An attribution model in Adobe Analytics is the rule set that decides which marketing touchpoints get credit when a visitor completes a conversion. If someone clicks a paid search ad, reads three blog posts, receives an email, then converts via direct traffic, the attribution model dictates whether the sale goes to paid search, email, all five touchpoints equally, or something in between. Adobe ships several preset models. Last touch gives 100% credit to the final interaction before conversion—the default in most implementations but often misleading because it ignores earlier research phases. First touch awards everything to the initial visit, useful for measuring top-of-funnel awareness spend. Linear splits credit evenly across all touches, which sounds fair but dilutes the impact of high-intent actions. Time decay weights recent interactions more heavily, reflecting that a demo request two days before purchase likely matters more than a blog visit three months prior. Participation gives each touchpoint full credit, inflating totals but useful for channel-overlap analysis. Algorithmic attribution uses machine learning to assign fractional credit based on actual conversion patterns in your data—the most sophisticated option but requires meaningful volume to train properly. You set attribution at the report level in Workspace or as a default in virtual report suites. The model applies to success events like revenue, leads, or form fills, and it reattributes the eVar or marketing channel dimension accordingly. One visitor's CAD 500 order might show as CAD 500 for email under last touch but CAD 125 each for paid search, organic, email, and direct under linear. At Ottawa SEO we typically compare last touch against linear and time decay side by side when auditing client analytics. Last touch makes SEO look weak because organic visits often happen early in the journey, while paid retargeting snags the final click. Switching to time decay or algorithmic usually reveals that organic and content drive 20–40% more pipeline value than last-touch reports suggest. The right model depends on your sales cycle—B2B with long consideration windows benefits from time decay or algorithmic, while e-commerce impulse buys often align fine with last touch.