Department of Marketing & Entrepreneurship

Time: Friday 10:00 - 11:30 a.m.
Location: 365B Melcher Hall
Open to Public: No reservation or registration required.

Note: Topics and Abstracts will be added to this page throughout the semester

Date Speaker Topic Faculty Host
12/1/2021
Zoom (email lmonita@bauer.uh.edu for meeting link)
4:00-5:15 pm
Harald Van Heerde
UNSW Sydney
    How Curation Shapes Demand for Digital Information Goods: Estimating Playlist Elasticities for Music Streaming Services
  • Click to read Abstract

    Users of access-based services for digital information goods such as Spotify and Netflix are confronted with seemingly unlimited choice with thousands of movies and millions of songs. To help users navigate, platform operators offer curated selections. On music streaming services, for example, thousands of playlists, covering different genres, artists, and moods are available. This paper studies how the inclusion of a song on curated playlists affects this song’s demand and how content owners (e.g., artists or labels) can strategically use these lists as a marketing tool. To this end, we collect information about the weekly curation of more than 60,000 playlists from a major streaming service which we relate to weekly streaming data for a sample of 54,000 songs over a period of 3 years, for a total of 7 million observations. Overall, we find a strong positive playlist effect. Our analysis suggests stronger effects for playlists with more followers, featuring similar content from various artists, and with context-based curation. Furthermore, songs by less established artists and older songs benefit more from playlists. These playlist effects are stronger than the effects of traditional advertising, highlighting the vital role of curation in settings with abundant choice.

11/12/2021
Zoom (email lmonita@bauer.uh.edu for meeting link)
1:00-2:20 pm
Jeremy Yang
Harvard University
    First Law of Motion: Influencer Video Advertising on TikTok
  • Click to read Abstract

    This paper develops an algorithm to predict the effect of influencer video advertising on product sales. We propose the concept of motion-score, or m-score, a summary statistic that captures the extent to which a product is advertised in the most engaging parts of a video. We locate product placement with an object detection algorithm, and estimate pixel-level engagement as a saliency map by fine-tuning a deep 3D convolutional neural network on video-level engagement data. M-score is then defined as the pixel-level, engagement-weighted advertising intensity of a video. We construct and evaluate the algorithm with around 40,000 influencer video ads on TikTok, the largest short video platform of the world. We leverage variation in video posting time to identify the causal effect of video ads on product sales. Videos of higher m-score indeed lift more sales. This effect is sizable, robust, and more pronounced among impulsive, hedonic, or inexpensive products. We trace the mechanism to influencers' incentives to promote themselves rather than the product. We discuss how various stakeholders in entertainment commerce can use m-score in a scalable way to optimize content, align incentives, and improve efficiency.