According to Google and Meta, generative AI tools are the future of creative testing and performance. Meta’s Advantage+ campaigns use AI to eliminate manual steps in ad creation. By providing a platform with all necessary assets, such as logos, product images, and colors, new creatives can be made, tested, and results can be improved. This advancement is particularly beneficial for small businesses with few design resources. The idea of developing brand-appropriate creatives quickly and in line with social media platforms’ design guidelines and formats is game-changing. For large brands, however, the role of AI in creative testing will be different. AI can create and test new creatives, but it falls short in understanding why one creative performs better than another.
Why AI Falls Short for Large Brands
Although AI can ingest information and optimize toward high-performing creatives, it cannot fully understand the “why” behind their success. For large brands that highly value their brand reputation, this is a concern. Any good media buyer would want to understand why one strategy, design, or approach works better than another. CMOs are accumulating more data-driven knowledge to justify every dollar spent, so understanding the “why” is critical.
One example of the importance of understanding the “why” is two banners developed for a quick-service restaurant with different product and design variations. An AI-driven creative testing algorithm identified “burnt orange” as a color associated with the higher-performing creative. However, the orange color was actually a cup of coffee with cream, which was not clear to the AI. A person would have realized that the most performant banners have cream in the coffee rather than plain black.
The Importance of Human Decision-Making in Creative Testing
Global brands have high-quality and design standards and are hesitant to leave their brand strategy or reputation up to AI. Feeding assets into a machine and letting it optimize can lead to a variety of issues. For example, whether to use “real” looking models in advertising versus overly polished, idealized versions of consumers has long been a conundrum for advertisers. Recent studies show that people react better to models that more fairly represent their consumer base. AI can create a variety of banners and test them, but it cannot weigh the pros and cons of which direction to take from a brand equity perspective.
Long-term vs. short-term campaign goals and the research that goes into making smart strategic decisions are areas where humans are still best suited. Deloitte found that 57% of consumers are more loyal to brands that commit to diversity, which may not be available to an AI performance algorithm at the moment of creative testing. An AI algorithm may not have the ability to weigh the various inputs that determine the right balance of representation.
The Future of AI in Creative Testing
AI is revolutionizing creativity for major brands and their agencies. Currently, AI can help with manual tasks, inspire new ideas and directions, and deliver insights. In the future, it has the potential to be part of the creative process at an even deeper level. Although AI may not understand the “why” immediately, it can improve with more training and interaction. By providing AI with findings from larger studies about brand perception, sales, and loyalty, AI-driven outputs can be tuned to the metrics that matter to large enterprise brands. Finding ways to deepen an algorithm enhances that algorithm’s ability to be useful.
For any business that deeply cares about its brand, AI will be most effective when used in conjunction with creative professionals, data analysts, brand managers, media teams, and other experts with the necessary expertise and context to understand the “why.” Although AI is still a valuable tool in creative testing and performance, it cannot replace human decision-making and understanding of brand equity.
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