With so many possibilities available to advertisers, it can be challenging to sift through and figure out which ads are most effective in realizing their marketing goals. Marketers are finding difficulty pinpointing which ads should be discontinued, which need to be optimized and which need to be prioritized. This is where attribution models come in to play, because it’s next to impossible to run efficient and effective ads if the source of conversions is a guessing game. Users often interact with multiple ads before a conversion takes place and attribution models are used to assign how much credit each ad interaction receives. It is important to note that attribution modeling in Google Ads is only available for Search Network and Shopping ads. In this series we will dive into different attribution models.

Linear attribution assigns equal credit to all ad interactions and associated keywords involved in the path to conversion. Let’s say a catering service has multiple ads on Google search and a user types in “catering near me” and clicks on one of their ads, but does not convert. The next day they type in “catering service” and click on an ad from the same catering service, but again do not convert. On the third day they type in “kosher catering” and call the catering service to set up an event. In the linear attribution model, each keyword and associated ad clicked would receive 1/3^rd of the credit for the conversion. This is a valuable method to see a general view of how ads are performing. After some time with this model, certain ads will perform better than others, so advertisers can see which of their ads are working and focus on the ads that are leading to more conversions. The potential downside of this model is its generality, certain ads may be more or less effective in driving action, but they all receive the same amount of credit. If an automated bidding strategy such as Target CPA, ECPC, or Target ROAS is being used, the attribution model will influence the way bids are optimized. Google has a “Model Comparison” feature that allows for side by side comparison of different models.

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