Nestlé Mexico Had More Scale Than It Knew What To Do With – Until It Unified Its Data

Most CPG marketers would walk over hot coals to have a presence in 90% of households nationwide.

But for Nestlé México, which shared that eye-popping stat about itself during a panel at Treasure AI’s Agentic World in Miami on Tuesday, popularity comes with its own set of challenges.

The company owns over 70 brands, with products ranging from coffee to candy bars to baby food, which means that understanding the full scope of a customer’s experience with Nestlé México is more complicated than just looking at their relationship with a particular brand or product. It requires tracking all the products they’ve bought, any correlations between them and their experience with each one.

The company’s immense scale is an opportunity to further its engagement and reach, said Elsa Vences, Nestlé México’s data and personalization manager, but it’s also “our greatest challenge.”

Or, as Emily Dickinson put it, “Fame is a bee.” It can be dangerous, but it also has wings.

Buried treasure

Dickinson probably didn’t think to recommend an AI platform that analyzes and acts on consumer data as a possible way to lessen the sting of fame – but we can forgive her for that.

Nestlé México, however, wanted to find a way to “break the siloes” it had historically operated in, said Vences. Previously, it had looked at data and targeting on a brand-by-brand basis but hadn’t used findings from one brand to target customers of another.

Three years ago, Nestlé México opted to partner with Treasure AI. The customer data platform recently rebranded itself as an “agentic experience platform,” which basically means AI agents are embedded into every step of its data analysis and campaign planning.

Now, Nestlé Mexico connects its data repository – which contains information like customers’ names and email addresses, along with demographic info – to Treasure AI. The platform can flag which customers are duplicates, said Vences, meaning they’re buying from multiple Nestlé México brands, and better understand those people’s wider interests.

Most of the individualized data that Nestlé México has is engagement signals within its CRM, like the links a consumer has clicked on. It also has access to hashed data from Amazon’s data clean room, which allows it to see purchase trends, like what age demographic is most often buying a product, without any personal information.

Sweetening the deal

Using engagement data from past campaigns, as well as demographic info, Nestlé México determined that La Lechera (its sweetened condensed milk brand) had three main audiences: traditionalists (people who use it in classic recipes), experts (bakers who are curious to try it in new ways) and explorers (generally younger consumers who are looking for a quick and easy way to add sweetness to a dish).

From there, Nestlé México was able to target each group with specific recipes that would appeal most to them, like flan for the traditionalists and a pancake topping for the explorers.

Since different brands can relate to the “same consumption moments,” Nestlé México often makes decisions about one brand based on data from several other brands, said Norma Martín, La Lechera’s marketing manager.

For instance, she added, La Lechera, Coffee-Mate and KitKat are all used in moments of “indulgence,” so the brand was able “to feed more users into the La Lechera base” by understanding shared commonalities between users of all three brands.

A recent La Lechera campaign saw an open rate that was nearly twice as high as its previous campaign baseline, said Martín, because of the AI’s ability to provide more personalized targeting.

But La Lechera, Coffee-Mate, and KitKat are all major brands that already have a lot of loyal customers. What’s harder, said Vences, is targeting the consumers of Nestlé México’s smaller brands, because it has less data on the customers and thus a less thorough understanding of what they want.

At least, that used to be the case.

Now, Nestlé México can determine what users to reach and how best to reach them by prompting Treasure AI’s agents with specific campaign parameters. An agent will generate suggestions based on data from relevant brands.

For instance, said Vences, she could tell the agent to generate an audience of Gen Z consumers who love chocolate, and it would pull from the data from larger brands like KitKat, or people who have engaged with dessert recipes within other brands’ campaigns. Brands can also upload the URL to their website and ask the agent to find recipes on the site that would resonate best with that audience.

For years, creative agencies have had to do this work manually, but with AI, Vences said, it’s not manual, and it’s also not just based on intuition.

Now, Nestlé México’s targeting is “more dedicated,” she said, using stronger data to ensure that its customers are being reached more accurately and holistically.