|导师||王飞跃 ; 袁勇|
|关键词||互联网广告 计算实验 关键字竞价 实时竞价 预算优化 市场细 分 广告展示频次控制|
Online advertising has become a novel business model with the rapid development and deep integration of Internet economy and big data analysis. It has also been regarded as the most effective marketing means for the advertisers and the most important revenue source for Internet companies. At present, keyword auction and Real Time Bidding (RTB) are the most two important business models of online advertising. Comparing with the traditional online advertising which sells the fixed ad slots with off-line negotiations or prices only according to the time slots, keyword auction realized the transition from "media buying" to "ad slot buying", and furthermore, RTB advertising realized the business logic of ad-delivery from buying "ad-impressions" in websites or ad slots to directly buying the best-matched "target audiences", and thus can help advertisers achieve the precision marketing.
Due to the advantages such as lower cost, higher efficiency, stronger ability for targeting the audiences and easier measurable, keyword auction and RTB advertising have been recognized as the most effective promotion ways for advertisers, and developed very quickly since they appeared. The appearance of keyword auction and RTB greatly enriched the connotation of the research in the field of computational advertising, Internet bidding and online marketing, and brings a new round of research and application in the field of computational advertising. At present, keyword auction and RTB advertising have become the most two popular business models in Internet advertising, and also with the most research and practical value in computational advertising. Due to the differences of their emerging time and backgrounds, the evolution of the business model from keyword auction to RTB advertising will inevitably lead to the changes of new bidding participants and decision making scenario. Based on such changes, this research focused on the advertising demand sides, and studied their decision issues in keyword auction and RTB, aiming to provide some decision supports and theoretical basis for the advertising demand sides.
With the rapid growth of the participants and the increasing complexity of the markets, keyword auction and RTB advertising markets have shown an unprecedented high complexity, dynamic, strong coupling and unpredictability, which bring great difficulties and challenges to the decision-making processes for the demand sides. On one hand, the difficulties in the budget allocation decisions for the advertisers in keyword auctions are mainly induced by the uncertain market environments and the coupled relationships among different campaigns. On the other hand, there are vast amount of ad requests in RTB market, and how to choose the best-matched ad requests and maximize the advertising effect for the advertisers have become the most important and difficult decision problems for Demand Side Platforms (DSPs) and advertisers in RTB advertising.
The purpose of this work is to deal with the above difficulties and challenges faced by the demand side in keyword auction and RTB advertising, and provide some necessary theoretical foundation for them. By utilizing computational experiments approach, optimal control theory, stochastic programming and mathematical programming approach, we study the budget optimization problem faced by the advertisers with one campaign or multiple campaigns in keyword auction, and the issues including market segmentation, frequency capping and the revenue models for the demand sides (i.e., DSP and advertisers) of RTB markets. The main researches and contributions of this work include the following five aspects:
(1) We studied the budget optimization problem over a series of sequential temporal slots faced by the advertisers in keyword auctions when only one campaign was promoted. Since there exists large uncertainty in search markets, we introduced random variable to characterize it, and presented a stochastic model for budget distribution over a series of sequential temporal slots under a finite time horizon. Some properties and possible solutions for the proposed budget distribution model were studied when the random variable was characterized by uniform and normal distributions. We also conducted some computational experiments to evaluate the proposed model, and normative findings from the experiments show that, the expected revenues of the advertisers can be greatly improved by considering the randomness in their budget planning strategies, and the performance of normal distribution is better than uniform distribution.
(2) Budget planning over several coupled campaigns in keyword auctions was studied based on the optimal control theory. Firstly, a three dimensional measure of substitution relationships between campaigns is presented, namely the overlapping degree in terms of promotional periods, target regions and campaign contents. After that, we proposed a dynamic multi-campaign budget planning approach using optimal control theories, with consideration of the substitution relationship between advertising campaigns, and some desirable properties of our budget model and possible solutions were also studied for the case that there are only two campaigns. Finally, computational experiments were conducted to evaluate our model using real-world data of practical campaigns in keyword auctions, and experimental results showed that, coupled campaigns with higher overlapping degree can reduce the optimal payoff, and the advertising effort can be seriously weakened by ignoring the overlapping degree between campaigns.
(3) We studied the market segmentation issue faced by the DSPs in RTB advertising. Based on a mathematical programming approach, we established an optimization model of the granularity for segmenting RTB advertising markets, and its solution algorithm was also provided. We proposed to validate our model using the computational experiment approach, and the experimental results show that, the market segmentation granularity has the potential of improving the total revenue of the advertisers, and with the increasing refinement of the market segmentation granularity, the total revenue has a tendency of a rise first and followed by a decline. Moreover, the optimal granularity of market segmentation will be significantly influenced by the number of advertisers on the DSP.
(4) We studied the frequency capping problem faced by advertisers in RTB advertising. Firstly, the discount rate was introduced to characterize the diminishing of the advertising effect when displaying the advertisements multiple number of times to the same target audience. After that, we established an optimization model for frequency capping considering the two-stage bidding process in RTB advertising. Finally, utilizing the computational experiment approach, we designed two experiments to validate the proposed model, and the experimental results show that under different discount rates, the optimal frequency caps are different. Moreover, the optimal frequency cap is smaller for lower discount rate, and when considering all the discount rates, there exists an optimal frequency cap, at which the expected maximum revenue can be obtained in the long run.
(5) We studied the existed two revenue models for DSPs in RTB advertising, namely the commission model and the two-stage resale model, respectively. We first proposed the methods for computing the revenues for DSPs and the advertisers under these two models, and then compared their revenues in different cases. By comparing their revenues, we deduce the optimal revenue model for DSPs and advertisers under different commission rates. We also design computational experiments to evaluate our proposed models and their properties. The experimental results show that different revenue models can bring different revenues for DSPs and advertisers, and the ratio of the commission from advertisers can greatly affect their choices of the revenue models.
|秦蕊. 基于计算实验方法的互联网广告优化策略研究[D]. 北京. 中国科学院大学,2016.|