[EXP-GA] Move Experimentation docs to top-level navigation

config/_default/menus/main.en.yaml

@@ -8789,7 +8789,7 @@ menu:
       parent: rum
       weight: 13
     - name: Product Analytics
-      url: product_analytics
+      url: product_analytics/
       pre: product-analytics
       identifier: product_analytics
       parent: digital_experience_heading
@@ -8840,30 +8840,31 @@ menu:
       identifier: pa_profiles
       weight: 4
     - name: Experiments
-      url: product_analytics/experimentation/
-      parent: product_analytics
+      url: experiments/
+      pre: experiment-wui
+      parent: digital_experience_heading
       identifier: pa_experiments
-      weight: 5
+      weight: 50000
     - name: Define Metrics
-      url: product_analytics/experimentation/defining_metrics
+      url: experiments/defining_metrics
       parent: pa_experiments
       identifier: pa_experiments_metrics
-      weight: 501
+      weight: 1
     - name: Reading Experiment Results
-      url: product_analytics/experimentation/reading_results
+      url: experiments/reading_results
       parent: pa_experiments
       identifier: pa_experiments_results
-      weight: 502
+      weight: 2
     - name: Minimum Detectable Effects
-      url: product_analytics/experimentation/minimum_detectable_effect
+      url: experiments/minimum_detectable_effect
       parent: pa_experiments
       identifier: pa_experiments_mde
-      weight: 503
+      weight: 3
     - name: Troubleshooting
-      url: product_analytics/experimentation/troubleshooting
+      url: experiments/troubleshooting
       parent: pa_experiments
       identifier: pa_experiments_troubleshooting
-      weight: 504
+      weight: 4
     - name: Guides
       url: product_analytics/guide/
       parent: product_analytics

content/en/product_analytics/experimentation/_index.md → content/en/experiments/_index.md

@@ -1,11 +1,13 @@
 ---
 title: Planning and Launching Experiments
-description: Experimentation allows you to measure the causal relationship new experiences or features have on user outcomes.
+description: Use Datadog Experiments to measure the causal relationship that new experiences or features have on user outcomes.
+aliases:
+  - /product_analytics/experimentation/
 further_reading:
 - link: "https://www.datadoghq.com/blog/datadog-product-analytics"
   tag: "Blog"
   text: "Make data-driven design decisions with Product Analytics"
-- link: "/product_analytics/experimentation/defining_metrics"
+- link: "/experiments/defining_metrics"
   tag: "Documentation"
   text: "Defining Experiment Metrics"
 ---
@@ -15,11 +17,10 @@ Datadog Experiments is in Preview. Complete the form to request access.
 {{< /callout >}}
 
 ## Overview 
-Datadog Experimentation allows you to measure the causal relationship that new experiences and features have on user outcomes. To do this, Experimentation randomly allocates traffic between two or more variations, using one of the variations as a control group.
+Datadog Experiments allows you to measure the causal relationship that new experiences and features have on user outcomes. Experiments uses [Feature Flags][4] to randomly allocate traffic between two or more variations, using one of the variations as a control group.
 
 This page walks you through planning and launching your experiments. 
 
-
 ## Setup
 To create, configure, and launch your experiment, complete the following steps:
 
@@ -29,15 +30,13 @@ To create, configure, and launch your experiment, complete the following steps:
 2. Click **+ Create Experiment**.
 3. Enter your experiment name and hypothesis.
 
-{{< img src="/product_analytics/experiment/exp_create_experiment.png" alt="create an experiment and add a hypothesis for the experiment." style="width:80%;" >}}
-
+{{< img src="/product_analytics/experiment/exp_create_experiment.png" alt="The experiment creation form with fields for experiment name and hypothesis." style="width:80%;" >}}
 
 ### Step 2 - Add metrics
 
 After you’ve created an experiment, add your primary metric and optional guardrails. See [Defining Metrics][2] for details on how to create metrics.
 
-{{< img src="/product_analytics/experiment/exp_decision_metrics1.png" alt="create an experiment and add a hypothesis for the experiment." style="width:80%;" >}}
-
+{{< img src="/product_analytics/experiment/exp_decision_metrics1.png" alt="The metrics configuration panel with options for primary metric and guardrails." style="width:80%;" >}}
 
 #### Add a sample size calculation (optional)
 
@@ -48,44 +47,29 @@ After selecting your experiment’s metrics, use the optional sample size calcul
 
 1. Click **Run calculation** to see the [Minimum Detectable Effects][3] (MDE) your experiment has on your metrics. The MDE is the smallest difference that you are able to detect between your experiment’s variants.
 
-{{< img src="/product_analytics/experiment/exp_sample_size.png" alt="Sleect an entrypoint event to run a sample size calculation" style="width:90%;" >}}
+{{< img src="/product_analytics/experiment/exp_sample_size.png" alt="Select an entry point event to run a sample size calculation" style="width:90%;" >}}
 
 ### Step 3 - Launch your experiment
 
 After specifying your metrics, you can launch your experiment.
 
 1. Select a Feature Flag that captures the variants you want to test. If you have not yet created a feature flag, see the [Getting Started with Feature Flags][4] page.
 
-1. Click **Set up experiment on feature flag** to specify how you want to roll out your experiment. You can either launch the experiment to all traffic, or schedule a gradual rollout.
-
+1. Click **Set Up Experiment on Feature Flag** to specify how you want to roll out your experiment. You can either launch the experiment to all traffic, or schedule a gradual rollout.
 
 {{< img src="/product_analytics/experiment/exp_feature_flag.png" alt="Set up an experiment on a Feature Flag." style="width:90%;" >}}
 
-
 ## Next steps
-1. **[Defining metrics][2]**: Define the metrics you want to measure during your experimentation.
-1. **[Reading Experiment Results][5]**: Review and explore your Experiment results.
-1. Learn more about **[Minimum Detectable Effects][3]**: Choose an appropriately sized MDE.
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+1. **[Defining metrics][2]**: Define the metrics you want to measure during your experiments.
+1. **[Reading Experiment Results][5]**: Review and explore your experiment results.
+1. **[Minimum Detectable Effects][3]**: Choose an appropriately sized MDE.
 
 
 ## Further reading
 {{< partial name="whats-next/whats-next.html" >}}
 
 [1]: https://app.datadoghq.com/product-analytics/experiments
-[2]: /product_analytics/experimentation/defining_metrics
-[3]: /product_analytics/experimentation/minimum_detectable_effect
+[2]: /experiments/defining_metrics
+[3]: /experiments/minimum_detectable_effect
 [4]: /getting_started/feature_flags/
-[5]: /product_analytics/experimentation/reading_results
+[5]: /experiments/reading_results

content/en/product_analytics/experimentation/defining_metrics.md → content/en/experiments/defining_metrics.md

@@ -1,20 +1,22 @@
 ---
 title: Defining Metrics
-description: Define the metrics you want to measure during your experimentation.
+description: Define the metrics you want to measure during your experiments.
+aliases:
+  - /product_analytics/experimentation/defining_metrics/
 further_reading:
 - link: "https://www.datadoghq.com/blog/datadog-product-analytics/"
   tag: "Blog"
   text: "Make data-driven design decisions with Product Analytics"
-- link: "/product_analytics/experimentation/reading_results"
+- link: "/experiments/reading_results"
   tag: "Documentation"
   text: "Reading Experiment Results"
 ---
 
 ## Overview
 
-Define the metrics you want to measure during your experimentation. Metrics can be built using Product Analytics and Real User Monitoring (RUM) data.
+Define the metrics you want to measure during your experiments. Metrics can be built using Product Analytics and Real User Monitoring (RUM) data.
 
-<div class="alert alert-info"> In order to create a metric, you must have Datadog’s client SDK installed in your application and be actively capturing data.
+<div class="alert alert-info"> To create a metric, you must have Datadog’s client SDK installed in your application and be actively capturing data.
 </div>
 
 ## Create your first metric
@@ -35,7 +37,6 @@ After you’ve selected your event of interest, you can specify an aggregation m
 
 {{< img src="/product_analytics/experiment/exp_default_metric_agg.png" alt="Dropdown menu to select the method of aggregation for metrics." style="width:90%;" >}}
 
-
 ### Default metric normalization 
 
 All metrics are normalized by the number of enrolled subjects. For example, a **count of unique users** metric is computed as: 
@@ -68,17 +69,13 @@ For example, an e-commerce company that wants to measure the _Average Order Valu
 
 Datadog’s statistical engine accounts for correlations between the numerator and denominator using the [delta method][2]. 
 
-
 ## Add filters
 You can also add filters to your metrics, similar to other [Product Analytics dashboards][3]. For instance, you might want to filter page views based on referring URL or UTM parameters. Similarly, you might want to filter actions to a specific page or value of a custom attribute. As you add filters, you can check metric values in real time using the chart on the right.
 
-
 {{< img src="/product_analytics/experiment/exp_filter_by.png" alt="Filter flow to scope your metric by specific properties." style="width:90%;" >}}
 
-
-
 ## Advanced options
-Datadog supports several advanced options specific to experimentation:
+Datadog Experiments supports several advanced options:
 
 `Timeframe filters`
 : - By default, Datadog will include all events between a user's first exposure and the end of the experiment. If you want to measure a time-boxed value such as “sessions within 7 days”, you can add a timeframe filter.
@@ -92,10 +89,6 @@ Datadog supports several advanced options specific to experimentation:
 : - Real world data often includes extreme outliers that can impact experiment results. 
   - Use this setting to set a threshold at which data is truncated. For instance, set a 99% upper bound to truncate all results at the metric’s 99th percentile.
 
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-
 ## Further reading
 {{< partial name="whats-next/whats-next.html" >}}
 

content/en/product_analytics/experimentation/minimum_detectable_effect.md → content/en/experiments/minimum_detectable_effect.md

@@ -1,6 +1,8 @@
 ---
 title: Minimum Detectable Effects
 description: Determine the smallest detectable difference that may result in a statistically significant experiment result.
+aliases:
+  - /product_analytics/experimentation/minimum_detectable_effect/
 further_reading:
 - link: "https://www.datadoghq.com/blog/datadog-product-analytics/"
   tag: "Blog"
@@ -40,10 +42,5 @@ If the MDE is too small, the experiment may require excessive traffic or run tim
 
 A common way to choose an MDE is to examine results from past experiments. For example, if historical experiments in a particular domain typically yield effects of 5–10%, selecting an MDE near the lower end of that range (such as 5%) can be a reasonable starting point.
 
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 ## Further reading
 {{< partial name="whats-next/whats-next.html" >}}

content/en/product_analytics/experimentation/reading_results.md → content/en/experiments/reading_results.md

@@ -1,6 +1,8 @@
 ---
 title: Reading Experiment Results
-description: Read and understand the results of your Experimentation.
+description: Read and understand the results of your experiments.
+aliases:
+  - /product_analytics/experimentation/reading_results/
 further_reading:
 - link: "https://www.datadoghq.com/blog/datadog-product-analytics/"
   tag: "Blog"
@@ -12,9 +14,9 @@ further_reading:
 
 ## Overview 
 
-After [launching your experiment][1], Datadog immediately begins calculating results for your selected metrics. You can add additional metrics at any time, organize metrics into groups, and explore related user sessions to understand the impact of each variant.
+After [launching your experiment][1], Datadog begins calculating results for your selected metrics. You can add additional metrics, organize metrics into groups, and explore related user sessions to understand the impact of each variant.
 
-{{< img src="/product_analytics/experiment/exp_reading_exps_overview.png" alt="A view of the metrics and their variations in the control and experiment groups ." style="width:90%;" >}}
+{{< img src="/product_analytics/experiment/exp_reading_exps_overview.png" alt="A view of the metrics and their variations in the control and experiment groups." style="width:90%;" >}}
 
 ## Confidence intervals
 For each metric, Datadog shows the average per-subject value (typically per user) for both the control and treatment variants. It also reports the relative lift and the associated confidence interval.
@@ -34,18 +36,13 @@ If the entire confidence interval is above zero, then the result is statisticall
 ## Exploring results 
 To dive deeper into experiment results, hover over a metric and click **Chart**. This gives you the option to compare the experiment’s impact across different user segments.
 
-
 ### Segment-level results
-Subject level properties are based on attributes at the initial time of exposure (for example, region, new vistor vs repeat visitor etc.). This is useful for understanding when certain cohorts of users reacted differently to the new experience. 
-
+Subject level properties are based on attributes at the initial time of exposure (for example, region, new visitor vs repeat visitor). This is useful for understanding when certain cohorts of users reacted differently to the new experience. 
 
 {{< img src="/product_analytics/experiment/exp_segment_view.png" alt="Segment-level view of metric 'click on ADD TO CART' split by four country ISO code." style="width:90%;" >}}
 
 
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-
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 ## Further reading
 {{< partial name="whats-next/whats-next.html" >}}
 
-[1]: /product_analytics/experimentation/
+[1]: /experiments/

content/en/product_analytics/experimentation/troubleshooting.md → content/en/experiments/troubleshooting.md

@@ -1,6 +1,8 @@
 ---
 title: Troubleshooting
 description: Troubleshoot issues when running experiments.
+aliases:
+  - /product_analytics/experimentation/troubleshooting/
 further_reading:
 - link: "https://www.datadoghq.com/blog/datadog-product-analytics"
   tag: "Blog"