 @@ Event type tables provide a complete history for each type of event. Segment syn
Identity Resolution processes these events, and includes a `segment_id`, enabling the data to be joined into a single Profile record.
> success ""
-> Event type tables have 2 months of historical data on backfill. Contact support if you need access to data beyond this period.
+> Event type tables have two months of historical data on backfill. Contact [Segment support](https://segment.com/help/contact/){:target="_blank"} if you need access to data beyond this period.
Event type tables includes the following tables:
@@ -213,7 +213,7 @@ These event tables are similar to the tables landed by Segment warehouse integra
- Events are combined in a single schema. For example, if you have three sources going into a single space, Segment produces one schema, not three.
- These tables have two extra columns:
* `segment_id`: the profile ID at the time the event came through. That profile may have since merged.
- * `event_source_id`: the specific source ID of the incoming event
+ * `event_source_id`: the specific source ID of the incoming event.
The previous result would generate two entries in the `pages` table:
@@ -305,7 +305,7 @@ If you're not using materialized views for Profile Sync and would like to switch
- Once the backfill is complete, review the data in your warehouse to confirm all necessary historical information has been included.
> warning ""
-> For materialized view tables, you must have delete permissions for your data warehouse.
+> For materialized view tables, you must delete permissions for your data warehouse.
### Why materialized views?
@@ -317,7 +317,7 @@ Materialized views offer several advantages:
### The user_traits table
-With the `user_traits` table, you'll see all traits that belong to a profile, represented by the `canonical_segment_id`. Use this table for a complete picture of your Profiles Sync data with external data sources such as customer purchase history, product usage, and more.
+The `user_traits` table lists all traits that belong to a profile, represented by the `canonical_segment_id`. Use this table for a complete picture of your Profiles Sync data with external data sources such as customer purchase history, product usage, and more.
- This view is a fixed schema, and contains a row for each trait associated with the profile.
- As new traits are added to the profile, new rows are added to the table.