Abstract
Programmatic Advertising allows advertisers to bid for single advertising impressions, i.e., each time a user visits a website advertisers can decide whether they would like to bid for the opportunity to being displayed to that specific user and at what price. Programmatic Advertising, which emerged around 2009, thereby comes with a huge amount of data that can be used for decision making purposes (e.g., bidding). This article will provide an overview of the two fundamental decision making fields in Programmatic Advertising: budget allocation across the media mix and micro decision making in Programmatic Advertising ad auctions at the individual user-level. In this article, we outline state of the art modeling techniques used in both decision making areas as well as the specific challenges faced by analysts when developing models. In addition, we present common heuristics used by practitioners and potential drawbacks related to the use of heuristics vs. statistical models.
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Notes
- 1.
Statistical models are fundamentally able to explain complicated user behavior. They can be applied to make predictions such as which product is likely to be purchased and how high sales are anticipated to be. For the sake of simplicity, we will assume that we are attempting to predict whether or not a user will become a customer.
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Funk, B., Nabout, N.A. (2016). Cross-Channel Real-Time Response Analysis. In: Busch, O. (eds) Programmatic Advertising. Management for Professionals. Springer, Cham. https://doi.org/10.1007/978-3-319-25023-6_12
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DOI: https://doi.org/10.1007/978-3-319-25023-6_12
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