The Reserve Price Optimization for Publishers on Real-Time Bidding on-Line Marketplaces with Time-Series Forecasting

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.

Patterns of the Distribution of the Demand of End-Consumers among Retailers in the Zone of their Residence

“…The development of sales strategies in e-commerce is associated with the need to solve a number of complex problems (Wodecki, 2020). A user who saves his time and has visited the e-shop can, according to his wishes, choose the product very quickly, pay for it quickly and, of course, also wants to get it quickly (Mixed-Use Properties: A Convenient Option for Shoppers, 2019).…”