With a career spanning over 18 years, our guest is a recognized leader in the retail media industry, having successfully launched more than 40 retail media programs and driven significant revenue growth for major retailers. Her journey includes pivotal leadership roles at Epsilon Retail Media and Mi9 Retail Media Network, where she was instrumental in building some of the market’s most successful networks. Now, as the new Chief Operating Officer at Kevel, she is focused on scaling operations and empowering businesses to build their own differentiated, AI-powered ad platforms. In this interview, we explore the critical lessons from her extensive experience, the importance of data ownership for retailers, her strategic vision for operational excellence, the practical application of AI in ad tech, and the evolving landscape of monetization for non-traditional retailers.
Having launched over 40 retail media programs, what is a critical lesson you’ve learned about driving sustained revenue growth? Can you walk us through the first 90 days of establishing a new retail media network and the key metrics you prioritize for success?
The most critical lesson I’ve learned is that technology alone doesn’t build a successful network; a deep, strategic partnership does. Sustained growth comes from moving beyond a simple transactional model and truly understanding a retailer’s unique assets to create something differentiated that brands can’t get anywhere else. In the first 90 days, the approach has to be methodical. The first 30 days are all about discovery—I immerse myself in the retailer’s business, mapping out every potential asset, from their first-party data and onsite search to their in-store promotional placements. The next 30 days are about building the foundation, defining the initial ad products, and creating a compelling narrative for brand partners. In the final 30 days, we launch with a core group of trusted brands. The initial metrics are crucial: we look at advertiser adoption and campaign performance, of course, but I’m most interested in the qualitative feedback. Is this solving a real problem for brands? That feedback loop is the most valuable metric for long-term success.
You’ve emphasized the importance of retailers maintaining full control over their first-party data. How does Kevel’s API-first approach facilitate this, and what are the tangible benefits for a retailer compared to using a more closed, “walled garden” platform? Please provide an example.
This is a point I’m incredibly passionate about. Kevel’s API-first approach is fundamentally about empowerment. Instead of forcing a retailer to fit into a rigid, pre-built box, we provide a powerful set of building blocks. This means a retailer can integrate our ad-serving technology directly into their own ecosystem, so their invaluable first-party data never has to be uploaded to an external platform. It stays securely within their control. The tangible benefit is profound: it unlocks true differentiation. A “walled garden” offers the same limited ad formats to every retailer, leading to a commoditized, “me-too” network. With our approach, a specialty retailer, say for outdoor gear, could build a completely unique ad unit that integrates with their content about hiking trails, targeting users who have previously purchased hiking boots. They own the ad experience, the data, and the customer relationship. It’s their platform, powered by our tech, not the other way around. That control is the key to building a long-term, defensible business.
As COO, you’re tasked with optimizing efficiency and scaling go-to-market strategy. What is the first operational change you plan to implement to accelerate Kevel’s growth, and how will you measure its impact on strengthening customer partnerships within your first year?
My first priority is to deepen the integration between our go-to-market functions—Sales, Operations, and Account Strategy. I’ve seen firsthand that the most successful customer relationships are built on a foundation of seamless, cross-functional collaboration. My first major initiative will be to formalize this into a more robust, unified client engagement model where teams are aligned from the very first conversation through long-term strategic growth. This means every customer has a dedicated team that holistically understands their business goals and challenges. We’re moving from being just a technology provider to being indispensable strategic advisors. I’ll measure the impact directly through customer-centric KPIs. Within the first year, I’m targeting a significant increase in our Net Promoter Score (NPS) and, most importantly, a rise in net revenue retention. When your existing partners are investing more with you, it’s the ultimate proof that the partnership is not just strong but is actively delivering exceptional and growing value.
Kevel is accelerating innovation in AI features like Yield Forecast and AI Segment Builder. Can you provide a step-by-step example of how a retailer would use these tools to create a more effective, differentiated ad campaign for a brand partner?
These AI tools are game-changers because they turn data into actionable strategy. Let’s walk through an example with a major retailer like Dollar General. Imagine a beverage brand wants to launch a new energy drink. First, the retailer’s team would use the AI Segment Builder. Instead of basic targeting like “shoppers who buy drinks,” they can ask the AI to create a custom segment of “customers who purchase single-serve beverages and salty snacks between 2-5 PM on weekdays,” effectively identifying an “afternoon slump” audience. Next, before even pitching the brand, they use Yield Forecast. This tool analyzes historical site traffic and purchasing data to project the potential impressions and revenue this new, highly specific segment could generate. They can now approach the brand with a powerful, data-backed proposal: “We can put your new drink in front of 750,000 shoppers who are actively looking for an afternoon pick-me-up, and we project a 4x return on ad spend.” This transforms the conversation from a simple media buy into a strategic, insight-led partnership that delivers far better results.
With demand growing from non-traditional retailers like fintechs and delivery apps, what unique monetization challenges do these sectors face? Could you share how the platform strategy must adapt to serve a company like PayPal or Lyft compared to a traditional retailer?
The core challenge for non-traditional players like PayPal or Lyft is that their “moment of commerce” is very different. They aren’t digital aisles where users browse; they are functional platforms where users are trying to accomplish a specific task, like paying a friend or getting a ride. A disruptive, irrelevant ad here is a death sentence for the user experience. Their challenge is to monetize attention without creating friction. This is precisely where a rigid, one-size-fits-all platform fails and our flexible, API-driven approach excels. For PayPal, the strategy isn’t about banner ads. It’s about using our platform to build native ad experiences right into the checkout flow, like a sponsored cash-back offer from a merchant. For Lyft, it’s about monetizing the in-transit experience, perhaps using our tools to power relevant offers from businesses near the rider’s destination. The platform must adapt to be contextual and value-additive, enabling these companies to build ad products that feel like a natural and helpful part of their service.
What is your forecast for the future of retail media?
I believe we are moving into the next era of retail media, which will be defined by differentiation and full-funnel integration. The days of simply replicating a basic sponsored products model are numbered. The retailers who win in the future will be those who harness their unique assets—both online and in-store—to build a truly distinct advertising platform. We will see a surge in innovation connecting digital campaigns to physical store experiences, closing the loop for advertisers in a powerful new way. AI will be the engine that powers this, moving beyond simple automation to enable hyper-relevant audience creation and predictive analytics. Ultimately, my forecast is that the most successful retailers will be those who reject the limitations of closed, walled-garden systems. They will take ownership of their technology stack, using flexible platforms to build, innovate, and control their own destiny in this incredibly exciting space.
