{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/6834f906817442fdb84ef2adf8da32b6\" 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/6834f906817442fdb84ef2adf8da32b6-f3d8175394a8cfb9.gif","duration":1384.951,"title":"Crux Attribution Model, Two Layer Approach","description":"This Loom explains Crux’s two-layer attribution approach to reduce conversions incorrectly landing in direct, unknown, or brand channels. It describes a campaign-level attribution waterfall that uses up to six data sources, including raw GA4 data via BigQuery, discount code mapping, Shopify order sources, post-purchase surveys, order tags, and Klaviyo attribution, typically assigning 60 to 70 percent of orders at the first step and preventing sampling and unassigned spikes seen in GA4 UI. A second modeled re-attribution layer uses post-purchase survey responses to remap remaining unknown and direct conversions for channel and budget strategy, without changing total order or revenue totals. It also outlines common issues with Last Click, in-platform reports, GA4 sampling delays, and media mix modeling."}