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A machine learning algorithm leverages advanced statistical techniques such as linear and nonlinear regression, cooperative game theory and other data mining methods, to allocate credit in the fairest possible way possible, based on a touchpoint’s propensity to increase an audience’s likelihood to convert. It looks at all the touchpoints — both the presence and absence of touchpoints — and their role in driving incremental value — looking at both the baseline, converting paths and non-converting paths.

It is often perceived to be the most bias-free of distributing credit, but receives pushback from marketing organizations due to the perceived black box nature of its algorithm, particularly for those unfamiliar with its specific methodology or data science techniques in general. Most attribution vendors will have their own proprietary implementations of data science methodologies and will mix in some of their “secret sauce” in order to provide what they believe, would yield the most optimal set of incrementality calculations for their customers.

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