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In marketing, Attribution is used as a term to describe and identify a set of user actions, events or touchpoints that contribute in some way or another to a desired outcome. Attribution also includes the assignment of a value to each of these events. It also describes the understanding of all combinations of events in order to engage individuals in a desired way leading to a so called conversion.

The purpose of marketing attribution is to quantify the influence each advertising impression has on a consumer’s decision to make a purchase decision, or convert. Visibility into what influences the audience, when and to what extent, allows marketers to optimize media spend for conversions and compare the value of different marketing channels. Such marketing channels can include among others paid search, organic search, email marketing, affiliate marketing, display ads, retargeting, social media, video advertising, TV ads and much more. Understanding the entire conversion path across the whole marketing mix eliminates the accuracy challenge of analyzing data from isolated channels. Typically, attribution data is used by marketers to plan future ad campaigns by analyzing which media placements (ads) were the most cost-effective as determined by metrics such as effective cost per action (eCPA).

There are different attribution models that can be seperated.

Single Source Attribution: Also called Single Touch Attribution is an attribution model that assigns all the credit to one event, such as the last click, the first click or the last channel to show an ad (post view). Simple or last-click attribution is widely considered as less accurate than alternative forms of attribution as it fails to account for all contributing factors that led to a desired outcome.

Fractional Attribution: This attribution model includes equal weights, customer credit and multi-touch or curve models. Equal weight models assign the same amount of credit to the events. In comparison to that customer credit uses past experience and also simple guesswork to allocate credit to each touchpoint and channel. Finally multi-touch attribution assigns various credit to across all the touchpoints in the buyer journey at predefined amounts.

Algorithmic or Probabilistic Attribution: This attribution model uses statistical modeling and machine learning techniques to derive probability of conversion across all marketing touchpoints which can then be used to weigh the value of each touchpoint preceding the conversion. Algorithmic attribution analyzes both converting and non-converting paths across all channels to determine probability of conversion. With a probability assigned to each touchpoint, the touchpoint weights can be aggregated by a dimension of that touchpoint (channel, placement, creative, etc.) to determine a total weight for that dimension.