<?xml version="1.0" encoding="UTF-8"?><oembed><type>video</type><version>1.0</version><html>&lt;iframe src=&quot;https://www.loom.com/embed/fcb34ef720e144be9811692b3aa21968&quot; frameborder=&quot;0&quot; width=&quot;2490&quot; height=&quot;1867&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>1867</height><width>2490</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>1867</thumbnail_height><thumbnail_width>2490</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/fcb34ef720e144be9811692b3aa21968-95c2f49ac96d76aa.gif</thumbnail_url><duration>747.983833</duration><title>AI Mastermind - UTM Translator (SFDC)</title><description>This Loom explains a Salesforce UTM to channel translator workflow, focusing on a future-proof approach using Custom Metadata Types. The author builds a taxonomy-driven flow that triggers on changes to UTM source, medium, campaign, last referring site, and attribution completed date time, while bypassing execution when the user has a bypass flow automation permission. They argue for custom metadata tables over hard-coded flow values or custom objects, creating a Channel Mapping custom metadata type with an active flag and 11 fields populated with 30 records. They also confirm handling for blank values with a fallback to Website Direct and include match logic that sets or clears channel and related fields based on taxonomy rules.</description></oembed>