<?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/3b4ec1336edb4e73a3fd3f32c9fe84a3&quot; frameborder=&quot;0&quot; width=&quot;1920&quot; height=&quot;1440&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>1440</height><width>1920</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>1440</thumbnail_height><thumbnail_width>1920</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/3b4ec1336edb4e73a3fd3f32c9fe84a3-00001.gif</thumbnail_url><duration>208.508</duration><title>Alert Feature Demo at Chattermill</title><description>Hi, I&apos;m Jack from Chattamil, showcasing our new Aladdin feature for anomaly detection in customer feedback. I demonstrate setting up workflows to detect and act on feedback anomalies, including automated alerts and actions. Watch to see how we handle unexpected spikes in negative sentiment data. Action: Review the demo for insights on managing feedback anomalies effectively.</description></oembed>