From 53cdaa757cb7a9351f4776af7eac6e6ed06d6ef3 Mon Sep 17 00:00:00 2001
From: Isaiah Robinson <95643215+internetisaiah@users.noreply.github.com>
Date: Tue, 11 Jun 2024 21:11:56 -0700
Subject: [PATCH 01/11] BD-3161: Feature flag experiments

---
 .../platform_wide/feature_flags/canvas.md     |  2 +-
 .../feature_flags/experiments.md              | 49 +++++++++++++------
 2 files changed, 34 insertions(+), 17 deletions(-)

diff --git a/_docs/_developer_guide/platform_wide/feature_flags/canvas.md b/_docs/_developer_guide/platform_wide/feature_flags/canvas.md
index f5d6821f09a..4d087ee9d45 100644
--- a/_docs/_developer_guide/platform_wide/feature_flags/canvas.md
+++ b/_docs/_developer_guide/platform_wide/feature_flags/canvas.md
@@ -1,6 +1,6 @@
 ---
 nav_title: Feature Flags in Canvas
-page_order: 30
+page_order: 40
 noindex: true
 tool: Feature Flags
 platform:
diff --git a/_docs/_developer_guide/platform_wide/feature_flags/experiments.md b/_docs/_developer_guide/platform_wide/feature_flags/experiments.md
index fe55c09789e..75220d0919a 100644
--- a/_docs/_developer_guide/platform_wide/feature_flags/experiments.md
+++ b/_docs/_developer_guide/platform_wide/feature_flags/experiments.md
@@ -1,7 +1,7 @@
 ---
 nav_title: Feature Flag Experiments
 article_title: Feature Flag Experiments
-page_order: 40
+page_order: 30
 description: "Feature flag experiments let you A/B test changes to your applications to optimize conversion rates."
 tool: Feature Flags
 platform:
@@ -11,7 +11,7 @@ platform:
 
 ---
 
-# Creating a feature flag experiment
+# Feature flag experiments
 
 > Feature flag experiments let you A/B test changes to your applications to optimize conversion rates. Marketers can use feature flags to determine whether a new feature positively or negatively impacts conversion rates, or which set of feature flag properties is most optimal.
 
@@ -32,27 +32,25 @@ if (featureFlag?.enabled) {
 
 ```
 
-## Step 1: Create an experiment
+## Creating a feature flag experiment
+
+### Step 1: Create an experiment
 
 1. Go to **Messaging** > **Campaigns** and click **+ Create Campaign**.
 2. Select **Feature Flag Experiment**.
 3. Name your campaign something clear and meaningful.
 
-## Step 2: Add experiment variants
-
-Next, create variations. For each variant, choose the feature flag you want to turn on or off and review the assigned properties.
+### Step 2: Add experiment variants
 
-To test the impact of your feature, use variants to split traffic into two or more groups. Name one group "My control group" and turn its feature flags off.
+Next, create variations. For each variant, choose the feature flag you want to turn on or off and review the assigned properties. To test the impact of your feature, use variants to split traffic into two or more groups. Name one group "My control group" and turn its feature flags off.
 
-### Overwriting properties
+#### Overwriting properties
 
-Though you specified default properties when you originally set up your feature flag, you can choose to overwrite those values for users who receive a specific campaign variant.
+Though you specified default properties when you originally set up your feature flag, you can choose to overwrite those values for users who receive a specific campaign variant. To edit, add, or remove additional default properties, edit the feature flag itself from **Messaging** > **Feature Flags**.
 
 ![][image1]{: style="max-width:80%"}
 
-To edit, add, or remove additional default properties, edit the feature flag itself from **Messaging** > **Feature Flags**.
-
-## Step 3: Choose users to target
+### Step 3: Choose users to target
 
 Next, you need to [target users][4] by choosing segments or filters to narrow down your audience. Segment membership is calculated when feature flags are refreshed for a given user.
 
@@ -60,18 +58,37 @@ Next, you need to [target users][4] by choosing segments or filters to narrow do
 Your target audience will be eligible for the feature flag as soon as you save a rollout greater than 0%. Changes are made available once your app refreshes feature flags, or when a new session is started.
 {% endalert %}
 
-## Step 4: Distribute variants
+### Step 4: Distribute variants
 
 Choose the percentage distribution for your experiment. As a best practice, you should not change the distribution once your experiment has been launched.
 
-## Step 5: Assign conversions
+### Step 5: Assign conversions
 
 Braze lets you to track how often users perform specific actions, [conversion events][5], after receiving a campaign. Specify up to a 30-day window during which a conversion will be counted if the user takes the specified action.
 
-## Step 6: Review and launch
+### Step 6: Review and launch
+
+After you’ve finished building the last of your experiment, review its details, then click **Launch Experiment**. When your experiment is finished, you can [analyze the results](#analyzing-your-experiment).
+
+## Reviewing the results
+
+After your feature flag experiment is finished, you can review your results for...
+
+Go to **Messaging** > **Campaigns** and select the campaign containing your feature flag experiment.
+
+### Campaign analytics
+
+Campaign analytics offers a high-level overview of your experiment's performance.
+
+Review this panel to see overall metrics such as the total number of impressions logged from this experiment, the unique impressions logged from the experiment, the primary conversion rate, the total revenue generated by this message, and the estimated audience. You can also review delivery, audience, and conversion settings from this page.
+
+![ALT_TEXT]()
+
+### Feature flag experiment performance
 
-After you’ve finished building the last of your experiment, review its details, then click **Launch Experiment**.
+The Feature Flags Experiments Performance panel outlines how well your message has performed across various dimensions. The metrics in this panel vary depending on your chosen messaging channel, and whether or not you are running a multivariate test. You can click on the  Preview icon to see the feature flag values that pertain to each variant. 
 
+![ALT_TEXT]()
 
 [1]: {{site.baseurl}}/user_guide/administrative/manage_your_braze_users/teams/
 [2]: {{site.baseurl}}/user_guide/administrative/app_settings/manage_app_group/tags/

From d43a0f9c94a5958beb8b5d1a2e892c532c9ad71e Mon Sep 17 00:00:00 2001
From: internetisaiah <95643215+internetisaiah@users.noreply.github.com>
Date: Wed, 21 Aug 2024 08:46:25 -0700
Subject: [PATCH 02/11] Second round

---
 .../platform_wide/feature_flags/experiments.md     | 14 ++++++++++----
 .../data_and_analytics/report_metrics.md           |  4 ++++
 .../reporting/campaign_analytics.md                |  3 +++
 3 files changed, 17 insertions(+), 4 deletions(-)

diff --git a/_docs/_developer_guide/platform_wide/feature_flags/experiments.md b/_docs/_developer_guide/platform_wide/feature_flags/experiments.md
index 75220d0919a..cfa4ad34f0e 100644
--- a/_docs/_developer_guide/platform_wide/feature_flags/experiments.md
+++ b/_docs/_developer_guide/platform_wide/feature_flags/experiments.md
@@ -74,19 +74,25 @@ After you’ve finished building the last of your experiment, review its details
 
 After your feature flag experiment is finished, you can review your results for...
 
-Go to **Messaging** > **Campaigns** and select the campaign containing your feature flag experiment.
+Go to **Messaging** > **Campaigns** and select the campaign with your feature flag experiment.
 
 ### Campaign analytics
 
-Campaign analytics offers a high-level overview of your experiment's performance.
+**Campaign Analytics** offers a high-level overview of your experiment's performance, such as:
 
-Review this panel to see overall metrics such as the total number of impressions logged from this experiment, the unique impressions logged from the experiment, the primary conversion rate, the total revenue generated by this message, and the estimated audience. You can also review delivery, audience, and conversion settings from this page.
+- The total number of impressions
+- The number of unique impressions
+- The primary conversion rate
+- The total revenue generated by the message
+- The estimated audience
+
+You can also view the experiment's settings for delivery, audience, and conversion.
 
 ![ALT_TEXT]()
 
 ### Feature flag experiment performance
 
-The Feature Flags Experiments Performance panel outlines how well your message has performed across various dimensions. The metrics in this panel vary depending on your chosen messaging channel, and whether or not you are running a multivariate test. You can click on the  Preview icon to see the feature flag values that pertain to each variant. 
+**Feature Flags Experiments Performance** shows how well your message performed across various dimensions. The specific metrics you see will vary depending on your chosen messaging channel, and whether you're running a multivariate test. To see the feature flag values associated with each variant, select **Preview**.
 
 ![ALT_TEXT]()
 
diff --git a/_docs/_user_guide/data_and_analytics/report_metrics.md b/_docs/_user_guide/data_and_analytics/report_metrics.md
index 458febee797..7c351d17de7 100644
--- a/_docs/_user_guide/data_and_analytics/report_metrics.md
+++ b/_docs/_user_guide/data_and_analytics/report_metrics.md
@@ -115,6 +115,10 @@ Total number of clicks on Button 2 of the message.
 
 {% api %}
 
+### Campaign analytics
+
+The performance of the message across various dimensions. The metrics shown depend on the selected messaging channel, and whether the [Feature Flag experiment]({{site.baseurl}}/developer_guide/platform_wide/feature_flags/experiments/#campaign-analytics) is a multivariate test.
+
 ### Choices Submitted
 
 {% apitags %}
diff --git a/_docs/_user_guide/data_and_analytics/reporting/campaign_analytics.md b/_docs/_user_guide/data_and_analytics/reporting/campaign_analytics.md
index c72c4967286..3a07b0657df 100644
--- a/_docs/_user_guide/data_and_analytics/reporting/campaign_analytics.md
+++ b/_docs/_user_guide/data_and_analytics/reporting/campaign_analytics.md
@@ -18,6 +18,9 @@ guide_featured_list:
   - name: Email
     link: /docs/user_guide/message_building_by_channel/email/reporting_and_analytics/email_reporting/
     image: /assets/img/braze_icons/mail-01.svg
+  - name: Feature Flags
+    link: docs/developer_guide/platform_wide/feature_flags/experiments/
+    image: /assets/img/braze_icons/whatsapp.svg
   - name: In-App Messages
     link: /docs/user_guide/message_building_by_channel/in-app_messages/reporting/
     image: /assets/img/braze_icons/message-text-circle-01.svg

From e46caa202a7219d120845e531167a9d69489f550 Mon Sep 17 00:00:00 2001
From: internetisaiah <95643215+internetisaiah@users.noreply.github.com>
Date: Wed, 21 Aug 2024 08:53:03 -0700
Subject: [PATCH 03/11] replacing placeholder icon

---
 .../data_and_analytics/reporting/campaign_analytics.md          | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/_docs/_user_guide/data_and_analytics/reporting/campaign_analytics.md b/_docs/_user_guide/data_and_analytics/reporting/campaign_analytics.md
index 3a07b0657df..79661173f1f 100644
--- a/_docs/_user_guide/data_and_analytics/reporting/campaign_analytics.md
+++ b/_docs/_user_guide/data_and_analytics/reporting/campaign_analytics.md
@@ -20,7 +20,7 @@ guide_featured_list:
     image: /assets/img/braze_icons/mail-01.svg
   - name: Feature Flags
     link: docs/developer_guide/platform_wide/feature_flags/experiments/
-    image: /assets/img/braze_icons/whatsapp.svg
+    image: /assets/img/braze_icons/flag-06.svg
   - name: In-App Messages
     link: /docs/user_guide/message_building_by_channel/in-app_messages/reporting/
     image: /assets/img/braze_icons/message-text-circle-01.svg

From cd6e9f6dc021cf4e22e3d5b6251cddb5dd9a3b81 Mon Sep 17 00:00:00 2001
From: internetisaiah <95643215+internetisaiah@users.noreply.github.com>
Date: Thu, 22 Aug 2024 17:55:22 -0700
Subject: [PATCH 04/11] finishing report_metrics.md

---
 .../data_and_analytics/report_metrics.md      | 20 +++++++++++++++++++
 1 file changed, 20 insertions(+)

diff --git a/_docs/_user_guide/data_and_analytics/report_metrics.md b/_docs/_user_guide/data_and_analytics/report_metrics.md
index 7c351d17de7..385cc8e63cb 100644
--- a/_docs/_user_guide/data_and_analytics/report_metrics.md
+++ b/_docs/_user_guide/data_and_analytics/report_metrics.md
@@ -117,8 +117,16 @@ Total number of clicks on Button 2 of the message.
 
 ### Campaign analytics
 
+{% apitags %}
+Feature Flags
+{% endapitags %}
+
 The performance of the message across various dimensions. The metrics shown depend on the selected messaging channel, and whether the [Feature Flag experiment]({{site.baseurl}}/developer_guide/platform_wide/feature_flags/experiments/#campaign-analytics) is a multivariate test.
 
+{% endapi %}
+
+{% api %}
+
 ### Choices Submitted
 
 {% apitags %}
@@ -299,6 +307,18 @@ The WhatsApp message could not send because the Internet Service Provider return
 
 {% api %}
 
+### Feature flag experiment performance
+
+{% apitags %}
+Feature Flags
+{% endapitags %}
+
+Performance metrics for the message in a Feature Flag experiment. The specific metrics shown will vary depending on the messaging channel, and whether or not the experiment was a multivariate test.
+
+{% endapi %}
+
+{% api %}
+
 ### Influenced Opens
 
 {% apitags %}

From 4d05980ca7a271b26ec145b85093ca424145b544 Mon Sep 17 00:00:00 2001
From: isaiah robinson <95643215+internetisaiah@users.noreply.github.com>
Date: Wed, 26 Feb 2025 08:00:23 -0800
Subject: [PATCH 05/11] Update
 _docs/_developer_guide/platform_wide/feature_flags/experiments.md

---
 .../_developer_guide/platform_wide/feature_flags/experiments.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/_docs/_developer_guide/platform_wide/feature_flags/experiments.md b/_docs/_developer_guide/platform_wide/feature_flags/experiments.md
index cfa4ad34f0e..96655583d84 100644
--- a/_docs/_developer_guide/platform_wide/feature_flags/experiments.md
+++ b/_docs/_developer_guide/platform_wide/feature_flags/experiments.md
@@ -72,7 +72,7 @@ After you’ve finished building the last of your experiment, review its details
 
 ## Reviewing the results
 
-After your feature flag experiment is finished, you can review your results for...
+After your feature flag experiment is finished, you can review impression data for your experiment.
 
 Go to **Messaging** > **Campaigns** and select the campaign with your feature flag experiment.
 

From bd3dd23a9f8e26c572e777d9ccf0aa773934e985 Mon Sep 17 00:00:00 2001
From: isaiah robinson <95643215+internetisaiah@users.noreply.github.com>
Date: Wed, 26 Feb 2025 08:01:15 -0800
Subject: [PATCH 06/11] Update
 _docs/_user_guide/data_and_analytics/report_metrics.md

---
 _docs/_user_guide/data_and_analytics/report_metrics.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/_docs/_user_guide/data_and_analytics/report_metrics.md b/_docs/_user_guide/data_and_analytics/report_metrics.md
index 385cc8e63cb..bb762126e27 100644
--- a/_docs/_user_guide/data_and_analytics/report_metrics.md
+++ b/_docs/_user_guide/data_and_analytics/report_metrics.md
@@ -121,7 +121,7 @@ Total number of clicks on Button 2 of the message.
 Feature Flags
 {% endapitags %}
 
-The performance of the message across various dimensions. The metrics shown depend on the selected messaging channel, and whether the [Feature Flag experiment]({{site.baseurl}}/developer_guide/platform_wide/feature_flags/experiments/#campaign-analytics) is a multivariate test.
+The performance of the message across various channels. The metrics shown depend on the selected messaging channel, and whether the [Feature Flag experiment]({{site.baseurl}}/developer_guide/platform_wide/feature_flags/experiments/#campaign-analytics) is a multivariate test.
 
 {% endapi %}
 

From 39a0c496fdcaf26059358d77ad59d2ce51c9fcd6 Mon Sep 17 00:00:00 2001
From: internetisaiah <95643215+internetisaiah@users.noreply.github.com>
Date: Wed, 26 Feb 2025 08:11:48 -0800
Subject: [PATCH 07/11] undoing merge for experiments.md

---
 .../feature_flags/experiments.md              |  73 ++++--------
 .../feature_flags/experiments.md              | 106 ++++++++++++++++++
 2 files changed, 128 insertions(+), 51 deletions(-)
 create mode 100644 _docs/_developer_guide/platform_wide/feature_flags/experiments.md

diff --git a/_docs/_developer_guide/feature_flags/experiments.md b/_docs/_developer_guide/feature_flags/experiments.md
index 96655583d84..6f9fb891ac8 100644
--- a/_docs/_developer_guide/feature_flags/experiments.md
+++ b/_docs/_developer_guide/feature_flags/experiments.md
@@ -1,7 +1,7 @@
 ---
 nav_title: Feature Flag Experiments
 article_title: Feature Flag Experiments
-page_order: 30
+page_order: 40
 description: "Feature flag experiments let you A/B test changes to your applications to optimize conversion rates."
 tool: Feature Flags
 platform:
@@ -19,7 +19,7 @@ platform:
 
 Before you can track user data in the experiment, your app needs to record when a user interacts with a feature flag. This is called a feature flag impression. Make sure to log a feature flag impression whenever a user sees or could have seen the feature you're testing, even if they're in the control group.
 
-To learn more about logging feature flag impressions, see [Creating feature flags][6].
+To learn more about logging feature flag impressions, see [Creating feature flags]({{site.baseurl}}/developer_guide/platform_wide/feature_flags/create/#impressions).
 
 ```javascript
 const featureFlag = braze.getFeatureFlag("my-new-feature");
@@ -36,71 +36,42 @@ if (featureFlag?.enabled) {
 
 ### Step 1: Create an experiment
 
-1. Go to **Messaging** > **Campaigns** and click **+ Create Campaign**.
+1. Go to **Messaging** > **Campaigns**, then select **+ Create Campaign**.
 2. Select **Feature Flag Experiment**.
-3. Name your campaign something clear and meaningful.
+3. Give your campaign a clear and meaningful name.
 
 ### Step 2: Add experiment variants
 
-Next, create variations. For each variant, choose the feature flag you want to turn on or off and review the assigned properties. To test the impact of your feature, use variants to split traffic into two or more groups. Name one group "My control group" and turn its feature flags off.
+Next, create variations. For each variant, choose the feature flag you want to turn on or off, then review its assigned properties.
 
-#### Overwriting properties
+To test the impact of your feature, use variants to split traffic into two or more groups. Name one group "My control group" and turn its feature flags off.
 
-Though you specified default properties when you originally set up your feature flag, you can choose to overwrite those values for users who receive a specific campaign variant. To edit, add, or remove additional default properties, edit the feature flag itself from **Messaging** > **Feature Flags**.
+### Step 3: Overwrite properties (optional)
 
-![][image1]{: style="max-width:80%"}
+You can choose to overwrite the default properties you initially set up for users who receive a specific campaign variant.
 
-### Step 3: Choose users to target
+To edit, add, or remove additional default properties, edit the feature flag itself from **Messaging** > **Feature Flags**. When a variant is disabled, the SDK will return an empty properties object for the given feature flag.
 
-Next, you need to [target users][4] by choosing segments or filters to narrow down your audience. Segment membership is calculated when feature flags are refreshed for a given user.
+![The 'Experiment Variants' section with the 'link' variable key overwritten with '/sales'.]({% image_buster /assets/img/feature_flags/feature_flag_experiment_override.png %}){: style="max-width:80%"}
 
-{% alert note %}
-Your target audience will be eligible for the feature flag as soon as you save a rollout greater than 0%. Changes are made available once your app refreshes feature flags, or when a new session is started.
-{% endalert %}
-
-### Step 4: Distribute variants
-
-Choose the percentage distribution for your experiment. As a best practice, you should not change the distribution once your experiment has been launched.
-
-### Step 5: Assign conversions
-
-Braze lets you to track how often users perform specific actions, [conversion events][5], after receiving a campaign. Specify up to a 30-day window during which a conversion will be counted if the user takes the specified action.
-
-### Step 6: Review and launch
+### Step 4: Choose users to target
 
-After you’ve finished building the last of your experiment, review its details, then click **Launch Experiment**. When your experiment is finished, you can [analyze the results](#analyzing-your-experiment).
+Use one of your segments or filters to choose your [target users]({{site.baseurl}}/user_guide/engagement_tools/campaigns/building_campaigns/targeting_users/). For example, you can use the **Received Feature Flag Variant** filter to retarget users who have already received an A/B test.
 
-## Reviewing the results
+![The 'Target' page in a feature flag experiment with 'Received Feature Flag Variant' highlighted in the filter group search bar.]({% image_buster /assets/img/feature_flags/variant-filter-dropdown.png %}){: style="max-width:70%"}
 
-After your feature flag experiment is finished, you can review impression data for your experiment.
-
-Go to **Messaging** > **Campaigns** and select the campaign with your feature flag experiment.
-
-### Campaign analytics
-
-**Campaign Analytics** offers a high-level overview of your experiment's performance, such as:
-
-- The total number of impressions
-- The number of unique impressions
-- The primary conversion rate
-- The total revenue generated by the message
-- The estimated audience
-
-You can also view the experiment's settings for delivery, audience, and conversion.
+{% alert note %}
+Segment membership is calculated when feature flags are refreshed for a given user. Changes are made available after your app refreshes feature flags, or when a new session is started.
+{% endalert %}
 
-![ALT_TEXT]()
+### Step 5: Distribute variants
 
-### Feature flag experiment performance
+Choose the percentage distribution for your experiment. As a best practice, you should not change the distribution after your experiment has been launched.
 
-**Feature Flags Experiments Performance** shows how well your message performed across various dimensions. The specific metrics you see will vary depending on your chosen messaging channel, and whether you're running a multivariate test. To see the feature flag values associated with each variant, select **Preview**.
+### Step 6: Assign conversions
 
-![ALT_TEXT]()
+Braze lets you to track how often users perform specific actions, [conversion events]({{site.baseurl}}/user_guide/engagement_tools/campaigns/building_campaigns/conversion_events/), after receiving a campaign. Specify up to a 30-day window during which a conversion will be counted if the user takes the specified action.
 
-[1]: {{site.baseurl}}/user_guide/administrative/manage_your_braze_users/teams/
-[2]: {{site.baseurl}}/user_guide/administrative/app_settings/manage_app_group/tags/
-[3]: {{site.baseurl}}/user_guide/data_and_analytics/reporting/report_builder/
-[4]: {{site.baseurl}}/user_guide/engagement_tools/campaigns/building_campaigns/targeting_users/
-[5]: {{site.baseurl}}/user_guide/engagement_tools/campaigns/building_campaigns/conversion_events/
-[6]: {{site.baseurl}}/developer_guide/platform_wide/feature_flags/create/#impressions
+### Step 7: Review and launch
 
-[image1]: {% image_buster /assets/img/feature_flags/feature_flag_experiment_override.png %} 
+After you’ve finished building the last of your experiment, review its details, then select **Launch Experiment**.
diff --git a/_docs/_developer_guide/platform_wide/feature_flags/experiments.md b/_docs/_developer_guide/platform_wide/feature_flags/experiments.md
new file mode 100644
index 00000000000..96655583d84
--- /dev/null
+++ b/_docs/_developer_guide/platform_wide/feature_flags/experiments.md
@@ -0,0 +1,106 @@
+---
+nav_title: Feature Flag Experiments
+article_title: Feature Flag Experiments
+page_order: 30
+description: "Feature flag experiments let you A/B test changes to your applications to optimize conversion rates."
+tool: Feature Flags
+platform:
+  - iOS
+  - Android
+  - Web
+
+---
+
+# Feature flag experiments
+
+> Feature flag experiments let you A/B test changes to your applications to optimize conversion rates. Marketers can use feature flags to determine whether a new feature positively or negatively impacts conversion rates, or which set of feature flag properties is most optimal.
+
+## Prerequisites
+
+Before you can track user data in the experiment, your app needs to record when a user interacts with a feature flag. This is called a feature flag impression. Make sure to log a feature flag impression whenever a user sees or could have seen the feature you're testing, even if they're in the control group.
+
+To learn more about logging feature flag impressions, see [Creating feature flags][6].
+
+```javascript
+const featureFlag = braze.getFeatureFlag("my-new-feature");
+braze.logFeatureFlagImpression("my-new-feature");
+if (featureFlag?.enabled) {
+   return <NewFeature />
+} else {
+   return <ExistingFeature />
+}
+
+```
+
+## Creating a feature flag experiment
+
+### Step 1: Create an experiment
+
+1. Go to **Messaging** > **Campaigns** and click **+ Create Campaign**.
+2. Select **Feature Flag Experiment**.
+3. Name your campaign something clear and meaningful.
+
+### Step 2: Add experiment variants
+
+Next, create variations. For each variant, choose the feature flag you want to turn on or off and review the assigned properties. To test the impact of your feature, use variants to split traffic into two or more groups. Name one group "My control group" and turn its feature flags off.
+
+#### Overwriting properties
+
+Though you specified default properties when you originally set up your feature flag, you can choose to overwrite those values for users who receive a specific campaign variant. To edit, add, or remove additional default properties, edit the feature flag itself from **Messaging** > **Feature Flags**.
+
+![][image1]{: style="max-width:80%"}
+
+### Step 3: Choose users to target
+
+Next, you need to [target users][4] by choosing segments or filters to narrow down your audience. Segment membership is calculated when feature flags are refreshed for a given user.
+
+{% alert note %}
+Your target audience will be eligible for the feature flag as soon as you save a rollout greater than 0%. Changes are made available once your app refreshes feature flags, or when a new session is started.
+{% endalert %}
+
+### Step 4: Distribute variants
+
+Choose the percentage distribution for your experiment. As a best practice, you should not change the distribution once your experiment has been launched.
+
+### Step 5: Assign conversions
+
+Braze lets you to track how often users perform specific actions, [conversion events][5], after receiving a campaign. Specify up to a 30-day window during which a conversion will be counted if the user takes the specified action.
+
+### Step 6: Review and launch
+
+After you’ve finished building the last of your experiment, review its details, then click **Launch Experiment**. When your experiment is finished, you can [analyze the results](#analyzing-your-experiment).
+
+## Reviewing the results
+
+After your feature flag experiment is finished, you can review impression data for your experiment.
+
+Go to **Messaging** > **Campaigns** and select the campaign with your feature flag experiment.
+
+### Campaign analytics
+
+**Campaign Analytics** offers a high-level overview of your experiment's performance, such as:
+
+- The total number of impressions
+- The number of unique impressions
+- The primary conversion rate
+- The total revenue generated by the message
+- The estimated audience
+
+You can also view the experiment's settings for delivery, audience, and conversion.
+
+![ALT_TEXT]()
+
+### Feature flag experiment performance
+
+**Feature Flags Experiments Performance** shows how well your message performed across various dimensions. The specific metrics you see will vary depending on your chosen messaging channel, and whether you're running a multivariate test. To see the feature flag values associated with each variant, select **Preview**.
+
+![ALT_TEXT]()
+
+[1]: {{site.baseurl}}/user_guide/administrative/manage_your_braze_users/teams/
+[2]: {{site.baseurl}}/user_guide/administrative/app_settings/manage_app_group/tags/
+[3]: {{site.baseurl}}/user_guide/data_and_analytics/reporting/report_builder/
+[4]: {{site.baseurl}}/user_guide/engagement_tools/campaigns/building_campaigns/targeting_users/
+[5]: {{site.baseurl}}/user_guide/engagement_tools/campaigns/building_campaigns/conversion_events/
+[6]: {{site.baseurl}}/developer_guide/platform_wide/feature_flags/create/#impressions
+
+[image1]: {% image_buster /assets/img/feature_flags/feature_flag_experiment_override.png %} 

From 6849584293024912c04ba4c7dd100f0d5b344b11 Mon Sep 17 00:00:00 2001
From: internetisaiah <95643215+internetisaiah@users.noreply.github.com>
Date: Wed, 26 Feb 2025 08:16:03 -0800
Subject: [PATCH 08/11] remerging

---
 .../feature_flags/experiments.md              |  22 ++++
 .../feature_flags/experiments.md              | 106 ------------------
 2 files changed, 22 insertions(+), 106 deletions(-)
 delete mode 100644 _docs/_developer_guide/platform_wide/feature_flags/experiments.md

diff --git a/_docs/_developer_guide/feature_flags/experiments.md b/_docs/_developer_guide/feature_flags/experiments.md
index 6f9fb891ac8..d2561562833 100644
--- a/_docs/_developer_guide/feature_flags/experiments.md
+++ b/_docs/_developer_guide/feature_flags/experiments.md
@@ -75,3 +75,25 @@ Braze lets you to track how often users perform specific actions, [conversion ev
 ### Step 7: Review and launch
 
 After you’ve finished building the last of your experiment, review its details, then select **Launch Experiment**.
+
+## Reviewing the results
+
+After your feature flag experiment is finished, you can review impression data for your experiment.
+
+Go to **Messaging** > **Campaigns** and select the campaign with your feature flag experiment.
+
+### Campaign analytics
+
+**Campaign Analytics** offers a high-level overview of your experiment's performance, such as:
+
+- The total number of impressions
+- The number of unique impressions
+- The primary conversion rate
+- The total revenue generated by the message
+- The estimated audience
+
+You can also view the experiment's settings for delivery, audience, and conversion.
+
+### Feature flag experiment performance
+
+**Feature Flags Experiments Performance** shows how well your message performed across various dimensions. The specific metrics you see will vary depending on your chosen messaging channel, and whether you're running a multivariate test. To see the feature flag values associated with each variant, select **Preview**.
diff --git a/_docs/_developer_guide/platform_wide/feature_flags/experiments.md b/_docs/_developer_guide/platform_wide/feature_flags/experiments.md
deleted file mode 100644
index 96655583d84..00000000000
--- a/_docs/_developer_guide/platform_wide/feature_flags/experiments.md
+++ /dev/null
@@ -1,106 +0,0 @@
----
-nav_title: Feature Flag Experiments
-article_title: Feature Flag Experiments
-page_order: 30
-description: "Feature flag experiments let you A/B test changes to your applications to optimize conversion rates."
-tool: Feature Flags
-platform:
-  - iOS
-  - Android
-  - Web
-
----
-
-# Feature flag experiments
-
-> Feature flag experiments let you A/B test changes to your applications to optimize conversion rates. Marketers can use feature flags to determine whether a new feature positively or negatively impacts conversion rates, or which set of feature flag properties is most optimal.
-
-## Prerequisites
-
-Before you can track user data in the experiment, your app needs to record when a user interacts with a feature flag. This is called a feature flag impression. Make sure to log a feature flag impression whenever a user sees or could have seen the feature you're testing, even if they're in the control group.
-
-To learn more about logging feature flag impressions, see [Creating feature flags][6].
-
-```javascript
-const featureFlag = braze.getFeatureFlag("my-new-feature");
-braze.logFeatureFlagImpression("my-new-feature");
-if (featureFlag?.enabled) {
-   return <NewFeature />
-} else {
-   return <ExistingFeature />
-}
-
-```
-
-## Creating a feature flag experiment
-
-### Step 1: Create an experiment
-
-1. Go to **Messaging** > **Campaigns** and click **+ Create Campaign**.
-2. Select **Feature Flag Experiment**.
-3. Name your campaign something clear and meaningful.
-
-### Step 2: Add experiment variants
-
-Next, create variations. For each variant, choose the feature flag you want to turn on or off and review the assigned properties. To test the impact of your feature, use variants to split traffic into two or more groups. Name one group "My control group" and turn its feature flags off.
-
-#### Overwriting properties
-
-Though you specified default properties when you originally set up your feature flag, you can choose to overwrite those values for users who receive a specific campaign variant. To edit, add, or remove additional default properties, edit the feature flag itself from **Messaging** > **Feature Flags**.
-
-![][image1]{: style="max-width:80%"}
-
-### Step 3: Choose users to target
-
-Next, you need to [target users][4] by choosing segments or filters to narrow down your audience. Segment membership is calculated when feature flags are refreshed for a given user.
-
-{% alert note %}
-Your target audience will be eligible for the feature flag as soon as you save a rollout greater than 0%. Changes are made available once your app refreshes feature flags, or when a new session is started.
-{% endalert %}
-
-### Step 4: Distribute variants
-
-Choose the percentage distribution for your experiment. As a best practice, you should not change the distribution once your experiment has been launched.
-
-### Step 5: Assign conversions
-
-Braze lets you to track how often users perform specific actions, [conversion events][5], after receiving a campaign. Specify up to a 30-day window during which a conversion will be counted if the user takes the specified action.
-
-### Step 6: Review and launch
-
-After you’ve finished building the last of your experiment, review its details, then click **Launch Experiment**. When your experiment is finished, you can [analyze the results](#analyzing-your-experiment).
-
-## Reviewing the results
-
-After your feature flag experiment is finished, you can review impression data for your experiment.
-
-Go to **Messaging** > **Campaigns** and select the campaign with your feature flag experiment.
-
-### Campaign analytics
-
-**Campaign Analytics** offers a high-level overview of your experiment's performance, such as:
-
-- The total number of impressions
-- The number of unique impressions
-- The primary conversion rate
-- The total revenue generated by the message
-- The estimated audience
-
-You can also view the experiment's settings for delivery, audience, and conversion.
-
-![ALT_TEXT]()
-
-### Feature flag experiment performance
-
-**Feature Flags Experiments Performance** shows how well your message performed across various dimensions. The specific metrics you see will vary depending on your chosen messaging channel, and whether you're running a multivariate test. To see the feature flag values associated with each variant, select **Preview**.
-
-![ALT_TEXT]()
-
-[1]: {{site.baseurl}}/user_guide/administrative/manage_your_braze_users/teams/
-[2]: {{site.baseurl}}/user_guide/administrative/app_settings/manage_app_group/tags/
-[3]: {{site.baseurl}}/user_guide/data_and_analytics/reporting/report_builder/
-[4]: {{site.baseurl}}/user_guide/engagement_tools/campaigns/building_campaigns/targeting_users/
-[5]: {{site.baseurl}}/user_guide/engagement_tools/campaigns/building_campaigns/conversion_events/
-[6]: {{site.baseurl}}/developer_guide/platform_wide/feature_flags/create/#impressions
-
-[image1]: {% image_buster /assets/img/feature_flags/feature_flag_experiment_override.png %} 

From 97eb90136fadf754136db3b651a3a1868656ac50 Mon Sep 17 00:00:00 2001
From: isaiah robinson <95643215+internetisaiah@users.noreply.github.com>
Date: Wed, 26 Feb 2025 08:18:00 -0800
Subject: [PATCH 09/11] Update _docs/_developer_guide/feature_flags/canvas.md

---
 _docs/_developer_guide/feature_flags/canvas.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/_docs/_developer_guide/feature_flags/canvas.md b/_docs/_developer_guide/feature_flags/canvas.md
index 4d087ee9d45..f5d6821f09a 100644
--- a/_docs/_developer_guide/feature_flags/canvas.md
+++ b/_docs/_developer_guide/feature_flags/canvas.md
@@ -1,6 +1,6 @@
 ---
 nav_title: Feature Flags in Canvas
-page_order: 40
+page_order: 30
 noindex: true
 tool: Feature Flags
 platform:

From 3ed46a67791504cc470b6b0ecf76341db301f595 Mon Sep 17 00:00:00 2001
From: isaiah robinson <95643215+internetisaiah@users.noreply.github.com>
Date: Wed, 26 Feb 2025 08:28:53 -0800
Subject: [PATCH 10/11] Update experiments.md

---
 _docs/_developer_guide/feature_flags/experiments.md | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/_docs/_developer_guide/feature_flags/experiments.md b/_docs/_developer_guide/feature_flags/experiments.md
index d2561562833..a444d7cb085 100644
--- a/_docs/_developer_guide/feature_flags/experiments.md
+++ b/_docs/_developer_guide/feature_flags/experiments.md
@@ -78,9 +78,7 @@ After you’ve finished building the last of your experiment, review its details
 
 ## Reviewing the results
 
-After your feature flag experiment is finished, you can review impression data for your experiment.
-
-Go to **Messaging** > **Campaigns** and select the campaign with your feature flag experiment.
+After your feature flag experiment is finished, you can review impression data for your experiment. Go to **Messaging** > **Campaigns** and select the campaign with your feature flag experiment.
 
 ### Campaign analytics
 

From fedb8473c963009aa790d9251fc7f4f66e930a65 Mon Sep 17 00:00:00 2001
From: isaiah robinson <95643215+internetisaiah@users.noreply.github.com>
Date: Wed, 26 Feb 2025 11:22:50 -0800
Subject: [PATCH 11/11] Update
 _docs/_user_guide/analytics/reporting/campaign_analytics.md

---
 _docs/_user_guide/analytics/reporting/campaign_analytics.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/_docs/_user_guide/analytics/reporting/campaign_analytics.md b/_docs/_user_guide/analytics/reporting/campaign_analytics.md
index 45946e4db7d..efb4da89dde 100644
--- a/_docs/_user_guide/analytics/reporting/campaign_analytics.md
+++ b/_docs/_user_guide/analytics/reporting/campaign_analytics.md
@@ -19,7 +19,7 @@ guide_featured_list:
     link: /docs/user_guide/message_building_by_channel/email/reporting_and_analytics/email_reporting/
     image: /assets/img/braze_icons/mail-01.svg
   - name: Feature Flags
-    link: docs/developer_guide/platform_wide/feature_flags/experiments/
+    link: /docs/developer_guide/feature_flags/experiments/
     image: /assets/img/braze_icons/flag-06.svg
   - name: In-App Messages
     link: /docs/user_guide/message_building_by_channel/in-app_messages/reporting/