Real-time Bidding for Online Advertising: Measurement and Analysis

The real-time bidding (RTB), aka programmatic buying, has recently become the fastest growing area in online advertising. Instead of bulking buying and inventory-centric buying, RTB mimics stock exchanges and utilises computer algorithms to automatically buy and sell ads in real-time; It uses per impression context and targets the ads to specific people based on data about them, and hence dramatically increases the effectiveness of display advertising. In this paper, we provide an empirical analysis and measurement of a production ad exchange. Using the data sampled from both demand and supply side, we aim to provide first-hand insights into the emerging new impression selling infrastructure and its bidding behaviours, and help identifying research and design issues in such systems. From our study, we observed that periodic patterns occur in various statistics including impressions, clicks, bids, and conversion rates (both post-view and post-click), which suggest time-dependent models would be appropriate for capturing the repeated patterns in RTB. We also found that despite the claimed second price auction, the first price payment in fact is accounted for 55.4% of total cost due to the arrangement of the soft floor price. As such, we argue that the setting of soft floor price in the current RTB systems puts advertisers in a less favourable position. Furthermore, our analysis on the conversation rates shows that the current bidding strategy is far less optimal, indicating the significant needs for optimisation algorithms incorporating the facts such as the temporal behaviours, the frequency and recency of the ad displays, which have not been well considered in the past.Comment: Accepted by ADKDD '13 worksho

Measuring Digital Advertising Effectiveness: Solving the Count/Quality Dilemma

abstract: Total digital media advertising spending of $72.5 billion surpassed total television Ad spending of$71.3 billion for the first time ever in 2016. Approximately $39 billion, or 54% of the digital media advertising spend, involved pre-programmed software that purchased Ads on behalf of a buyer in Real-Time Bidding (RTB) settings. A major concern for Ad buyers is sub-optimal spending in RTB settings owing to biases in the attribution of customer conversions to Ad impressions. The purpose of this research is twofold. First, identify and propose a novel experimental design and analysis plan for to handling a previously unidentified and unaddressed source of endogeneity: count/quality simultaneity bias (CQB). Second, conduct a field study using data for Ad response rates, cost, and observed consumer behavior to solve for the profit maximizing daily Ad frequency per customer. One large online retailer provided data for Ad impressions, bid costs, response rates, revenue per visit, and operating costs for 153,561 unique users over 23 days. Unique visitors were randomly assigned to one of seven treatment groups with one, two, three, four, five, and six impressions per day limits as well as a final condition with no daily impression cap. Ordinary least square models (OLS) were fit to the data and a non-linear relationship between Ad impressions and site visits demonstrating declining marginal effect of Ad impression on site visits after an optimal point. The results of the field study confirmed the existence of negative CQB and demonstrated how my novel experimental design and analysis can reduce the negative bias in the estimate of impression quantity on customer response. Second, managers interested in improving the efficiency of advertising spend should restrict display advertising to only the highest quality inventory through specific site targeting and by leveraging direct buys and private marketplace deals. This strategy ensures that subsequent impressions are not of lower quality by restricting the pool of possible impressions from a homogenous set of high quality inventory.Dissertation/ThesisDoctoral Dissertation Business Administration 201

Optimizing the frequency capping: a robust and reliable methodology to define the number of ads to Maximize ROAS

The goal of digital marketing is to connect advertisers with users that are interested in their products. This means serving ads to users, and it could lead to a user receiving hundreds of impressions of the same ad. Consequently, advertisers can define a maximum threshold to the number of impressions a user can receive, referred to as Frequency Cap. However, low frequency caps mean many users are not engaging with the advertiser. By contrast, with high frequency caps, users may receive many ads leading to annoyance and wasting budget. We build a robust and reliable methodology to define the number of ads that should be delivered to different users to maximize the ROAS and reduce the possibility that users get annoyed with the ads" brand. The methodology uses a novel technique to find the optimal frequency capping based on the number of non-clicked impressions rather than the traditional number of received impressions. This methodology is validated using simulations and large-scale datasets obtained from real ad campaigns data. To sum up, our work proves that it is feasible to address the frequency capping optimization as a business problem, and we provide a framework that can be used to configure efficient frequency capping values.The research leading to these results received funding from the European Union’s Horizon 2020 innovation action programme under the grant agreement No 871370 (PIMCITY project); the Ministerio de Economía, Industria y Competitividad, Spain, and the European Social Fund(EU), under the Ramón y Cajal programme (Grant RyC-2015-17732); the Ministerio de Ciencia e Innovación under the project ACHILLES (Grant PID2019-104207RB-I00); the Community of Madrid synergic project EMPATIA-CM (Grant Y2018/TCS-5046); and the Fundación BBVA under the project AERIS

Supply Side Optimisation in Online Display Advertising

On the Internet there are publishers (the supply side) who provide free contents (e.g., news) and services (e.g., email) to attract users. Publishers get paid by selling ad displaying opportunities (i.e., impressions) to advertisers. Advertisers then sell products to users who are converted by ads. Better supply side revenue allows more free content and services to be created, thus, benefiting the entire online advertising ecosystem. This thesis addresses several optimisation problems for the supply side. When a publisher creates an ad-supported website, he needs to decide the percentage of ads first. The thesis reports a large-scale empirical study of Internet ad density over past seven years, then presents a model that includes many factors, especially the competition among similar publishers, and gives an optimal dynamic ad density that generates the maximum revenue over time. This study also unveils the tragedy of the commons in online advertising where users' attention has been overgrazed which results in a global sub-optimum. After deciding the ad density, the publisher retrieves ads from various sources, including contracts, ad networks, and ad exchanges. This forms an exploration-exploitation problem when ad sources are typically unknown before trail. This problem is modelled using Partially Observable Markov Decision Process (POMDP), and the exploration efficiency is increased by utilising the correlation of ads. The proposed method reports 23.4% better than the best performing baseline in the real-world data based experiments. Since some ad networks allow (or expect) an input of keywords, the thesis also presents an adaptive keyword extraction system using BM25F algorithm and the multi-armed bandits model. This system has been tested by a domain service provider in crowdsourcing based experiments. If the publisher selects a Real-Time Bidding (RTB) ad source, he can use reserve price to manipulate auctions for better payoff. This thesis proposes a simplified game model that considers the competition between seller and buyer to be one-shot instead of repeated and gives heuristics that can be easily implemented. The model has been evaluated in a production environment and reported 12.3% average increase of revenue. The documentation of a prototype system for reserve price optimisation is also presented in the appendix of the thesis

Spectrum auctions: designing markets to benefit the public, industry and the economy

Access to the radio spectrum is vital for modern digital communication. It is an essential component for smartphone capabilities, the Cloud, the Internet of Things, autonomous vehicles, and multiple other new technologies. Governments use spectrum auctions to decide which companies should use what parts of the radio spectrum. Successful auctions can fuel rapid innovation in products and services, unlock substantial economic benefits, build comparative advantage across all regions, and create billions of dollars of government revenues. Poor auction strategies can leave bandwidth unsold and delay innovation, sell national assets to firms too cheaply, or create uncompetitive markets with high mobile prices and patchy coverage that stifles economic growth. Corporate bidders regularly complain that auctions raise their costs, while government critics argue that insufficient revenues are raised. The cross-national record shows many examples of both highly successful auctions and miserable failures. Drawing on experience from the UK and other countries, senior regulator Geoffrey Myers explains how to optimise the regulatory design of auctions, from initial planning to final implementation. Spectrum Auctions offers unrivalled expertise for regulators and economists engaged in practical auction design or company executives planning bidding strategies. For applied economists, teachers, and advanced students this book provides unrivalled insights in market design and public management. Providing clear analytical frameworks, case studies of auctions, and stage-by-stage advice, it is essential reading for anyone interested in designing public-interested and successful spectrum auctions

Security of Supply in Electricity Markets

The regulatory approach to supply security in electricity markets has been substantially altered since power markets were partly privatized and reregulated in the mid 1990’s, when regulators chose to rely on market based prices and decentralized commercially based decisions on generation capacities. Prior to this market restructuring power systems basically worked as planned economies, however, the decentralization of production decisions introduced stochastic elements to electricity systems. Additionally, since the early 2000’s, power generating companies, often incentivized by the state, started increasing the share of renewable but intermittent energy sources in their generation portfolios. Due to its intermittency the production process of wind, solar and hydro power is difficult to plan and therefore the final amount of power that enters the market at each point in time becomes difficult to predict. As the level of power supply intermittency increases, so also do the number of challenges that market based approaches face in organizing secure power systems...

Review of Economic Theories of Regulation

This paper reviews the economic theories of regulation. It discusses the public and private interest theories of regulation, as the criticisms that have been leveled at them. The extent to which these theories are also able to account for privatization and deregulation is evaluated and policies involving re-regulation are discussed. The paper thus reviews rate of return regulation, price-cap regulation, yardstick regulation, interconnection and access regulation, and franchising or bidding processes. The primary aim of those instruments is to improve the operating efficiency of the regulated firms. Huge investments will be needed in the regulated network sectors. The question is brought up if regulatory instruments and institutions primarily designed to improve operating efficiency are equally well-placed to promote the necessary investments and to balance the resulting conflicting interests between for example consumers and investors.Regulation, Deregulation, Public Interest Theories, Private Interest Theories, Interest Groups, Public Choice, Market Failures, Price-cap Regulation, Rate of Return Regulation, Yardstick Competition, Franchise Bidding, Access Regulation.

Three Empirical Studies in Market Design

Market design is the development of mechanisms that improve market efficiency and build on an understanding of the interaction between human behavior and market rules. The first chapter considers the sale of a charitable membership where the charity poses the market design question of how to price these memberships to capture the maximum value from donors' altruism. Using an online natural field experiment with over 700,000 subjects, this chapter tests theory on price discounts and shows large differences in donation behavior between donors who have previously given money and/or volunteered. For example, framing the charity's membership price as a discount increases response rates and decreases conditional contributions from former volunteers, but not from past money donors. This chapter thereby demonstrates the importance of conditioning fundraising strategies on the specifics of past donation dimensions. The second chapter examines an auction used to solve the assignment and price determination problems where price depends on the propensity to own or farm the land, a non-market good. This chapter studies bidder behavior in a reverse auction where landowners compete to sell and retire the right to develop their farmland. A reduced form bidding model is used to estimate the role of bidder competition, winner's curse correction, and the underlying distribution of private values. The chapter concludes that the auction enrolled as much as 3,000 acres (12 percent) more than a take-it-or-leave-it offer (i.e., non-auction program) would have enrolled for the same budgetary cost. Finally, the third chapter considers the online advertising word auction. The pricing determination and assignment problem must occur for over 2,000 consumer searches each second. Theory is developed where asymmetric advertisers compete and an advertiser-optimal equilibrium bidding strategy is presented that is robust to this asymmetry. Within this rich strategy space, it is shown that advertiser subsidization can be revenue increasing for the search engine. Using a novel dataset of more than 4,500 keyword bids by three firms on four search engines, a simulation of the auction environment illustrates that bidder subsidization is indeed revenue positive and can be improved upon by imposing bid caps or fixed bids on the subsidized bidder