What Is Attribution Modeling? Models Compared and Their Limits

Customers rarely convert after one ad. They may see a video ad, click a search ad, read an email and then buy. An attribution model decides who gets the credit. Last-click gives it all to the final touch. First-click gives it to the first. Linear splits it evenly. Time-decay gives more to touches nearer the conversion. Position-based weights the first and last touches most. Data-driven models use statistical analysis of many conversion paths to assign credit.

Every model is a simplification and each favours different channels: last-click tends to flatter bottom-of-funnel channels such as branded search and retargeting, while first-click favours discovery channels. Platforms also report on their own terms and their own attribution windows, so the same sale can be claimed by more than one platform, and totals rarely add up to your actual revenue.

Privacy changes, such as tracking restrictions on mobile and cookie limits in browsers, have made user-level tracking less complete. Practical teams often use a platform view for in-platform optimisation, then cross-check with blended numbers: total revenue against total ad spend (see what-is-ad-spend), or ROAS (see what-is-roas), and sometimes holdout or lift tests.

Infinall AI turns a product URL into competitor research, strategy, ad copy and creative that land in an Approval Queue for you to review before export. It is not an analytics or attribution tool: it does not track conversions, assign credit across channels or report on live campaigns. It helps upstream, by drafting the research, copy and creative you then run in your channels. Which model you use to judge those channels, and how you track the results, is something you set up in your analytics and ad platforms.