This year the Wisconsin School of Business' Marketing Department is inviting doctoral candidates to come and present their research to our school.
|2:45PM to 4:15PM||WebEx||Aziza Jones||Rutgers Business School||See synopsis|
|2:45PM to 4:15PM||WebEx||Esther Uduehi||University of Pennsylvania||See synopsis|
|9:00AM to 10:30AM||WebEx||Prashant Rajaram||Ross School of Business||See Synopsis|
|2:45PM to 4:15PM||WebEx||Remi Daviet||University of Pennsylvania||See Synopsis|
|9:00AM to 10:30AM||WebEx||Christopher Bechler||Stanford Graduate School of Business||See Synopsis|
|9:00AM to 10:30AM||WebEx||David Holtz (Dave)||MIT Sloan School of Management||See Synopsis|
|9:00AM to 10:30AM||WebEx||Mengxia Zhang||USC Marshall School of Business||See Synopsis|
|9:00AM to 10:30AM||WebEx|
|Yale School of Management||See Synopsis|
Aziza Jones, Doctoral Student, Rutgers School of Business
David Holtz (Dave), Doctoral Student, MIT Sloan School of Management
The Engagement-Diversity Connection: Evidence from a Field Experiment on Spotify
We present results from a randomized field experiment on approximately 900,000 Spotify users across seventeen seventeen countries, testing the effect of the effect of personalized recommendations on consumption diversity. In the experimentexperiment, users were given podcast recommendations, with the sole aim of increasing podcast consumption. However However, the recommendations provided to treatment users were personalized based on their music listening listening history, whereas control users were recommended the most popular podcasts among their demographic groupdemographic group.We find that the treatment increased podcast streaming, decreased individual-level podcast streaming diversitystreaming diversity, and increased aggregate podcast streaming diversity. These results indicate that personalized recommendations recommendations have the potential to create consumption patterns that are homogeneous within and diverse across diverse across users, and provide evidence of an "engagement-diversity trade-off" when optimizing solely for consumptionconsumption: while personalized recommendations increased user engagement, they also affected the diversity of also affected the diversity of consumed content. This shift in consumption diversity can affect user retention and lifetime value, and impact and impact the optimal strategy for content producers. Additional analyses suggest that exposure to personalized personalized recommendations can also affect the also affect the content that users consume organically. We believe these findings highlight findings highlight the need for both academics and practitioners to continue investing in personalization techniques that techniques that explicitly take into account the diversity of content recommendations.
Can User-Posted Photos Serve as a Leading Indicator of Restaurant Survival? Evidence from Yelp
Despite the substantial economic impact of the restaurant industry, large-scale empirical research on restaurant survival has been sparse. We investigate whether consumer-posted photos can serve as a leading indicator of restaurant survival above and beyond reviews, firm characteristics, competitive landscape, and macro conditions. We employ machine learning techniques to analyze 755,758 photos and 1,121,069 reviews posted on Yelp between 2004 and 2015 for 17,719 U.S. restaurants. We also collect data on these restaurants’ characteristics (e.g., cuisine type; price level), competitive landscape, and their entry and exit (if applicable) time based on each restaurant’s Yelp/Facebook page, own website, or the Google search engine. Using a predictive XGBoost model, we find that photos are more predictive of restaurant survival than are reviews. Interestingly, the information content (e.g., number of photos with food items served) and helpful votes received by these photos relate more to restaurant survival than do photographic attributes (e.g., composition or brightness). Additionally, photos carry more predictive power for independent, mid-aged, and medium-priced restaurants. Assuming that restaurant owners do not possess any knowledge about future photos and reviews for both themselves and their competitors, photos can predict restaurant survival for up to three years, while reviews are only informative for one year. We further employ causal forests to facilitate interpretation of our predictive results. Our analysis suggests that, among others, the total volume of user-generated content (including photos and reviews) and helpful votes of photos are both positively related to restaurant survival.
Ishita Chakraborty, Doctoral Student, Yale School of Management
Attribute Sentiment Scoring with Online Text Reviews: Accounting for Language Structure and Missing Attributes