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.
The Data-Driven model is unique among attribution models in that it uses algorithms and conversion data to determine how much credit to assign ad interactions. This is done by comparing the interactions of users who fail to convert with those who perform conversion actions. With enough data, patterns begin to emerge and certain ads will show higher probabilities of leading to a conversion. The model then gives higher credit to the ads that have been shown to lead to more interactions. Data-driven attribution relies heavily on data and there are general minimum requirements in order to use this model in Google Ads. Advertisers must have at least 15,000 ad interactions in supported networks and at least 600 conversions in a single conversion action (for example: phone calls) within 30 days. If these requirements are not met, then data-driven attribution will not appear as an option. Once this model is available for use, there are minimum requirements to continue use; 10,000 ad interactions and 400 conversions in a certain conversion action within 30 days. If these requirements are not met, Google Ads will default to the linear attribution model. The strength of this model lies in its tailored approach, the other models rely on assumptions of ad importance (for example last click assumes the last ad interaction is the most important), where this model analyzes user paths and whether or not ads are more likely to lead to a conversion using machine learning. The downside of this model is that it requires a lot of data, more than many advertisers can provide. 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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