This article was based on the interview with Rochelle Thielen, CEO at Traject Data by Greg Kihlström, AI Adoption keynote speaker for The Agile Brand with Greg Kihlström podcast. Listen to the original episode here:
As leaders in the marketing and retail space, we’ve all felt the ground shifting beneath our feet for years. What was once a gradual evolution has become a series of seismic shocks, from unexpected platform changes to the rapid ascent of generative AI. The old playbooks, the reliable dashboards, and the comfortable strategies of yesterday are proving to be liabilities today. In this environment, agility isn’t just a buzzword; it’s the fundamental requirement for survival and, more importantly, for dominance. The question is no longer *if* we need to adapt, but *how* we build an operational model that can thrive amidst constant instability.
The answer, as is often the case, lies in data. But not the data we’ve grown accustomed to—the static reports, the weekly summaries, or the black-box dashboards we were told to trust implicitly. The new imperative is a command of real-time, granular, and forensically accurate data that allows brands to see the market as it is, not as it was a week ago. This shift requires us to move from being passive consumers of insights to active commanders of our own data intelligence. It’s about taking ownership, asking harder questions, and building the capabilities to anticipate the next move rather than simply reacting to the last one. In a recent conversation, Rochelle Thielen, CEO of Traject Data, offered a clear-eyed perspective on how the most sophisticated retailers are making this transition.
The Great Dashboard Deception
For years, the dashboard has been the command center for marketing and retail leaders. We logged in, checked the KPIs, and trusted the neatly visualized data to guide our decisions. The problem, Thielen points out, is that this trust was often misplaced. The “real-time” data fueling many of these tools is often anything but, a realization that can be as jarring as it is strategically dangerous.
“When you’re finding out these dashboards you’ve been trusting your whole career, that data is actually not as real time as you always were led to believe… it’s basically the same thing as when we’re a kid and you look up in the sky and one day you’re told the stars you’re looking at died millions of years ago. Like the exact same reaction.”
This is more than just a colorful analogy; it’s a critical diagnosis of a widespread problem. Relying on data that is a week old, or even an average of the last 30 days, is like navigating a high-speed race by looking in the rearview mirror. In an era of AI-driven dynamic pricing and algorithm-based ad placements, stale data doesn’t just lead to missed opportunities—it actively feeds poor decisions that can cost millions. This realization is forcing a necessary and healthy convergence of business and technology teams. Leaders on the business side are now compelled to look “in the engine room,” asking crucial questions about data provenance, latency, and reliability. The era of outsourcing visibility is over; the future belongs to brands that build and trust their own data streams.
The Real-Time “War Room”: Turning Data into Dominance
If stale data is the problem, what does the solution look like in practice? Thielen provides a powerful, tangible example of how leading brands leverage truly real-time data to gain a decisive competitive edge during retail’s most high-stakes moments. It’s not about incremental improvements; it’s about weaponizing data for immediate impact.
“They actually employ a Tiger team that sits in like a war room, adjusting our data and dynamically actually adjusting prices… they’re ensuring that they’re the best possible, winning that buy box and winning that number one position literally like by the second for those periods of time. And there’s a lot of AI that’s also hopping in and making those decisions in and around prime days.”
This “war room” scenario during Amazon Prime Day perfectly illustrates the pinnacle of data-driven agility. By pulling in competitive pricing, product descriptions, reviews, and availability from across the retail landscape—not just Amazon—this electronics brand transforms a chaotic sales event into a controllable, winnable battlefield. This isn’t just about being fast; it’s about being precise. The combination of AI making automated suggestions and a human “Tiger Team” providing strategic oversight allows for a level of responsiveness that is impossible with traditional methods. It’s a clear demonstration of the direct ROI of investing in real-time data infrastructure, where every second of insight can translate into hundreds of thousands of units sold.
Navigating the New Frontier of AI-Powered Search
Just as we were mastering the art and science of keyword-based search, the landscape has been upended once again by generative AI. Consumers are no longer just typing “best sunscreen for sensitive skin” into a search bar. They are engaging in complex, conversational prompts, fundamentally changing how products and brands are discovered.
“People aren’t anymore saying, I want to buy sunscreen from this company. They’re saying things like, ‘I’m going on a vacation to Greece, what type of things do you recommend?’ And you have to be so far ahead in order to be sourced. It’s really important to understand what type of prompts rather than keywords are being put out there.”
This shift from keywords to contextual prompts represents an existential threat to brands that aren’t prepared. The game is no longer about ranking for a specific term; it’s about ensuring your brand is part of the solution ecosystem that an AI agent will recommend. To do this, retailers must ingest a new class of data that goes beyond search volume. They need to understand sentiment, the credibility of sources, and the contextual relationships between products, problems, and occasions. As Thielen notes, this requires partnering with data providers to ingest and analyze these conversational trends, allowing brands to get ahead of the prompts and position themselves to be “sourced” by the AI agents that are increasingly becoming the gatekeepers to the consumer.
Beyond Marketing: Data as a Brand Protection Tool
While much of the conversation around retail data centers on customer acquisition and pricing, its application extends deep into the operational and legal functions of the business. For many brands, particularly in the enterprise space, the persistent threats of fraud, counterfeits, and unauthorized sellers are a significant drain on resources and a danger to brand equity. Real-time data offers a powerful tool for defense.
“It’s so important to have really forensic level data… We work with them to basically forensically capture every single time that they’re doing something and build these massive files for them and cases essentially that allow them to actually get Amazon and other sellers out there to kick these guys out and it actually works.”
The challenge with combating bad actors isn’t always a lack of suspicion; it’s a lack of court-ready evidence. Platforms like Amazon require an overwhelming burden of proof to take action. This is where, as Thielen describes, “forensic level data” becomes indispensable. By systematically and continuously capturing every instance of a violation—from MAP pricing breaches to fraudulent listings—brands can build an irrefutable case. This proactive, data-driven approach transforms brand protection from a reactive game of whack-a-mole into a strategic defense. It demonstrates that a sophisticated data infrastructure is not merely a marketing asset but a core business function that protects revenue, market share, and hard-won brand integrity.
The message for retail leaders is clear: the passive, trust-based relationship with data is over. The current market demands a proactive, command-and-control approach where real-time, granular data is the central nervous system of the entire organization. From informing minute-by-minute pricing decisions in a Prime Day war room to building legal cases against fraudulent sellers, a robust data strategy is the bedrock of modern retail agility. It empowers brands to not only react to market shifts but to anticipate and shape them.
This transition is not without its challenges. It requires a new level of data literacy across the organization, a closer collaboration between business and technical teams, and an investment in the tools and partnerships that can deliver truly real-time intelligence. However, the alternative—continuing to operate based on data from a past that no longer exists—is far riskier. The brands that emerge as leaders in this new era will be those that embrace this reality, take ownership of their visibility, and wield data not as a report, but as their most potent strategic weapon.
