{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/0deadf5dddd64c0ab5b4de5bea16a8f6\" frameborder=\"0\" width=\"1920\" height=\"1440\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1440,"width":1920,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1440,"thumbnail_width":1920,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/0deadf5dddd64c0ab5b4de5bea16a8f6-9528e3e50ec101d9.gif","duration":191.125,"title":"Automating Pipeline Control with Deal Risk|||","description":"This Loom explains how a revenue CRM pipeline workflow uses data and AI to answer recurring sales questions before a Monday meeting. It shows example metrics for Northwin Analytics such as Commit of $198,000 against a $300,000 target, best case $411,000, pipeline of $215,300, and coverage around 1.74x, with math computed by code while Cloud only writes narrative. Rep notes are pasted into a meeting record within 15 minutes, then R2 drafts deal summaries, risk, and recap emails, while R1 creates daily deal briefs based on website and last 30 days of news. R3 checks news every Sunday, only flagging changes that affect budget, timeline, or decision makers, and the system pulls dates directly from data to avoid errors."}