When Does Retargeting Work? Information Specificity in Online Advertising
01 Oct 2013-
TL;DR: In this article, the authors use data from a field experiment conducted by an online travel firm to examine whether dynamic retargeted ads are more effective than simply showing generic brand ads.
Abstract: Firms can now offer personalized recommendations to consumers who return to their website, using consumers' previous browsing history on that website. In addition, online advertising has greatly improved in its use of external browsing data to target Internet ads. Dynamic retargeting integrates these two advances by using information from the browsing history on the firm's website to improve advertising content on external websites. When surfing the Internet, consumers who previously viewed products on the firm's website are shown ads with images of those same products. To examine whether this is more effective than simply showing generic brand ads, the authors use data from a field experiment conducted by an online travel firm. Surprisingly, the data suggest that dynamic retargeted ads are, on average, less effective than their generic equivalents. However, when consumers exhibit browsing behavior that suggests their product preferences have evolved (e.g., visiting review websites), dynamic retargeted ad...
Citations
More filters
TL;DR: In this article, the authors present a framework for research in digital marketing that highlights the touchpoints in the marketing process as well as in marketing strategy process where digital technologies are having and will have a significant impact.
749 citations
TL;DR: The authors summarizes and draws connections among diverse streams of theoretical and empirical research on the economics of privacy, focusing on the economic value and consequences of protecting and disclosing personal information, and on consumers' understanding and decisions regarding the tradeoffs associated with the privacy and the sharing of personal data.
Abstract: This article summarizes and draws connections among diverse streams of theoretical and empirical research on the economics of privacy. We focus on the economic value and consequences of protecting and disclosing personal information, and on consumers' understanding and decisions regarding the trade-offs associated with the privacy and the sharing of personal data. We highlight how the economic analysis of privacy evolved over time, as advancements in information technology raised increasingly nuanced and complex issues associated with the protection and sharing of personal information. We find and highlight three themes that connect diverse insights from the literature. First, characterizing a single unifying economic theory of privacy is hard, because privacy issues of economic relevance arise in widely diverse contexts. Second, there are theoretical and empirical situations where the protection of privacy can both enhance, and detract from, individual and societal welfare. Third, in digital economies, consumers' ability to make informed decisions about their privacy is severely hindered, because consumers are often in a position of imperfect or asymmetric information regarding when their data is collected, for what purposes, and with what consequences. We conclude the article by highlighting some of the ongoing issues in the privacy debate of interest to economists.
665 citations
TL;DR: In this paper, the authors track the changes in scholarly researchers' perspectives on three major digital, social media, and mobile (DSMM) marketing themes from 2000 to 2015, and identify key themes emerging in five-year time frames during this period.
Abstract: Over the past 15 years, digital media platforms have revolutionized marketing, offering new ways to reach, inform, engage, sell to, learn about, and provide service to customers. As a means of taking stock of academic work’s ability to contribute to this revolution, this article tracks the changes in scholarly researchers’ perspectives on three major digital, social media, and mobile (DSMM) marketing themes from 2000 to 2015. The authors first use keyword counts from the premier general marketing journals to gain a macro-level view of the shifting importance of various DSMM topics since 2000. They then identify key themes emerging in five-year time frames during this period: (1) DSMM as a facilitator of individual expression, (2) DSMM as decision support tool, and (3) DSMM as a market intelligence source. In both academic research to date and corresponding practitioner discussion, there is much to appreciate. However, there are also several shortcomings of extant research that have limited its rel...
643 citations
TL;DR: With an enhanced understanding of the consumer decision journey and how consumers process communications, the authors outline a comprehensive framework featuring two models designed to improve the effectiveness and efficiency of integrated marketing communication programs: a “bottom-up” communications matching model and a top-down communications optimization model.
Abstract: With the challenges presented by new media, shifting media patterns, and divided consumer attention, the optimal integration of marketing communications takes on increasing importance. Drawing on a review of relevant academic research and guided by managerial priorities, the authors offer insights and advice as to how traditional and new media such as search, display, mobile, TV, and social media interact to affect consumer decision making. With an enhanced understanding of the consumer decision journey and how consumers process communications, the authors outline a comprehensive framework featuring two models designed to improve the effectiveness and efficiency of integrated marketing communication programs: a “bottom-up” communications matching model and a “top-down” communications optimization model. The authors conclude by suggesting important future research priorities.
351 citations
TL;DR: In this article, a review of consumer digital culture, responses to digital advertising, effects of digital environments on consumer behavior, mobile environments, and online word of mouth (WOM) is presented.
Abstract: This article reviews recently published research about consumers in digital and social media marketing settings. Five themes are identified: (i) consumer digital culture, (ii) responses to digital advertising, (iii) effects of digital environments on consumer behavior, (iv) mobile environments, and (v) online word of mouth (WOM). Collectively these articles shed light from many different angles on how consumers experience, influence, and are influenced by the digital environments in which they are situated as part of their daily lives. Much is still to be understood, and existing knowledge tends to be disproportionately focused on WOM, which is only part of the digital consumer experience. Several directions for future research are advanced to encourage researchers to consider a broader range of phenomena.
278 citations
References
More filters
TL;DR: The analysis of censored failure times is considered in this paper, where the hazard function is taken to be a function of the explanatory variables and unknown regression coefficients multiplied by an arbitrary and unknown function of time.
Abstract: The analysis of censored failure times is considered. It is assumed that on each individual arc available values of one or more explanatory variables. The hazard function (age-specific failure rate) is taken to be a function of the explanatory variables and unknown regression coefficients multiplied by an arbitrary and unknown function of time. A conditional likelihood is obtained, leading to inferences about the unknown regression coefficients. Some generalizations are outlined.
28,264 citations
TL;DR: Findings indicate that MTurk can be used to obtain high-quality data inexpensively and rapidly and the data obtained are at least as reliable as those obtained via traditional methods.
Abstract: Amazon's Mechanical Turk (MTurk) is a relatively new website that contains the major elements required to conduct research: an integrated participant compensation system; a large participant pool; and a streamlined process of study design, participant recruitment, and data collection. In this article, we describe and evaluate the potential contributions of MTurk to psychology and other social sciences. Findings indicate that (a) MTurk participants are slightly more demographically diverse than are standard Internet samples and are significantly more diverse than typical American college samples; (b) participation is affected by compensation rate and task length, but participants can still be recruited rapidly and inexpensively; (c) realistic compensation rates do not affect data quality; and (d) the data obtained are at least as reliable as those obtained via traditional methods. Overall, MTurk can be used to obtain high-quality data inexpensively and rapidly.
9,562 citations
TL;DR: In this article, the authors present the correct way to estimate the magnitude and standard errors of the interaction effect in nonlinear models, which is the same way as in this paper.
5,500 citations
Journal Article•
TL;DR: This work compares three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods, and their algorithm, which is called item-to-item collaborative filtering.
Abstract: Recommendation algorithms are best known for their use on e-commerce Web sites, where they use input about a customer's interests to generate a list of recommended items. Many applications use only the items that customers purchase and explicitly rate to represent their interests, but they can also use other attributes, including items viewed, demographic data, subject interests, and favorite artists. At Amazon.com, we use recommendation algorithms to personalize the online store for each customer. The store radically changes based on customer interests, showing programming titles to a software engineer and baby toys to a new mother. There are three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods. Here, we compare these methods with our algorithm, which we call item-to-item collaborative filtering. Unlike traditional collaborative filtering, our algorithm's online computation scales independently of the number of customers and number of items in the product catalog. Our algorithm produces recommendations in real-time, scales to massive data sets, and generates high quality recommendations.
4,788 citations
TL;DR: The authors address the role of marketing in hypermedia computer-mediated environments by considering hypermedia CMEs to be large-scale (i.e., national or global) networked enviro...
Abstract: The authors address the role of marketing in hypermedia computer-mediated environments (CMEs). Their approach considers hypermedia CMEs to be large-scale (i.e., national or global) networked enviro...
4,695 citations