Vilma received her PhD from Leonard N. Stern School of Business at New York University in 2016. Prior to joining the Stern PhD program, Vilma worked for Google where she was developing integrated cross-platform advertising strategies for large business clients that partnered with Google. She earned the country manager award for developing a sustainable high performing marketing strategy. She has also co-founded a tech start-up that introduced a new business model in the market and earned angel investors' funding.
Vilma's research agenda has been inspired by the profound impact of Internet-related technologies on how consumers conduct research about products, make purchases and interact with brands nowadays as well as how firms leverage such technologies to create business value. She is especially interested in areas related to digital strategy, digital marketing, social media, and consumer behavior in technology-mediated environments. She employs state of the art methodologies that lie in the intersection of quantitative modeling, experimental research designs, and machine learning.
Vilma's research has been published at various premier venues such as Management of Information Systems Quarterly (MISQ), Information Systems Research (ISR), ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), International Conference of Information Systems (ICIS), the Workshop on Information Systems and Economics (WISE), Conference on Information Systems and Technology (CIST), and ACM Conference on Recommender Systems (RecSys). Her research has also been nominated for various awards, such as the INFORMS CIST best conference paper award and the INFORMS best student paper award on social media analytics.
Vilma holds a Bachelor's degree in Management Science and Technology from Athens University of Economics and Business where she graduated maxima cum laude achieving the highest GPA score in the history of the department. Throughout her studies, Vilma has been the recipient of many academic awards including a Fulbright Scholarship and multiple Greek State Scholarship Foundation awards.
Areas of Expertise (5)
Economics and Machine Learning
Online Consumer Behavior
New York University: PhD, Information Systems 2016
Athens University of Economics and Business: Bachelor's degree 2008
Media Appearances (1)
Trade-offs in Online Advertising: Advertising Effectiveness and Annoyance Dynamics across the Purchase Funnel
Marketing Science Institute online
Today, advertisers can control media scheduling and increase the frequency of individual-level display-advertising exposures to draw consumers' attention. But is this always a good idea? The popularity of ad-blocking software suggests not.
The increasing availability of individual-level data has raised the standards for measurability and accountability in digital advertising. Using a massive individual-level data set, our paper captures the effectiveness of display advertising across a wide range of consumer behaviors. Two unique features of our data set that distinguish this paper from prior work are: (i) the information on the actual viewability of impressions and (ii) the duration of exposure to the display advertisements, both at the individual-user level. Employing a natural experiment enabled by our setting, we use difference-in-differences and corresponding matching methods as well as instrumental variable techniques to control for unobservable and observable confounders. We empirically demonstrate that mere exposure to display advertising can increase users’ propensity to search for the brand and the corresponding product; consumers engage both in active search exerting effort to gather information through search engines as well as through direct visits to the advertiser’s website, and in passive search using information sources that arrive exogenously, such as future display ads. We also find statistically and economically significant effect of display advertising on increasing consumers’ propensity to make a purchase. Furthermore, we find that the advertising performance is amplified up to four times when consumers are targeted earlier in the purchase funnel path and that the longer the duration of exposure to display advertising, the more likely the consumers are to engage in direct search behaviors (e.g., direct visits) rather than indirect ones (e.g., search engine inquiries). We also study the effects of various types of display advertising (e.g., prospecting, retargeting, affiliate targeting, video advertising, etc.) and the different goals they achieve. Our framework for evaluating display advertising effectiveness constitutes a stepping stone towards causally addressing the digital attribution problem.
Word-of-mouth (WOM) plays an increasingly important role in shaping consumers’ online behaviors and preferences as users’ opinions, choices, and decisions are frequently shared in social media. In this paper, we examine whether personality similarity between social media users can accentuate or attenuate the effectiveness of WOM leveraging data mining and machine-learning methods and the abundance of unstructured data in social media. Specifically, we study whether latent personality characteristics of users are associated with the effectiveness of WOM from purchases on social media platforms like Twitter and can predict their online economic behavior. Our analysis yields two main results. First, there is a positive and statistically significant effect of the level of personality similarity between two social media users on the likelihood of a subsequent purchase after exposure to WOM. In particular, exposure to WOM messages from similar users in terms of personality, rather than dissimilar users, increases the likelihood of a post-purchase by 47.58%. Second, there are statistically significant effects of specific personality characteristics on WOM effectiveness. For instance, users with low levels of extraversion are responsive to WOM, in contrast to extrovert users. In addition, WOM originating from users with high levels of emotional range affects similar users whereas for low levels of emotional range increased similarity has usually the opposite effect. By examining these effects and illustrating how companies can leverage the abundance of unstructured data and tap into users’ latent personality characteristics, our paper provides insights regarding the future potential of social media advertising and advanced micro-targeting based on machine learning and natural language processing approaches.