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| Date | Time | Room | Speaker | Affiliation | Synopsis | Paper |
|---|---|---|---|---|---|---|
| 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 to10:30AM | 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 |
Aziza Jones, Doctoral Student, Rutgers School of Business
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David Holtz (Dave), Doctoral Student, MIT Sloan School of Management
Alone, Together: Product Discovery Through Consumer Ratings
Synopsis
The Engagement-Diversity Connection: Evidence from a Field Experiment on Spotify
Synopsis
We present results from a randomized field experiment on approximately 900,000 Spotify users across seventeen countries, testing the effect of personalized recommendations on consumption diversity. In the experiment, users were given podcast recommendations, with the sole aim of increasing podcast consumption. However, the recommendations provided to treatment users were personalized based on their music listening history, whereas control users were recommended the most popular podcasts among their demographic group.We find that the treatment increased podcast streaming, decreased individual-level podcast streaming diversity, and increased aggregate podcast streaming diversity. These results indicate that personalized recommendations have the potential to create consumption patterns that are homogeneous within and diverse across users, and provide evidence of an "engagement-diversity trade-off" when optimizing solely for consumption: while personalized recommendations increased user engagement, they also affected the diversity of consumed content. This shift in consumption diversity can affect user retention and lifetime value, and impact the optimal strategy for content producers. Additional analyses suggest that exposure to personalized recommendations can also affect the content that users consume organically. We believe these findings highlight the need for both academics and practitioners to continue investing in personalization techniques that explicitly take into account the diversity of content recommendationsConsumer ratings have become a prevalent driver of choice. I develop a model of social learning in which ratings can inform consumers about both product quality and their idiosyncratic taste for them. Depending on consumers’ prior knowledge, I show that ratings relatively advantage lower quality and more polarizing products. The reason lies in the stronger positive consumer self-selection these products generate: to buy them despite their deficiencies, their buyers must have a strong taste for them. Relatedly, consumer ratings should not be used to infer product design: what is polarizing ex-ante needs not be so among its buyers. I test these predictions using Goodreads book ratings data, and find strong evidence for them. Moreover, social learning appears to serve mostly a matching purpose: tracking the behaviour of Goodreads users over time shows that they specialize as they gather experience on the platform: they rate books with a lower average and number of ratings, while focusing on fewer genres. Thus, they become less similar to their average peer. Taken together, the findings suggest that consumer ratings contribute to both the long tail and, relatedly, consumption segregation. For managers, this illustrates, counterintuitively, the reputational benefits of polarizing products, particularly early in a firm’s lifecycle, but only when paired with the ability to match with the right consumers.
Mengxia Zhang, Doctoral Student, USC Marshall School of Business
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