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Programmatic Advertising A.Y. 2019/20

When you submit Google (or another search engine) a request, you fire an online auction: in much less than a second, many advertisers’ algorithms launch their offers and a Google algorithm chooses the winners, which gain the right to show you their advertisements. Similar events happen when you enter a web site, a social network or an app delivering you advertisements.
In this course, we describe the logic underpinning these events. A set of agents (social networks, search engines, sites, and apps) generate opportunities for showing advertisements and sell them. Another set of agents (advertisers) buy these opportunities. An auctioneer matches supply and demand. All these players use algorithms, sometimes elementary sometimes very complex ones.
We will explore how these algorithms work, how a search engine or a newspaper maximize their profit, how an advertiser targets its potential customers. The goal is to provide the student with the background required to join a real-life project team in a media agency and design a strategy for optimal management of an advertising campaign. The same framework is also a basis for e-commerce campaigns.
The description is high-level, focusing the conceptual framework instead of the programming task. Indeed, often one can algorithmically manage advertising and e-commerce campaigns without explicit programming, using existing algorithmic platforms. Nonetheless, the mindset required is still analytical and algorithms-oriented. Our ambition is to help the student to develop such a mindset, pertaining this specific field.
No advanced background in mathematics or computer science is required.


  • Digital advertising and e-commerce: business context; programmatic vs. traditional approach; the profession of programmatic advertising; the economy of online auctions.
  • Decision-making: expected utility, optimization, heuristics.
  • Multi-armed bandits: a conceptual framework for maximizing profit in a dynamic uncertain environment.
  • Basic statistical tools: distributions (like Normal, Binomial, Beta); concept of simulation; Bayesian inference.
  • Forecasting: regression; time series analysis; matrix-based methods.
  • Advertising and e-commerce with customer profiles available.


Teaching Material

mds/pa/start.txt · Ultima modifica: 08/09/2021 alle 12:25 (3 anni fa) da Nicola Ciaramella