Anastasia Braitsik stands at the intersection of data science and creative strategy, helping global brands navigate the complex landscape of marketing measurement. As a leader in SEO and content marketing, she has witnessed the evolution of performance tracking from simple spreadsheets to sophisticated, AI-driven models. Her insights reflect a modern approach to advertising, where the focus has shifted from merely reaching an audience to truly moving them. By integrating emotional intelligence with hard financial data, she bridges the gap between the art of storytelling and the science of profitability.
In this conversation, we explore the revolutionary partnership between Mortar AI and DAIVID, focusing on how creative effectiveness can be quantified and unified with traditional media metrics. We delve into the financial risks of ignoring the emotional quality of advertisements and discuss the practical steps needed to break down the silos that have traditionally separated creative teams from media planners. Finally, we look toward a future where real-time predictive data allows brands to optimize their campaigns with unprecedented precision and commercial impact.
Traditional marketing mix models often overlook creative quality despite its massive impact on profitability. How do you quantify the financial risk of ignoring creative variables, and what specific steps are required to link emotional resonance with hard financial data to generate actionable insights?
Ignoring creative quality is a massive gamble because data from Accelero suggests that high-quality creative can multiply profitability by up to 12 times. To quantify this risk, we must stop treating creative as a “fixed” cost and start viewing it as a variable that dictates the return on every dollar spent on media. The process starts by mapping performance across 39 distinct emotions, then correlating those emotional triggers with commercial outcomes like brand intent. By integrating these emotional insights directly into the financial model, we build an intelligence infrastructure that treats creative resonance as a hard metric rather than a subjective feeling. This allows us to see exactly how much money is being left on the table when an ad fails to connect with its audience on an emotional level.
Measuring performance across dozens of distinct emotions helps predict outcomes like brand intent and recall. What challenges arise when translating these subjective emotional triggers into objective metrics for media optimization, and how does this change the way you evaluate the commercial impact of a campaign across different channels?
The primary challenge lies in the fact that customers are driven as much by emotion as they are by reason, yet most legacy systems only measure these two drivers in silos. Translating a feeling like “surprise” or “joy” into an objective data point requires sophisticated mapping to commercially predictive measures such as Attention, Recall, and Brand Intent. When we bridge this gap, our evaluation shifts from simply counting impressions to analyzing the emotional efficiency of each channel. This means a high-spend channel might actually be underperforming if the creative isn’t triggering the specific emotions necessary to drive purchase behavior, forcing us to re-evaluate the commercial weight of each touchpoint based on its ability to drive real action.
Legacy measurement systems are often slow, yet unified real-time data is now a priority for advertisers. How can marketing teams successfully break down silos between creative and media data, and what are the practical trade-offs when moving from historical reporting to predictive decision-making platforms?
To break down these silos, teams must move away from retrospective reporting and adopt a unified decision-making platform that blends creative effectiveness with media performance in real-time. This requires a cultural shift where creative teams and media buyers work from the same live dashboard, rather than waiting months for a post-campaign analysis. The practical trade-off is often the complexity of the initial setup, as you are moving from simple historical tracking to a model that predicts future performance based on emotional data. However, the result is a game-changing level of insight that allows advertisers to pivot while the campaign is still active, rather than realizing a mistake only after the entire budget has been spent.
Integrating a creative effectiveness score directly into media performance calculations offers a new level of campaign oversight. How should a brand pivot its strategy if a specific channel shows high reach but low creative quality, and what specific metrics should they monitor to ensure this integration drives long-term results?
If a brand sees high reach coupled with low creative quality, it essentially means they are paying to be ignored, which is an expensive way to fail in any market. In this scenario, the brand should pivot by either swapping the creative assets to better align with the audience’s emotional drivers or by shifting the budget to channels where the Creative Effectiveness score is higher. Monitoring metrics like Attention and Brand Intent alongside traditional CPMs ensures that the reach is actually building long-term equity rather than just filling a spreadsheet. By keeping a close eye on the Creative Effectiveness score within a real-time engine, marketers can ensure that every impression has the potential to move the needle on profitability.
What is your forecast for the future of creative data within marketing mix modeling?
I believe we are entering an era where creative data will be the primary driver of marketing investment decisions rather than an afterthought. As tools continue to refine the way we measure the impact of 39 different emotions, the traditional siloed approach to marketing will become obsolete. We will see a shift toward completely automated optimization where media buying and creative selection are linked by a single financial model in real-time. Ultimately, the future of Marketing Mix Modeling lies in its ability to treat the human element of marketing—the emotional connection—with the same mathematical rigor as we treat click-through rates and conversion funnels.
