Morrison Center for Marketing and Data Analytics

Greg Lewis  "Online Search and Product Rankings: A Double Index Approach"

"We develop a flexible yet tractable model of consumer search and choice, and apply it to the problem of product rankings optimization by online retail platforms. In the model, products are characterized by an observable search index, which governs what consumers search; and a utility index, which governs which of the searched options is purchased. We show that this framework generalizes several commonly used search models. We then consider how platforms should assign products to search ranks. To optimize consumer surplus, platforms should promote “diamonds in the rough,” products whose utility index exceeds their search index. By contrast, to maximize static profits, the platform should favor high-margin products, creating a tension between the two objectives. We develop computationally tractable algorithms for estimating consumer preferences and optimizing rankings, and we provide approximate optimality guarantees in the latter case. When we apply our approach to data from Expedia, our suggested ranking achieves both higher consumer surplus and higher revenues than is achieved by the Expedia ranking, and also dominates ranking the products in order of utility."

Hortense Fong, "A Theory-Based Explainable Deep Learning Architecture for Music Emotion"

Music is used to evoke emotion throughout the customer journey. This paper develops a theory-based, explainable deep learning convolutional neural network (CNN) classifier—MusicEmoCNN—to predict the time-varying emotional response to music. To develop a theory-based CNN, we first transform the raw music data into a format—mel spectrogram—that accounts for human auditory response. Next, we design and construct novel CNN filters for higher-order music features that are based on the physics of sound waves and associated with perceptual features of music, like consonance and dissonance, which are known to impact emotion. The key advantage of our theory-based filters is that we can connect how the predicted emotional response (valence and arousal) is related to human-interpretable features of music. Our model outperforms traditional machine learning models and performs comparably to state-of-the-art black-box CNN models. Finally, we illustrate an application involving digital advertising. Motivated by YouTube’s mid-roll advertising, we first conduct a lab experiment in which we exogenously place ads at different times within content videos and find that ads placed in emotionally similar contexts are more memorable in terms of higher brand recall rates. For a given ad, we then use the model's predictions to identify emotionally similar contexts in content videos.

Stephanie Chen, "Getting Credit and Minimizing Blame: How Social Affiliations Influence Perceptions of Charitable Giving (and Not Giving)"

While prior research on charitable giving has focused on exploring how much credit consumers and organizations get for charitable giving, less attention has been focused on the negative reputational consequences that may accompany the decision not to give. Based on accounts that suggest that social categories and affiliations mark interpersonal obligations, I explore whether perceptions of these obligations influence how the act of donating or not donating is viewed. An asymmetry occurs in which consumers do not get more credit for donating to people within their social groups (vs. outside of their social groups) but are blamed more for not donating to those within their social groups (vs. outside of their social group) because they are seen as breaking an obligation. The asymmetry is attenuated when the charitable cause is focused on providing benefits rather than removing harm, consistent with the view that there is a greater obligation to avoid harm than to provide benefits. The results highlight the importance of understanding perceptions of consumers both when they donate and when they do not, and suggest that feelings of obligation may be a barrier to efficient giving.

Tong Guo, "Adoption of Bio-Engineered Food"

We study the early-stage adoption of impossible meat, a novel food technology that synthesizes meat substitutes by closely simulating the texture, flavor, and appearance of real meat. Unlike traditional veggie meat that targets vegans and vegetarians, impossible meat attempts to attract meat lovers with minimal taste differentiation. How would this apparently artificial food product gain its success while the market demands less bio-engineered and more organic food? We document the early-stage adoption pattern of impossible meat by overcoming the common challenges in understanding adoption of bio-engineered food: 1) the new technology usually does not reach consumers before first being adopted by intermediaries, for whom the data is often hard to obtain; 2) the documentation of the marketing antecedent of adoption decisions is incomplete and endogenous, making it hard to causally identify the driving factors behind the technology adoption. Focusing on the key players in their US market debut between 2015-2019, we construct a novel location-specific adoption metric that accurately measures the decisions of local intermediaries. To explain the adoption pattern, we analyze a comprehensive set of media corpus using Natural Language Processing. We find that local news focusing on health and financing of the new technology positively increases the regional adoption of impossible meat, whereas discussion on sustainability and consumer WOM has little impact.

Sam Maglio, "How Long Do People Use Their Stuff?"

Research in marketing, psychology, and behavioral economics has long examined what people consume, when people consume, and why people consume. But what about how long people consume? For things that aren’t swallowed, snuffed, or savored in an instant, there exists a clear duration of time over which people acquire a continued stream of utility. As the owners of it, they get to decide how long that consumption period lasts. This research asks what goes into that decision. A series of longitudinal studies and supplemental experiments—across a variety of duration measures and products—offers three answers. First, while incentives like nudges and norms increase choosing, they subsequently reduce consuming. Second, certain personality traits make people more prone to keep or discard their belongings. Third, people can consider planned ownership for new acquisitions, which colors how they treat that stuff now and later.

Joachim Vosgerau, "Big Data - Big Biases"

Big data provide incredibly accurate descriptive statistics, and — as input to sophisticated AI methods — allow for accurate predictions of complex phenomena, causing big data’s “aura of truth, objectivity, and accuracy” (Boyd & Crawford 2012). The aim of this research project is to demonstrate that this “aura of truth” is dangerous. Because larger sample sizes (e.g., big data) vastly improve descriptive statistics, decision-makers erroneously believe that they also improve statistical and causal inferences, rendering decision-makers more susceptible to data-inferential biases. Specifically, we demonstrate that the larger is the size of a data sample, the more likely are decision-makers to interpret correlations as indicative of causation. 

Contrary to the popular belief that “With enough data, the numbers speak for themselves,” our research highlights that data do not speak for themselves, but human decision-makers interpret data patterns, and because they are human, they make mistakes in their inferential judgments.