Originally appeared in The Current
Forget the cookie delay — AI is already rewriting the rules of advertising.
While the industry was busy debating yet another postponement of Chrome’s third-party cookie phaseout, AI quietly became the most disruptive force in marketing. But here’s the twist: AI doesn’t work without identity. If marketers want results — real outcomes, not just impressions — they need to prioritize the data that makes AI go.
First-party data strategies are now mainstream. Interoperable identity solutions like Unified I.D. 2.0 (UID2) and ID5 are gaining adoption across the open web. Connected TV (CTV) has grown into a performance-focused, cookieless channel. Contextual and geo-based targeting have become smarter and more scalable. Identity graphs are helping marketers stitch together signals across devices, platforms, and channels.
The foundation for a better ecosystem isn’t being built — it’s already here.
The AI hype is over — and the stakes are higher
It’s no longer buzz. AI is here, and it’s already reshaping how we plan, activate, and measure advertising.
We’re seeing the rise of agentic AI: systems that don’t just surface insights but act on them. These AI agents are identifying patterns, building audiences, optimizing media buys, and analyzing performance. AI is helping marketers stop guessing and start improving.
But there’s a catch — one we can’t afford to overlook.
AI is only as good as the data it works with. “Garbage in, garbage out.” as the saying goes. And in advertising, that means if you don’t know who you’re reaching, even the smartest AI won’t drive results. To unlock AI’s full potential, marketers need a strong, privacy-safe identity foundation.
Identity is the fuel that makes AI work
AI can personalize creative, optimize in-flight campaigns, and even recommend which channels to prioritize — but it can’t do any of that well without context. And context starts with identity.
Identity connects signals from different devices, logins, channels, and interactions to real people. It tells your AI models who you’re talking to — not just what they clicked. That kind of clarity gives AI the power to make smarter predictions, uncover insights, and deliver relevance at scale. Without identity, AI is guessing. With identity, it’s delivering.
Identity is the foundation of the outcomes era
We’re living in a performance-driven age. Impressions and clicks are no longer enough. Marketers today are being judged by real outcomes: incremental sales, customer acquisition, revenue lift, and long-term value.
To measure those outcomes, you need to know who you reached — and whether they took action. Identity makes that connection possible. It links ad exposure to real-world results. It enables accurate attribution across channels. It powers personalization at every stage of the journey, making every impression more valuable.
This is the outcomes era, and identity is what makes it measurable.
Commerce media and CTV show what’s possible
Two of the fastest-growing channels — commerce media and CTV — are great examples of identity in action.
Commerce media
In commerce media, identity helps retailers and marketplaces organize their customer data, enrich it with external insights, and activate it across their own sites and off-site channels. It makes accurate targeting possible and gives marketers a clear ROI they can prove.
CTV
In CTV, identity helps solve a fundamental challenge: turning anonymous viewers into addressable audiences. On free ad-supported streaming platforms (FAST), identity solutions resolve viewership to the household level. On logged-in platforms, identity enriches profiles with behavioral and purchase data, boosting demand, improving CPMs, and growing revenue.
At Experian, we’ve invested in this future. Our recent acquisition of Audigent brings together data, identity, and activation — under one roof — built to support both AI-driven planning and outcome-based performance.
How marketers can win now
To stay ahead in a world defined by AI and outcomes, marketers need to:
Invest in omnichannel identity
To unify signals across platforms and environments.
Make identity actionable in real time
To inform both targeting and measurement.
Utilize first-party data, clean rooms, and privacy-safe partnerships
To future-proof performance.
Tailor identity strategies
To fit their media mix — because what works in CTV may not apply to in-app or web.
It’s not about rebuilding everything. It’s about building on what’s already working.
Final thought: Identity is the bridge
AI is raising the bar, and outcomes are the new standard. But neither works without identity. The marketers who win won’t treat identity as a compliance checkbox — they’ll treat it as their competitive edge.
Get started with us today
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After a six-month beta period, collaboration in Snowflake Data Clean Rooms using Experian's offline or digital graph is now generally available for all clients. As part of this, Experian is excited to announce that Experian's identity graph will be integrated into Snowflake's Data Clean Rooms. With the growing importance of data privacy and marketing efficiency, this partnership builds off of Experian's previously-announced integration into Snowflake's AI Data Cloud for Media. Adding Experian's identity graph to Snowflake Data Clean Rooms helps advertisers, advertising platforms, and measurement partners work more effectively. Built upon Experian’s rich offline and digital identity foundation, with support for various identifiers across platforms, collaboration in Snowflake Data Clean Rooms helps clients maximize the value of their data and meet the diverse needs of modern business: Collaborate with partners for richer data insights Achieve higher match rates Improve audience building Produce more accurate and complete reports Ensure data privacy Seamless integration of AdTech and MarTech platforms Regardless of the identifier type you are looking to collaborate on, Experian has the identity data in Snowflake Data Clean Rooms to support you and your partner. This leads to higher match rates and more resolved data for you to use to benefit your media initiatives. "Integrating Experian's identity graph into Snowflake Data Clean Rooms marks a transformative leap for digital marketing. This collaboration empowers advertisers, programmatic platforms, and measurement partners with unparalleled accuracy, privacy, and efficiency. Together, we are excited to provide innovative solutions to meet the evolving needs of our clients."Kamakshi Sivaramakrishnan, Head of Data Clean Rooms at Snowflake The Experian and Snowflake partnership showcases how collaboration can enhance scalability and cost-efficiency. Data clean rooms provide a secure environment where multiple parties can share, join, and analyze their data assets without leaving the clean room or exposing the underlying data. By integrating Experian's identity graph within Snowflake's secure platform businesses of all sizes can receive advanced data collaboration and identity tools without the high costs usually involved. The integration prioritizes consumer privacy and data security. Backed by Experian’s Global Data Principles, Experian's deep roots in data protection and security provide customers with the most trusted way to share data and protect consumer privacy. With Experian's graph in Snowflake Data Clean Rooms, customers will get a solution that respects customer consent, safeguards sensitive data, and ensures that processing occurs with the utmost respect for user confidentiality and preferences. Further, Snowflake Data Clean Rooms uses advanced methods to preserve privacy, such as differential privacy and secure computations on encrypted data, enabling data security and integrity. Together, these methods prevent unauthorized access by keeping sensitive data within the secure confines of the cleanroom on a strict, collaboration-to-collaboration basis. The collaboration between Experian and Snowflake significantly enhances data matching and identity resolution within the Snowflake Data Cleanroom. Experian’s identity solution uses digital identifiers like hashed emails, MAIDs, and CTV IDs and offline identifiers like name and address. This allows advertisers to reach more consumers and enrich their data. Marketers can easily use their first-party data in the cleanroom, and with Experian's Graph, they get higher match rates for more accurate targeting and campaign measurement. The continued partnership between Snowflake and Experian provide advertisers, platforms, and measurement providers a secure and effective way to collaborate. This sets the stage for continued innovation in programmatic advertising, ensuring that our solutions evolve in step with our clients' needs. If you're not utilizing clean rooms for collaboration but have advanced identity needs, you can license our Graph and seamlessly integrate it into your Snowflake account. Reach out to our team to learn more Latest posts
With U.S. brands expected to invest over $28 billion in connected TV (CTV) in 2024, balancing linear TV and CTV is now a top priority. Advertisers need to integrate these platforms as the TV landscape evolves to reach audiences with various viewing habits. A successful strategy requires both linear and CTV approaches to effectively reach audiences at scale. We interviewed experts from Comcast Advertising, Disney, Fox, Samsung Ads, Snowflake, and others to gain insights on the evolving landscape of linear and CTV. In our video, they discuss audience fragmentation, data-driven targeting, measurement challenges, and more. Watch now to hear their perspectives. Five considerations for connecting with linear TV and CTV audiences 1. Adapt to audience fragmentation With consumers' rapid shift toward streaming, it's easy to overlook the enduring significance of linear TV, which still commands a large portion of viewership. According to Jamie Power of the Walt Disney Company, roughly half of the current ad supply remains linear, highlighting the need for brands to adapt their strategies to target traditional TV viewers and cord-cutters. As streaming continues to rise, ensuring your strategy integrates both CTV and linear TV is crucial for reaching the full spectrum of audiences. "I don't think that we thought the world would shift so quickly to streaming, but it's not always just all about streaming; there's still such a massive audience in linear."Jamie Power, Disney 2. Combine linear TV’s reach with CTV’s precision Blending the reach of linear TV with the granular targeting capabilities of CTV allows advertisers to engage both broad and niche audiences. Data is critical in understanding audience behavior across these platforms, enabling brands to create highly relevant campaigns tailored to specific audience segments. This strategic use of data enhances engagement and ensures that the right viewers see advertising campaigns. "The future of TV is really around managing the fragmentation of audiences and making sure that you can reach those audiences addressably wherever they're watching TV."Carmela Fournier, Comcast Advertising 3. Manage frequency across platforms Cross-platform campaigns require managing ad frequency to avoid oversaturation while ensuring adequate exposure. With a variety of offline and digital IDs resolved to consumers, our Digital and Offline Graphs can help maintain consistent messaging across linear TV and CTV. This approach allows advertisers to strike the right balance, preventing ad fatigue and delivering the right audience reach for campaign impact. "You've got to make sure that you're not reaching the same homes too many times, that you're reaching everybody the right amount of times."Justin Rosen, Ampersand 4. Focus on consistent measurement Linear TV and CTV offer different data granularities, necessitating tailored approaches for accurate cross-platform campaign measurement. Bridging these data gaps requires advanced tools that streamline reporting for both mediums. As the industry moves toward consistent measurement standards, advertisers must adopt solutions that provide a comprehensive view of campaign performance, enabling them to optimize their cross-platform efforts. "Where I think there are pitfalls are with the measurement piece, it's highly fragmented, there's more work to be done, we're not necessarily unified in terms of a consistent approach to measurement."April Weeks, Basis 5. Align with shifts in audience behavior The success of cross-platform campaigns hinges on staying agile and responsive to shifting audience preferences. As CTV adoption grows, advertisers must proactively adjust their strategies to align with how viewers engage across linear and streaming platforms. Ideas include: Regularly updating creative Adjusting the media mix Utilizing real-time data insights to ensure campaigns remain relevant "At Fox we were a traditional linear company, and essentially what we're trying to do is merge the reach and the scale of TV as well as the reach and the scale of all the cord-cutters and cord-nevers that Tubi possesses." Darren Sherriff – FoxDarren Sherriff, Fox As streaming TV rapidly changes, brands must stay ahead of trends and shifts in consumer behavior to tap into CTV's growing potential. By focusing on these opportunities, advertisers can blend linear TV and CTV, ensuring their campaigns reach audiences wherever they watch. Connect with Experian's TV experts As a trusted leader in data and identity services, Experian offers the expertise to help you succeed in television marketing. With our strong partnerships with key players in the TV industry, we provide access to unique marketing opportunities. Learn how Experian’s data and identity solutions can deliver outstanding results in advanced TV advertising. Partner with us today to enhance your marketing strategies using our Consumer View and Consumer Sync solutions. Connect with our TV experts Contact us Latest posts
In this article…Understanding the AI revolution in commerceFour benefits of the AI revolution coming to commerceFuture trends and predictionsChart the future of commerce with Experian Technology is pushing the boundaries of commerce like never before. Artificial intelligence (AI) is one of the primary driving technologies at the forefront of the commerce evolution, using advanced algorithms to revolutionize marketing and personalize customer experiences. As of 2024, AI adoption in e-commerce is skyrocketing, with 84% of brands already using it or gearing up to do so. This article explores the AI revolution coming to commerce, focusing on what makes AI a driving force for e-commerce in particular, and the ways it's reshaping how businesses engage with consumers. Understanding the AI revolution in commerce AI is quickly reshaping commerce as we know it by democratizing access to sophisticated tools once reserved for large corporations, breaking down functional silos within organizations, and integrating data from multiple sources to achieve deeper customer understanding. It’s paving the way for a future where every brand interaction is uniquely crafted for the individual, powered by AI systems that anticipate preferences proactively. AI is a broad term that encompasses: Data mining: The gathering of current and historical data on which to base predictions Natural language processing (NLP): The interpretation of human language by computers Machine learning: The use of algorithms to learn from past experiences or examples to enhance data understanding The capabilities of AI have significantly matured into powerful tools that can improve operational efficiency and boost sales, even for smaller businesses. They have also fundamentally changed how businesses interact with customers and handle operations. As AI continues to develop, it has the potential to provide even more seamless, personalized, and ethically informed commerce experiences and establish new benchmarks for engagement and efficiency in the marketplace. Four benefits of the AI revolution coming to commerce Major commerce players like Amazon have benefited from AI and related technologies for a while. Through machine learning, they’ve optimized logistics, curated their product selection, and improved the user experience. As this technology quickly expands, businesses have unlimited opportunities to see the same efficiency, growth, and customer satisfaction as Amazon. Here are four primary benefits of AI adoption in commerce. 1. Data-driven decision making AI gives businesses powerful tools to analyze large amounts of data more quickly and accurately than a person. Through advanced algorithms and machine learning, AI can sift through historical sales data, customer behavior patterns, and market trends to uncover insights and suggest actions that might not be immediately obvious to human analysts. By transforming raw data into actionable insights, AI empowers businesses to make more informed decisions, reduce risks, and capitalize on opportunities. As a real-world example, Foxconn, the largest electronics contract manufacturer worldwide, worked with Amazon Machine Learning Solutions Lab to implement AI-enhanced business analytics for more accurate forecasting. This move improved forecasting accuracy by 8%, saved $533,000 annually, reduced labor waste, and improved customer satisfaction through data-driven decisions. 2. A better customer experience AI is set to make customer interactions smoother, faster, and more personalized by recommending products based on preferences and behaviors, making it easier for customers to find what they need. When consumers visit an online store, AI also provides instantaneous help via a chatbot that knows their order history and preferences. These AI-powered assistants offer real-time help like a knowledgeable store clerk. They give the appearance of higher-touch support and can answer basic questions at any hour, provide personalized product recommendations, and even troubleshoot issues. Chatbots free up human customer service agents for more complicated matters, and these agents can then use AI to obtain relevant information and suggestions for the customer during an interaction. 3. Personalized marketing Data-driven personalization of the customer journey has been shown to generate up to eight times the ROI, as data shows 71% of consumers now expect personalized brand interactions. Until AI came around, personalization at scale was complex to achieve. Now, gathering and processing data about a customer’s shopping experience is easier than ever based on lookalike customers and past behavior. Many businesses have adopted AI to glean deeper insights into purchase history, web browsing, and social media interactions to drive better segmentation and targeting. With AI, advertisers can analyze behavioral and demographic data to suggest products someone is likely to love. Consumers can now browse many of their favorite online stores and see product recommendations that perfectly match their tastes and needs. AI can also offer special discounts based on purchasing habits, and send personalized emails with products and content that interest customers to make their shopping experience more engaging and relevant. This personalization helps businesses forge stronger customer relationships. Personalization across digital storefronts Retail media involves placing advertisements within a retailer's website, app, or other digital platform to help brands target consumers based on their behavior and preferences within that environment. Retail media networks (RMNs) expand this capability across multiple retail platforms to create seamless advertising opportunities throughout the customer journey. Integrating AI into RMNs can improve personalization across digital storefronts with personalized, relevant ads and custom offers in real time that improve the customer experience. 4. Operational efficiency AI can also be beneficial on the back end, enabling more efficient resource allocation, pricing optimization, efficiency, and productivity. Customers can be frustrated when they visit a store for a specific product only to find it out of stock or unavailable in a particular size. With AI, these situations can be prevented through algorithms that forecast demand for certain items. Retailers like Amazon and Walmart both use AI to predict demand, with Walmart even tracking inventory in real time so managers can restock items as soon as they run out. AI can automate and streamline operational tasks to help businesses run smoother, faster, and more cost-effective operations. It can: Offload tedious data entry, scheduling, and order processing tasks for greater fulfillment accuracy. Analyze historical data and market trends, predicting demand to help businesses optimize inventory, reduce waste, track online and in-store sales, and prevent shortages. Forecast demand levels, transit times, and shipment delays to make better predictions about logistics and supply chains. Improve data quality using machine learning algorithms that find and correct product information errors, duplicates, and inconsistencies. Adjust prices based on competitor pricing, seasonal fluctuations, and market conditions to maximize profits. Pinpoint bottlenecks, identify issues before they escalate, and provide improvements for suggestions. Future trends and predictions If you want to stay ahead in e-commerce, it’s just as important to know what’s coming as it is to understand where things are today. Here are some of the trends expected to shape the rest of 2024 and beyond. Conversational commerce Conversational commerce allows real-time, two-way communication through AI-based text and voice assistants, social messaging apps, and chatbots. Generative AI advancements may soon enable more seamless, personalized interactions between customers and online retailers. This technology can improve customer engagement and satisfaction while providing helpful insights into preferences and behaviors for better personalization and targeting. Delivery optimization AI-driven delivery optimization uses AI to predict ideal routes for each individual delivery, boosting efficiency, reducing costs, promoting sustainability, and improving customer satisfaction throughout the delivery process. Visual search AI-driven visual search is quickly improving in accuracy, speed, and contextual understanding. Future developments may integrate seamlessly with augmented reality (AR) so shoppers can search for products by pointing their devices at physical objects. Social media and e-commerce platforms may soon incorporate visual search more prominently, allowing users to find products directly from images. AI content creation AI is already automating and optimizing aspects of content production: Algorithms can generate product descriptions, blog posts, and social media captions personalized to specific customer segments. AI tools also enable the creation of high-quality visuals and videos. NLP advancements ensure content is compelling and grammatically correct. AI-driven content strategies analyze consumer behavior and refine messaging to meet changing preferences and trends. This automation speeds up content creation while freeing resources for strategic planning and customer interaction. IoT integration Integrating AI with Internet of Things (IoT) devices could help make the ecosystem more interconnected in the future. AI algorithms can use data from IoT devices like smart appliances, wearables, and sensors to gather real-time insights into consumer behavior, preferences, and product usage patterns. This data enables personalized marketing strategies, predictive maintenance for products, and optimized inventory management. AI-driven IoT data analytics can also streamline supply chain operations to reduce costs and inefficiencies. Fraud detection and security There will likely be an increased focus on the ethical use of AI and data privacy regulations to strengthen consumer trust and transparency. AI-powered systems will get better at detecting and preventing fraud in e-commerce transactions, which will heighten security measures for both businesses and consumers. Chart the future of commerce with Experian AI has changed how marketers approach e-commerce in 2024. With AI-driven analytics and predictive capabilities, marketers can extract deeper insights from extensive data sets to gain a clearer understanding of consumer behavior. This enables refined segmentation, precise targeting, and real-time customization of messages and content to fit individual preferences. Beyond insights, AI automates routine tasks like ad placement, content creation, and customer service responses, freeing marketers to concentrate on strategic planning and creativity. Through machine learning, marketers can predict trends, optimize budgets, and fine-tune strategies faster and more accurately than ever. The time to embrace AI is now. At Experian, we’re here to help you make more data-driven decisions, deliver more relevant content, and reach the right audience at the right time. Using AI in your commerce marketing strategy with our Consumer View and Consumer Sync solutions can help you stay competitive with effective, engaging campaigns. Contact us to learn how we can empower your commerce advertising strategy today. Contact us Latest posts