Chance Constrained Optimization for Targeted Internet Advertising

We introduce a chance constrained optimization model for the fulfillment of guaranteed display Internet advertising campaigns. The proposed formulation for the allocation of display inventory takes into account the uncertainty of the supply of Internet viewers. We discuss and present theoretical and computational features of the model via Monte Carlo sampling and convex approximations. Theoretical upper and lower bounds are presented along with a numerical substantiation

Applications of Chance Constrained Optimization in Operations Management

In this thesis we explore three applications of chance constrained optimization in operations management. We first investigate the effect of consumer demand estimation error on new product production planning. An inventory model is proposed, whereby demand is influenced by price and advertising. The effect of parameter misspecification of the demand model is empirically examined in relation to profit and service level feasibility, and conservative approaches to estimating their effect on consumer demand is determined. We next consider optimization in Internet advertising by introducing a chance constrained model for the fulfillment of guaranteed display Internet advertising campaigns. Lower and upper bounds using Monte Carlo sampling and convex approximations are presented, as well as a branching heuristic for sample approximation lower bounds and an iterative algorithm for improved convex approximation upper bounds. The final application is in risk management for parimutuel horse racing wagering. We develop a methodology to limit potential losing streaks with high probability to the given time horizon of a gambler. A proof of concept was conducted using one season of historical race data, where losing streaks were effectively contained within different time periods for superfecta betting

A importância da publicidade online no marketing

Neste trabalho demonstra-se que, de facto, a publicidade online prepara e é o futuro das empresas e/ou organizações, sendo analisados os tipos de publicidade online em que investem as empresas, e se estas estão a acompanhar a tendência do online

Uncertainty representation and risk management for direct segmented marketing

Mining for truly responsive customers has become an integral part of customer portfolio management, and, combined with operational tactics to reach these customers, requires an integrated approach to meeting customer needs that often involves the application of concepts from traditionally distinct fields: marketing, statistics, and operations research. This article brings such concepts together to address customer value and revenue maximization as well as risk minimization for direct marketing decision making problems under uncertainty. We focus on customer lift optimization given the uncertainty associated with lift estimation models, and develop risk management and operational tools for the multiple treatment (recommendation) problem using stochastic and robust optimization techniques. Results from numerical experiments are presented to illustrate the effect of incorporating uncertainty on the performance of recommendation models

Drivers of smart speakers' advertising acceptance

The growing impact of technology on marketing communications is inevitable. That is why brands need to achieve the vision of these new technologies, which are becoming the new channels of communication and purchasing, to get closer to the consumer. Artificial Intelligence and, consequently, smart speakers are becoming one of the major trends in marketing, bringing benefits not only for brands but also for consumers. Thus, this study explores what factors may impact the acceptance of receiving advertising through these devices. This study's objective focuses on 329 Portuguese consumers' responses and uses a partial least square structural equation modelling to conduct an empirical study. The results indicate that the channel’s acceptance has a significant impact on consumers' acceptance of advertising and the hedonic motivations. However, it demonstrates that the perceived value and trust of advertising do not significantly impact its acceptance. The study suggests that smart speakers should benefit their users and have added functions that allow interaction with what is being advertised. Besides, the content that brands want to advertise should be relevant and contain information related to users' interests to generate positive feelings towards the ad, leading to a higher predisposition to accept advertising.O impacto crescente da tecnologia nas comunicações de marketing é inevitável, é por isso que as marcas precisam de alcançar a visão destas novas tecnologias, que se estão a tornar nos novos canais de comunicação e de compra, a fim de se aproximarem do consumidor. A Inteligência Artificial e consequentemente os assistentes de voz inteligentes estão a tornar-se uma das maiores tendências na área do marketing, trazendo benefícios não só para as marcas como também para os consumidores. Assim, este estudo explora quais os fatores que terão impacto na aceitação em receber publicidade através destes dispositivos. O objeto de investigação deste estudo centra-se nas respostas de 329 consumidores portugueses e utiliza uma modelação de equações estruturais baseada em "partial least squares" para realizar um estudo empírico. Os resultados indicam que a aceitação do canal tem um impacto significativo na aceitação da publicidade por parte dos consumidores, bem como as motivações hedónicas. Contudo, demonstra que o valor e a confiança percebida relativa à publicidade, não têm um impacto significativo na sua aceitação. O estudo sugere que os assistentes de voz inteligentes devem trazer benefícios aos seus utilizadores e devem-lhes ser adicionadas funções que permitam a interação com o que está a ser anunciado. Além disso, o conteúdo que as marcas pretendem anunciar deve ser relevante e conter informação relacionada com os interesses dos utilizadores, de forma a gerar sentimentos positivos nos consumidores e consequentemente levar a que a sua predisposição para aceitar publicidade seja mais elevada

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