Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/237013 
Year of Publication: 
2020
Citation: 
[Journal:] Foundations of Management [ISSN:] 2300-5661 [Volume:] 12 [Issue:] 1 [Publisher:] De Gruyter [Place:] Warsaw [Year:] 2020 [Pages:] 167-180
Publisher: 
De Gruyter, Warsaw
Abstract: 
Today's Internet marketing ecosystems are very complex, with many competing players, transactions concluded within milliseconds, and hundreds of different parameters to be analyzed in the decision-making process. In addition, both sellers and buyers operate under uncertainty, without full information about auction results, purchasing preferences, and strategies of their competitors or suppliers. As a result, most market participants strive to optimize their trading strategies using advanced machine learning algorithms. In this publication, we propose a new approach to determining reserve-price strategies for publishers, focusing not only on the profits from individual ad impressions, but also on maximum coverage of advertising space. This strategy combines the heuristics developed by experienced RTB consultants with machine learning forecasting algorithms like ARIMA, SARIMA, Exponential Smoothing, and Facebook Prophet. The paper analyses the effectiveness of these algorithms, recommends the best one, and presents its implementation in real environment. As such, its results may form a basis for a competitive advantage for publishers on very demanding online advertising markets.
Subjects: 
online marketing
real-time bidding
reserve price optimization
machine learning
forecasting
JEL: 
C53
C57
M37
M39
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article

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