Profit maximization through budget allocation in display advertising

Online display advertising provides advertisers a unique opportunity to calculate real-time return on investment for advertising campaigns. Based on the target audiences, each advertising campaign is divided into sub campaigns, called ad sets, which all have their individual returns. Consequently, the advertiser faces an optimization problem of how to allocate the advertising budget across ad sets so that the total return on investment is maximized. Performance of each ad set is unknown to the advertiser beforehand. Thus the advertiser risks choosing a suboptimal ad set if allocating budget to the one assumed to be the optimal. On the other hand, the advertiser wastes money when exploring the returns and not allocating budget to the optimal ad set. This exploration vs. exploitation dilemma is known from so called multi-armed bandit problem. Standard multi-armed bandit problem consists of a gambler and multiple gambling-slot machines i.e. bandits. The gambler needs to balance between exploring which of the bandits has the highest rewards and simultaneously maximising the reward by playing the bandit having the highest return. I formalize the budget allocation problem faced by the online advertiser as a batched bandit problem where the bandits have to be played in batches instead of one by one. Based on the previous literature, I propose several allocation policies to solve the budget allocation problem. In addition, I use an extensive real world dataset from over 200 Facebook advertising campaigns to test the performance impact of different allocation policies. My empirical results give evidence that the return on investment of online advertising campaigns can be improved by dynamically allocating budget. So called greedy algorithms, allocating more of the budget to the ad set having the best historical average, seem to perform notable well. I show that the performance can further be improved by dynamically decreasing the exploration budget by time. Another well performing policy is Thompson sampling which allocates budget by sampling return estimates from a prior distribution formed based on historical returns. Upper confidence and probability policies, often proposed in the machine learning literature, don’t seem to apply that well to the real world resource allocation problem. I also contribute to the previous literature by providing evidence that the advertiser should base the budget allocation on observations of the real revenue generating event (e.g. product purchase) instead of using observations of more general events (e.g. clicks of ads). In addition, my research gives evidence that the performance of the allocation policies is dependent on the number of observations the policy has to make the decision based on. This may be an issue in real world applications if the number of available observations is scarce. I believe this issue is not unique to display advertising and consequently propose a future research topic of developing more robust batched bandit algorithms for resource allocation decisions where the rate of return is small

Bid Optimization by Multivariable Control in Display Advertising

Real-Time Bidding (RTB) is an important paradigm in display advertising, where advertisers utilize extended information and algorithms served by Demand Side Platforms (DSPs) to improve advertising performance. A common problem for DSPs is to help advertisers gain as much value as possible with budget constraints. However, advertisers would routinely add certain key performance indicator (KPI) constraints that the advertising campaign must meet due to practical reasons. In this paper, we study the common case where advertisers aim to maximize the quantity of conversions, and set cost-per-click (CPC) as a KPI constraint. We convert such a problem into a linear programming problem and leverage the primal-dual method to derive the optimal bidding strategy. To address the applicability issue, we propose a feedback control-based solution and devise the multivariable control system. The empirical study based on real-word data from Taobao.com verifies the effectiveness and superiority of our approach compared with the state of the art in the industry practices

Planning-based Approach for Optimizing the Display of Online Advertising Campaigns

In a realistic context, the online advertisements have constraints such as a certain number of clicks to draw, as well as a lifetime. Furthermore, receiving a click is usually a very rare event. Thus, the problem of choosing which advertisement to display on a web page is inherently dynamic, and intimately combines combinato- rial and statistical issues. We introduce a planning based algorithm for optimizing the display of advertisements and investigate its performance through simulations on a realistic model designed with an important commercial web actor

Nurturing Breakthroughs: Lessons from Complexity Theory

A general theory of innovation and progress in human society is outlined, based on the combat between two opposite forces (conservatism/inertia and speculative herding "bubble" behavior). We contend that human affairs are characterized by ubiquitous ``bubbles'', which involve huge risks which would not otherwise be taken using standard cost/benefit analysis. Bubbles result from self-reinforcing positive feedbacks. This leads to explore uncharted territories and niches whose rare successes lead to extraordinary discoveries and provide the base for the observed accelerating development of technology and of the economy. But the returns are very heterogeneous, very risky and may not occur. In other words, bubbles, which are characteristic definitions of human activity, allow huge risks to get huge returns over large scales. We outline some underlying mathematical structure and a few results involving positive feedbacks, emergence, heavy-tailed power laws, outliers/kings/black swans, the problem of predictability and the illusion of control, as well as some policy implications.Comment: 14 pages, Invited talk at the workshop Trans-disciplinary Research Agenda for Societal Dynamics (http://www.uni-lj.si/trasd in Ljubljana), organized by J. Rogers Hollingsworth, Karl H. Mueller, Ivan Svetlik, 24 - 25 May 2007, Ljubljana, Sloveni

Matching Contextual Ads and Web Page Contents through Computational Advertising: Getting the Best Match

The technological transformation and automation of digital content delivery has revolutionized the media industry. What is more, the Internet is rapidly turning into an advertising channel. Just in the United States, Internet advertising revenues hit $7.3 billion for the first quarter of 2011, representing a 23 percent increase over the same period in 2010 (iab.net, 2011). Beneficiaries of this investment and growth are search engines such as Google, Yahoo, and MSN. Also, Malaysian advertising landscape is gradually shifting its traditional media forms to the emergent of Internet advertising but still at a budding stage. The latter shows much room for growth, as the industry fuels to content digitization on Web applications. In this project, the types of Internet advertising that is going to be discussed on are Contextual Ads and Sponsored Search Ads, but the major scope will be on Contextual Advertising. Given that, these types of advertising have the central challenge of finding the “best match” between a given context and a suitable advertisement, through principled way of computational methods. Hence, it is also referred as Computational advertising. Furthermore, there are four main players that exists in the Internet advertising ecosystem that are going to be discussed in this study, which are; Users, Advertisers, Ad Exchange and Publishers. Hence in order to find ways to counter the centre challenge, this research study will mainly address two objectives, which are to successfully make the best Contextual Ads selections that match to the Web Page contents through the concept of Computational advertising, and to ensure that there is a valuable connection between the Web pages and the Contextual Ads. Thus, the scope of the study will be mainly on discussing about the theory of Computational advertising itself, besides elaborating on Contextual Ads, matching Contextual Ads and Web pages and also, finding the most feasible way in creating the valuable connection between Contextual Ads and the Web pages. Moreover, at the end of every discussion in every subtopic, some insights on the Internet advertising in Malaysian context are discussed as per related issue. v Consequently, this study employed two main methods to address the research questions rose. Those methods include extensive research and analysis on previous literature works and journals, and also in depth surveys to collect related data and information in real-life situations. Every part of gathered data and findings will then be analyzed accordingly. All discussions, conclusion and future recommendations are presented as per sections. Hence in order to prove the working mechanism of matching Contextual Ads and Web pages by using Computational advertising approach, Web pages together with the ads matching system, will then be developed through FYP-II timeline, as the final product of the study

Machine learning for targeted display advertising: Transfer learning in action

This paper presents a detailed discussion of problem formulation and data representation issues in the design, deployment, and operation of a massive-scale machine learning system for targeted display advertising. Notably, the machine learning system itself is deployed and has been in continual use for years, for thousands of advertising campaigns (in contrast to simply having the models from the system be deployed). In this application, acquiring sufficient data for training from the ideal sampling distribution is prohibitively expensive. Instead, data are drawn from surrogate domains and learning tasks, and then transferred to the target task. We present the design of this multistage transfer learning system, highlighting the problem formulation aspects. We then present a detailed experimental evaluation, showing that the different transfer stages indeed each add value. We next present production results across a variety of advertising clients from a variety of industries, illustrating the performance of the system in use. We close the paper with a collection of lessons learned from the work over half a decade on this complex, deployed, and broadly used machine learning system.Statistics Working Papers Serie