• by Savannah Westbrock , December 13, 2024

Here’s an uncomfortable truth: Every ad ops specialist in our industry can think of at least one time they’ve been handed a creative package they knew would tank the campaign before it even began.

It’s not necessarily that the concepts are bad, they're just out of sync from the media plan. Most of our industry is still working in the “hand-off” model, where brands engage creative teams to produce their big ideas and pass them on to media for activation. This remains the industry norm because it’s faster for media planners to lock in their tactical plans concurrently with creative development, but this leads to the kind of out-of-sync work that harms campaign results.

Creative is the critical performance lever that can make or break digital campaign success. This isn’t a new argument -- but with the rapid automation and increasingly opaque algorithmic changes being made by our industry’s biggest buying platforms, we can’t afford to keep working in the same divided paradigm.

Big platforms will continue to push for reliance on their algorithmic A/B testing post-launch to inform the “best” message, but the theory behind this approach is still dated even when the tech gets a Gen AI facelift. Rather than waiting weeks for platforms to tell us which creative is “better” with no insights on why, let’s take the time to gain shared strategic alignment with a clear learning agenda prior to investing media dollars. Here’s how:

Actually use all the data we’re sitting on. Working together, creative and media can predict messaging performance and test concepts pre-launch. For instance, our data shows no performance-driven rationale for producing 60-second versions of a social video. Using predictive analytics and messaging analysis, we can uncover similar best practices for conceptual elements like tone, sentiment, and rhetorical devices to better understand true messaging effectiveness by channel and industry.

Embrace more human-centric use cases for AI. There is a middle ground between never using AI in our campaigns and automating our way to every ad looking exactly the same. Increased collaboration from media and creative allows for more grounded, audience-centric exploration of new AI developments to bring innovation to our work without turning it into schlock.

For example, with improvements to Dynamic Creative Optimization, we can finally openly acknowledge that there is no one single consumer journey or path-to-purchase for any product, and build our campaigns and messaging strategies accordingly without adding several days to our turnaround times.

Gain a better understanding of “why” our ads work. AI is also bringing neuroscience out of academia. Biometric panel studies have been too impractical for the average brand to explore, but AI trained on biometric signals now offers valuable analysis of how audiences will process, comprehend, and retain messaging, allowing marketers to optimize individual creative elements more effectively. Reporting on how the 15-second spot is doing compared to the 30-second spot is largely pointless; reporting on the audience’s ability to sustain attention frame-by-frame gives us actual insight. Doing this prior to spending any media budget is even better.