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Attribution Model

定义

An attribution model is a rule or algorithm that determines how credit for conversions is assigned to different touchpoints in a customer's journey. Choosing the right attribution model affects how you evaluate campaign performance, allocate budget, and optimize bids across your advertising channels.

Types of Attribution Models

Last-click attribution gives 100% credit to the final interaction before conversion (Google Ads' historical default). First-click gives all credit to the first touchpoint. Linear distributes credit equally across all touchpoints. Time-decay gives more credit to interactions closer to the conversion. Position-based (U-shaped) gives 40% to the first and last touch with 20% split among middle touches. Data-driven attribution uses machine learning to assign credit based on actual contribution patterns. Google Ads now defaults to data-driven attribution for most conversion actions.

How Attribution Affects Campaign Decisions

Your attribution model directly influences which campaigns appear profitable. Under last-click, branded search gets outsized credit because it is often the final touch. Under first-click, top-of-funnel awareness campaigns get more credit. Data-driven attribution attempts to distribute credit more accurately based on actual conversion paths. If you use last-click attribution, you will systematically undervalue Display, YouTube, and prospecting campaigns that initiate customer journeys but do not close them. This leads to underinvestment in awareness and over-investment in bottom-funnel capture.

Choosing the Right Attribution Model

Use data-driven attribution if available (requires sufficient conversion volume). For smaller accounts, time-decay or position-based models are reasonable approximations. Avoid last-click for anything except simple, single-touchpoint businesses. When comparing attribution models, use Google Ads' Model Comparison report to see how conversion credit shifts between campaigns under different models. This reveals which campaigns are undervalued under your current model. Cross-platform attribution (Google + Meta) requires additional tools since each platform only attributes conversions to its own ads.

Attribution Analysis with AdWhiz

AdWhiz evaluates your attribution model setup and identifies potential misallocations. The audit compares your current model's conversion credit distribution against alternative models, highlighting campaigns that may be over or undervalued. For accounts using last-click attribution with multi-touch customer journeys, AdWhiz recommends transitioning to data-driven attribution and estimates the budget reallocation impact.

常见问题

Google Ads now defaults to data-driven attribution for new conversion actions when sufficient data is available. This model uses machine learning to assign credit based on actual conversion path patterns in your account. For accounts with limited data, Google may fall back to last-click attribution.

No, changing your attribution model does not change the total number of conversions. It changes how credit is distributed across touchpoints. Your total conversions remain the same; the difference is which campaigns, keywords, and ads receive credit for those conversions, which affects optimization decisions.

Each advertising platform (Google, Meta, TikTok) attributes conversions to its own ads independently, leading to overcounting across platforms. For accurate cross-platform attribution, use a unified analytics tool (Google Analytics 4, triple attribution platforms, or server-side tracking) that can deduplicate conversions across channels.

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