모바일 매체의 수익 최적화를 위한 디스플레이 광고 요소 및 워터폴 입찰 전략 평가

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 협동과정 기술경영·경제·정책전공, 2020. 8. 윤명환.Advertising revenue has become an important revenue source for mobile publishers, along with in-app purchase. Based on empirical data and academic methodology, this study attempted to solve two key problems that mobile publishers face when trying to maximize advertising revenue. This study analyzed transaction history data of mobile advertising from AD(x) Inc., a company that provides services to optimize ad revenue for mobile publishers by operating multiple ad networks simultaneously, including Google AdMob and Facebook Audience Network. The first problem mobile publishers face when trying to gain revenue through advertising is determining the optimal ad position and ad format for the service UX of mobile publishers. To provide guidelines for the first decision, this study analyzed characteristics of mobile advertising, including native ads and rewarded video ads, which have been relatively recently introduced. As a result, in addition to various ad factors defined by previous research through traditional advertising media, three new ad factors were summarized: ad density, disclosure position, and disclosure method. Moreover, the relationships among the three new derived ad factors, ad revenue, and ad effectiveness were analyzed. First, in relation to ad density, which is the proportion of an advertisements physical area relative to the full-screen area, the higher the ad density, the higher both the ad revenue and advertising effectiveness. On the other hand, among advertisements with similar ad density, there was a difference in ad revenue and advertising effectiveness according to ad format. Among advertisements with low ad density, native banner ads showed higher ad revenue and advertising effectiveness than banner ads. Among advertisements with high ad density, rewarded video ads showed the highest ad revenue, and interstitial ads showed the highest advertising effectiveness. As for the second new ad factor, disclosure position, the effectiveness of advertisements displayed at the top of the screen was higher in the PC web environment, but advertisements displayed at the bottom of the screen in the mobile environment were higher in terms of ad revenue and advertising effectiveness. Lastly, in the analysis of the third new ad factor, disclosure method, advertisements with the same ad format as native ads were classified in three categories, based on their development by mobile publishers: Separated area, List UI, and Pop-up. This study analyzed the relationship between disclosure method, ad revenue, and advertising effectiveness. The results showed that the highest ad revenue and advertising effectiveness were found in the Pop-up disclosure method. The second problem that mobile publishers face after determining ad position and ad format is the optimization of waterfall settings such as the priority and reserve prices of each ad network to maximize ad revenue when mobile advertising is served from multiple ad networks. On the other hand, between ad networks and mobile publishers, there is information asymmetry. Hence, ad networks have more information, so this study proposed a reserve price strategy for the operation of waterfall bidding among multiple ad networks to maximize ad revenue, even under information asymmetry. First, a demand curve-based model was designed to explain the loss of ad revenue when a mobile publisher sells its ad inventory at a non-optimized price using waterfall bidding. In addition, sensitivity analysis was conducted to show that the proposed model performs better than the companys existing bidding strategy. Moreover, this model enabled mobile publishers to have better performance with independent correlation, not a positive correlation of ad networks bid prices. Therefore, mobile publishers can use the key finding that the proposed model is more effective in reducing expected advertising losses under information asymmetry. In addition, it was found that performance improved to a greater extent when ad networks have less bid price similarity. This study provides guidelines that can be utilized not only in an academic sense but also in a real business environment. Standardized knowledge for small- and medium-sized mobile publishers, in particular, which have a relatively high ad network dependency, is suggested to improve their understanding of ad network usage and to establish optimized advertising operation policies.광고 수익은 모바일 매체에게 있어서, 인앱 판매 (in-app purchase) 와 함께 중요한 수익원 중 하나가 되었다. 본 연구에서는 모바일 매체가 광고 수익을 최대화하고자 할 때 마주하게 되는 두 가지 핵심 과제를 실증적인 데이터와 학술적인 방법론을 통해 해결하고자 하였다. 본 연구는, Google AdMob, Facebook Audience Network 를 포함하는 다수의 광고 네트워크를 동시에 운영하여 모바일 매체의 광고 수익을 최적화하는 서비스를 제공하고 있는 기업, 주식회사 애드엑스의 2019년 광고 결과 통계 데이터에서 추출하여 분석과 평가를 진행하였다. 모바일 매체가 광고를 통해 수익을 얻고자 할 때 가장 처음으로 마주하는 과제는, 모바일 매체의 서비스 UX에 최적화된 광고 위치와 광고 포맷을 결정하는 것이다. 이 결정에 가이드라인을 제공하기 위해, 상대적으로 최근 도입된 네이티브 광고, 리워드 비디오 광고를 포함한 모바일 광고가 가지는 특징을 분석하였다. 그 결과, 전통적인 광고 매체에 노출되는 광고를 통해 정의된 다양한 광고 요소 외에, 세 가지 신규 광고 요소; 광고 밀도, 노출 위치, 노출 방법을 정리하였으며, 도출된 신규 광고 요소와 광고 수익, 광고 효과 간의 관계를 분석하였다. 먼저, 서비스 화면 내에 광고가 차지하는 비율인 광고 밀도와 관련하여, 광고 밀도가 높을수록 광고 수익과 광고 효과, 모두 높은 결과를 얻었다. 한편, 유사한 광고 밀도를 가진 광고 간에도 광고 포맷에 따라 광고 수익, 광고 효과가 차이를 보였다. 낮은 광고 밀도를 가진 광고 중에서는 네이티브 배너 광고가 배너 광고 보다 더 높은 광고 수익과 광고 효과를 보였으며, 높은 광고 밀도를 가진 광고 중에서는 리워드 비디오 광고가 가장 높은 광고 수익을 나타냈고, 전면 광고가 가장 높은 광고 효과를 보였다. 두번째 신규 광고 요소인 노출 위치와 관련하여, 기존 PC 또는 웹 환경에서는 화면 상단에 노출된 광고의 광고 효과가 가장 높았으나, 모바일 환경에서는 화면 아래에 노출된 광고가 광고 수익, 광고 효과, 모두 더 높게 나타났다. 마지막으로, 노출 방법 와 관련한 분석에서는, 동일한 네이티브 광고 포맷이지만, 모바일 매체에 의해 개발된 노출 방법에 따라, 분리된 영역, 리스트 UI, Pop-up 로 구분하였고, 다양한 노출 방법에 따른 광고 수익, 광고 효과를 비교 분석해보았다. 그 결과 Pop-up 형태의 노출 방법에서 가장 높은 광고 수익과 광고 효과가 나타났다. 모바일 매체가 광고 위치와 광고 포맷을 결정한 뒤에 직면하는 두번째 핵심 과제는, 다수의 광고 네트워크로부터 광고를 제공받아 노출할 때, 광고 수익이 최대화 될 수 있도록 각 광고 네트워크의 우선순위, 예약 가격 (reserve price) 등 워터폴 세팅을 최적화 하는 것이다. 한편, 광고 네트워크와 모바일 매체 사이에는 광고 네트워크가 더 많은 정보를 가지고 있는 정보 비대칭이 존재하는데, 본 연구는 이런 정보 비대칭 하에서 광고 수익을 최대화 위하여, 최저 가격 (reserve price) 전략을 통한 워터폴 세팅 방법을 제안하였다. 먼저, 모바일 매체의 광고 판매 가격이 최적화 되어 있는지를 설명하기 위하여 수요 곡선 기반 모델을 설계하였다. 그리고, 민감도 분석을 통해 제안된 모델이 기존 운영 전략보다 우수함을 비교해 보였다. 또한, 제안된 모델을 통해, 광고 네트워크 간의 입찰 가격이 상관 관계가 있을 때보다 독립적일 때 더 높은 광고 수익을 얻을 수 있음을 밝혔다. 본 연구를 통해, 학술적인 의미 뿐만 아니라, 실제 경영 환경에서 모바일 매체가 광고 수익을 창출하고 극대화하기 위해서 활용할 수 있는 가이드라인을 제공하였다. 특히 광고 네트워크에 대한 의존도가 높고, 내부 자원의 제약이 있는 중소 개발자들에게 별도의 R&D 없이 최적화된 광고 운영 정책을 수립할 수 있는 방법을 제시하였다.Chapter 1. Introduction 1 Chapter 2. Literature Review 11 2.1 Real-Time Bidding 11 2.2 Ad Format 15 2.2.1 Native Ads 15 2.2.2 Rewarded Video Ads 17 2.3 Advertisement Performance Index 19 Chapter 3. Evaluation of Ad Factor 23 3.1 Introduction 23 3.1.1 Advertisement Factors 26 3.1.2 Environmental Factors 29 3.1.3 Audience Factors 32 3.2 Hypotheses and Dataset 34 3.2.1 Advertisement Density 34 3.2.2 Ad Format with the Same Advertisement Density 35 3.2.3 Disclosure Position with the Same Advertisement Density 36 3.2.4 Disclosure Method of Native Ads 37 3.2.5 Dataset 38 3.3 Results 41 3.3.1 Influence of Advertisement Density on Advertising Revenue and effectiveness 41 3.3.2 Heterogenous Influence with the Same Advertisement Density 43 3.3.3 Heterogenous Influence of Disclosure Position 46 3.3.4 Heterogeneous effect by Disclosure Method 47 3.4 Discussion 49 Chapter 4. Waterfall Strategy Development 57 4.1 Introduction 57 4.1.1 Information Asymmetry 60 4.1.2 Bidding Strategy 61 4.1.3 Price and Demand 63 4.2 Estimation of Ad Networks Demand Curves 65 4.2.1 Dataset 65 4.2.2 Demand Curve Estimation 67 4.3 Waterfall Bidding Strategy 76 4.4 Sensitivity Analysis 82 Chapter 5. Conclusion 91 5.1 Summary of Research Findings 91 5.2 Contribution of this Study 94 5.3 Limitation and Further Studies 96 Bibliography 97 Appendix 109 Abstract (Korean) 121Docto

YourEnergyOptions, Inc.: A Web Business Plan

YourEnergyOptions\u27 website lacked many features that a fully-functional Web 2.0 site typically requires: revenue generation, outreach, social-networking, and visual appeal and usability. Our goal was to generate low-cost recommendations that could be implemented to help YEO. Research was conducted via interviews, focus groups, user testing, articles, and web content. It was concluded that Emarketing, better ad selection and placement, a referral system, enhanced profile and forum functionality, and improved site structure would greatly aid YEO in its energy autonomy mission

Viewability prediction for display advertising

As a massive industry, display advertising delivers advertisers’ marketing messages to attract customers through graphic banners on webpages. Display advertising is also the most essential revenue source of online publishers. Currently, advertisers are charged by user response or ad serving. However, recent studies show that users barely click or convert display ads. Moreover, about half of the ads are actually never seen by users. In this case, advertisers cannot enhance their brand awareness and increase return on investment. Publishers also lose much revenue. Therefore, the ad pricing standards are shifting to a new model: ad impressions are paid if they are viewable, not just being responded to or served. The Media Ratings Council’s standard for a viewable display impression is a minimum of 50% of pixels in view for a minimum of one second. To implement viewable impressions as pricing currency, ad viewability should be accurately predicted. Ad viewability prediction can improve the performance of guaranteed ad delivery, real-time bidding, as well as recommender systems. This research is the first to address this important problem of ad viewability prediction. Inspired by the standard definition of viewability, this study proposes to solve the problem from two angles: 1) scrolling behavior and 2) dwell time. In the first phase, ad viewability is predicted by estimating the probability that a user will scroll to the page depth where an ad is located in a specific page view. Two novel probabilistic latent class models (PLC) are proposed. The first PLC model computes constant use and page memberships offline, while the second PLC model computes dynamic memberships in real-time. In the second phase, ad viewability is predicted by estimating the probability that the page depth will be in-view for certain seconds. Machine learning models based on Factorization Machines (FM) and Recurrent Neural Network (RNN) with Long Short Term Memory (LSTM) are proposed to predict the viewability of any given page depth in a specific page view. The experiments show that the proposed algorithms significantly outperform the comparison systems

Online Advertising Assignment Problems Considering Realistic Constraints

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 산업공학과, 2020. 8. 문일경.With a drastic increase in online communities, many companies have been paying attention to online advertising. The main advantages of online advertising are traceability, cost-effectiveness, reachability, and interactivity. The benefits facilitate the continuous popularity of online advertising. For Internet-based companies, a well-constructed online advertisement assignment increases their revenue. Hence, the managers need to develop their decision-making processes for assigning online advertisements on their website so that their revenue is maximized. In this dissertation, we consider online advertising assignment problems considering realistic constraints. There are three types of online advertising assignment problems: (i) Display ads problem in adversarial order, (ii) Display ads problem in probabilistic order, and (iii) Online banner advertisement scheduling for advertising effectiveness. Unlike previous assignment problems, the problems are pragmatic approaches that reflect realistic constraints and advertising effectiveness. Moreover, the algorithms the dissertation designs offer important insights into the online advertisement assignment problem. We give a brief explanation of the fundamental methodologies to solve the online advertising assignment problems in Chapter 1. At the end of this chapter, the contributions and outline of the dissertation are also presented. In Chapter 2, we propose the display ads problem in adversarial order. Deterministic algorithms with worst-case guarantees are designed, and the competitive ratios of them are presented. Upper bounds for the problem are also proved. We investigate the display ads problem in probabilistic order in Chapter 3. This chapter presents stochastic online algorithms with scenario-based stochastic programming and Benders decomposition for two probabilistic order models. In Chapter 4, an online banner advertisement scheduling model for advertising effectiveness is designed. We also present the solution methodologies used to obtain valid lower and upper bounds of the model efficiently. Chapter 5 offers conclusions and suggestion for future studies. The approaches to solving the problems are meaningful in both academic and industrial areas. We validate these approaches can solve the problems efficiently and effectively by conducting computational experiments. The models and solution methodologies are expected to be convenient and beneficial when managers at Internet-based companies place online advertisements on their websites.온라인 커뮤니티의 급격한 성장에 따라, 많은 회사들이 온라인 광고에 관심을 기울이고 있다. 온라인 광고의 장점으로는 추적 가능성, 비용 효과성, 도달 가능성, 상호작용성 등이 있다. 온라인에 기반을 두는 회사들은 잘 짜여진 온라인 광고 할당결정에 관심을 두고 있고, 이는 광고 수익과 연관될 수 있다. 따라서 온라인 광고 관리자는 수익을 극대화 할 수 있는 온라인 광고 할당 의사 결정 프로세스를 개발하여야 한다. 본 논문에서는 현실적인 제약을 고려한 온라인 광고 할당 문제들을 제안한다. 본 논문에서 다루는 문제는 (1) adversarial 순서로 진행하는 디스플레이 애드문제, (2) probabilistic 순서로 진행하는 디스플레이 애드문제 그리고 (3) 광고효과를 위한 온라인 배너 광고 일정계획이다. 이전에 제안되었던 광고 할당 문제들과 달리, 본 논문에서 제안한 문제들은 현실적인 제약과 광고효과를 반영하는 실용적인 접근 방식이다. 또한 제안하는 알고리즘은 온라인 광고 할당 문제의 운영관리에 대한 통찰력을 제공한다. 1장에서는 온라인 광고 할당 문제에 대한 문제해결 방법론에 대해 간단히 소개한다. 더불어 연구의 기여와 개요도 제공된다. 2장에서는 adversarial 순서로 진행하는 디스플레이 애드문제를 제안한다. worst-case를 보장하는 결정론적 알고리즘을 설계하고, 이들의 competitive ratio를 증명한다. 더불어 문제의 상한도 입증된다. 3장에서는 probabilistic 순서로 진행하는 디스플레이 애드문제를 제안한다. 시나리오 기반의 확률론적 온라인 알고리즘과 Benders 분해방법을 혼합한 추계 온라인 알고리즘을 제시한다. 4장에서는 광고효과를 위한 온라인 배너 광고 일정계획을 설계한다. 또한, 모델의 유효한 상한과 하한을 효율적으로 얻는 데 사용되는 문제해결 방법론을 제안한다. 5장에서는 본 논문의 결론과 향후 연구를 위한 방향을 제공한다. 본 논문에서 제안하는 문제해결 방법론은 학술 및 산업 분야 모두 의미가 있다. 수치 실험을 통해 문제해결 접근 방식이 문제를 효율적이고 효과적으로 해결할 수 있음을 보인다. 이는 온라인 광고 관리자가 본 논문에서 제안하는 문제와 문제해결 방법론을 통해 온라인 광고 할당관련 의사결정을 진행하는 데 있어 도움이 될 것으로 기대한다.Chapter 1 Introduction 1 1.1 Display Ads Problem 3 1.1.1 Online Algorithm 4 1.2 Online Banner Advertisement Scheduling Problem 5 1.3 Research Motivations and Contributions 6 1.4 Outline of the Dissertation 9 Chapter 2 Online Advertising Assignment Problem in Adversarial Order 12 2.1 Problem Description and Literature Review 12 2.2 Display Ads Problem in Adversarial Order 15 2.3 Deterministic Algorithms for Adversarial Order 17 2.4 Upper Bounds of Deterministic Algorithms for Adversarial Order 22 2.5 Summary 28 Chapter 3 Online Advertising Assignment Problem in Probabilistic Order 30 3.1 Problem Description and Literature Review 30 3.2 Display Ads Problem in Probabilistic Order 33 3.3 Stochastic Online Algorithms for Probabilistic Order 34 3.3.1 Two-Stage Stochastic Programming 35 3.3.2 Known IID model 37 3.3.3 Random permutation model 41 3.3.4 Stochastic approach using primal-dual algorithm 45 3.4 Computational Experiments 48 3.4.1 Results for known IID model 55 3.4.2 Results for random permutation model 57 3.4.3 Managerial insights for Algorithm 3.1 59 3.5 Summary 60 Chapter 4 Online Banner Advertisement Scheduling for Advertising Effectiveness 61 4.1 Problem Description and Literature Review 61 4.2 Mathematical Model 68 4.2.1 Objective function 68 4.2.2 Notations and formulation 72 4.3 Solution Methodologies 74 4.3.1 Heuristic approach to finding valid lower and upper bounds 75 4.3.2 Hybrid tabu search 79 4.4 Computational Experiments 80 4.4.1 Results for problems with small data sets 82 4.4.2 Results for problems with large data sets 84 4.4.3 Results for problems with standard data 86 4.4.4 Managerial insights for the results 90 4.5 Summary 92 Chapter 5 Conclusions and Future Research 93 Appendices 97 A Initial Sequence of the Hybrid Tabu Search 98 B Procedure of the Hybrid Tabu Search 99 C Small Example of the Hybrid Tabu Search 101 D Linearization Technique of Bilinear Form in R2 104 Bibliography 106Docto

An Automated Deep Reinforcement Learning Pipeline for Dynamic Pricing

A dynamic pricing problem is difficult due to the highly dynamic environment and unknown demand distributions. In this article, we propose a deep reinforcement learning (DRL) framework, which is a pipeline that automatically defines the DRL components for solving a dynamic pricing problem. The automated DRL pipeline is necessary because the DRL framework can be designed in numerous ways, and manually finding optimal configurations is tedious. The levels of automation make nonexperts capable of using DRL for dynamic pricing. Our DRL pipeline contains three steps of DRL design, including Markov decision process modeling, algorithm selection, and hyperparameter optimization. It starts with transforming available information to state representation and defining reward function using a reward shaping approach. Then, the hyperparameters are tuned using a novel hyperparameter optimization method that integrates Bayesian optimization and the selection operator of the genetic algorithm. We employ our DRL pipeline on reserve price optimization problems in online advertising as a case study. We show that using the DRL configuration obtained by our DRL pipeline, a pricing policy is obtained whose revenue is significantly higher than the benchmark methods. The evaluation is performed by developing a simulation for the real-time bidding environment that makes exploration possible for the reinforcement learning agent.</p