An attribution model is a framework that assigns credit to different marketing touchpoints along a customer's path to conversion. It answers the question: which marketing effort actually drove this sale or lead? Most customers don't convert on their first visit. They might find you through organic search, come back via a Google Ad, then convert after clicking an email. Attribution models decide how much credit each of those three touchpoints receives. Google Analytics offers several standard models. Last-click gives 100% credit to the final interaction before conversion, which is simple but ignores everything that built awareness. First-click does the opposite, crediting only the initial touchpoint. Linear splits credit equally across all interactions, while time-decay gives more weight to recent touches. Position-based (U-shaped) credits 40% to first and last interactions, splitting the remaining 20% across middle touches. The model you choose changes how you evaluate channel performance. If you run last-click attribution and your paid search gets all the credit, you might over-invest there while starving the blog content that actually introduced most customers to your brand. Data-driven attribution, available in GA4 and Google Ads with sufficient conversion volume, uses machine learning to assign credit based on actual impact, but you need at least 400 conversions per month for it to function properly. At Ottawa SEO, we typically start clients on position-based attribution because it acknowledges both discovery and closing channels without the naive equality of linear models. For ecommerce clients doing $50K+ monthly, we push toward data-driven once they hit volume thresholds. The key is remembering that no model is objectively correct. They're lenses for understanding contribution, not absolute truth. Run reports under two different models occasionally to see how your channel mix story changes. That delta tells you where conventional wisdom about your funnel might be wrong.