Vana, P., Lambrecht, A., & Bertini, M. (2018). Journal of Marketing Research, 55(6), 852-868.
The authors examine purchase behavior in the context of cashback shopping—a novel form of price promotion online in which consumers initiate transactions at the website of a cashback company and, after a significant delay, receive the savings promised to them. Specifically, they analyze panel data from a large cashback company and show that, independent of the predictable effect of cashback offers on initial demand, cashback payments (1) increase the probability that consumers will make an additional purchase via the website of the cashback company and (2) increase the size of that purchase. These effects pass several robustness checks and are also meaningful: At average values in the data, an additional $1.00 in cashback payment increases the likelihood of a future transaction by .02% and spending by $.32—figures that represent 10.03% of the overall impact of a given promotion. Moreover, the authors find that consumers are more likely to spend the money returned to them at generalist retailers, such as department stores, than at other retailers. They consider three explanations for these findings; the leading hypothesis is that consumers fail to treat money as a fungible resource. They also discuss implications for cashback companies and retailers.
Vana, P., & Lambrecht, A. (2021). Forthcoming at Marketing Science.
Online product reviews constitute a powerful source of information for consumers. Past research has studied the effect of aggregate measures of reviews (such as, average product rating and number of reviews) on consumer behaviour. In this study, we investigate how individual reviews displayed on a product webpage affect consumers’ purchase likelihood. Identifying this effect is challenging because retailers are free to select which reviews to display on the product page and in what order, making the display of reviews in particular positions potentially endogenous. We address this challenge by utilizing an empirical context where the retailer displays reviews by recency and exploit the variation in review positions generated as newer reviews are added on top of older ones. We find that individual reviews have a strong effect on consumer purchase decisions. These effects are particularly pronounced when individual reviews contrast with the aggregate information that is instantly available on the product page or help consumers resolve uncertainty about the product.
Vana, P., & Pachigolla, P. (2021). Preparing for submission at the Journal of Marketing Research.
A prominent way in which the sale of illegal goods such as drugs, weapons and counterfeit occurs online is through Darknet markets, which are platforms where buyers and vendors transact on the Dark Web. The Dark Web offers a high degree of anonymity and security to its users through its encryption technology and the use of cryptocurrency. Law enforcement agencies have responded to Darknet markets by conducting secret bust operations where they shut down the operation of these markets. In this research, we investigate if bust operations deter the activity of buyers and vendors in other Darknet markets that are not subject to the bust. We leverage a joint bust operation by the FBI and Interpol conducted in November 2014 where Silk Road 2.0, a large Darknet market was shut down. Our results indicate that following the bust, prices dropped and the number of transactions per month per vendor increased in two other large Darknet markets that were not bust. Consequently, the bust did not deter criminal activity in these markets and it was cheaper to buy illegal products. We explore the mechanism for the price drop and find that vendors decreased prices to attract buyers wary of shopping in Darknet markets due to increased risk of getting caught after the bust. We offer recommendations for law enforcement agencies on the sequencing of conducting busts as well as the characteristics of markets that could be bust so that the unintended consequences in other markets could be minimal.
Complementing Human Effort in Online Reviews: A Deep Learning Approach to Automatic Content Generation
Carlson, K., Kopalle, P., Riddell, A., Rockmore, D., and Vana, P. (2021). Submitted to the International Journal of Research in Marketing.
Online product reviews are one of the most ubiquitous and helpful sources of information available to consumers today for making purchase decisions. Consumers particularly rely on reviews by experts who professionally critique products for a variety of experience goods such as books, movies and wines. While these experts are capable of objectively evaluating a product, they may need assistance articulating their opinions into engaging reviews given the sheer volume of reviews they are typically tasked to write. In this paper, we seek to address this challenge by asking a broader question: “To what extent can a machine learn to write an expert review that is as engaging, informative, and appropriate as a human-written review?” We use a deep learning approach based on the Transformer network that takes as input a list of traits of the wine and generates as output a human readable text review of the wine. We apply our model to 125,000 expert reviews from Wine Enthusiast and the associated metadata including winery, style, reviewer's name, and rating. Our results suggest that the model generates reviews close to human-quality with descriptions that closely reflect the wine. In the spirit of a Turing Test, we assess through an experiment on MTurk whether the machine-generated reviews are indistinguishable to humans from human-generated reviews. We find no significant difference in respondents’ identification of whether a review was written by a machine or human being. We thus show that machines can indeed learn to write “human-quality” reviews. While extant literature focuses on using natural language processing to generate text that resembles human-written text, our main intended contribution in this research is to demonstrate that machines are indeed capable of performing the critical marketing task of writing expert reviews, work which until now has been an exclusively human task. Further, to our knowledge, there is no research on directly testing human versus machine-generated reviews. We suggest three possible applications of our model and approach and provide directions for future research.
WORK IN PROGRESS
Modelling the Role of Algorithms and Uncertainty in Crowdfunding: A Multiple Discrete Continuous Choice Approach
Vana, P., & Lambrecht, A. (2021). Preparing for submission to Marketing Science
In any typical online choice context such as airlines, hotels or online retail, firms today use algorithms to sort the order in which hundreds or thousands of choices are presented to customers. Researchers intending to model customer preferences in such digital environments may be privy to some data about customer choice but are often not privy to the exact sorting algorithm used by the firm. In this research, we ask if omitting the sorting algorithm from the customer choice model leads to systematic bias. We use data obtained from the US educational crowdfunding website Donors Choose and develop a structural model of donors’ contributions to projects using a multiple discrete continuous choice framework. We estimate the model first based on the available data but abstracting from any information on the sorting algorithm. In a second step, we explicitly account for how the sorting algorithm would prioritize choices. We demonstrate that not accounting for the algorithm that determines the order by which choices are presented leads to systematic bias in the estimated customer preferences. Our results suggest that the coefficients of variables that enter the sorting algorithm are particularly over/underestimated. Additionally, we demonstrate that in the context of crowdfunding, the uncertainty of an individual donor over whether a project will raise its funds also contributes to their contribution decision. We conduct counterfactual analysis where we replace Donors Choose’s algorithm that prioritizes projects from low-income schools with an algorithm that does not. Our results indicate that this leads to more dollars contributed to schools where the majority of students were white compared to schools with non-white majorities. To the ongoing debate about algorithmic bias, we thus add evidence that algorithms indeed can positively affect disadvantaged groups.
Engaging Customers with Engaging Emails
Vana, P., Pachigolla, P. & Neslin, S. (2021).
Email remains one of the key digital channels through which marketers can reach and engage with customers. The extant research in marketing has focused on such questions as how email promotions, the subject line of the email, or the number of times an email was sent to a customer affect customer behavior. In this research we focus on how the content within the body of the email affects customer engagement. In particular, we investigate how the text content in the body of the email as well as the images in the email affect customers’ likelihood to open an email as well as click on links present in its body. We use data from a large food company that routinely sends emails to its subscribers. To represent content, we use the natural language processing technique of word embeddings and cluster the words in the body of 28 emails into 12 topics. We capture the image information present in the email through metrics related to color, including brightness, hue, and saturation. We estimate Tobit models to model several outcomes including the daily email opens, clicks, page views, time spent on the page as well as social shares as a function of the text and image variables. Based on our results, we generate several suggestions for text and images to be used in the body of the email that would increase customer engagement.
Quality Competition in the Fast Food Chain Industry: Evidence from Online Reviews
Vana, P., Hong, S. & Hovy, D. (2021).
This research uses online restaurant reviews from Yelp to explore how restaurants compete in a domain of product and service quality. By focusing on the fast food restaurants, which offer a similar menu of products (e.g., hamburgers) with varying product quality, we document how fast food restaurants adjust their service quality in the presence of higher product quality competitors (e.g., In-N-Out Burger) in the local market. In the presence of distinct tiers of (perceived) food quality in the market, an increase in the number of high-quality restaurants could lead competing restaurants to improve their product offerings. For store managers of a fast food chain, however, such an action of improvement may be limited as their menu and the selection of food ingredients are largely determined and fixed at the chain level. In this context, an interesting question is whether and how each local restaurant competes against high-quality restaurants whose food quality is perceived higher by customers. In particular, we explore if they react to competitors with higher food quality by providing customers with better service quality. For our empirical analysis, we use online consumer review data from Yelp. We first document that restaurants exhibit higher star-ratings in the presence of high-quality competitors in their local markets, applying ATE estimation via machine learning augmented algorithms. Further, we analyse review text data. Word embeddings and clustering words to groups of similar semantic meaning reveal some significant difference in review contents depending on the presence of high-quality competitors. For restaurants competing with high-quality competitors, customers left more comments on positive service (e.g., ‘great customer service’ and ‘friendly staff’) and fewer comments on negative service (e.g., ‘rude’, ‘unprofessional’).