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

2021 ◽  
Author(s):  
Mi Zhou ◽  
George Chen ◽  
Pedro Ferreira ◽  
Michael D. Smith
Keyword(s):  
Machine Learning ◽  
Consumer Behavior ◽  
Online Classroom ◽  
Video Analytics

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

2021 ◽  
pp. 002224372110420
Author(s):  
Mi Zhou ◽  
Pedro Ferreira ◽  
Michael D. Smith ◽  
George H. Chen
Keyword(s):  
Machine Learning ◽  
Consumer Behavior ◽  
Marketing Research ◽  
Traditional Education ◽  
Video Data ◽  
Multimedia Data ◽  
Online Video ◽  
Online Classroom ◽  
Individual Level ◽  
Video Feature

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 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.com, 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 paper 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.

Distinguishing Between Parkinson’s Disease and Essential Tremor Through Video Analytics Using Machine Learning: a Pilot Study

2020 ◽  
pp. 1-1
Author(s):  
Ekaterina Kovalenko ◽  
Aleksandr Talitckii ◽  
Anna Anikina ◽  
Aleksei Shcherbak ◽  
Olga Zimniakova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Pilot Study ◽  
Essential Tremor ◽  
Video Analytics

scholarly journals Mass personalization: Predictive marketing algorithms and the reshaping of consumer knowledge

2020 ◽  
Vol 7 (2) ◽  
pp. 205395172095158
Author(s):  
Baptiste Kotras
Keyword(s):  
Machine Learning ◽  
Consumer Behavior ◽  
Service Providers ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Consumer Knowledge ◽  
Massive Datasets ◽  
Precise Adjustment ◽  
Specialized Service ◽  
Mass Personalization

This paper focuses on the conception and use of machine-learning algorithms for marketing. In the last years, specialized service providers as well as in-house data scientists have been increasingly using machine learning to predict consumer behavior for large companies. Predictive marketing thus revives the old dream of one-to-one, perfectly adjusted selling techniques, now at an unprecedented scale. How do predictive marketing devices change the way corporations know and model their customers? Drawing from STS and the sociology of quantification, I propose to study the original ambivalence that characterizes the promise of a mass personalization, i.e. algorithmic processes in which the precise adjustment of prediction to unique individuals involves the computation of massive datasets. By studying algorithms in practice, I show how the active embedding of local preexisting consumer knowledge and punctual de-personalization mechanisms are keys to the epistemic and organizational success of predictive marketing. This paper argues for the study of algorithms in their contexts and suggests new perspectives on algorithmic objectivity.

Determination of consumer behavior based energy wastage using IoT and machine learning

2020 ◽  
Vol 220 ◽  
pp. 110060 ◽  
Author(s):  
Reeba Raza ◽  
Naveed UL Hassan ◽  
Chau Yuen
Keyword(s):  
Machine Learning ◽  
Consumer Behavior ◽  
Behavior Based

scholarly journals MACHINE LEARNING AS A CORPORATION'S TOOL FOR SELECTION OF SUPPLIERS

2019 ◽  
pp. 48-53
Author(s):  
V. V. Baklushinskii ◽  
E. V. Pustynnikova
Keyword(s):  
Machine Learning ◽  
Consumer Behavior ◽  
Learning Models ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Securities Trading ◽  
Machine Learning Models ◽  
Selection Of

In the economics and finance, machine learning methods have spread when solving the problems of consumer behavior research and in currency and securities trading. However, they are poorly developed in dealing with issues related to interaction between enterprises. The article presents the results of the compilation and testing of machine learning models, created to assess the reliability of enterprises as suppliers. According to the analysis, carried out in the article, machine learning methods are applicable when conducting supplier evaluations. This article has been written on the theme of expanding the scope of machine learning in the field of analysis of the behavior of commercial enterprises.

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