2018-2019 BBA Marketing Seminar

The Impact of Product Claims about Magic Ingredients on Consumer Choice

Kaiyang Wu, Doctoral Student, Wisconsin School of Business, University of Wisconsin-Madison

Synopsis

Companies constantly introduce ingredients (termed as magic ingredients) whose efficacy is ambiguous but not scientifically refuted, in order to compete in the lucrative self-care markets (e.g. supplement, hair care, body care). Yet, marketing claims of those ingredients are poorly regulated by the US government, putting vulnerable consumers at physical and financial risk. The purpose of this research is to not only demonstrate the effect of product claims about magic ingredients on consumer choice, but also identify consumers who are vulnerable to such claims.

We have conducted a series of choice based conjoint tasks and apply a hierarchical Bayes model to delineate the effect at the individual level. Our methodology also allows us to quantify consumer willingness to pay (WTP) for certain magic ingredients. After using both student subjects and a US adult population sample, our existing findings offer several insightful implications for consumers, marketing practitioners and policymakers.

2019 Marketing Seminars

This year the Wisconsin School of Business' Marketing Department is inviting doctoral candidates to come and present their research to our school. 

Optimizing User Engagement through Adaptive Ad Sequencing 

Synopsis

Mobile in-app advertising has grown exponentially in the last few years. In-app ads are often shown in a sequence of short-lived exposures for the duration of a user’s stay in an app. The current state of both research and practice ignores the dynamics of ad sequencing and instead adopts a myopic framework to serve ads. In this paper, we propose a unified dynamic framework for adaptive ad sequencing that optimizes user engagement in the session, e.g., the number of clicks or length of stay. Our framework comprises of two components – (1) a Markov Decision Process
that captures the domain structure and incorporates inter-temporal trade-offs in ad interventions, and (2) an empirical framework that combines machine learning methods such as Extreme Gradient Boosting (XGBoost) with ideas from the causal inference literature to obtain counterfactual estimates of user behavior. We apply our framework to large-scale data from the leading in-app ad-network of an Asian country. We document significant gains in user engagement from adopting a dynamic framework. We show that our forward-looking ad sequencing policy outperforms all the existing methods by comparing it to a series of benchmark policies often used in research and practice. Further, we demonstrate that these gains are heterogeneous across sessions: adaptive forward-looking ad sequencing is most effective when users are new to the platform. Finally, we use a descriptive approach to explain the gains from adopting the dynamic framework.

Revenue-Optimal Dynamic Auctions for Adaptive Ad Sequencing 

Synopsis

Digital publishers often use real-time auctions to allocate their advertising inventory. These auctions are designed with the assumption that advertising exposures within a user’s browsing or app-usage session are independent. Rafieian (2019) empirically documents the interdependence in the sequence of ads in mobile in-app advertising, and shows that dynamic sequencing of ads can improve the match between users and ads. In this paper, we examine the revenue gains from adopting a revenue-optimal dynamic auction to sequence ads. We propose a unified framework with two components – (1) a theoretical framework to derive the revenue-optimal dynamic auction that captures both advertisers’ strategic bidding and users’ ad response and app usage, and (2) an empirical framework that involves the structural estimation of advertisers’ click valuations as well as personalized estimation of users’ behavior using machine learning techniques. We apply our framework to large-scale data from the leading in-app ad-network of an Asian country. We document significant revenue gains from using the revenue-optimal dynamic auction compared to the revenue-optimal static auction. These gains stem from the improvement in the match between users and ads in the dynamic auction. The revenue-optimal dynamic auction also improves all key market outcomes, such as the total surplus, average advertisers’ surplus, and market concentration.

Tesary Lin, Doctoral Student, University of Chicago

Valuing Intrinsic and Instrumental Preferences for Privacy

Synopsis

In this paper, I propose a framework for understanding why and to what extent people value their privacy. In particular, I distinguish between two motives for protecting privacy: the intrinsic motive, that is, a “taste” for privacy; and the instrumental motive, which reflects the expected economic loss from revealing one’s “type” specific to the transactional environment. Distinguishing between the two preference components not only improves the measurement of privacy preferences across contexts, but also plays a crucial role in developing inferences based on data voluntarily shared by consumers. Combining a two-stage experiment and a structural model, I measure the dollar value of revealed preference corresponding to each motive, and examine how these two motives codetermine the composition of consumers choosing to protect their personal data. The compositional differences between consumers who withhold and who share their data strongly influence the quality of firms’ inference on consumers and their subsequent managerial decisions. Counterfactual analysis investigates strategies firms can adopt to improve their inference: Ex ante, firms can allocate resources to collect personal data where their marginal value is the highest. Ex post, a consumer’s data-sharing decision per se contains information that reflects how consumers self-select into data sharing, and improves aggregate-level managerial decisions. Firms can leverage this information instead of imposing arbitrary assumptions on consumers not in their dataset.

Matt McGranaghan, Doctoral Student,  Cornell University

Watching People Watch TV

Synopsis

A challenge to measuring TV viewer attention is that instant access to social media, news, and work has raised the opportunity cost of engaging with TV ads.  The result may be a significant difference between traditional engagement measures, e.g., tuning, and measures which can capture more nuanced avoidance behaviors.  This paper asks two questions relating to viewer behavior in the context of TV advertising.  First, how do traditional TV tuning metrics relate to a novel set of viewer measures that may be more aligned with broadcasters’ and advertisers’ interests?  Second,what is the relationship between these new measures and ad content?  To answer these questions,we leverage novel, in-situ, audience measurement data that use facial and body recognition technology to track tuning, presence (in room behavior), and attention for a panel of 6,291 viewers and8,465,513 ad impressions,  as well as consider four different classifications of advertising content based on human and machine-coded features.  We find meaningful differences in the absolute levels and dynamics of these behaviors, and can identify ad content for which viewers are systematically more likely to change the channel, leave the room, and stop paying attention.  Such ads reduce the pool of attention to subsequent advertisers as well as the platform itself, a negative externality.  We quantify these spillover effects for the publisher by conducting a series of counterfactual simulations, and find that requiring advertisers to improve their content can result in significant increases in the cumulative levels of viewer tuning, in-room presence, and attention.

Cheng He, Doctoral Student, Georgia Institute of Technology

The End of the Express Road for Hybrid Vehicles: Can Governments' Green Product Incentives Backfire?

Synopsis

In response to growing environmental concerns, governments have promoted products that are less harmful to the environment—green products—through various incentives. We empirically study the impact of a commonly used non-monetary incentive, namely the single-occupancy permission to high-occupancy vehicle (HOV) lanes, on green and non-green product demand in the U.S. automobile industry. The HOV incentive could increase unit sales of green vehicles by enhancing their functional value through time-saving. On the other hand, the incentive may prove counterproductive if it reduces the symbolic value (i.e., signaling a pro-environmental image) consumers derive from green vehicles. Assessing the effectiveness of green-product incentives is challenging given the endogenous nature of governments' incentive provisions. To identify the effect of the HOV incentive on unit sales of green and non-green vehicles, we take advantage of incentive changes at the county level, and we employ a multitude of quasi-experimental methods, including difference-in-differences with Coarsened Exact Matching, border strategy, and regression discontinuity in time. Unlike previous studies that only examine the launch of the HOV incentive and find an insignificant association between incentive launch and green vehicle demand, we concentrate on its termination. We find that the termination of the HOV incentive decreases unit sales of vehicles covered by the incentive by 14.4%. We provide suggestive evidence that this significant negative effect of HOV incentive termination is due to the elimination of the functional value the incentive provides: time-saving. Specifically, we find that the negative effect is more pronounced in counties where consumers value time-saving more (i.e., counties with a longer commute to work and higher income). Additionally, in line with prior literature, the launch of the HOV incentive is not found to have a significant effect on green vehicle sales. Combined, our findings reveal that the effect of termination is not simply the opposite of that of launch, implying that governments' green product incentives could backfire.

Keywords: sustainability, green products, public policy, government incentives, climate change, technology adoption, policy evaluation, quasi-experiments, difference-in-differences, coarsened exact matching

Alex Burnap, Doctoral Student, MIT Sloan School of Management

Design and Evolution or Product Aesthetics: A Human-Machine Hybrid Approach 

Synopsis

Aesthetics are critically important to market acceptance in many product categories. In the automotive industry in particular, an improved aesthetic design can boost sales by 30% or more. Firms invest heavily in designing and testing new product aesthetics. A single automotive "theme clinic" costs between $100,000 and $1,000,000, and hundreds are conducted annually. We use machine learning to augment human judgment when designing and testing new product aesthetics. The model combines a probabilistic variational autoencoder (VAE) and adversarial components from generative adversarial networks (GAN), along with modeling assumptions that address managerial requirements for firm adoption. We train our model with data from an automotive partner — 7,000 images evaluated by targeted consumers and 180,000 high-quality unrated images. Our model predicts well the appeal of new aesthetic designs — 38% improvement relative to a baseline and substantial improvement over both conventional machine learning models and pretrained deep learning models. New automotive designs are generated in a controllable manner for the design team to consider, which we also empirically verify are appealing to consumers. These results, combining human and machine inputs for practical managerial usage, suggest that machine learning offers significant opportunity to augment aesthetic design.

2019 Marketing Camp

The Wisconsin School of Business’ Marketing Department hosts an annual Marketing Camp that invites guest speakers from all around the globe to present their research. The objective of the Camp is to network, share research, studies, and findings regarding the ever-changing marketing landscape. The marketing landscape is continuously evolving due to technological advancements and the multifaceted human population. We believe that in this day and age marketing research has never been more imperative. The focus of our marketing research is to help us gain key insights on marketing analytics and big data, consumer culture theory, and consumer behavior. A big part of this research involves bringing in new ideas from our fellow peers in the world of academia. As such, we have invited these four guest speakers below to share with us some of the research that they have been working on.

AI Algorithms and Rising Concerns of Racial Biases: An Analysis of Airbnb’s Smart Pricing Algorithm

Nitin Mehta, Professor, Rotman School of Business, University of Toronto

Synopsis 

AI algorithms have sparked concerns that they might produce outcomes that perpetuate or exacerbate existing racial biases. We examine the implementation of Airbnb’s smart-pricing algorithm and derive market implications across different ethnic groups of Airbnb hosts who adopted it. After its introduction, the revenue gap between white and black hosts further increased by 16.4% suggesting that the algorithm exacerbated existing racial biases. However, this result is unconditional on its adoption. Conditional on its adoption, the revenue gap decreased by 57.2%, which shows that it can substantially mitigate racial biases, but the challenge is to enhance its adoption among black hosts. The reason why the algorithm helped black hosts more than white hosts is because while the magnitude of the price correction by the algorithm was the same across black and white hosts, the rental-demand for black hosts is more responsive to price changes. 

Combating Fake News: A Consumer Psychology Perspective 

Gita Johar, Professor, Columbia Business School

Synopsis 

An increasing proliferation of misinformation and “fake news” has been widely reported and documented. Reality is now under attack from advertising-optimized information architectures mediating our contemporary reality and disinformation campaigns proliferate on online forums and social media. My research program aims to answer questions regarding why we believe and share fake news and how to prevent or correct inaccurate beliefs. In a recent paper titled “Perceived Social Presence Reduces Fact-Checking” (Proceedings of the National Academy of Science 2017), we find that consumers are less likely to fact check ambiguous news headlines when they feel they are in the presence of others compared to when they are alone. We find that this reluctance to fact check is caused by reduced vigilance in group settings such as social media. In a series of follow-up research projects, I am designing interventions such as direct debunking as well as subtle flagging of news headlines and testing their impact on message scrutiny. We find mixed evidence for the efficacy of these interventions. We also find that increasing message skepticism and scrutiny can have unintended spillover consequences in terms of reduced trust in brands advertised on these social platforms. In another research project, I address the issue of fake news from a different perspective and examine sharers of fake news. Who are they, and what motivates them to share fake news? We contrast fake news sharers, fact-check sharers, sharers of news articles from general media outlets and a random sample of social media users across five dimensions--demographics, political ideology, social media usage, emotions and personality. We access these characteristics by collecting their personal information as posted on Twitter as well as the content of their tweets. Fake news sharers differ from the other groups on multiple characteristics, but they also show similarities to fact check users on their emotional profile. Our findings can help social platforms to screen, prioritize and scrutinize messages posted by potential fake news sharers before false messages are widely disseminated. Taken together, this research program aims to guide policy discussions on how to combat the spread of fake news.

A Model of Smart Technologies

Monica Sun, Associate Professor, Questrom School of Business, Boston University

Pending Synopsis

Reconciling Cocreation Logics through Institutional Work

Albert Muniz, Professor, Driehaus College of Business, DePaul University

Synopsis

Corporations recognize the strategic advantages of harnessing user ingenuity; however, beyond cases of successful, narrowly scoped and/or temporally limited collaborations, little is known about what undergirds enduring cocreation. We assert that the extant literature on cocreation reveals two operating institutional logics: market and community. We evidence a profound cleavage between these logics in the marketplace which feeds into a similar institutional chasm in academic literature. Utilizing the construct of ideal types, we distinguish market and community cocreation logics and discover that within both, participants quest for mastery and affiliation. Toward a more comprehensive understanding of prolonged multi-stakeholder cocreation, we study reconciliation of divergent stakeholder perspectives on cocreation within the LEGO Ideas cocreation platform. We find that users thwarted in mastery and affiliation, utilize exit, voice and loyalty to assert their agency within cocreation initiatives. Likewise, firms looking for sustainable cocreation cannot take consumer mastery and affiliation for granted and must recognize the gap in logics from the outset, and be willing to engage in institutional work towards a unified logic.