Milena Traikovich is a powerhouse in the demand generation space, currently steering high-impact campaigns that turn raw data points into meaningful customer connections. With her deep background in performance optimization and analytics, she understands the delicate balance between effective targeting and consumer privacy better than most. In her current role, she helps businesses navigate the complexities of modern lead generation by focusing on quality over quantity and building trust-based relationships with audiences.
This discussion focuses on the duality of consumer sentiment toward data usage, where the discomfort of being monitored competes with the undeniable convenience of relevant offers. We dive into the rise of retailers as media moguls, the specific technological shifts allowing for hyperpersonalization through hundreds of variables, and the measurable financial impact of refined targeting strategies that eliminate wasted ad spend across the market.
How do you interpret the psychological tension where more than half of consumers feel uncomfortable with personalized ads, yet an even larger majority proceeds to make a purchase because of them?
It is a fascinating paradox where utility often triumphs over the initial feeling of privacy invasion. While 51% of people express that these targeted offers feel intrusive, the reality is that 62% of them find the resulting convenience too valuable to ignore. This tells us that the friction isn’t necessarily with the personalization itself, but with the lack of transparency regarding how that data was acquired. If a brand can make the experience feel like a helpful part of a long-term relationship rather than a cold, transactional hunt, that discomfort begins to fade. The key to turning that “creepy” factor into a competitive advantage is ensuring consumers feel they have the power to authorize or refuse how their information is being used.
With platforms like Spotify seeing a 14% increase in monthly active users since 2025, reaching 63.5 million in Brazil alone, how are brands moving beyond basic demographics to find the right audience?
The shift we are seeing is a move toward deep contextual and behavioral understanding rather than just looking at age or location. On a platform like Spotify, advertisers are now looking at specific listening behaviors, purchase intents, and even the emotional affinity a user has with certain artists. This allows for the use of artificial intelligence to identify potential customers who might not seem like obvious prospects on paper but whose habits suggest a high alignment with a product. By layering these signals, brands can insert themselves into a user’s day in a way that feels natural and timely. It’s no longer about shouting at a crowd, but about whispering the right suggestion to a specific person at the exact moment they are most receptive.
Retailers like GPA are now managing databases of over 27 million registered customers and seeing 30% annual growth in their data operations; what does this signify for the future of retail media?
We are witnessing a massive transformation where retailers are effectively becoming high-value media channels because they own the most important piece of the puzzle: the actual point of purchase. Brands are flocking to these platforms because they offer a direct line to consumers, often bypassing the standard 2% to 3% conversion rates seen on traditional sites. A great example of this success was a recent Heineken campaign that delivered a return on investment of 12, meaning they saw R$12 in sales for every R$1 they invested. By using AI to optimize creative assets and media plans within these retail environments, companies can track results with a level of precision that was previously impossible. This allows them to move from simply launching a campaign to managing a highly profitable, data-driven ecosystem.
How is the integration of telecommunications data and geolocation helping companies solve the problem of wasted ad impressions?
The “shotgun approach” to advertising is rapidly becoming a thing of the past because we now have the technology to process complex signals like anonymized carrier data and real-time geolocation. By cross-referencing this information, we can deliver messages to highly specific groups with shared characteristics, ensuring that an ad is only shown to someone who is actually in a position to act on it. This precision is vital because as the target group gets smaller and more defined, the need for sophisticated processing technology grows. The ultimate benefit here is the drastic reduction in wasted impressions, meaning brands stop spending money on people who have zero interest or physical proximity to their offer. It’s a win-win where the brand saves its budget and the consumer isn’t bothered by irrelevant noise.
When you talk about “hyperpersonalization” involving over 400 variables, how does that actually change the financial performance of a campaign?
Hyperpersonalization is the ultimate evolution of lead generation because it doesn’t just look at what someone likes; it looks at their actual ability and propensity to buy. By combining interests with credit data and historical relationship patterns with a brand, we can identify the most qualified leads with startling accuracy. We have seen cases where this rigorous model has allowed companies to lower their media costs by as much as 25% while actually increasing their conversion volume. It’s about working smarter by focusing only on the high-probability segments of the market. When you can account for hundreds of variables simultaneously, you aren’t just guessing who your customer is—you are identifying them with mathematical certainty.
What is your forecast for the evolution of consumer trust in data-driven marketing?
I believe we are entering an era where the most successful brands will be those that treat data privacy not as a legal hurdle, but as a core pillar of their brand identity. As AI continues to refine our ability to find non-obvious prospects, the industry will have to become much more transparent about the “why” and “how” of data usage to keep consumers on board. We will see a shift where consumers are more willing to trade their data for high-value, personalized experiences, provided they feel they are in the driver’s seat. Ultimately, the goal is to reach a point where personalization is so seamless and respectful that it feels less like advertising and more like a personalized concierge service that anticipates your needs before you even voice them.
