How Kellanova’s Data Clean Room & AI Pilot Helped Develop Real-Time Consumer Personalization
Kellanova’s Special K brand in the UK was plagued by fierce competition from the private label landscape and lagging performance related to volume and penetration.
The company found its solution in data clean rooms, according to its SVP and chief growth officer Charisse Hughes — an approach that it is now testing across key platforms in the U.S. to transform marketing effectiveness and develop an enterprise-wide blueprint for better engaging with consumers.
How It Began
Last year, Kellanova launched a pilot program that combined clean room, artificial intelligence and machine learning technologies. Using this three-pronged approach, the company was able to analyze 20 million-plus addressable records from Experian, unifying them with consumer purchase behavior, demographics information, and attitudes data, to identify three new, high-value audience segments.
It then used this information to inform personalized campaigns that it launched across Meta and Pinterest. As a result of these marketing activations, Kellanova saw Special K sales increase from 9% to 36% in 2024 — up to 12x industry benchmarks, according to Hughes. Additionally, brand consideration increased 0.9 points and the company reversed its decline in household penetration.
Also read: Ramesh Kollepara shares how Kellanova is fine-tuning its internal AI muscle
The company is looking to replicate the data clean room environment to transform marketing effectiveness, now testing its applications across key platforms in the U.S., Hughes tells CGT.
“While our Special K pilot in the U.K. has set the benchmark globally, we’re adapting the approach across our portfolio and tailoring it to local data availability, consumer behaviors, and privacy requirements,” she says. “In the U.S., our focus is on using clean rooms to refine audience targeting, enhance personalization at scale and accelerate closed-loop measurement in partnership with key retailers, data providers and platforms.”
Developing a New Blueprint
Out of these efforts, Kellanova has developed a new, more dynamic and precise approach of connecting to fluid consumers. It includes using more targeted audience modeling that is grounded in real-time purchase behavior, attitudes, and household composition.
“Instead of static segments, we identified cohorts with very specific triggers: price-conscious lapsers or loyalists needing a nudge,” says Hughes.
The company then matched creative and media to each audience, adjusting platform selection, messaging and calls to action to achieve a new level of personalization that is powered by AI and supports a privacy-safe data environment.
“[It] has allowed us to shift from guesswork to precision, identifying which campaigns are driving conversion, not just awareness,” says Hughes, adding that it’s also a move away from siloed tech experiments to a more integrated approach designed to solve real business challenges.
Laying the Groundwork
Kellanova’s foundational work in data is paving the way for more tech-powered innovation. For example, Hughes says the company is also now testing creative intelligence tools such as VidMob to better understand the types of visuals, copy, and formats that resonate with different audiences and contexts.
It hasn’t been without its challenges, however. The biggest obstacle has been navigating the complexity of data integration across markets and partners, particularly as the privacy landscape and stances on regulation shift.
It’s work that requires measured steps. “Clean rooms are powerful, but they’re not plug-and-play; each requires thoughtful setup, data alignment, and experimentation,” Hughes emphasizes.
