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Traditional methods of measuring creative advertising effectiveness have severe limitations today. ..
. More Global tech platform DAIVID uses artificial intelligence complemented by a human touch to offer creative ad testing in today's crowded advertising environment The evolution of advertising over the past 20 years has been nothing short of remarkable. It was not until 2005 that digital advertising became recognized as a legitimate advertising medium .
According to e-marketer, as of 2024, digital advertising expenditures accounted for more than 75% of global advertising revenue , with streaming and digital video (e.g., podcasts, display video) accounting for a substantial portion of the increase.
With such rapid evolution, changes in how advertising creative is tested are needed. With more communication avenues available and many ads being developed to be more personalized than in the past, most advertisers are producing far more ads than they used to. Generative AI has accelerated this trend, allowing for the mass personalization of digital ads .
In the meantime, many markets are more competitive than others making efficient allocation of the advertising budget a key for many businesses. Moreover, with single brands running many different ads , it becomes difficult to monitor whether an individual ad is effective. Yet, passing on creative testing is not a good option given the significant investments made in advertising.
One company that is responsive to industry trends that affect creative testing is DAIVID , which has launched a human-informed AI-powered platform designed to measure creative effectiveness. The platform operates by predicting the attention levels and emotions that an ad will generate along with their likely impact on outcome measures including brand and sales metrics. As it is not dependent on panels, the innovative system is trained using tens of millions of human responses to ads and can allow advertisers to know within minutes the emotional impact of an ad along DAIVID’s insight on predicted business outcomes, offering advertisers to test the effectiveness of their campaigns at scale even in contexts where there are large numbers of individual ads.
Peter Daboll, Head of U.S., Daivid To gain insight into the use of artificial intelligence in measuring creative effectiveness, I spoke to Peter Daboll, DAIVID’s Head of US and former CEO of Ace Metrix .
Regarding why creative ad testing is so important in 2025, Daboll observes that it has always been important, but emphasizes that it has become more challenging to do at scale in a timely and cost-efficient way. “First off, Bud Light has warned the industry that everything in our marketing plan needs to be tested,” he says, “Social media blowback can amplify negatives that the brand team never thought about and can cause lasting brand damage. Second, is the explosion in the volume of ad creative.
Whether generative AI, or just increasing personalization, expanding media outlets the number of ads being produced is skyrocketing.” Daboll notes that Adobe recently predicted that the number of ads will increase 5 times in the next 2 years alone. He states, “What’s new is that we can now leverage AI correctly to handle testing these high volumes when there was no way to do it before.
Now it is possible to test everything. These facts point to the importance of creative testing and the promise of new, scalable ways of testing leveraging AI to handle the volume.” Essentially, whereas 10 to 15 years ago an ad had to be shown to a sample of people to get their reactions.
Daboll observes that such an approach does not work today: saying, “That worked when you had one or 2 TV spots a year, but not when you have 1000 creative assets today. Creative testing, like DAIVID, has evolved to the point where we can test thousands of ads per day leveraging disruptive AI tech, while maintaining the human connection.” Daboll views the industry as being at an inflection point where it will soon be mandatory to test creative assets ahead of time and that the old system will be viewed as a relic.
He says, “We'll look back at the inefficiency of testing one ad with a few people and waiting weeks for results and laugh.” Daivid's system uses human informed AI to measure attention, emotions, recall, and intentions. DAIVID’s model for creative testing is based on many practitioner and academic studies that show that creative effectiveness is a function of four “creative pillars,” Attention, Emotion, Memory, and Intentions.
The basic idea is that a successful ad attracts attention in a way that creates an emotional reaction from the viewer that, in turn, evokes information stored in memory and leads to intention. The system compares these metrics to all other ads to make normative comparisons. Different research technologies including facial coding, eye tracking and survey responses are used to build the database used to train the database.
Regarding the specifics of the system and how it produces effective output measures for measuring creative effectiveness, Daboll says: “Attention is critical or viewers won’t remember the ad (we measure at first 3 seconds, mid 3 seconds, and final 3 seconds and can look at decay over time). Emotions are DAIVID's strong suit-- measuring 39 distinct human emotions, positive and negative. Memory is measured by traditional brand recall metrics in the human sample and markers are compared to the test ads.
Intentions are purchase intent, search, and sharing intention as a result of seeing the ad. All of these 4 creative metric pillars are essential to create a successful ad.” Daivid's system measures 39 different emotions.
In terms of the advantages of his own company’s measurement system over other AI-based systems, Daboll emphasizes that DAIVID adds a valuable human touch in addition to drawing on AI’s advantages. “A few AI-based companies measure what I call "suitability “of the creative—focusing on the aspect ratio of the ad, whether it will fit into the unit, degree of blurriness, color or length of logo shown, etc.,” he says, “ These are deterministic characteristics of the ad that AI can identify--useful but not sufficient.
But they don't measure human reaction. DAIVID's use of AI compares ads to a massive human training dataset. This training dataset-- thousands of ads with thousands of people, becomes our "true north" on how and why people respond.
” Daboll continues, “ We then leverage the AI to pattern match and find ads that have the same characteristic markers. These markers include things like storyline, script, visuals, colors, audio, imagery, characters, etc. at the frame by frame and aggregate level.
By using the 4 creative effectiveness pillars described above DAIVID can assess WHY and how an ad works on human behavior; and establishes guideposts for brands to hit with future ads. So, our deliverables are prescriptive as well as descriptive informing brands on how to make new ads better. Further, most other systems measure just 1 of the pillars I mentioned.
All 4 are required to get the full impact of the creative on behavior.” In terms of what he has learned from running DAIVID’s creative measurement system, Daboll offers the following insights: He is fascinated by how human reaction in terms of emotional response can vary second-by-second in response to a new image or sound. Many AI-generated ads lack the “warmth” emotion in comparison to those produced in standard fashion by humans.
DAVIID’s tools are useful in evaluating whether humor works in an ad. Measuring humor’s effectiveness is tricky as it can vary based on humor or external events, so quantifying risk is what is important. He believes creative testing is all about measuring successful advertising outcomes and what creative causes those outcomes.
Specifically, it is important to focus on how creative markers improve a brand's key performance indicators-- brand perception, consumer favorability, and increased sales, and how successful outcomes can be repeated in future campaigns. As with many aspects of AI, it is important to figure out how to use its positive features while avoiding pitfalls or ethical issues associated with technology. DAIVID’s systems do an excellent job of this by taking advantage of AI’s high level of effectiveness in recognizing patterns while putting an informed human touch on it.
Advertising creative testing is better for it..
