Anastasia Braitsik stands at the cutting edge of the digital marketing revolution, where data science meets creative strategy to redefine how brands interact with consumers. With an extensive background in SEO, content marketing, and high-level data analytics, she has spent years decoding the complex patterns of human behavior in the digital space. Her expertise is particularly relevant today as the industry moves away from the “reach-at-all-costs” mentality and toward a more nuanced, intent-driven approach. By leveraging deep learning and behavioral AI, Anastasia helps global leaders navigate the shift from static advertising models to dynamic systems that predict and capture the exact moment of purchase intent. This interview explores the transformative potential of behavioral data and how the next generation of performance marketing is being built on a foundation of precision and privacy.
Many advertising strategies prioritize reach over relevance, yet high visibility often fails to drive actual sales. How is the shift toward behavioral AI changing the way we define successful engagement and purchase intent?
The traditional programmatic advertising industry has hit a ceiling because it has spent years optimizing for reach rather than true relevance. We have seen that simply putting a brand in front of millions of people doesn’t guarantee they are ready to buy, which is why behavioral AI is such a significant game-changer. By focusing on consented decision-making data and learning from patterns across massive populations, we can move away from static segments that often miss the mark. This approach allows us to identify the exact moments when consumers are most likely to make a decision, shifting the focus from “how many people saw this” to “is this person ready to purchase right now.” It is a fundamental pivot from casting a wide net to using precision tools that capture high-intent demand at its peak.
With a dataset as massive as 5.5 trillion transaction signals and 70 billion daily ad events, how does this level of data scale allow for better targeting without compromising consumer privacy?
Operating at this scale requires a sophisticated technical foundation, which is why using deep learning models on platforms like Microsoft Azure is so critical. Instead of storing raw, identity-level records which could pose privacy risks, the system transforms behavioral signals into vector embeddings that capture activity patterns while preserving individual anonymity. When you are processing 5 billion search queries and data from 50 million active TVs, you gain a macro-level understanding of consumer movement that no traditional audience platform can match. This non-public, licensed data from banks, retailers, and telecom providers provides a “complete behavioral context” that respects the consumer’s choice and transparency. The result is a sharper analytics foundation that connects exposure to conversion without ever needing to exploit personal identities.
A recent retail campaign saw an 81% reduction in cost per order using this behavioral approach. What does this massive efficiency gain tell us about the limitations of standard custom algorithms?
The 81% reduction in cost-per-order for 1-day orders—along with a 79% reduction for 7-day and 70% for 14-day orders—proves that the strength of the audience model is a far more powerful driver than the size of the media spend. Standard custom algorithms often struggle to identify the “when” of demand, leading to wasted spend on consumers who aren’t in a buying window. By aligning media strategy and timing with real-time demand signals, brands can actually afford the premium of higher CPMs because the conversion rate is so much higher. This efficiency more than offsets the cost of premium inventory, demonstrating that knowing when demand exists is the ultimate “unlock” in modern performance marketing. It reinforces the idea that precision targeting isn’t just about finding the right person, but finding them at the right second.
Rain utilizes an approach called “Transactional Brand Building” to achieve sales and branding goals simultaneously. How do behavioral models support this holistic strategy across different media environments?
Transactional Brand Building is about removing the compromise between long-term brand health and immediate sales targets by linking creative production with advanced analytics. By deploying proprietary audience models across premium publisher inventory, we can ensure that the right creative is placed in the right environment at the moment of highest intent. This isn’t just about a single transaction; it’s about gaining insights into net new consumer records that extend well beyond the campaign itself. For brands ranging from DTC challengers to established names like Shopify or 1-800 Flowers, this means every dollar spent on performance also contributes to a deeper understanding of their audience. The dynamic optimization ensures that as the campaign runs, it adapts in real-time, allowing the brand to grow its presence while simultaneously hitting aggressive CPA targets.
Since these AI models continue to learn throughout the duration of a campaign flight, how does this structural advantage change the way marketers should plan their long-term budgets?
Unlike static targeting approaches that degrade in effectiveness over time, behavioral AI models possess a structural advantage because their performance actually compounds as they gather more data. This means that instead of seeing a “tail-off” in results, marketers can expect their campaigns to become more efficient the longer they run. When you have a team of machine learning experts from places like Amazon, Uber, and Meta refining these recommendation systems, the model becomes more adept at navigating the nuances of human behavior. Marketing leaders should view their budgets not as a one-time “burst” of spend, but as an investment in a learning engine that continuously optimizes customer acquisition costs. By moving beyond basic segmentation toward this deeper behavioral context, brands can build a sustainable competitive edge that adapts to the market’s shifts.
What is your forecast for performance-driven advertising?
I believe we are entering an era where “intent-at-the-moment” will completely replace the concept of demographic targeting. As traditional cookies disappear and programmatic reach hits its final limits, the brands that win will be those that can leverage massive, consented datasets to predict consumer needs before they even type a search query. We will see a total convergence of brand strategy and data science, where the “scientific study of human behavior” becomes the primary engine for creative inspiration. Advertising will become less about interruption and more about being a helpful, timely presence in the consumer’s journey, driven by models that understand the complete context of why and when we buy.
