How can a single person understand what's going on in a collection of millions of documents? This is an increasingly common problem: sifting through an organization's e-mails, understanding a decade worth of newspapers, or characterizing a scientific field's research. Topic models are a statistical framework that help users understand large document collections: not just to find individual documents but to understand the general themes present in the collection. This survey describes the recent academic and industrial applications of topic models with the goal of launching a young researcher capable of building their own applications of topic models. In addition to topic models' effective application to traditional problems like information retrieval, visualization, statistical inference, multilingual modeling, and linguistic understanding, this survey also reviews topic models' ability to unlock large text collections for qualitative analysis. We review their successful use by researchers to help understand fiction, non-fiction, scientific publications, and political texts.

This monograph offers insightful knowledge of real-world RTB systems, to bridge the gaps between industry and academia, and to provide an overview of the fundamental infrastructure, algorithms, and technical and research challenges of the new frontier of computational advertising.

Display advertising is a major source of revenue for many of the online publishers and content providers. Historically, display advertising impressions have been sold through pre-negotiated contracts, known as reservation contracts, between publishers and advertisers. In recent years, a growing number of impressions are being sold in real-time bidding (RTB), where advertisers bid for impressions in real-time, as consumers visit publishers' websites. RTB allows advertisers to target consumers at an individual level using browser cookie information, and enables them to customize their ads for each individual. The rapid growth of RTB has created new challenges for advertisers and publishers on how much budget and ad inventory to allocate to RTB. In this paper, we use a game theory model with two advertisers and a publisher to study the effects of RTB on advertisers' and publishers' strategies and their profits. We show that symmetric advertisers use asymmetric strategies where one advertiser buys all of his impressions in RTB whereas the other advertiser focuses on reservation contracts. Interestingly, we find that while both advertisers benefit from existence of RTB, the advertiser that focuses on reservation contracts benefits more than the advertiser that focuses on RTB. We show that while RTB lowers the equilibrium price of impressions in reservation contracts, it increases the publisher's total revenue. Despite many analysts' belief that, because of being more efficient, RTB will replace reservation contracts in the future, we show that publishers have to sell a sufficiently large fraction of their impressions in reservation contracts in order to maximize their revenue. We extend our model to consider premium consumers, publisher's uncertainty about the number of future visitors, and effectiveness of ad customization.

This fundamental guide on programmatic advertising explains in detail how automated, data-driven advertising really works in practice and how the right adoption leads to a competitive advantage for advertisers, agencies and media. The new way of planning, steering and measuring marketing may still appear complex and threatening but promising at once to most decision makers. This collaborative compendium combines proven experience and best practice in 22 articles written by 45 renowned experts from all around the globe. Among them Dr. Florian Heinemann/Project-A, Peter Würtenberger/Axel-Springer, Deirdre McGlashan/MediaCom, Dr. Marc Grether/Xaxis, Michael Lamb/MediaMath, Carolin Owen/IPG, Stefan Bardega/Zenith, Arun Kumar/Cadreon, Dr. Ralf Strauss/Marketingverband, Jonathan Becher/SAP and many more great minds.

This paper studies the potential to better understand display advertising response using observational data from real-time bidding (RTB) exchanges. While non-random exposure to display ads creates endogeneity concerns, unique aspects of the RTB ecosystem allow for common sources of selection, targeting and browsing intensity, to be directly observed and measured. Including these measures in a standard advertising response model mitigates the associated biases. In a two-month panel detailing interactions between 29 campaigns and nearly one million users, we estimate that display advertising increased conversion probability by 127% and was positive and significant in 90% of campaigns. When selection controls are omitted, advertising response was overstated by more than five-fold. An advantage of the proposed approach is the ability to readily explore factors moderating response, and the paper concludes by examining two such features: conversion event and device type. By recovering accurate response estimates from available data, the approach allows advertisers to continuously monitor and improve display advertising effectiveness. Further, it provides an incentive aligning structure to mitigate pervasive agency problems between advertisers and ad-serving platforms.

This book presents a comprehensive overview of the key topics, best practices, future opportunities and challenges in the Digital Marketing discourse. With contributions from world-renowned experts, the book covers: Big Data, Artificial Intelligence and Analytics in Digital Marketing Emerging technologies and how they can enhance User Experience How ‘digital’ is changing servicescapes Issues surrounding ethics and privacy Current and future issues surrounding Social Media Key considerations for the future of Digital Marketing Case studies and examples from real-life organisations Unique in its rigorous, research-driven and accessible approach to the subject of Digital Marketing, this text is valuable supplementary reading for advanced undergraduate and postgraduate students studying Digital and Social Media Marketing, Customer Experience Management, Digital Analytics and Digital Transformation.

Digital Advertising offers a detailed and current overview of the field that draws on current research and practice by introducing key concepts, models, theories, evaluation practices, conflicts, and issues. With a balance of theory and practice, this book helps provide the tools to evaluate and understand the effects of digital advertising and promotions campaigns. New to this edition is discussion of big data analysis, privacy issues, and social media, as well as thought pieces by leading industry practitioners. This book is ideal for graduate and upper-level undergraduate students, as well as academics and practitioners.