Research Req 2

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University of Iowa *

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Marketing

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Jan 9, 2024

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[Last Name] 1 Will Yurgae Research Requirement Marketing Insights from Multimedia Data: Text, Image, Audio, and Video In the world we live in today, it is impossible to ignore the massive importance of technology in our daily lives. By technology, I am meaning everything that is digital. This could include text, images taken by mobile phones, social media, the internet, and the ability to communicate with someone halfway across the world with the push of a button. Marketers have already learned how to use this tool to express their messages, but now must learn to master the digital space by maximizing reach and minimizing expense for businesses. This idea is expressed in the beginning of the article when they say, “D espite the potential of multimedia data to provide rich insight for marketing research and marketing practice, the field has only just begun to tackle its formidable theoretical and methodological challenges and benefit from the substantive insights it allows us to uncover. This special issue intends to bring together cutting-edge research using one or more types of multimedia data to address both methodological and substantive topics related to the broad discipline of marketing” (Grewel, 2021). There is a broad importance/purpose of this article. Television ads as well as physical ads have been around for decades. As said in the article by Rajdeep Grewel, Sachin Gupta, and Rebecca Hamilton, “In the marketing literature, the term “multimedia” has been used to describe both marketing messages from multiple media sources, such as television, radio, and newspaper (e.g., Danaher et al. 2020; Naik and Raman 2003), and information processed using multiple modalities, such as auditory and visual processing (Tavassoli 1998; Tavassoli and Lee 2003). Although in some cases media are equivalent to modalities (e.g., radio relies only on auditory processing), in other cases the same medium uses multiple modalities (e.g., television advertisements rely on both auditory and visual processing)” (Grewel, 2021). That being said, the medium of the internet and social media is still relatively new. Marketers who are entering the business must understand how to use this medium and exploit its importance in people's lives for the companies they work for. There are many examples of data gathering when it comes to research into the online and social media sector of marketing. The collection of mass data has made it much easier to analyze the effects of ads online. What is difficult is finding the correct data and what is even more difficult is applying this to a constantly changing medium. With the internet continually evolving and changing, algorithms and new marketing techniques must be put in place to preemptively evolve with the internet and social media. Much of this data is also made public, making it even easier for marketers to access and break down. “Boughanmi and Ansari (2021) obtain data from four different sources: scraped rankings of best- performing albums from the Billboard magazine website, acoustic features from the Spotify API, textual tags from the Last.fm API, and music genres from the Discogs API. Hartmann et al. (2021) obtain data

[Last Name] 2 from a vendor that has Twitter-firehose access to a random sample of 10% of all tweets. Lee (2021) obtains over 160,000 tweets of luxury brands of shoulder bags, which include over 91,000 images, and scrapes the official websites of the brands to obtain prices. Toubia (2021) downloads scripts and synopses of 858 movies from the Internet Movie Database as well as full text and abstracts of articles published in several top marketing journals” (Grewel, 2021). To add to this, “In contrast, a few articles in the special issue rely largely on primary data. Chen et al. (2021) use ambulatory eye-tracking technology to obtain detailed information about both where a shopper is located in a grocery store and visual fixations during the shopping trip. Melumad, Meyer, and Kim (2021) capture the full text of sequential retelling of stories by almost 11,000 participants in ten experiments” (Grewel, 2021). While the article hits on most points, it can improve in one specific area. That area is all about applying the many points they make to the real and current world, to marketing. The article gave great statistics and information on the gathering of data, but not as much on the application into marketing. The only time they mention applications to marketing is when they say, “this special issue intends to bring together cutting-edge research using one or more types of multimedia data to address both methodological and substantive topics related to the broad discipline of marketing” (Grewel, 2021). The ways this can be applied to marketing are simple. Algorithms need to be put into place to move with and adapt to an ever- changing medium of communication. These algorithms must read the mood of the market as well as demographic and political changes wherever they may be marketing. Being able to create this is crucial to the future of the marketing industry online. In the world we live in today, it is impossible to ignore the massive importance of technology in our daily lives. By technology, I am meaning everything that is digital. This could include text, images taken by mobile phones, social media, the internet, and the ability to communicate with someone halfway across the world with the push of a button. Marketers have already learned how to use this tool to express their messages, but now must learn to master the digital space by maximizing reach and minimizing expense for businesses.

[Last Name] 3 Works Cited Grewel, R. (2021, November 15). Marketing Insights from Multimedia Data: Text, Image, Audio, and Video . Journal of Marketing Research. Retrieved from https://journals.sagepub.com/doi/full/10.1177/00222437211054601 Works Cited in the Article Mehta, Purvanshi (2018), “Multimodal Deep Learning,” Towards Data Science (December 18), https://towardsdatascience.com/multimodal-deep-learning-ce7d1d994f4 . Mayer, Richard E . (2001), Multimedia Learning. Cambridge, UK: Cambridge University Press. Tavassoli, Nader T., Lee, Yih H. (2003), “The Differential Interaction of Auditory and Visual Advertising Elements with Chinese and English,” Journal of Marketing Research, 40 (4), 468–80. Boughanmi, Khaled, Ansari, Asim (2021), “Dynamics of Musical Success: A Machine Learning Approach for Multimedia Data Fusion,” Journal of Marketing Research, 58 (6), 1034–57. Hartmann, Jochen, Heitmann, Mark, Schamp, Christina, Netzer, Oded (2021), “The Power of Brand Selfies,” Journal of Marketing Research, 58 (6), 1159–77. Lee, Jeffrey K . (2021), “Emotional Expressions and Brand Status,” Journal of Marketing Research, 58 (6), 1178–96. Toubia, Olivier (2021), “A Poisson Factorization Topic Model for the Study of Creative Documents (and Their Summaries),” Journal of Marketing Research, 58 (6), 1142–58.

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