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There are several popular attribution models in the market. Based on the channel's position in the sequence, they can be classified as follows:
๐๐ถ๐ฟ๐๐ ๐๐น๐ถ๐ฐ๐ธ: attributes the entire conversion value to the first source in the sequence that introduced the user to the brand.
๐๐ฎ๐๐ ๐๐น๐ถ๐ฐ๐ธ: a popular model where conversion is attributed to the last channel that led to the desired action.
๐๐ฎ๐๐ ๐ก๐ผ๐ป-๐๐ถ๐ฟ๐ฒ๐ฐ๐ ๐๐น๐ถ๐ฐ๐ธ: was default in Google Analytics reports. The full conversion value is assigned to the last channel in the sequence. However, if it's a direct visit, the value is attributed to the preceding source.
๐๐ฎ๐๐ ๐ฃ๐ฎ๐ถ๐ฑ ๐๐น๐ถ๐ฐ๐ธ: similar to Last Click, but only considers paid (cpc/cpm) traffic.
๐๐ถ๐ป๐ฒ๐ฎ๐ฟ: the value of the transaction is evenly distributed among all channels in the sequence. It is more advanced than single-channel models, as it considers all sessions before the order.
๐ง๐ถ๐บ๐ฒ ๐๐ฒ๐ฐ๐ฎ๐: the value of the transaction is distributed incrementally among the channels, especially favoring the last one in the sequence and those closer in time to the conversion.
๐จ-๐๐ต๐ฎ๐ฝ๐ฒ: two sources receive higher value (40% each): the one that attracted the customer and the one that motivated the conversion. The remaining 20% is evenly distributed among all channels in the middle of the funnel.
๐ ๐ฎ๐ฟ๐ธ๐ผ๐ ๐๐ต๐ฎ๐ถ๐ป๐: an algorithmic model that assesses the mutual influence of channels on conversion and identifies the most significant channel.
๐๐ฎ๐๐ฎ-๐๐ฟ๐ถ๐๐ฒ๐ป ๐๐๐๐ฟ๐ถ๐ฏ๐๐๐ถ๐ผ๐ป: another algorithmic model, typically built on Shapley Vector. Considers the order of the channel in the sequence and evaluates how the presence of this channel overall influenced the conversion. Changing session order won't alter the Shapley Vector's value.
๐ฃ๐ผ๐๐-๐ฉ๐ถ๐ฒ๐: attributes value not after a click but after the user sees the ad and within the attribution window (e.g., 7 days), visits the site, and performs a conversion action.
๐ฃ๐ฟ๐ฒ๐ฑ๐ถ๐ฐ๐๐ถ๐๐ฒ ๐๐๐๐ฟ๐ถ๐ฏ๐๐๐ถ๐ผ๐ป: Predicts future revenue from an acquired user based on behavioral factors during a specific session. Used in conjunction with machine learning algorithms by services like Segmentstream and Tomi AI.
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