Linear attribution divides conversion credit equally across all touchpoints a user engaged with before completing a goal. If someone clicked a Facebook ad, read a blog post, got a retargeting display ad, then searched your brand name and converted, each of those four touchpoints gets 25% credit. The math is simple: 100% divided by the number of interactions. This model treats every touchpoint as equally valuable, which makes it straightforward to explain to stakeholders and easy to implement in Google Analytics or any platform that supports multi-touch attribution. You're not making judgment calls about which channels matter more. The first click that introduced your brand gets the same weight as the final search that closed the deal. The main advantage is fairness across your marketing mix. Channels that assist conversions throughout the journey, like content marketing or social media, get recognized instead of being invisible under last-click attribution. You see a more complete picture of how your channels work together. The downside is that linear attribution ignores reality. Not all touchpoints influence a purchase equally. The blog post that educated a cold prospect probably mattered more than a retargeting banner they scrolled past. A demo request form is more valuable than opening an email. Linear treats them the same, which can misallocate budget if you optimize based solely on this model. At Ottawa SEO, we use linear attribution as a middle-ground view when clients run integrated campaigns across SEO, paid search, and content. It helps justify investment in top-of-funnel activities that last-click models undervalue. But we never rely on it alone. We compare it against first-click, last-click, and time-decay models in GA4's attribution reports to understand the full story. If your conversion paths are short (two or three touches), linear works fine. If paths span weeks and a dozen interactions, you need more sophisticated weighting or data-driven attribution to allocate budget effectively.