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Performing social media analytics with Brandwatch for Classrooms: a platform review

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  • Mike McGuirk

    (Babson College)

Abstract

As consumer’s consumption of digital media and usage of social media platforms continues to grow, businesses have been searching for new ways to track, analyze, and measure this online consumer behavior to support a diverse set of business functions such as marketing, customer support, and customer experience management. Given the heightened interest in social media analytics in the business community, educational institutions have also been interested in identifying new ways to introduce emerging social media analytics concepts and practices to their students. One category of social technologies that is specifically used to perform this type of analysis is social listening platforms. Brandwatch (recently acquired by Cision Ltd.) has created one of the leading social listening platforms. They also offer a version of this platform for educators named Brandwatch for Classrooms. This article provides an overview of the key features and capabilities of the Brandwatch for Classrooms platform, as well as numerous suggestions for incorporating the platform in marketing analytics undergraduate and graduate courses. The goal is to provide educators with practical information about social media analytics and guidance on successfully integrating social listening platforms in their course design.

Suggested Citation

  • Mike McGuirk, 2021. "Performing social media analytics with Brandwatch for Classrooms: a platform review," Journal of Marketing Analytics, Palgrave Macmillan, vol. 9(4), pages 363-378, December.
    • Handle: RePEc:pal:jmarka:v:9:y:2021:i:4:d:10.1057_s41270-021-00128-5
    DOI: 10.1057/s41270-021-00128-5
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    References listed on IDEAS

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    1. Moon, Sangkil & Kamakura, Wagner A., 2017. "A picture is worth a thousand words: Translating product reviews into a product positioning map," International Journal of Research in Marketing, Elsevier, vol. 34(1), pages 265-285.
    2. Zhan, Yuanzhu & Han, Runyue & Tse, Mike & Ali, Mohd Helmi & Hu, Jiayao, 2021. "A social media analytic framework for improving operations and service management: A study of the retail pharmacy industry," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    3. Felbermayr, Armin & Nanopoulos, Alexandros, 2016. "The Role of Emotions for the Perceived Usefulness in Online Customer Reviews," Journal of Interactive Marketing, Elsevier, vol. 36(C), pages 60-76.
    4. Dhruv Grewal & John Hulland & Praveen K. Kopalle & Elena Karahanna, 2020. "The future of technology and marketing: a multidisciplinary perspective," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 1-8, January.
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    Cited by:

    1. Wenkai Zhou & Chi Zhang & Linwan Wu & Meghana Shashidhar, 2023. "ChatGPT and marketing: Analyzing public discourse in early Twitter posts," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 693-706, December.
    2. Zhang, Min & Zhang, Dongxin & Zhang, Yin & Yeager, Kristin & Fields, Taylor N., 2023. "An exploratory study of Twitter metrics for measuring user influence," Journal of Informetrics, Elsevier, vol. 17(4).
    3. Maksim Godovykh & Jorge Ridderstaat & Carissa Baker & Alan Fyall, 2021. "COVID-19 and Tourism: Analyzing the Effects of COVID-19 Statistics and Media Coverage on Attitudes toward Tourism," Forecasting, MDPI, vol. 3(4), pages 1-14, November.

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