Snowplow recognized as leader in Snowflake's Modern Marketing Data Stack report for third consecutive year. Daniela Howard June 22, 2026 Snowplow today announced at Cannes Lions 2026 that it has been recognized by Snowflake, the AI Data Cloud company, as a leader in The Modern Marketing Data Stack: Governing the Agentic Enterprise. The report recognizes Snowplow in the Analytics & Measurement category for delivering high-quality, real-time, event-level behavioral data directly into the AI Data Cloud, powering agentic marketing analytics and hyper-personalized customer experiences. Now in its fifth year, Snowflake's Modern Marketing Data Stack report reflects a major shift in how marketing organizations operate - from fragmented tools toward AI-driven, agentic systems built on governed data foundations. This edition draws on insights from more than 11,500 Snowflake customers and ecosystem partners across 13 categories, highlighting how organizations are bringing industry-leading applications directly to their data to drive faster execution and proven business outcomes across the marketing lifecycle, while addressing the growing demands of data gravity, privacy and trust. "Marketing measurement is only as trustworthy as the data underneath it, and for most teams that data is still client-side, sampled, and arriving in the warehouse too late to act on," said Alex Dean, co-founder and CEO of Snowplow. "Snowplow captures behavioral data server-side and delivers it in real time into Snowflake, governed at the schema level, which is how joint customers like HelloFresh improved data accuracy from 33% to 95% and built measurement they could actually run their business on. That same high-quality, real-time customer context now powers the AI agents marketing teams are putting into production. Being named a Leader for the third year running shows that the context layer marketers have needed for a decade is finally here." For marketing teams on the AI Data Cloud, Snowplow's deep, native integration with Snowflake unlocks the full spectrum of modern measurement from within their own data platform - accurate attribution, multi-touch and marketing mix modeling, granular customer analytics - while establishing the same trusted foundation that powers joint customers' AI agents. For organizations consolidating on the AI Data Cloud, Snowplow is the real-time customer context layer that makes both motions possible on a single, governed set of data. "In an AI-driven era, the trustworthiness of marketing measurement comes down to whether the underlying behavioral data is captured, governed and acted on in real time inside the data platform," said Denise Persson, Chief Marketing Officer, Snowflake. "Snowplow stands out in the Snowflake ecosystem for offering exactly that - event-level data validated and enriched in real time inside the AI Data Cloud, integrating deep customer context directly into the analytics and AI applications our joint customers build on Snowflake." Customer Spotlight: HelloFresh - By migrating from legacy analytics tooling to a composable analytics approach with Snowplow, HelloFresh improved data accuracy from 33% to 95%, giving data science, marketing, and product teams a single trusted view to build customer behavior models and make smarter, faster optimization decisions. "The full integration has been very transformative in regards to how we can centralize all our data. Because we now have this capability, we can empower teams to have more rapid, accurate insights and enable more agile data-driven decisions." David Castro Gavino, Former Global Vice President of Data, HelloFresh About Snowplow: Snowplow is the real-time customer context layer that collects, validates, enriches, and delivers behavioral data for advanced analytics, ML, and AI agent decisioning. Snowplow's event tracking is leveraged across 2M+ websites and applications globally, processing over one trillion events per month. More than 250 companies, including Experian, AutoTrader, Strava, Condé Nast, and HelloFresh, rely on Snowplow to build a well-governed, first-party data foundation that powers their customer-facing AI agents, in-session personalization and recommendations, and real-time analytics. To learn more, visit snowplow.io. (Please note: Snowplow and Snowflake are entirely separate, independent entities with no corporate affiliation or relationship beyond their technology partnership.)

Five takeaways from Snowflake Summit 2026. Taylor Libby June 10, 2026 Last week, the Snowplow team joined thousands of data leaders, engineers, and AI practitioners at Snowflake Summit 2026 in San Francisco. At last year's Summit, the open question was whether AI belonged in the enterprise data stack. This year nobody was asking that. The conversation had moved on to whether the stack is actually ready for what AI is starting to demand of it. Snowflake CEO Sridhar Ramaswamy framed it directly in his opening keynote: unifying enterprise data and making it governed, secure, and AI-ready is what separates real business transformation from AI experiments that never scale. The Snowflake product announcements followed that line, positioning Snowflake as the Enterprise Data Layer, while the session its co-founder and CTO Yali Sassoon gave anchors Snowplow as the Customer Context Layer. Five things stood out. 1. Enterprise AI has moved from insight to action. For years the promise of the data stack was insight: better dashboards, better models, a better understanding of what already happened. The theme running through Snowflake Summit 2026 was action. The announcements and the sessions assumed AI isn't there to tell you what customers did, but to do things on their behalf and on yours. That shift sounds subtle. However, it changes what the data underneath has to be capable of, and it's the thread that connects everything below. Snowplow at Snowplow call this the Customer Context Layer, and that has been its bread and butter since day one (over 14 years ago). 2. Snowflake now has two agents, one for doing the work and one for building it. Snowflake opened the week by renaming Snowflake Intelligence to Snowflake CoWork, a personal work agent that helps knowledge workers reason across enterprise data, automate workflows, and take governed actions across the tools their teams already use. Alongside it, Cortex Code became Snowflake CoCo, a coding agent aimed at the data and engineering teams building the infrastructure underneath. CoCo generates pipelines, ML models, and agents from natural language, grounded in your actual Snowflake catalog, lineage, and permissions from the first prompt. The split is the point. CoWork does the work; CoCo builds the systems that do it. Together they are Snowflake's answer to what an agentic environment looks like for the people doing the work and the people building the tools. 3. Three Cortex announcements worth watching. Beyond the two agents, three Cortex announcements stood out: Cortex Sense (private preview soon) learns how an organisation defines its own business, including its workflows and the relationships between its data assets, so business reasoning is grounded from day one. Cortex Training (in private preview) lets teams fine-tune open-weight foundation models to their own domain and cost requirements without managing GPU capacity. Streaming feature support (generally available soon) serves online features in 10ms from Snowflake Feature Store, with under two seconds of data freshness from ingestion to serving. Streaming feature support is the one to watch for anyone trying to serve agents in real time, for reasons that become clear in takeaway 5 below. Spoiler: the answer is Snowplow Signals. 4. Agentic AI is quietly breaking customer data infrastructure. This was the core of Yali's session, "When the Interface is an Agent: Re-imagining Your Customer Context Layer." His argument was that agentic AI is creating data infrastructure problems most teams haven't reckoned with, and the window to get ahead of them is closing. Two shifts drive it. The first is that agents now make up more than half of all website traffic, and filtering them out is no longer enough. Whether an agent is acting for your customer, your competitor, or no one useful at all changes how you should respond to it. The second, and more interesting, is what happens when you put an agent inside your own experience. Yali used a simple example. Three customers walk into a shoe shop: one wants to buy, one wants to return, one is browsing. Today they all get the same interface and do the work of navigating to what they need. With an agent, the experience composes itself around each person's intent. The customer stops reading the map. That generates an order of magnitude more data than a traditional digital experience. It also makes two questions answerable for the first time. Yali pointed to Gary Angel, one of the early figures in digital analytics, who observed that the two things teams most wanted to know about a customer had always been opaque: what is this person trying to do, and did Snowplow give them what they needed? Tracked properly, agentic experiences finally close that gap. The hard part is activation. A customer-facing agent has a finite context window, and dumping a full event stream into it helps no one. The real engineering problem is compressing a high-velocity behavioural stream into something dense enough to be useful without eating the budget the agent needs to reason. As Yali put it, that means turning detailed tabular data into a tight paragraph of text. It's a new problem. 5. Most teams can't yet answer the three questions that matter. Three things earn a place in an agent's context window: who the customer is, what they are trying to do right now, and whether they need help. Real-time identity stitching, intent inference, and frustration detection aren't additions to that model. They are the model. This is where the Snowflake announcements and Yali's session turn out to be one conversation rather than two. CoWork and the agentic infrastructure around it assume the data feeding those agents is accurate, real-time, and current enough to reason on. Historical warehouse data tells an agent what a customer did. It doesn't tell the agent what they're trying to do right now, whether they've hit a wall, or whether they're even human. Snowplow Signals is built to fill that gap. It takes the behavioral event stream, processes it in real time, and delivers a tight context object an agent can act on. Paired with Snowflake's 10ms streaming features, the latency objection falls away. The infrastructure to give agents real-time customer context now exists. Yali closed with three questions he put to the room. Can you see, in your Snowflake data, how your customer-facing agents are reasoning versus what your customers are actually doing? Can you take your behavioral data and feed it to those agents in a form they can use? Can you tell the agents visiting your applications apart from the humans? Most teams Snowplow spoke with at Summit couldn't answer yes to all three. Where the work is. The teams investing now in how they collect, structure, and deliver behavioral data are the ones whose agents will work intelligently. The rest will spend the next couple of years debugging outputs and wondering why the context window isn't doing its job. That distance is the infrastructure gap, and it's widening. Snowplow built a blueprint showing how Snowplow and Snowflake CoWork fit together in practice. If you're working through any of this, it's a good place to start. Or get in touch directly to go deeper.