Multi-touch attribution and budget allocation

Comparison between different multi-touch attribution models in a budget allocation problem

Budget allocation in digital marketing is nowadays widely based on multi-touch attribution and return on investment (ROI). Multi-touch attribution often relies on heuristic methodologies (e.g. last-touch, first-touch, linear, and weighted attribution) despite advanced methods being available (e.g. logistic regression, Shapley value, and Markov model). Heuristics are easy to understand and implement while advanced methodologies instead are not so immediate and often require more computations. In this paper, we will go in-depth on multi-touch attribution and budget allocation when high-frequency and/or dependent touch-points are present and we will present a simulation use case in which different attribution methodologies will be compared in terms of generated profit. We will show that if attribution is performed using odds calculated from Markov model then a greater profit can be reached with respect to other models.

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