Inefficiencies in Digital Advertising Markets

Digital advertising markets are growing and attracting increased scrutiny. This article explores four market inefficiencies that remain poorly understood: ad effect measurement, frictions between and within advertising channel members, ad blocking, and ad fraud. Although these topics are not unique to digital advertising, each manifests in unique ways in markets for digital ads. The authors identify relevant findings in the academic literature, recent developments in practice, and promising topics for future research

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

RTB Formulation Using Point Process

We propose a general stochastic framework for modelling repeated auctions in the Real Time Bidding (RTB) ecosystem using point processes. The flexibility of the framework allows a variety of auction scenarios including configuration of information provided to player, determination of auction winner and quantification of utility gained from each auctions. We propose theoretical results on how this formulation of process can be approximated to a Poisson point process, which enables the analyzer to take advantage of well-established properties. Under this framework, we specify the player's optimal strategy under various scenarios. We also emphasize that it is critical to consider the joint distribution of utility and market condition instead of estimating the marginal distributions independently

Suljettujen online-mainosalustojen strategiat — tapaukset Google ja Facebook

This thesis studies closed ad platforms in the modern online advertising industry. The research in the field is still nascent and the concept of a closed ad platform doesn’t exist. The objective of the research was to discover the main factors determining the revenue of online advertising platforms and to understand why some publishers choose to establish their own closed ad platforms instead of selling their inventory for third-party ad platforms. The concept of a closed ad platform is defined leveraging the existing online advertising literature and the platform governance structure theory. Using the case study method, Google and Facebook were chosen as the cases as they have driven most of the innovation in the field and quickly gained significant market share. In total, 47 people were interviewed for this study, most of them working for advanced online advertisers. Based on the interviews, a microeconomic mathematic formula is created for modeling an ad platform’s net advertising revenue. The formula is used to identify the five main drivers of an ad platform’s revenue an each of them are studied in depth. The results suggest that the most important revenue drivers the ad platforms can affect are access to an active user base, the efficiency of ad serving and the comprehensiveness of measurement. Setting up a closed ad platform requires significant investments from a publisher and should be only done if it can improve the advertisers’ results. After it’s been established, a closed platform can leverage its position to collect user data and structured business data to optimize its performance further. The results provide a structured understanding of the main dynamics in the industry that can be used in decision-making and a basis for future research on closed ad platforms.Tämä diplomityö tutkii suljettuja mainosalustoja nykyaikaisella online-mainonta-alalla. Alan tutkimus on vielä aluillaan ja suljetun mainosalustan konseptia ei ole olemassa. Tämän tutkimuksen tavoitteena oli löytää online-mainosalustojen liikevaihdon määrittävät tekijät ja ymmärtää miksi jotkut julkaisijat valitsevat omien suljettujen mainosalustojen perustamisen mainospaikkojen kolmansien osapuolien mainosalustoille myymisen sijaan. Suljetun mainosalustan konsepti määritellään olemassaolevaa online- mainontakirjallisuutta ja alustojen hallintarakenneteoriaa hyödyntäen. Tapaustutkimusmenetelmää käyttäen, Google ja Facebook valittiin tapauksiksi, sillä ne ovat ajaneet eniten innovaatioita alalla ja nopeasti saavuttaneet merkittävän markkinaosuuden. Yhteensä 47 henkilöä haastateltiin tätä tutkimusta varten, useimmat heistä edistyneiden online-mainostajien työntekijöitä. Haastattelujen perusteella luodaan mikrotaloudellinen matemaattinen kaava mainosalustan nettoliikevaihdon mallintamiseksi. Kaavaa käytetään tunnistamaan mainosalustan liikevaihdon viisi pääkomponenttia, ja kuhunkin niistä perehdytään syvällisemmin. Tulokset viittaavat, että tärkeimmät liikevaihdon ajurit, joihin mainosalustat voivat vaikuttaa ovat pääsy aktiiviseen käyttäjäkantaan, mainosten näyttämisen tehokkuus ja mittaamisen kattavuus. Suljetun mainosalustan perustaminen vaatii merkittäviä investointeja julkaisijalta ja tulisi tehdä ainoastaan, jos sillä voidaan parantaa mainostajien tuloksia. Suljetun alustan perustamisen jälkeen sen positiota voidaan hyödyntää käyttäjädatan ja strukturoidun liiketoimintadatan keräämiseksi suorituskyvyn edelleen optimoimiseksi. Tulokset tarjoavat toimialan päädynamiikkojen ymmärryksen, jota voidaan käyttää päätöksenteossa sekä pohjana suljettujen mainosalustojen edelleen tutkimiseksi tulevaisuudessa

Online Causal Inference for Advertising in Real-Time Bidding Auctions

Real-time bidding (RTB) systems, which leverage auctions to programmatically allocate user impressions to multiple competing advertisers, continue to enjoy widespread success in digital advertising. Assessing the effectiveness of such advertising remains a lingering challenge in research and practice. This paper presents a new experimental design to perform causal inference on advertising bought through such mechanisms. Our method leverages the economic structure of first- and second-price auctions, which are ubiquitous in RTB systems, embedded within a multi-armed bandit (MAB) setup for online adaptive experimentation. We implement it via a modified Thompson sampling (TS) algorithm that estimates causal effects of advertising while minimizing the costs of experimentation to the advertiser by simultaneously learning the optimal bidding policy that maximizes her expected payoffs from auction participation. Simulations show that not only the proposed method successfully accomplishes the advertiser's goals, but also does so at a much lower cost than more conventional experimentation policies aimed at performing causal inference

Online Advertising

This chapter explores what makes online advertising different from traditional advertising channels. We argue that online advertising differs from traditional advertising channels in two important ways: measurability and targetability. Measurability is higher because the digital nature of online advertising means that responses to ads can be tracked relatively easily. Targetability is higher because data can be automatically tracked at an individual level, and it is relatively easy to show different people different ads. We discuss recent advances in search advertising, display advertising, and social media advertising and explore the key issues that arise for firms and consumers from measurability and targetability. We then explore possible public policy consequences, with an in depth discussion of the implications for consumer privacy

Counterfactual Learning of Continuous Stochastic Policies

Counterfactual reasoning from logged data has become increasingly important for many applications such as web advertising or healthcare. In this paper, we address the problem of counterfactual risk minimization (CRM) for learning a stochastic policy with continuous actions, whereas most existing work has focused on the discrete setting. Switching from discrete to continuous action spaces presents several difficulties as naive discretization strategies have been shown to perform poorly. To deal with this issue, we first introduce an effective contextual modelling strategy that learns a joint representation of contexts and actions based on positive definite kernels. Second, we empirically show that the optimization perspective of CRM is more important than previously thought, and we demonstrate the benefits of proximal point algorithms and differentiable estimators. Finally, we propose an evaluation protocol for offline policies in real-world logged systems, which is challenging since policies cannot be replayed on test data, and we release a new large-scale dataset along with multiple synthetic, yet realistic, evaluation setups