In this article…

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.
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I’ve officially been at Experian Marketing Services for one month. That’s long enough to get past the onboarding checklists, meet an incredible number of people, and start connecting the dots between what I believed from the outside and what I now see clearly from the inside. What’s surprised me most is not the scale of Experian's assets. Everyone knows Experian operates at massive scale. It’s the uniqueness of how those assets come together. Identity. Activation. Curation. Optimization. Measurement. And a culture that understands the responsibility that comes with being the identity and data backbone for the AdTech ecosystem. There’s real energy here around not just what’s possible, but how to do it the right way. Very early on, this felt like the right move. The people confirmed it immediately. The leadership team reinforced it just as quickly. There’s alignment around how we go to market, how we think about identity, and how seriously we take client trust. That matters, especially in a moment when marketers are being asked to do more with less, prove everything, and still protect the consumer at every turn. The reality marketers are facing right now I’ve spent my career working with brands and agencies navigating change. What’s different right now is the level of fragmentation. Signals are everywhere. They’re coming from transactions, media exposure, location, content consumption, commerce, and increasingly from AI-driven interactions that don’t follow traditional linear paths. The challenge is no longer access to data. It’s coherence. If I’m a marketer today, my core question is simple: How do I tie a durable identity structure to constantly evolving consumer signals, and feed that intelligence into the right places at the right time? Especially as I start interacting with AI buying agents that will make decisions on my behalf. If the signals those systems receive are noisy, incomplete, or misaligned with my brand, I lose control fast. Identity has to be the foundation That’s where identity stops being a background capability and becomes foundational. Without a strong, continuously refreshed identity framework, everything downstream breaks. Planning becomes guesswork. Activation becomes inefficient. Measurement becomes misleading. I see too many brands treating identity as a one-time project. Build a graph. Do some householding. Declare victory. But people change. Households change. Signals multiply. Identity has to evolve just as fast. One of the biggest misconceptions I walked into was how narrowly Experian is often viewed. Many marketers still think of us as a place to buy attributes. Full stop. What I see now is a connected system that supports the full marketing lifecycle: Audience creation Activation Curation Optimization Measurement All grounded in identity and executed in a way that’s measurable and privacy-forward. Audigent + Experian’s data marketplace. This is where things click. This becomes even more powerful when you layer in Audigent. Audigent was foundational in defining curation, the idea that it’s not just about having data or inventory, but about intentionally pairing the right audience signals with the right supply to drive outcomes. When you combine Audigent’s curation expertise with Experian’s identity, data, and marketplace capabilities, something meaningful happens. That same philosophy extends directly into our data marketplace. It’s not just about accessing unique data sets. It’s about safely combining Experian data with partner data, or even multiple partner data sets together, to create audiences that simply don’t exist anywhere else. Then tying those audiences to real-world exposure and conversion across online and offline environments. This matters across industries, but especially in two places: Regulated verticals like healthcare and financial services, where accuracy and privacy are non-negotiable. Industries sitting on valuable first-party data like retail, travel, and automotive. No single company has all the signals they need. The opportunity is in collaboration. Partnering data in a trusted environment to create better outcomes and, in many cases, entirely new revenue streams. Looking ahead As AI continues to reshape how media is planned and bought, signals will become the currency. Not just any signals. The right ones. Curated, contextual, and connected to identity in a way that reflects real consumer behavior. Marketers who win will be the ones who control that signal flow, rather than reacting to it. After one month, what excites me most is that Experian is built for this moment. Years of investment in identity. A data marketplace designed for collaboration. And teams who understand that our job is not just to help marketers reach people, but to help them do it responsibly, efficiently, and in a way that actually drives outcomes. We’re just getting started. About the author Kevin Dunn Chief Revenue Officer, Experian Kevin Dunn joins Experian Marketing Services with more than 20 years of leadership experience across marketing and advertising technology, most recently serving as Senior Vice President of Brands and Agencies at LiveRamp. In that role, he led growth across retail, CPG, travel, hospitality, financial services, and healthcare, overseeing new business, account expansion, and channel partnerships. Kevin is known for building cohesive, accountable teams and leading with optimism, clarity, and a strong sense of shared purpose. His leadership philosophy centers on empowering people, driving positive outcomes for clients and fostering a culture where teams can grow, take smart risks, and succeed together. Latest posts

How should CMOs think about data as part of their audience strategy? The best digital marketers possess excellent storytelling capabilities—and they fuel the plot with data. When you think about it, your audience strategy is the whole story, and the type of data you use helps create each chapter. Just as any good book incorporates numerous literary devices, you must use more than one type of data to develop a dynamic, relevant, and timely narrative that captures your target users’ attention. In 2026, marketers should prioritize and invest in data and targeting strategies beyond just first-party to drive growth, improve efficiency, and strengthen customer relationships. Our 2026 Digital trends and predictions report is available now and reveals five trends that will define 2026. From curation becoming the standard in programmatic to AI moving from hype to implementation, each trend reflects a shift toward more connected, data-driven marketing. The interplay between them will define how marketers will lead in 2026. Download Why is first-party data not sufficient on its own? First-party data provides a strong foundation for targeting and measurement. It reflects information consumers have shared directly through brand interactions. That makes it reliable and central to audience strategy. That foundation alone does not tell the full story. First-party data defines known customers, but limits reach and frequency. Growth depends on expanding beyond existing relationships. Think of first-party data as a way to create an outline, not the whole story, about your target audiences—the main characters in your marketing. To flesh out the entire narrative about them, you must source, connect, and activate additional data. The ability to unify different data sources with accuracy, scale, and privacy at the forefront sits at the core of Experian’s business. We unify household, individual, device, demographic, behavioral, and first-party signals, along with contextual and geographic data points, to build a reliable view of consumers, even when specific signals are missing. This clarity helps you personalize, target, activate, and measure with confidence. By layering third-party data, contextual data, and geolocation data onto your first-party data foundation, your advertising strategies become stronger than if you used any of these sources as standalone solutions. How do different types of third-party data add depth to audience profiles? Third-party data expands understanding beyond known customers. If first-party data is the outline, third-party data helps with “character development”—a.k.a., adding detail to your audience profiles. Good third-party marketing data complements first-party insights with demographic, behavioral, and transactional context, providing the missing puzzle pieces to complete the full customer profile. Filling in gaps in customer understanding helps you identify, reach, and engage current and new customers more effectively. Third-party data allows brands to build loyalty with consumers by speaking to their interests and intent behind purchases. Third-party data opens up new targeting tactics for advertisers, such as: Behavioral How people engage with brands or how they use social media Demographic Age, gender, education, income, and religion Health A combination of demographics, behaviors, and health needs Interest Delivering ads based on interests, hobbies, or online activities Location Where people live, work, or spend large amounts of time Psychographics Shared characteristics like attitudes, lifestyles, and interests Purchases Using previous purchase behavior to identify the right audiences In addition to targeting, third-party data also remains critical to AI models, which must train on both structured and unstructured data. At Experian, our AI-powered technology interprets live bidstream data, device activity, content, and timing to optimize in the moment, ensuring campaigns deliver meaningful relevance, not just broader reach. How are contextual and geographic approaches reshaping audience targeting? Contextual and geographic approaches to targeting focus on environment and behavior rather than identifiers. Regulatory scrutiny, stricter and more fragmented compliance standards, and rising consumer expectations are transforming how marketers approach third-party data targeting. Evolving privacy laws and inconsistent identifiers across environments require new approaches that balance performance and privacy. Contextual and geographic targeting help marketers reach relevant audiences while maintaining privacy. What is data-informed contextual targeting? Contextual targeting connects audience attributes to the content environments people choose. It helps determine the setting of your story—where your characters spend their time. Solutions like Experian’s Contextually-Indexed Audiences harness advanced machine learning technology to combine contextual signals (a tried–and-true targeting tactic) with third-party targeting to ensure marketers reach their target audiences on the content they tend to consume, regardless of environment or location. What’s excellent about data-informed contextual targeting is that it moves beyond traditional keyword-based strategies to reach consumers on websites that over-index for visitors with the demographics, behaviors, or interests they are looking to target. What is data-informed geotargeting? Geotargeting uses shared location patterns to support relevance at scale. Geotargeting is another possibility for further developing the scene of your story. People with similar behaviors and interests tend to live in similar areas, which is why so much effort goes into location planning for brick-and-mortar stores. Data-informed geotargeting combines geos with third-party data to make more informed media buys based on common behaviors within a geographic location. We launched our Geo-Indexed audiences, which use advanced indexing technology to identify and reach consumers based on their geographic attributes. These audiences help marketers discover, segment, and craft messaging for consumers without relying on sensitive personal information, enabling them to reach target audiences while maintaining data privacy confidently. What role does AI play in third-party data targeting? AI acts like an automated editor of your book, refining and finding new ways to put valuable third-party audiences and data to work without relying on segments linked to known or disparate identifiers. We’ve used AI and machine learning at Experian for decades to bring identity, insight, and generative intelligence together so brands and agencies can reach the right people, with relevance, respect, and simplicity. Why does a balanced, integrated approach that combines first-party, third-party, contextual, and geo-targeting data matter? The combined effects of integrating third-party, contextual, and geotargeting data (and the marketing tactics it underpins) with first-party data will drive your success. Think of how any good author crafts a story. Regardless of whether it’s fiction or non-fiction, they draw on both first-person experience and external research and sources to develop their plot. No single data source tells the full story. Integration allows marketers to understand audiences more completely and act with confidence. Pooling these inputs together moves you closer to your goal of understanding the whole story about your target customers. In fact, an almost even number of marketers plan to use contextual targeting (41%) and first-party data (40%) as their main targeting strategies, amid privacy laws and the loss of persistent advertisers. Primary data strategyPercent of marketers that plan to use this data strategyContextual targeting41%First-party data40% A brand with strong first-party insights can extend reach by layering in additional signals. For example, a nutrition brand that knows who purchases protein supplements can expand prospecting by combining: First-party signals Customers who purchase protein supplements Contextual signals Engagement with fitness blogs, healthy recipe content, or workout apps Geographic signals Consumers located in the Greater Philadelphia area By connecting these inputs, the brand can identify new health-conscious audiences with similar interests and behaviors. This approach supports privacy-safe targeting while improving engagement and performance. How can marketers build an integrated data strategy in 2026? An integrated data strategy reduces friction and supports scale. The right data partner offers a unified solution that helps unify data, activate audiences, and adapt as the ecosystem evolves. Here’s how: Organize data Create a clean, usable data foundation by eliminating fragmented silos. Experian’s solutions unify disparate data, enabling identity resolution and a single customer view. Create a complete profile Experian links a persistent offline core of personally identifiable information (PII) data with fresh digital signals, giving you a high-fidelity view of consumers to decorate with marketing data. This allows for improved customer understanding and personalized marketing that competitors struggle to replicate. Build addressable audience segments Create audiences using a mixture of signals, including first-party data, third-party behavioral, interest, and demographic data, as well as contextual signals. If you partner with Experian, you can use audiences built on our identity graph to guarantee accuracy, scale, and maximum addressability. Drive innovation Look for partners and platforms that prioritize innovation in finding new ways to reach target audiences across the ecosystem. You don’t want a vendor or a system that can’t keep pace and adapt with our rapidly evolving industry. Marketers who want to create and activate campaigns more efficiently and effectively in 2026 need an integrated approach that combines first-party, third-party, contextual, and geotargeting data. Streamlining data integration and activation positions brands and agencies for sustainable growth and stronger consumer relationships in a privacy-conscious marketplace. Build your next chapter on a connected data foundation As audience strategies evolve, connection and interoperability matter more than ever. Connect with our team to learn how Experian helps marketers unify data, identity, and activation across channels. About the author Scott Kozub VP, Product Management, Experian Scott Kozub is the Vice President of the Product Management team at Experian Marketing Services working across the entire product portfolio. He has over 20 years of product experience in the marketing and advertising space. He’s been with a few startups and spent many years at FICO and Oracle Data Cloud heavily focused on loyalty marketing and advertising technology. FAQs How should CMOs think about data as part of their 2026 audience strategy? In 2026, CMOs should prioritize and invest in data and targeting strategies that combine first-party, third-party, contextual, and geographic data to drive growth, improve efficiency, and strengthen customer relationships. Why is first-party data not sufficient on its own? First-party data is not sufficient on its own because first-party data defines known customers but limits reach and frequency. Growth depends on expanding beyond existing relationships. The ability to unify different data sources with accuracy, scale, and privacy at the forefront sits at the core of Experian’s business. We unify household, individual, device, demographic, behavioral, and first-party signals, along with contextual and geographic data points, to build a reliable view of consumers, even when specific signals are missing. This clarity helps you personalize, target, activate, and measure with confidence. How do different types of third-party data add depth to audience profiles? Third-party data expands understanding beyond known customers. Third-party data opens up new targeting tactics for advertisers, such as: – Location: Where people live, work, or spend large amounts of time- Health: A combination of demographics, behaviors, and health needs- Purchases: Using previous purchase behavior to identify the right audiences – Behavioral: How people engage with brands or how they use social media – Interest: Delivering ads based on interests, hobbies, or online activities- Psychographics: Shared characteristics like attitudes, lifestyles, and interests- Demographic: Age, gender, education, income, and religion In addition to targeting, third-party data also remains critical to AI models, which must train on both structured and unstructured data. At Experian, our AI-powered technology interprets live bidstream data, device activity, content, and timing to optimize in the moment, ensuring campaigns deliver meaningful relevance, not just broader reach. What is data-informed contextual targeting? Data-informed contextual targeting connects audience attributes to the content environments people choose. It helps determine the setting of your story—where your characters spend their time. Experian’s Contextually-Indexed Audiences harness advanced machine learning technology to combine contextual signals (a tried–and-true targeting tactic) with third-party targeting to ensure marketers reach their target audiences on the content they tend to consume, regardless of environment or location. What is data-informed geotargeting? Data-informed geotargeting uses shared location patterns to support relevance at scale. Experian launched our Geo-Indexed audiences, which use advanced indexing technology to identify and reach consumers based on their geographic attributes. These audiences help marketers discover, segment, and craft messaging for consumers without relying on sensitive personal information, enabling them to reach target audiences while maintaining data privacy confidently. What role does AI play in third-party data targeting? In third-party data targeting, AI refines and finds new ways to put valuable third-party audiences and data to work without relying on segments linked to known or disparate identifiers. We’ve used AI and machine learning at Experian for decades to bring identity, insight, and generative intelligence together so brands and agencies can reach the right people, with relevance, respect, and simplicity. Latest posts

For years, marketers have worked around a familiar disconnect. Campaigns go live first. Measurement follows later. Insights arrive after audiences are reached, and budgets are committed. That gap has slowed decisions, blurred performance signals, and limited marketers’ ability to respond when it counts. In 2026, that model changes. Activation and measurement no longer operate as separate steps. They function as a single system, where insight informs action as campaigns unfold. Consistency across identity, data, and decision-making sits at the center of this shift, connecting the full campaign lifecycle from planning through outcomes. How is marketing measurement shifting from post-campaign reporting to in-flight intelligence in 2026? Marketing measurement in 2026 is moving from retrospective reporting to real-time input that shapes campaigns while they run. Instead of explaining performance after delivery, measurement now guides creative, audience, and channel decisions as verified outcomes appear. Historically, measurement worked like a post-mortem. Dashboards showed what happened after campaigns ended, or weeks after impressions were delivered. Those insights supported long-term planning but rarely influenced performance in the moment. That dynamic has changed. Today, marketers embed measurement directly into activation. Campaigns adapt while they run. Creative evolves based on engagement quality. Audience strategies adjust as verified outcomes come into view. Channel investments respond to performance signals, not assumptions. Connected ecosystems make this possible. Experian helps marketers plan, activate, and measure within a single framework by linking audiences, identity, and outcomes. When planning and performance live in the same environment, insight becomes actionable in the moment. Why is identity the connective layer between activation and measurement? Identity provides the consistent thread that links planning, activation, and outcomes into a unified system. Without it, marketers rely on proxy signals and disconnected views of performance. For years, fragmented identity frameworks made it difficult to connect media exposure to real-world outcomes. Without a consistent way to recognize audiences across planning, activation, and measurement, marketers relied on proxy metrics and modeled assumptions. That's changing as identity becomes interoperable across the ecosystem. Experian’s Digital and Offline Graphs help marketers onboard and resolve their data into a clean, connected foundation that supports everything that follows. From building audiences enriched with behavioral, demographic, and lifestyle insights, to activating those audiences across channels like connected TV (CTV), social, and programmatic through direct integrations with more than 200 platforms. When identity stays consistent from the first impression through final outcome, marketers gain a clearer view of what drives performance and where to act next. Our 2026 Digital trends and predictions report is available now and reveals five trends that will define 2026. From curation becoming the standard in programmatic to AI moving from hype to implementation, each trend reflects a shift toward more connected, data-driven marketing. The interplay between them will define how marketers will lead in 2026. Download How does closed-loop measurement become standard in 2026? Closed-loop measurement is becoming the default as activation and measurement come together. Marketers now tie exposure directly to verified business outcomes instead of relying on inferred signals. In partnership with MMGY Global, we helped Windstar Cruises connect digital impressions directly to bookings. The result was more than 6,500 verified bookings and $20 million in revenue tied back to campaign exposure. That translated to a 13:1 return on ad spend. Download the full case study here This level of accountability changes how marketers optimize. Instead of relying on clicks or inferred intent, teams can measure outcomes that reflect business impact. Store visits. Purchases. Site activity. These signals now guide decisions while campaigns are live. Through curated private marketplace deals and supply-path optimization, Experian also helps reduce cost, and improve reach and performance. With Experian and Audigent operating as one, marketers gain access to scalable, privacy-conscious data solutions that support both addressability and accountability across the supply chain. What should marketers plan for as activation and measurement connect in 2026? Marketing teams should prepare for an operating model built around continuous feedback, unified systems, and verified outcomes. This shift changes how success is defined and managed. Marketers should plan for: Always-on feedback loops Real-time signals guide creative, audience, and channel decisions while campaigns are in flight. Unified planning, activation, and outcome validation Integrated identity and audience frameworks allow marketers to trace value across every impression, not just the last click. Outcome-based performance signals Measurement will focus less on surface-level performance and more on true business impact, including sales, bookings, and long-term value. Greater use of first-party data Connected first-party data supports consistent activation and outcome validation across channels. Whether you're activating your own data or reaching new audiences, Experian connects every stage of the campaign. From early planners to last-minute buyers, we help you show up in the moments that matter and prove what is working. The takeaway Marketing's next chapter centers on connection. As data systems unify, activation and measurement operate as one. Insight flows directly into action. Decisions are guided by intelligence, not delayed reporting. With Experian, marketers plan, reach, and measure in a connected cycle. Every impression is measurable. Every audience is accurate. Every decision is powered by data ranked #1 in accuracy by Truthset. To explore this trend and the others shaping marketing in 2026, download our 2026 Digital trends and predictions report. Download Ready to connect with our team? About the author Ali Mack VP, AdTech Sales, Experian Ali Mack leads Experian’s AdTech business, overseeing global revenue across the company’s expansive tech and media portfolio. With over a decade of experience in digital and TV advertising, Ali drives strategic growth by aligning sales, customer success, and solutions teams to deliver impactful outcomes for clients and partners. She has successfully guided teams through two major acquisitions, integrating sales organizations and product portfolios into unified go-to-market strategies. Under her leadership, Experian has consistently exceeded revenue targets while fostering collaborative, results-driven teams and mentoring emerging leaders. Working closely with finance, product, and marketing, Ali develops strategies that support a diverse ecosystem of publishers, brands, and technology partners, positioning Experian at the forefront of data-driven advertising and identity resolution. FAQS How is marketing measurement shifting from post-campaign reporting to in-flight intelligence in 2026? Marketing measurement in 2026 is moving from retrospective reporting to real-time input that shapes campaigns while they run. Instead of explaining performance after delivery, measurement now guides creative, audience, and channel decisions as verified outcomes appear. Connected ecosystems make this possible. Experian helps marketers plan, activate, and measure within a single framework by linking audiences, identity, and outcomes. When planning and performance live in the same environment, insight becomes actionable in the moment. Why is identity the connective layer between activation and measurement? Identity provides the consistent thread that links planning, activation, and outcomes into a unified system. Without it, marketers rely on proxy signals and disconnected views of performance. Experian’s Digital and Offline Graphs help marketers onboard and resolve their data into a clean, connected foundation that supports everything that follows. From building audiences enriched with behavioral, demographic, and lifestyle insights, to activating those audiences across channels like connected TV (CTV), social, and programmatic through direct integrations with more than 200 platforms. How does closed-loop measurement become standard in 2026? Closed-loop measurement is becoming the default as activation and measurement come together. Marketers now tie exposure directly to verified business outcomes instead of relying on inferred signals. In partnership with MMGY Global, we helped Windstar Cruises connect digital impressions directly to bookings. The result was more than 6,500 verified bookings and $20 million in revenue tied back to campaign exposure. That translated to a 13:1 return on ad spend. What should marketers plan for as activation and measurement connect in 2026? Marketers should plan for: always-on feedback loops, unified planning, activation, and outcome validation, outcome-based performance signals, and greater use of first-party data. Whether you're activating your own data or reaching new audiences, Experian connects every stage of the campaign. From early planners to last-minute buyers, we help you show up in the moments that matter and prove what is working. Latest posts