Attribution Model
StrategyDefinition
An attribution model is a rule or algorithm that determines how credit for conversions is distributed across the marketing touchpoints a customer interacted with before converting. Different models tell different stories about which channels drive results.
Attribution models range from simple single-touch approaches (last-click, first-click) to multi-touch models (linear, time-decay, position-based) and algorithmic data-driven attribution. Last-click gives all credit to the final interaction, which overvalues bottom-funnel channels. First-click credits the initial touchpoint, overvaluing awareness.
Google Ads moved to data-driven attribution as its default in 2023, using machine learning to distribute credit based on actual conversion path data. Meta uses its own modelling. The choice of attribution model directly affects how you evaluate channel performance and allocate budget.
When a user converts, the platform looks at all touchpoints in the conversion path. The attribution model applies its rules to assign fractional or full credit. Last-click gives 100% to the final click. Linear splits credit equally. Data-driven uses machine learning to weight each touchpoint by its actual contribution.
Attribution models shape every budget decision. Using last-click attribution undervalues awareness and mid-funnel campaigns, causing you to over-invest in branded search while starving the channels that fill the funnel. Choosing the right model ensures budget flows to the channels that actually drive incremental growth.
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Frequently Asked Questions
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Data-driven attribution is the best default for Google Ads. It uses your actual conversion data to assign credit. If you lack data volume, position-based is a reasonable alternative that credits both first and last touch.
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Indirectly, yes. If you switch from last-click to data-driven, campaigns that assist conversions (like Display or YouTube) receive more credit. This changes which campaigns appear profitable and influences budget allocation.
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Google deprecated first-click, linear, time-decay, and position-based models in 2023 in favor of data-driven attribution, arguing it provides more accurate credit assignment using machine learning rather than arbitrary rules.
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