Granular Creative Taxonomies: The Unsung Heroes of the AI Revolution
In recent months, AI is everywhere you look – and that includes advertising. Unprecedented advances in machine learning and data analysis have unlocked new frontiers in creative optimization and personalization, bringing an aura of excitement, anticipation, and even fear that resonates through the industry and beyond.
All eyes are now trained on AI and its potential to reshape everything. However, while the spotlight is often focused on the spectacular showpieces of generative AI and large language models, an equally crucial but less flashy aspect remains in the wings: the role of high-quality data, granular labeling, and taxonomy.
Before delving into the nuts and bolts of this vital cog in the AI machine, let's paint a broader picture of the AI revolution. It's a grand promise of automated workflows, precise targeting, and personalized content at a scale beyond human capability. In an age where time is at a premium and efficiency is king, these are attractive propositions. Yet, it's worth remembering that, as with any technology, AI is simply a tool. Its effectiveness hinges heavily on the inputs it receives.
This brings us to the unsung hero of the AI narrative for advertising – granular creative taxonomies.
AI Is Only as Good as its Inputs
Despite its lack of glamor, taxonomy, the classification and labeling of data, is the bedrock upon which AI systems are built. It is the lens through which an AI discerns patterns, makes decisions, and, ultimately, adds value to the creative process. Without a fine-grained taxonomy, AI's unprecedented powers lack a compass.
At its heart, granular labeling is about providing detailed, explicit descriptions of the data. This might include tags for color schemes, creative themes, sentiment, or product categories. The more specific the labels, the better AI can parse, understand and utilize the data; this enables a higher degree of personalization and optimization. Every creative asset and every moment of engagement contains multitudes of such variables. AI's ability to synthesize and decision on these variables is only as good as the ability to make sense of them in the first place.
Let's illustrate this with a simple example. Suppose you're running an omnichannel advertising campaign with an AI-powered automation tool. Without detailed tagging, the AI may identify a generic pattern, say, ads with blue themes perform well. But with granular labeling, it can unearth much more nuanced insights. For instance, it might be discovered that ads featuring blue themes with a calm sentiment and outdoor settings perform exceptionally well on Instagram during weekend afternoons for people between the ages of 25 to34. With even more nuanced taxonomies, creative teams can understand the ideas that animate each ad and how they are performing in certain contexts.
Unleashing the Power of AI at Scale
As we transition into a future dominated by omnichannel advertising strategies, the importance of granular taxonomy becomes even more pronounced. Different platforms have different dynamics, user behaviors, and preferences. To navigate this complex terrain and ensure a seamless user experience across all touchpoints, an intricate understanding of these nuances is indispensable. Here again, a granular taxonomy acts as a guiding light, enabling AI to adapt and optimize content across various channels.
In this context, the role of AI is not to replace the creative process but to enhance it. AI is supplemental and complementary. It automates tasks, provides deeper insights, and frees up human creatives to focus on what they do best – crafting compelling narratives and forging emotional connections with the audience.
Taxonomy is not the most glamorous part of the AI story, but it is a chapter we cannot afford to skip. AI's effectiveness in the creative process is contingent on the quality of the data it is trained on. The promise of AI is tantalizing, but it is only as good as the signals it can respond to. As we strive for better creative optimization and personalization, we must also commit to the less exciting yet equally important task of improving our metadata, taxonomies, and data labeling.
The views and opinions expressed are solely those of the contributor and do not necessarily reflect the official position of the ANA or imply endorsement from the ANA.
Ben Kartzman is COO at Mediaocean and Flashtalking.