<?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/9394c7fd698949a5b83c4cec7a8fe38a&quot; frameborder=&quot;0&quot; width=&quot;1728&quot; height=&quot;1296&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>1296</height><width>1728</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>1296</thumbnail_height><thumbnail_width>1728</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/9394c7fd698949a5b83c4cec7a8fe38a-c29ee71f82aa96dc.gif</thumbnail_url><duration>299.524</duration><title>User Segmentation Analysis 📊</title><description>Today, I delve into an analysis inspired by a 2023 Duolingo blog post, focusing on segmenting user bases and creating a growth model. We explore tracking user segment dynamics over time using standard and advanced segmentation approaches. By examining daily active users and engagement segments, we uncover insights into user behavior and potential churn risks. No action requested, just valuable insights shared.</description></oembed>