Eyeota and Zeotap Data partner to integrate new audience solutions for advertisers into Amazon DSP. Eyeota, a Dun & Bradstreet company, and Zeotap Data / ROQAD, a leading provider of privacy-first identity and data solutions, announced that they are partnering to make Eyeota's global audience ecosystem available within Amazon DSP, giving advertisers streamlined access to high-quality B2C and B2B audience data. This integration enables advertisers to activate Eyeota's global data marketplace, including Dun & Bradstreet's leading B2B data and audiences from Eyeota's extensive network of trusted partners, across Amazon's premium inventory. Through this new integration, advertisers can now reach highly qualified audiences in a privacy-compliant way across channels including Prime Video, Freevee, Twitch, and more, using rich intent, interest, and behavioral signals to support more precise targeting. Zeotap Data identity graph and data onboarding capabilities give advertisers a more streamlined way to activate Eyeota's consumer and business audiences in Amazon DSP, helping brands reach relevant audiences across premium streaming and digital inventory without managing separate activation paths. "This partnership makes it easier than ever for advertisers to access and activate Eyeota's global audience data within Amazon DSP," said Marc Fanelli, SVP, Global Digital Audiences & Operations at Dun & Bradstreet. "From consumer segments to Dun & Bradstreet's B2B data, brands can now reach high-value audiences with greater precision across premium streaming and digital environments." "We're excited to partner with Eyeota to make high-quality audience activation within Amazon DSP simpler and more scalable. By combining Eyeota's audience marketplace with Zeotap Data's privacy-first identity capabilities, we're helping advertisers activate more relevant audiences across premium digital environments." said Alex Moufle, VP Data Sales at Zeotap Data / ROQAD For advertisers, this new integration: Opens premium streaming to B2B audience strategies, helping brands reach decision-makers beyond the confines of traditional professional media. Connects consumer and business audience strategies, giving brands greater flexibility to address the increasingly fluid path between personal interests, professional roles, and purchasing decisions. Reduces operational complexity and accelerates campaign activation by providing a streamlined route to multiple audience types within Amazon DSP Supports consistent multinational audience strategies, with activation available across the U.S., Europe, and India About Eyeota: Eyeota, a Dun & Bradstreet company, is the global leader in audience data solutions, helping brands, agencies, and platforms activate data-driven strategies across digital channels. About Zeotap Data: Zeotap Data, together with ROQAD, provides privacy-first identity, data onboarding and audience activation solutions that help advertisers and partners unify, enrich and activate customer and audience data across digital channels.

TMW #286 | Customer identity is enterprise-wide infrastructure. The top stories in Martech. * Customer identity is enterprise-wide infrastructure * How do you measure influence without measuring clicks? * The agentic commerce trust hierarchy 1. Customer identity is enterprise-wide infrastructure. Customer identity moving into the warehouse is not a new story by any means; Martech teams have been regularly discussing the gravitational pull of Cloud Data Platforms for the last few years. What's changing is the role identity plays inside the enterprise. Identity was once treated primarily as marketing infrastructure: a way to recognize customers, build audiences and coordinate messages. But it is increasingly becoming whole-business infrastructure used across marketing, commerce, service, fraud, analytics, product and AI. That has a big impact on where it architecturally belongs. A customer identity layer locked inside a marketing application can only serve the teams and use cases that application was designed to support. An identity layer built on shared enterprise data infrastructure, on the other hand, can become available across the organization, governed through common controls and reused by any authorized application, or increasingly importantly any authorized agent. Recent product moves make that direction a lot clearer. Zeotap's new Snowflake Native App runs identity resolution and journey logic inside the customer's Snowflake account, using the customer's compute and governance. Not to be left behind, Databricks' recently launched CustomerLake similarly embeds identity resolution, Customer 360 and audience creation into the lakehouse. What do these moves tell Themartechweekly? Apart from a healthy dose of herd instinct, it's a clear reflection of identity becoming a shared layer of enterprise architecture that has to live on shared infrastructure. Recent Gartner research forecasts that 80% of net-new enterprise CDP deployments will be embedded in, or composable with, broader data platforms by 2030. The prediction matters less as a verdict on standalone CDPs than as confirmation that identity is being absorbed into infrastructure intended to serve the whole business. Marketers will still need usable tools for segmentation, decisioning, orchestration and activation. But those tools may increasingly consume a common identity foundation rather than each maintaining their own version of the customer. That makes the architectural questions more important than the category label. Where does the identity graph actually run? Who owns the matching logic and identifiers? Which teams can access and improve it? Does data need to be copied into another platform? Can the organization retain its identity foundation when an activation vendor changes? The future of customer identity isn'tt just warehouse-native, but enterprise-wide. 2. How do you measure influence without clicks? It's no secret that marketing teams have traditionally measured the impact of website content through observable behavior: rankings, clicks, visits, engagement and conversions. But like a lot of things being turned on its head by AI, AI search is quickly breaking that chain. A customer may ask ChatGPT, Gemini or another LLM for a recommendation, receive an answer shaped partly by a brand's website, and continue the journey without clicking the source. They might search for the brand later, visit directly, purchase through a retailer or simply become more likely to consider it. Picture yourself here; have you done this? I certainly have! The content influenced the customer, but conventional analytics attributes the outcome somewhere else. A recent study linking opt-in browsing data with users' ChatGPT, Claude and Gemini conversations found that an AI recommendation to a previously unengaged user increased same-name Google searches by 4.3 percentage points and visits to the brand's website by 2.4 points. The researchers found that much of the downstream response was mediated through search and therefore invisible to standard referral or last-click reporting. Separate Similarweb analysis found that users shown a brand recommendation in ChatGPT were 2.5 times more likely to visit that brand than a direct competitor. Again, most AI-influenced visits arrived through search rather than an identifiable AI referral. This creates an interesting parallel with above-the-line media. A television campaign is not judged by the number of viewers who immediately type a URL into their browser. Brands use controlled studies to measure changes in awareness, recall, consideration and preference among exposed audiences. Does this mean that changes to website content now need their own version of brand-lift measurement? If a revised product page, research article or knowledge hub changes how AI systems describe and recommend the brand, marketers need to detect the influence outside the page itself. That could mean tracking changes in AI recommendations alongside branded search, direct traffic, consideration and downstream conversion. Proving causation will require more experimental approaches: think content holdouts, repeated prompt panels, exposed-versus-unexposed audience studies and triangulation across brand and behavioral signals. The website is now both a destination for people and an information source consumed by machines and redistributed across other interfaces. When content shapes an answer delivered somewhere else, measuring it purely through clicks is no longer enough. 3.The agentic commerce trust hierarchy. Agentic commerce is usually presented as a binary adoption question: will customers trust AI to shop for them? That's a fair query, but in my humble opinion, the more useful question is whose AI they will trust. EMARKETER reports that 47.1% of US digital shoppers have not used AI for shopping and are not interested. Its analysis also cites a Bain survey finding that only 7% trust general AI platforms such as ChatGPT to manage shopping end to end, compared with 25% who trust retailers. Gartner found a similar reticence to adoption. Consumers were receptive to AI helping them research products and narrow choices, but only 11% were willing to allow it to make purchase decisions. Clearly there is some genuine resistance to surrendering control to a system whose incentives, permissions and accountability are unclear - and rightly so, too. Visa's research into agentic-commerce trust found that recognizable payment and commerce brands currently hold an advantage. PayPal, Amazon and Visa (nice self-pat on the back there from Visa!) occupied the highest trust tier, followed by Apple, Google and Mastercard. Consumers also emphasized reversibility, clear data use, recognizable payment providers and control over purchasing permissions. This suggests trust will form a hierarchy rather than spreading evenly across agents. A retailer's agent has access to product information, inventory, loyalty status, payments, delivery and returns. It also has a visible relationship with the customer and can be held accountable when something goes wrong. But it carries a conflict: is it acting for the customer or optimizing the retailer's margin? An independent agent may appear more impartial, but customers must trust it with broader data and permissions while accepting less certainty about who is responsible for a bad recommendation or purchase. The immediate opportunity is therefore likely to be assistive rather than autonomous: agents that help customers discover products, compare choices, apply loyalty benefits, assemble a cart and complete repetitive tasks while preserving approval at important moments. Brands should be careful not to mistake technical autonomy for customer value. The winning agents will be the ones that customers can easily control and hold accountable. 2026 State of CXM Global Report: Humans in the Machine examines nine forces reshaping customer experience management and the operational changes required to respond. Creative Production Is Moving to Marketers, Just Like Data Did: Florian Delval argues that AI will transfer more creative execution to marketers and create a new creative marketing operations function. The AI Adoption Race Is Over. The Maturity Race Has Begun: Time Under Tension finds that experimentation is widespread, but very few marketing teams have integrated AI into their core workflows. Register for the Enterprise Martech Outlook research briefing. Benchmark your Martech strategy against more than 400 enterprise consumer brands. Register for the Enterprise Martech Outlook research briefing to explore how your peers are approaching investment, operating models, data, composability, and AI. Register now. Join Themartechweekly at Martech World Forum London & New York 2026. Connect with senior Martech leaders for two days of practical insights, candid conversations and high-value networking in London (8-9 September) or New York (4-5 November). Secure your ticket for London or New York today. Generative CMS: From Library to Laboratory. 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