Attribution modeling assigns conversion credit across multiple marketing touchpoints instead of giving all the glory to the final click. When someone converts after seeing your Facebook ad, clicking an organic result two weeks later, then finally returning via a branded search, attribution models decide how to split that sale among those three interactions. The most common models are last-click (100% credit to the final touchpoint), first-click (all credit to the discovery channel), linear (equal credit across all touchpoints), time-decay (more credit to recent interactions), and position-based (40% to first, 40% to last, 20% split among middle touches). Google Analytics 4 replaced its old attribution reports with a data-driven model that uses machine learning to weight touchpoints based on actual conversion patterns in your account. Why this matters: Last-click attribution systematically undervalues top-of-funnel work like SEO and content marketing. If someone reads five blog posts over three months then converts via a direct visit, last-click gives SEO zero credit even though it did the heavy lifting. This creates budget allocation problems where you overfund retargeting and underfund awareness channels. The tradeoffs are real. Data-driven models need volume—Google recommends 400 conversions per month minimum for the algorithm to learn properly. Smaller accounts default to simpler rules-based models. Cross-device tracking remains messy despite improvements, so attribution is always an approximation, not truth. At Ottawa SEO we treat attribution models as directional tools, not gospel. For most of our portfolio sites generating 50–200 conversions monthly, we use position-based or time-decay models and triangulate with assisted conversion reports. We also track direct traffic spikes after content pushes as a sanity check, since attribution systems categorically fail to connect awareness work to later branded searches. The goal isn't perfect measurement, it's avoiding the worst decisions that pure last-click data would drive you toward.