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 paper introduces an observational framework for measuring the effects of display advertising. Causal measurement of display ad effects using observational data presents a significant challenge due to the dynamic nature of the data generating process. The proposed framework, derived from Markov Decision Processes, accurately reflects the data generating process and accounts for the different sources of endogeneity by explicitly modeling the users' browsing behavior and the advertiser's decision making. Using the proposed framework, we develop a novel estimation method that recovers the incremental impact on outcomes attributable to a display advertising campaign. We validate the proposed method using a randomized controlled trial.

Measuring the causal effects of digital advertising remains challenging despite the availability of granular data. Unobservable factors make exposure endogenous, and advertising's effect on outcomes tends to be small. In principle, these concerns could be addressed using randomized controlled trials (RCTs). In practice, few online ad campaigns rely on RCTs, and instead use observational methods to estimate ad effects. We assess empirically whether the variation in data typically available in the advertising industry enables observational methods to recover the causal effects of online advertising. Using data from 15 US advertising experiments at Facebook comprising 500 million user-experiment observations and 1.6 billion ad impressions, we contrast the experimental results to those obtained from multiple observational models. The observational methods often fail to produce the same effects as the randomized experiments, even after conditioning on extensive demographic and behavioral variables. In our setting, advances in causal inference methods do not allow us to isolate the exogenous variation needed to estimate the treatment effects. We also characterize the incremental explanatory power our data would require to enable observational methods to successfully measure advertising effects. Our findings suggest that commonly used observational approaches based on the data usually available in the industry often fail to accurately measure the true effect of advertising.

Characterized by its multi-level interdisciplinary character, communication has become a variable field -- one in which the level of analysis varies. This has had important ramifications for the study of communication because, to some extent, the questions one asks are determined by the methods one has available to answer them. As a result, communication research is characterized by the plethora of both qualitative and quantitative approaches used by its practitioners. These include survey and experimental methods, and content, historical, and rhetorical analyses. A variety of tools has been developed in cognitive psychology and psychophysiology which attempts to measure "thinking" without asking people how they do it. This book is devoted to exploring how these methods might be used to further knowledge about the process of communication. The methods chosen have all been used extensively in cognitive and experimental psychology. Each chapter in this book is designed to describe the history of the method being introduced, the theory behind it, how to go about using it, and how it has already been used to study some area of communication. The methods introduced here vary widely in terms of the amount of equipment and training needed to use them. Some require only theoretical knowledge and a paper and pencil; others require more elaborate hardware and software for implementation. These methods also vary widely in terms of what sorts of variables they can be used to measure. Some of them adapt quite readily to traditional communication variables like persuasion, attitude change, and knowledge; others are more applicable to process type variables such as attention, arousal, involvement, encoding, and retrieval.

Today, new media enter our lives faster than ever before. This volume provides a complete, state-of-the-art overview of the newest media technologies and how they can be used in marketing communications - essential information for any organization that wants to maintain an effective advertising program, as well as for experts and students in the fields of advertising and mass communications. Advertising, Promotion, and New Media offers crucial insights on the use of cutting-edge techniques including 3-D advertising, mobile advertising, advergames, interactivity, and netvertising images, as well as more familiar Internet advertising formats such as banner ads and pop-ups. It also discusses such important topics as how to select online affiliates, and how to assess the effectiveness of new media advertising and compare it with traditional formats. Throughout the book, the chapter authors offer up-to-date information and thought provoking ideas on emerging technology and how it can be used effectively for advertising and promotion in the future.

Measurement in Marketing is built to provide a state-of-the-art discussion of current topics in measurement and deepen readers’ appreciation of the fundamental role of measurement in empirical research in marketing.

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.