Milena Traikovich is a powerhouse in the world of demand generation, specializing in the delicate intersection of performance analytics and lead optimization. With a career dedicated to bridging the gap between high-level strategy and granular execution, she has become a leading voice in how brands navigate the complexities of modern retail media. In our conversation, she dives into the transformative potential of Walmart’s Scintilla In-Store platform, explaining how real-time shelf signals are redefining the relationship between supply chain logistics and digital marketing spend.
The following discussion explores the strategic shift toward first-party retailer ecosystems and the necessity of breaking down internal data silos. We delve into the mechanics of adjusting local media spend based on inventory, the role of AI in overcoming analysis paralysis across hundreds of locations, and the importance of viewing store execution as a fundamental marketing metric. Milena provides a roadmap for brands to ensure that their digital investments are never wasted on empty shelves, fostering a more resilient and responsive retail strategy.
When digital campaigns drive demand for out-of-stock items, marketing ROI suffers significantly. How should brands bridge the gap between media spend and real-time shelf availability, and what specific workflows ensure marketing pivots before performance drops?
To bridge this gap, brands must move away from viewing inventory as a “supply chain problem” and start seeing it as a primary trigger for media bidding. The strategy begins with a real-time data sync where shelf-level signals from platforms like Scintilla feed directly into your ad management interface. First, you establish a threshold—if a product’s on-shelf availability drops below a certain percentage in a specific region, an automated alert triggers a reduction in local ad spend for those zip codes. Second, you pivot that budget immediately to “high-stock” regions or switch the creative to highlight a comparable, available SKU. This ensures your cost-per-acquisition doesn’t skyrocket due to clicks that result in “out of stock” messages, keeping your marketing ROI protected by physical reality.
Privacy regulations are pushing brands toward closed first-party retailer ecosystems. What are the practical advantages of integrating transaction data with store execution signals, and how does this shift change how suppliers manage their internal data silos?
The primary advantage is the ability to see the “full picture” of a customer’s journey without relying on crumbling third-party cookies; you are looking at what they see on the shelf versus what they actually buy. This shift forces a radical reorganization of internal data silos, as marketing teams now need access to the logistics data once guarded by warehouse managers to justify their spend. I’ve seen cases where a brand noticed a 15% dip in sales during a major promotion and initially blamed the creative team for poor engagement. However, by integrating store execution signals, they discovered the product was simply sitting in the backroom instead of the display, allowing them to fix the physical execution rather than wasting money on a new ad campaign. This level of clarity turns data from a post-mortem tool into a proactive growth lever.
Managing hundreds of locations often leads to “analysis paralysis” for suppliers. As AI-driven prioritization tools emerge, how should teams decide which store-level alerts to act on first, and what metrics best define a “successful” intervention?
When faced with thousands of data points, teams should prioritize alerts based on “Value at Risk,” which calculates the potential lost revenue of a stock-out over the next 48 hours. You should focus first on high-velocity “A-tier” stores where the gap between current shelf inventory and predicted weekend demand is greatest. A successful intervention isn’t just “fixing the shelf,” but is measured by the speed of recovery—specifically, how quickly the sales velocity returns to its baseline after the corrective task is completed. By using AI to filter out noise, your field teams are no longer wandering aisles aimlessly; they are performing high-impact “surgical” fixes that directly correlate to protected revenue.
Historically, supply chain and marketing teams operated in isolation. What steps must a brand take to integrate shelf-level execution into their media buying cycle, and how do you prevent operational failures from being misread as weak consumer demand?
The first step is establishing a unified dashboard where “on-shelf availability” is a core KPI alongside “click-through rate,” creating a common language for both departments. Brands must implement a feedback loop where an operational failure—like a missed display setup—is flagged in the analytics platform so the marketing team doesn’t misinterpret the resulting sales slump as a lack of consumer interest. Without this joined-up view, you might kill a perfectly good product line simply because it wasn’t stocked correctly during a crucial launch window. By treating physical availability as a marketing variable, you ensure that your strategic decisions are based on true consumer sentiment rather than logistical hiccups.
What is your forecast for the integration of physical retail signals and digital advertising?
I believe we are heading toward a “frictionless retail” era where digital ads will become entirely dynamic based on hyper-local physical conditions. Within the next few years, your programmatic ads won’t just change based on who is watching, but will automatically swap products in real-time based on the exact inventory of the store three miles away from that specific user. We will see the rise of “self-healing” campaigns that pause themselves the moment a shelf goes empty and restart the second a pallet is scanned into the backroom. Ultimately, the distinction between “online” and “offline” marketing will vanish, leaving us with a singular, holistic commerce strategy driven by real-world availability.
