In marketing, attribution, also known as multi-touch attribution (MTA), is the identification of a set of user actions ("events" or "touchpoints") that contribute to a desired outcome, and then the assignment of a value to each of these events. Marketing attribution provides a level of understanding of what combination of events in what particular order influence individuals to engage in a desired behavior, typically referred to as a conversion.

The roots of marketing attribution can be traced to the psychological theory of attribution. By most accounts, the current application of attribution theory in marketing was spurred by the transition of advertising spending from traditional, offline ads to digital media and the expansion of data available through digital channels such as paid and organic search, display, and email marketing.

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, including paid and organic search, email, affiliate marketing, display ads, social media and more. Understanding the entire conversion path across the whole marketing mix diminishes the accuracy challenge of analyzing data from siloed channels. Typically, attribution data is used by marketers to plan future ad campaigns and inform the performance of previous campaigns by analyzing which media placements (ads) were the most cost-effective and influential as determined by metrics such as return on ad spend (ROAS) or cost per lead (CPL).

Resulting from the disruption created by the rapid growth of online advertising over the last ten years, marketing organizations have access to significantly more data to track effectiveness and ROI. This change has impacted how marketers measure the effectiveness of advertisements, as well as the development of new metrics such as cost per click (CPC), Cost per thousand impressions (CPM), Cost per action/acquisition (CPA) and click-through conversion. Additionally, multiple attribution models have evolved over time as the proliferation of digital devices and tremendous growth in data available have pushed the development of attribution technology.

Binary classification methods from statistics and machine learning can be used to build appropriate models. However, an important element of the models is model interpretability; therefore, logistic regression is often appropriate due to the ease of interpreting model coefficients.

Suppose observed advertising data are ${\displaystyle \{(X_{i},A_{i},Y_{i})\}_{i=1}^{n}}$ where

${\displaystyle u(x,a)=\mathbb {E} (Y|X=x,A=a)}$ ${\displaystyle \forall X\in \mathbb {R} }$ covariates and ${\displaystyle \forall A}$ ads

${\displaystyle u=\sum _{k}A\beta ^{k}\psi (x)+\epsilon }$