Artikel
The reserve price optimization for publishers on real-time bidding on-line marketplaces with time-series forecasting
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.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Foundations of Management ; ISSN: 2300-5661 ; Volume: 12 ; Year: 2020 ; Issue: 1 ; Pages: 167-180 ; Warsaw: De Gruyter
- Klassifikation
-
Management
Forecasting Models; Simulation Methods
Econometrics of Games and Auctions
Advertising
Marketing and Advertising: Other
- Thema
-
online marketing
real-time bidding
reserve price optimization
machine learning
forecasting
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Wodecki, Andrzej
- Ereignis
-
Veröffentlichung
- (wer)
-
De Gruyter
- (wo)
-
Warsaw
- (wann)
-
2020
- DOI
-
doi:10.2478/fman-2020-0013
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Wodecki, Andrzej
- De Gruyter
Entstanden
- 2020
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