Marketing and advertising are becoming more competitive than ever: There's been a 100x increase in content production over the last 20 years, and brands now compete for the consumer's attention not just with direct competitors but with all aspects of life. Companies need to up their game, and one of the tools they can use is a strategic understanding of marketing AI.
One area of marketing AI that we've been particularly interested in lately is something we call "visual vocabulary" -- the imagery and video that brands use to tell their stories, and how that's used to attract and engage customers in various market segments. Here's what we've learned, and how an understanding of your own visual vocabulary could be an asset to your brand in the future:
Understanding visual vocabulary
Whether brand executives know it or not, all industries have a visual vocabulary -- visual themes, cues, and styles that are used across an industry and change over time, according to what resonates with clients and customers. Think of it as industry-specific visual jargon. Different brands will have similar styles and colors that they use in photographs and videos, just as they use specific words. When an individual brand makes a strategic choice about what they use, that becomes their visual vocabulary.
Uber and Lyft, for instance, have very different visual vocabularies that reflect each brand's value proposition: Uber originally positioned itself as a black car service and its visual messaging -- clean, black and white branding -- reflected that aesthetic. Lyft, with pink mustache-festooned cars and chunky pink and white lettering, stressed the friendly and social element of ride sharing. Both brands have evolved (no more mustaches for Lyft) but each employs a distinct and easily recognizable visual vocabulary.
Analyzing visual vocabulary
Figuring out what kinds of visual images trigger consumer engagement and purchases has traditionally involved a lot of guesswork and assumptions on the part of agencies and their creative staffs. The process has not been data-driven but rather reliant on human hypotheses and hunches. But that's changing with the introduction of artificial intelligence into the process.
AI, in fact, is being more broadly employed for all kinds of market research. Tech giants such as Google, Facebook and Microsoft have made AI an integral part of their research divisions; Google, in fact, recently renamed its research division Google AI. But smaller companies, such as Persado, are also using machine learning; they help clients craft impactful marketing and promotional copy, sometimes by changing just a word or phrase for maximum impact. Similarly, AI can also be used to explicitly identify and quantify a brand's visual story. Companies are using these kinds of insights to identify new opportunities, to ensure their content resonates with audiences, and to eliminate waste from their production. What's most exciting to us is that this data -- either text or visual -- doesn't require a group of data scientists to come in and work for days or weeks on custom analysis. AI-driven analysis draws upon thousands or millions of consumer opinions, can be performed in a fraction of the time of human-driven data analysis, and is not tainted by human bias.
For example, when our company analyzed the grocery store market for a company that wanted to expand its footprint in Washington, we compared national content patterns to Seattle and Portland. Bottom line: images of dogs, top-down pictures of salads, bunches of produce, and kids were appealing on a national level. But while Portland consumers loved images of people in stores, Seattle did not; Portland loved dog images and Seattle consumers preferred flowers.
Using these kinds of findings, the customer was able to spend time and money creating content they were sure was going to connect with their audience.
The future of content intelligence
In the future, companies and agencies will increasingly use all kinds of marketing AI to understand marketing and creative choices. The information gathered with AI can be run against other performance data to make informed choices about how to construct a brand and run campaigns that align with the brand. All of those things can be illuminated by data that may include objects and images, primary colors, aspect ratio, the angle at which an image is shot, if the background is sharp or blurry, and countless other factors. As digital media becomes more and more noisy and flooded with content, it will become imperative for brands to eliminate guesswork when it comes to finding and connecting with their customer. I predict Marketing AI and Advertising AI will soon be a part of every brand's competitive strategy as a result.