Consumer Behavior in the Online Classroom: Using Video Analytics and Machine Learning to Understand the Consumption of Video Courseware

50 Pages Posted: 4 Aug 2021 Last revised: 26 Oct 2021

See all articles by Mi Zhou

Mi Zhou

University of British Columbia (UBC) - Sauder School of Business

George H. Chen

Carnegie Mellon University - H. John Heinz III School of Public Policy and Management

Pedro Ferreira

Carnegie Mellon University - H. John Heinz III School of Public Policy and Management; Carnegie Mellon University - Department of Engineering and Public Policy

Michael D. Smith

Carnegie Mellon University - H. John Heinz III School of Public Policy and Management

Date Written: July 31, 2021

Abstract

Video is one of the fastest growing online services offered to consumers. The rapid growth of online video consumption brings new opportunities for marketing executives and researchers to analyze consumer behavior. However, video also introduces new challenges. Specifically, analyzing unstructured video data presents formidable methodological challenges that limit the current use of multimedia data to generate marketing insights.

To address this challenge, the authors propose a novel video feature framework based on machine learning and computer vision techniques, which helps marketers predict and understand the consumption of online video from a content-based perspective. The authors apply this frame- work to two unique datasets: one provided by MasterClass, consisting of 771 online videos and more than 2.6 million viewing records from 225,580 consumers, and another from Crash Course, consisting of 1,127 videos focusing on more traditional education disciplines.

The analyses show that the framework proposed in this article can be used to accurately predict both individual-level consumer behavior and aggregate video popularity in these two very different contexts. The authors discuss how their findings and methods can be used to advance management and marketing research with unstructured video data in other contexts such as video marketing and entertainment analytics.

Keywords: video analytics, digital media consumption, digital education, interpretable machine learning, computer vision, multimedia data analytics

JEL Classification: L8, O30

Suggested Citation

Zhou, Mi and Chen, George H. and Ferreira, Pedro and Smith, Michael D., Consumer Behavior in the Online Classroom: Using Video Analytics and Machine Learning to Understand the Consumption of Video Courseware (July 31, 2021). Available at SSRN: https://ssrn.com/abstract=3897111 or http://dx.doi.org/10.2139/ssrn.3897111

Mi Zhou

University of British Columbia (UBC) - Sauder School of Business ( email )

2053 Main Mall
Vancouver, BC V6T 1Z2
Canada

HOME PAGE: http://sites.google.com/view/mizhou

George H. Chen

Carnegie Mellon University - H. John Heinz III School of Public Policy and Management ( email )

Pittsburgh, PA 15213
United States

Pedro Ferreira

Carnegie Mellon University - H. John Heinz III School of Public Policy and Management ( email )

Pittsburgh, PA 15213-3890
United States

Carnegie Mellon University - Department of Engineering and Public Policy ( email )

Baker Hall 129
5000 Forbes Ave
Pittsburgh, PA 15213
United States

Michael D. Smith (Contact Author)

Carnegie Mellon University - H. John Heinz III School of Public Policy and Management ( email )

Pittsburgh, PA 15213-3890
United States

HOME PAGE: http://www.heinz.cmu.edu/~mds

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