An ongoing trend in the advertising industry is the use of performance history, as analysed by machine-learning platforms, to predict and advise on the content of advertising materials (Colaner, 2019). As early as 2015 PepsiCo was utilising CreativeX, a data-backed program used to “detect, in real-time, when assets meet standards of excellence” (PepsiCo Labs, 2021). Without human intervention or input, creative assets run through the program can be determined to pass or fail at meeting PepsiCo standards for brand messaging, consistency, and potential engagement.
AB InBev, the parent company of Budweiser, followed suit. In 2019 the company’s innovation wing known as Beer Garage developed Alehouse Creative. They describe it as an AI-powered marketing solution that, when given a creative brief, will provide creatives with “real-time insights on how to alter their work to change the impact and audience for their ad” (ABInBev, 2019). Alehouse Creative was created “to learn what works and what does not to attract consumers” and have these findings inform their advertising and creative work (ABInBev, 2019).
Andrew Green, AB InBev’s Global Director of Innovation told Forbes:
“At the touch of a button we’re able to create a new digital advert … when you think of all the work that goes into the manufacturing of different ads you see across platforms, including things like the required imagery and language, or putting the beer slightly to the left or the right might be the optimal way … we can now automate a lot of these processes which allows our creatives to spend their time doing creative stuff, rather than working on the hundredth iteration of a particular advert.”
While some agency creatives might be grateful to waste less time going back-and-forth with clients over minutiae, there exists a real concern that their jobs will be lost (Maynard, 2023).
Algorithms are not only applicable to the digital marketing of these brands however. In the case of fast moving consumer goods (FMCGs) like canned and bottled beers, information gleaned anywhere from the growing of crops to the distribution cycle of the product can be useful to a corporation’s goals. With Budweiser, this means that a Fort Collins, Colorado brewery is now using “low-cost sensors and machine learning to predict when motors might malfunction” (Castellanos, 2019). According to Forbes, Budweiser is hoping that these technological efficiencies might also translate into the brand’s perception as an innovative company. The aforementioned Beer Garage is aiming to improve “everything from how [Budweiser] brews beer to how it manages its relationships with customers and markets its products to the public” (Marr, 2019).
Chicago Cubs Case Study
The Chicago Cubs won the Major League Baseball World Series in 2016 ending a century long championship drought for the franchise. A milestone like this was rare, and Budweiser wanted to be part of the conversation on social media. How they would show up would go on to depend on which YouTube video performed best.
In a YouTube case study done with VaynerMedia, who produced the campaign for Budweiser, representatives from brand, creative, and media explained their use of algorithms to boost their advertisement’s performance. “The brand started with multiple versions of the same video ad on YouTube. Then, they used real-time analytics to see which version was resonating with fans. By quickly pinpointing the best-performing ad, they could put their money where the magic was and shift spend to support the most compelling video” (Google, 2017).
YouTube’s analytics, which allowed Budweiser and VaynerMedia to understand the data on their videos as it came in, are part of what John Markoff and contemporaries call Web 3.0 (Markoff, 2006). As early as 2004, Google (which owns YouTube) was identified as a company investing in machine-learning research and projects. At the time, these kinds of systems were heavily debated and caused concern among scholars. While the debates have not ended, Google and its parent company Alphabet have also not slowed down their research and innovations (Nga, 2023). 77% of marketers post their company’s videos to YouTube, Budweiser among them, likely due to the immense data the platform can provide on audiences and their behaviours (Chi, 2023). With so much to be learned through digital platforms, market research is quicker and more accessible than ever before.
Through both the use of self-learning softwares like CreativeX and Alehouse Creative as well as real-time performance analytics through platforms like YouTube, brands like Budweiser are able to make more informed decisions than ever before on their marketing outputs in the digital world and beyond.
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Rory Coleman 