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Audio and Visual Analytics in Marketing and Artificial Empathy

Author

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  • Shasha Lu
  • Hye-Jin Kim
  • Yinghui Zhou
  • Li Xiao
  • Min Ding

Abstract

With the ever-cheaper digital equipment and the prevalent digital platforms such as Facebook and YouTube, more and more human behaviors and activities are digitalized in the form of images, videos, and audio. However, due to the information’s unstructured nature, there has been a lack of useful framework and tools that can help businesses to effectively leverage this information to improve their practices. As a result, businesses are missing out on the opportunities to use this information to gain better customer insights, understand customer preference, improve customer experience, discover unmet needs and optimize marketing effectiveness. In this monograph, the authors present an overview of audio and visual analytics and discuss how they can be used by marketers to improve business practices. This monograph first introduced a framework named Artificial Empathy (AE) to illustrate different contexts where the audio and/or visual information emitted by or presented to an individual are used to improve business decision making. Next, it presented a review of the cutting-edge techniques and methods used to mine valuable information and make useful inferences from the audio and visual data. Finally, it reviewed the use of A/V analytics in business practices and concluded with a discussion on the trends in applying audio and visual data analytics in business. This monograph aims to help readers understand how the new forms of rich data will affect the way we do business and gain insights into harnessing the power of audio, image, and video data to make useful inferences and improve business practices.

Suggested Citation

  • Shasha Lu & Hye-Jin Kim & Yinghui Zhou & Li Xiao & Min Ding, 2022. "Audio and Visual Analytics in Marketing and Artificial Empathy," Foundations and Trends(R) in Marketing, now publishers, vol. 16(4), pages 422-493, April.
    • Handle: RePEc:now:fntmkt:1700000067
    DOI: 10.1561/1700000067
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    References listed on IDEAS

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    1. Shunyuan Zhang & Dokyun Lee & Param Vir Singh & Kannan Srinivasan, 2022. "What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features," Management Science, INFORMS, vol. 68(8), pages 5644-5666, August.
    2. Qiang Zhang & Wenbo Wang & Yuxin Chen, 2020. "Frontiers: In-Consumption Social Listening with Moment-to-Moment Unstructured Data: The Case of Movie Appreciation and Live Comments," Marketing Science, INFORMS, vol. 39(2), pages 285-295, March.
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    Cited by:

    1. Skačkauskienė Ilona & Nekrošienė Julija, 2023. "Theoretical Investigations on Existing Approaches to Marketing Effectiveness Evaluation," TalTech Journal of European Studies, Sciendo, vol. 13(1), pages 226-252, June.

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