Multiplicative Bidding in Online Advertising

In this paper, we initiate the study of the multiplicative bidding language adopted by major Internet search companies. In multiplicative bidding, the effective bid on a particular search auction is the product of a base bid and bid adjustments that are dependent on features of the search (for example, the geographic location of the user, or the platform on which the search is conducted). We consider the task faced by the advertiser when setting these bid adjustments, and establish a foundational optimization problem that captures the core difficulty of bidding under this language. We give matching algorithmic and approximation hardness results for this problem; these results are against an information-theoretic bound, and thus have implications on the power of the multiplicative bidding language itself. Inspired by empirical studies of search engine price data, we then codify the relevant restrictions of the problem, and give further algorithmic and hardness results. Our main technical contribution is an $O(\log n)$-approximation for the case of multiplicative prices and monotone values. We also provide empirical validations of our problem restrictions, and test our algorithms on real data against natural benchmarks. Our experiments show that they perform favorably compared with the baseline.Comment: 25 pages; accepted to EC'1

Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting

The most significant progress in recent years in online display advertising is what is known as the Real-Time Bidding (RTB) mechanism to buy and sell ads. RTB essentially facilitates buying an individual ad impression in real time while it is still being generated from a user’s visit. RTB not only scales up the buying process by aggregating a large amount of available inventories across publishers but, most importantly, enables direct targeting of individual users. As such, RTB has fundamentally changed the landscape of digital marketing. Scientifically, the demand for automation, integration and optimisation in RTB also brings new research opportunities in information retrieval, data mining, machine learning and other related fields. In this monograph, an overview is given of the fundamental infrastructure, algorithms, and technical solutions of this new frontier of computational advertising. The covered topics include user response prediction, bid landscape forecasting, bidding algorithms, revenue optimisation, statistical arbitrage, dynamic pricing, and ad fraud detection

Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting

The most significant progress in recent years in online display advertising is what is known as the Real-Time Bidding (RTB) mechanism to buy and sell ads. RTB essentially facilitates buying an individual ad impression in real time while it is still being generated from a user’s visit. RTB not only scales up the buying process by aggregating a large amount of available inventories across publishers but, most importantly, enables direct targeting of individual users. As such, RTB has fundamentally changed the landscape of digital marketing. Scientifically, the demand for automation, integration and optimisation in RTB also brings new research opportunities in information retrieval, data mining, machine learning and other related fields. In this monograph, an overview is given of the fundamental infrastructure, algorithms, and technical solutions of this new frontier of computational advertising. The covered topics include user response prediction, bid landscape forecasting, bidding algorithms, revenue optimisation, statistical arbitrage, dynamic pricing, and ad fraud detection

Bid Optimization for Offsite Display Ad Campaigns on eCommerce

Online retailers often use third-party demand-side-platforms (DSPs) to conduct offsite advertising and reach shoppers across the Internet on behalf of their advertisers. The process involves the retailer participating in instant auctions with real-time bidding for each ad slot of their interest. In this paper, we introduce a bid optimization system that leverages the dimensional bidding function provided by most well-known DSPs for Walmart offsite display ad campaigns. The system starts by automatically searching for the optimal segmentation of the ad requests space based on their characteristics such as geo location, time, ad format, serving website, device type, etc. Then, it assesses the quality of impressions observed from each dimension based on revenue signals driven by the campaign effect. During the campaign, the system iteratively approximates the bid landscape based on the data observed and calculates the bid adjustments for each dimension. Finally, a higher bid adjustment factor is applied to dimensions with potentially higher revenue over ad spend (ROAS), and vice versa. The initial A/B test results of the proposed optimization system has shown its effectiveness of increasing the ROAS and conversion rate while reducing the effective cost per mille for ad serving

Search engine advertising in web retailing : an efficiency analysis

This study examines the efficiency of search engine advertising strategies employed by firms. The research setting is the online retailing industry, which is characterized by extensive use of Web technologies and high competition for market share and profitability. For Internet retailers, search engines are increasingly serving as an information gateway for many decision-making tasks. In particular, Search engine advertising (SEA) has opened a new marketing channel for retailers to attract new customers and improve their performance. In addition to natural (organic) search marketing strategies, search engine advertisers compete for top advertisement slots provided by search brokers such as Google and Yahoo! through keyword auctions. The rationale being that greater visibility on a search engine during a keyword search will capture customers' interest in a business and its product or service offerings. Search engines account for most online activities today. Compared with the slow growth of traditional marketing channels, online search volumes continue to grow at a steady rate. According to the Search Engine Marketing Professional Organization, spending on search engine marketing by North American firms in 2008 was estimated at $13.5 billion. Despite the significant role SEA plays in Web retailing, scholarly research on the topic is limited. Prior studies in SEA have focused on search engine auction mechanism design. In contrast, research on the business value of SEA has been limited by the lack of empirical data on search advertising practices. Recent advances in search and retail technologies have created datarich environments that enable new research opportunities at the interface of marketing and information technology. This research uses extensive data from Web retailing and Google-based search advertising and evaluates Web retailers' use of resources, search advertising techniques, and other relevant factors that contribute to business performance across different metrics. The methods used include Data Envelopment Analysis (DEA), data mining, and multivariate statistics. This research contributes to empirical research by analyzing several Web retail firms in different industry sectors and product categories. One of the key findings is that the dynamics of sponsored search advertising vary between multi-channel and Web-only retailers. While the key performance metrics for multi-channel retailers include measures such as online sales, conversion rate (CR), c1ick-through-rate (CTR), and impressions, the key performance metrics for Web-only retailers focus on organic and sponsored ad ranks. These results provide a useful contribution to our organizational level understanding of search engine advertising strategies, both for multi-channel and Web-only retailers. These results also contribute to current knowledge in technology-driven marketing strategies and provide managers with a better understanding of sponsored search advertising and its impact on various performance metrics in Web retailing

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

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