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Conversion Attribution
Conversion attribution assigns credit for a completed conversion (purchase, lead, sign-up, app install) to the specific ad exposure or exposures a user encountered before converting. It identifies which touchpoint, or combination of touchpoints, caused the outcome, using a model such as last-click, first-click, linear, time-decay, or data-driven/algorithmic. The output is a credit split across channels and creatives that planners use to reallocate budget toward what actually drove the conversion.
Worked example
A shopper in Warsaw sees a programmatic display ad for a retailer on Monday but does not click. On Wednesday she clicks a paid search ad for the same retailer. On Thursday she completes an €85 purchase. The advertiser's attribution setup uses a 7-day click / 1-day view window: the Monday display impression falls outside its 1-day view-through window by the time of purchase and gets no credit, while the Wednesday search click sits inside the 7-day click window and is credited with the full conversion. The buyer's dashboard shows the €85 sale attributed 100% to paid search, which hides the assist role the display impression played in prompting the visit in the first place.
How it is used
Planners use conversion attribution to decide which channels, placements, and creatives get credit for sales, and that credit split drives budget reallocation and channel-level ROI reporting. Instead of a formula, it works by tracking the sequence of exposures in a user's path (impressions, clicks, view-throughs) and applying a chosen rule set to split or assign credit across that path; the rule set is the whole method, there is no underlying sum. The mistake practitioners make is pulling attributed conversions from a single platform's own reporting (Meta, Google Ads) and treating it as the full picture, when each platform only sees its own exposures and structurally over-credits itself relative to channels it can't observe.
The common mistake
Don't reallocate budget off one platform's self-reported attribution; cross-check it against a neutral measurement layer (MMM, an incrementality test, or a third-party attribution tool) that can see the full cross-channel path.