diff --git a/content/en/change_tracking/feature_flags.md b/content/en/change_tracking/feature_flags.md
index b0561273fdc..44cdced40a2 100644
--- a/content/en/change_tracking/feature_flags.md
+++ b/content/en/change_tracking/feature_flags.md
@@ -40,6 +40,15 @@ For example, to link a flag to the `payments_api` service used in the examples b
 
 Send feature flag events from any provider using the [Events API][3]. Create a `change` category event and include a service tag to link the event to your service.
 
+#### Recommended tags
+
+When sending custom feature flag change events, include the following fields to enable accurate filtering and cross-product correlation within Datadog:
+
+- **impacted_resources** (with type `service`): Add the relevant service name to the `impacted_resources` array to associate the feature flag change with the affected service.
+- **env tag**: Specify the environment where the change occurred (for example, production, staging, or development).
+
+If these tags cannot be added at event creation time, see the next section for guidance on automatic enrichment.
+
 Example request:
 
 ```json
@@ -101,9 +110,12 @@ Example request:
 
 ## Automatically detect affected services
 
-In addition to tracking when a feature flag's configuration changes using the LaunchDarkly integration or the Events API, you can also automatically identify every service that evaluates a flag. This auto-enrichment provides deeper context by using APM traces to show the real-time impact of a flag, which is especially useful when a single flag is used by multiple services.
+In addition to tracking feature flag configuration changes through the LaunchDarkly integration or the Events API, Datadog can automatically detect which services evaluate a flag by using APM traces or metrics. This provides real-time visibility into flag usage across your system, especially when the same flag is evaluated by multiple services.
 
-### Setup
+### Trace-based enrichment
+Trace-based enrichment uses APM traces to automatically associate feature flag changes with the relevant Datadog services. The following section details how to implement it in your codebase.
+
+#### Setup
 
 To automatically detect services using a feature flag, instrument your feature flag evaluation code with the APM tracing library. This allows Datadog to automatically detect all services that evaluate a specific flag, even if they weren't originally tagged.
 
@@ -122,6 +134,30 @@ with tracer.trace("experiments.IsEnabled") as span:
     span.set_tag("experiment_value", flag_value) # adds the evaluated flag value as a span tag
 ```
 
+### Metrics-based enrichment
+
+Metric-based enrichment uses Datadog metrics to enrich your feature flag changes with service context. The following section explains how to implement it in your codebase.
+
+#### Setup
+
+Send the `dd.dynamic_config.is_experiment_enabled` metric with your experiment status.
+
+```python
+from datadog import initialize, statsd
+
+initialize(statsd_host='localhost', statsd_port=8125)
+
+# Any metric type is supported
+statsd.gauge(
+    'dd.dynamic_config.is_experiment_enabled',
+    1,  # 1 if enabled, 0 if disabled
+    tags=[
+        'experiment.id:fallback_payments_test',
+        'service:payments_api'
+    ]
+)
+```
+
 ## Remediate feature flag changes with Workflow Automation
 
 When you identify that a feature flag change caused an issue, you can immediately toggle its state without leaving Datadog. This feature uses [Workflow Automation][2] to toggle LaunchDarkly flags directly from Change Tracking timelines.