diff --git a/contents/docs/experiments/data-warehouse.mdx b/contents/docs/experiments/data-warehouse.mdx
new file mode 100644
index 000000000000..351e32b2535b
--- /dev/null
+++ b/contents/docs/experiments/data-warehouse.mdx
@@ -0,0 +1,59 @@
+---
+title: Using data warehouse tables in experiments
+---
+
+> 🚧 **Note:** Data warehouse integration is currently considered in `beta`. We are keen to gather as much feedback as possible so if you try this out please let us know. You can email [daniel.b@posthog.com](mailto:daniel.@posthog.com), send feedback via the [in-app support panel](https://us.posthog.com#panel=support%3Afeedback%3Aexperiments%3Alow), or use one of our other [support options](/docs/support-options).
+
+Evaluating experiment metrics always depends on events. They rely on something happening at a certain point in time. If one of your [data warehouse](/docs/data-warehouse) tables includes event-like data, you can use it as a primary or secondary metric for your trends experiment.
+
+To use a data warehouse table with an experiment, you'll first need to join the 'events' table to your data warehouse table:
+
+1. Navigate to the [Data warehouse](https://us.posthog.com/data-warehouse) tab and click on **Add join** from the triple dot menu next to your table.
+2. Join the `events` table to your data warehouse table:
+ a. Under **Source Table Key**, specify a column that holds the value of the `distinct_id` present for the `$feature_flag_called` event.
+ b. Check **Optimize for Experiments** to ensure only the most recent matching event is joined to your table.
+ c. Under **Source Timestamp Key**, specify a column that represents the timestamp of the table row. It will be compared with the event timestamp to determine the most recent `$feature_flag_called` event for the row.
+
+
+
+
+
+Once you've joined the `events` table to your data warehouse table, you can use the data warehouse table in the experiment goal or as a secondary metric:
+
+1. When picking an experiment goal, click on the **Data warehouse tables** category and select your table.
+2. Specify the columns in your data warehouse table that represent the unique ID, the distinct ID, and the timestamp.
+
+
+
+When you select a data warehouse table as an experiment goal or secondary metric, PostHog sees it as an 'events-like' table and is thus able to use it to calculate results.
+
+
+
+The PostHog uses the join to the `events` table to determine which variant a given data warehouse table row is associated with. For trends experiments, the most recent `$feature_flag_called` event before the row represents their active variant assignment:
+
+```
+Data Warehouse: timestamp=2024-01-03 12:00, distinct_id=user1
+Events:
+ - 2024-01-02 12:00: (user1, variant='control') <- This event will be joined
+ - 2024-01-03 18:00: (user1, variant='test') <- Ignored (occurs after data warehouse timestamp)
+```
diff --git a/src/navs/index.js b/src/navs/index.js
index d7e8d51c8468..3c5862ba0d23 100644
--- a/src/navs/index.js
+++ b/src/navs/index.js
@@ -2643,6 +2643,16 @@ export const docsMenu = {
icon: 'IconPeople',
color: 'orange',
},
+ {
+ name: 'Data warehouse',
+ url: '/docs/experiments/data-warehouse',
+ icon: 'IconDatabase',
+ color: 'blue',
+ badge: {
+ title: 'Beta',
+ className: 'uppercase !bg-blue/10 !text-blue !dark:text-white !dark:bg-blue/50',
+ },
+ },
],
},
{