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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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What makes data “good” in the age of AI? In AI-driven marketing, data quality now defines success. “Good data” in AI isn’t about volume; it’s about the balance of accuracy, freshness, consent, and interoperability. As algorithms guide decisions, they must learn from data that’s both accurate and ethical. At Experian, we believe good data must meet four conditions: 1. Accurate Verified and anchored in real human identity. 2. Fresh Continuously updated to reflect today’s consumers. 3. Consented Collected and governed transparently. 4. Interoperable Easily integrated across platforms through a secure, signal-agnostic identity spine, enabling seamless data activation. This is the data AI can trust and the data that keeps marketing relevant, predictive, and privacy-first. Listen to InfoSum\’s Identity Architect\’s podcast for more on AI, outcomes, and curation Why does data accuracy matter more than ever? AI models are only as intelligent as their inputs. Incomplete or inconsistent data leads to bad predictions and wasted spend. As the industry moves toward agentic advertising, where autonomous systems handle campaign buying and optimization, data accuracy becomes even more critical. If your ad server or audience data is flawed, these new AI agents will simply automate bad decisions faster. Experian applies rigorous quality filters and conflict resolution rules to ensure our data is both deterministic and accurate. Deterministic signals alone don’t guarantee accuracy; they must be verified, deduplicated, and contextualized. Our identity resolution process anchors every attribute to real people, giving brands and platforms the confidence that every insight stems from truth, not noise. Our data is ranked #1 in accuracy by Truthset, giving our clients confidence that every decision they make is backed by the industry’s most reliable insights. See how Experian\’s Digital Graph improved attribution accuracy for a demand-side platform (DSP) with 84% of IDs resolved Just because it is deterministic, doesn’t mean it’s highly accurate. You still need to refine and validate your data to make sure it tells a consistent story. You need to anchor your data around real people. Calculate the real impact of data accuracy Why does AI need fresh data? Outdated data can’t predict tomorrow’s behavior. AI thrives on recency. At Experian, our audiences are refreshed continuously to mirror real-world signals, from purchase intent to media habits, so every campaign reflects what’s happening now, not six months ago. And we don’t just advocate for fresh data, we rely on it ourselves. Our own AI-powered models, used across our audience and identity platforms, are continuously retrained on the most current, consented signals. This allows us to see firsthand how freshness drives better accuracy, faster optimization cycles, and more relevant outcomes. But freshness alone isn’t enough. With predictive insights, our models go beyond describing the past. They forecast behaviors, fill gaps with inferred attributes, and recommend next-best audiences, helping you anticipate opportunity before it happens. Fresh and predictive data means you’re reaching people in the moment that matters and shaping what comes next. With AI, that’s what defines performance. Explore Experian\’s most popular audiences, ready to activate now How do consent and governance build trust in AI? Responsible AI starts with responsible data. With 20 U.S. states now enforcing privacy laws, data compliance isn’t optional, it’s operational. At Experian, privacy and compliance are built in. Every data signal, attribute, audience, and partner goes through our rigorous review process to meet federal, state, and local consumer privacy laws. With decades of experience in highly regulated industries, we’ve built processes that emphasize risk mitigation, transparency, and accountability. Governance isn’t just about regulation, it’s also about innovation done right. We drive transparent and responsible innovation through safe, modular experimentation, from generative applications to agentic workflows. By balancing bold ideas with ethical guardrails and staying ahead of evolving legislation, we ensure our innovations protect consumers, brands, and the broader ecosystem while moving the industry forward responsibly. Compliance and governance aren’t just boxes to check; they’re the foundation that gives AI its license to operate. How does interoperability enable AI’s full potential? AI delivers its best insights when data connects seamlessly across fragmented environments. Our signal-agnostic identity spine allows data to move securely between platforms (connected TV, retail media networks, and demand-side platforms) without losing context or compliance. Interoperability isn’t just about moving data between systems; it’s about connecting insights across them. When signals connect across environments, AI gains a more complete view of the customer journey revealing true behavior patterns, intent signals, and cross-channel impact that would otherwise remain hidden. This unified perspective allows AI to connect insights in real time, improving predictions, performance, and personalization while protecting privacy. Where do AI and human oversight meet? AI can make marketing more predictive, but people make it meaningful. At Experian, our technology brings identity, insight, and generative intelligence together so brands, agencies, and platforms can reach the right people with relevance, respect, and simplicity. Our AI-powered models surface connections, recommend audiences, and uncover insights that would take humans months to find. But our experts shape the process, crafting the right inputs, ensuring data quality, reviewing model outputs, and refining recommendations based on industry knowledge and client goals. It’s this partnership between advanced AI and experienced people that turns predictions into actionable, trustworthy solutions. What “good data” looks like in action “Good data” becomes most powerful when it’s put to work. At Experian, our marketing data and identity solutions help brands and their partners connect accurate, consented, and interoperable data across the ecosystem, turning insight into measurable outcomes. Learn more about Experian\’s data solutions Learn more about Experian\’s identity solutions When Windstar Cruises and their agency partner MMGY set out to connect digital media spend to real-world bookings, they turned to Experian’s marketing data and identity solutions to close the attribution loop. By deploying pixels across digital placements and using Experian’s identity graph to connect ad exposure data with reservation records, we created a closed-loop attribution system that revealed the full traveler journey, from impression to confirmed booking. The results were clear: 6,500+ bookings directly tied to digital campaigns, representing more than $20 million in revenue, with a 13:1 ROAS and $236 average cost per booking. Attributed audiences booked $500 higher on average, and MMGY’s Terminal audience segments powered by Experian data achieved a 28:1 ROAS. This collaboration shows that responsible, high-quality data and AI-driven insights don’t just tell a better story; they deliver measurable business performance. Download the full case study How to choose the partner built for responsible AI Why the future of AI depends on “good” data The next phase of AI-driven marketing won’t be defined by who has the most data, but by who has the best. Leaders will: Operate with clear data principles grounded in transparency and truth Build consent and compliance into every workflow Keep data accurate, current, and interoperable Pair automation with human oversight AI success starts with good data. And good data starts with Experian, where accuracy, privacy, and purpose come together to make marketing more human, not less. Partner with Experian for AI you can trust About the author Budi Tanzi, VP, Product, Experian Budi Tanzi is the Vice President of Product at Experian Marketing Services, overseeing all identity products. Prior to joining Experian, Budi worked at various stakeholders of the AdTech ecosystem, such as Tapad, Sizmek, and StrikeAd. During his career, he held leadership roles in both Product Management and Solution Engineering. Budi has been living in New York for almost 11 years and enjoys being outdoors as well as sailing around NYC whenever possible. FAQs What defines “good data” according to Experian? At Experian, we define \”good data\” as the balance of accuracy, consent, freshness, and interoperability. We apply rigorous governance, validation, and cleansing across every signal to ensure that AI systems learn from real-time behaviors, not assumptions. This approach turns data into a foundation for reliable, ethical, and high-performing intelligence. How does Experian ensure AI-ready data accuracy? Experian ensures AI-ready data accuracy through advanced cleansing, conflict resolution, and human anchoring. Experian ensures AI models rely on verified, high-quality inputs. 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Artificial intelligence (AI) is becoming a bigger part of modern advertising, changing how brands connect with people. At Experian, we believe this technology should make marketing more human, not less. We use AI to help marketers understand consumer behavior, respect privacy, and deliver messages that matter. As part of our latest Cannes Content Studio series, we spoke with leaders from AdRoll, MiQ, OpenX, Optable, PMG, PubMatic, and Yieldmo. Their insights show a clear path forward; one where technology supports human strategy to create more meaningful connections. 1. How does AI help you see audiences more clearly? AI decodes complex behavioral signals to reveal the values and mindsets behind decisions, and increasingly, it predicts what audiences will care about next. This allows marketers to deliver timely, relevant messages that resonate with audiences. At Experian, we help brands use these insights to connect more meaningfully and ethically. 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In our Ask the Expert Series, we interview leaders from our partner organizations who are helping lead their brands to new heights in AdTech. Today’s interview is with George Castrissiades, General Manager of Connected TV at AdRoll. Premium reach and fragmentation As viewer attention fragments across platforms, how should marketers redefine “premium reach” in CTV to prioritize engagement and audience quality over scale alone? A few years ago, ad supported streaming over-indexed on younger adults, those without much financial history and much more budget conscious. It would have been fair for B2B brands to assume that maybe they weren’t going to find their C-Suite audiences on those channels, and so connected TV(CTV) was positioned as a top of funnel tactic aimed at retail. But that’s all changed – ad-free prices are going up, and ad supported tiers are the norm across the majority of channels. 66% of adults have at least one ad supported streaming channel, and adults today spend nearly as much time streaming movies and TV as they spend on their mobile phones. Now that ad viewing audiences on CTV really span the full spectrum of demo, techno, and firmographic segments, savvy marketers should partner with platforms that offer breadth and depth of targeting and measurement to find the highest value audiences wherever they’re watching CTV and serve them highly relevant ads that draw their attention towards the screen. I know I’m jumping out of my seat whenever I see an AdTech or MarTech ad. Identity and relevance What does a strong identity framework unlock for delivering household- and person-level relevance across screens, and how does it reshape audience planning? In privacy-safe ecosystems, people want to share less data and log in to websites and browsers less frequently. If you can’t resolve a household ID to a CTV device through Safari and other sources of obfuscated identity, you’re going to end up losing a lot of signal along the way. On top of that, targeting smaller, higher-value audiences means this attrition can have a profound impact on your ability to build meaningful reach and use audience forecasts to predict scale. A strong identity framework is the key to maintaining as much of your planned audience as possible and staying true to initial forecasts. AI and outcome planning How is AI evolving CTV from tactical bidding to strategic outcome planning, and what mechanisms are in place to validate true performance lift? Tomorrow isn’t guaranteed, especially not in advertising. Audiences change where and when they consume media, and so shifting budget to a placement that did well yesterday is like buying a stock when it’s outperforming – the gains might be gone by then! Predictive AI is bridging the gap to find the highest value and most engaged audiences in real time, versus being purely reactive. This helps drive outcomes which we see in the form of pipeline influence, ROAS, and site traffic lift – without exponentially increasing costs. The same is true for account-based marketing(ABM) outcomes – there’s a blend of signals, account “fit” and intent data that needs to be evaluated in a deeper, more intelligent way. AI is helping to find those highest value accounts, even before they’re in market, which means smart marketers aren’t showing up late to the party. Measurement and incrementality What privacy-safe, closed-loop measurement frameworks should become standard to prove incremental visits and sales from CTV campaigns? Working with a dedicated multichannel, full-funnel ad and marketing platform like AdRoll can easily let you know when a user arrives at your site and makes a purchase, but understanding how that customer arrived there and which tactics deserve the credit requires a deeper, more sophisticated workflow. Our partnership with Experian allows all devices in a household to ladder back up to a household ID, so we can ensure accuracy without pivoting on anything personally identifiable. This works perfectly in CTV, an environment that follows an app workflow of user resettable device IDs rather than IP address or email but always connects seamlessly to web tokens including cookies. Accuracy, scale, and privacy are maintained in a proven way – you see this tech underpinning the infrastructure of streaming across all the biggest players, so marketers can rest easy. Creative and commerce How can creative sequencing and shoppable TV experiences convert living-room attention into commerce without compromising user experience or feeling intrusive? I like to say that CTV trades attention for action. Users lean back and focus on the messaging and visuals of the big screen rather than scrambling for the mouse or tapping to close some intrusive pop-up. This focus means that the messaging is absorbed more quickly, but creatives can wear out their welcome just as fast. Sequential messaging really helps to move the messaging along without subjecting the viewer to longer ads where attention wanes, but also increases brand recall and specific product information because the story evolves with each impression. This is a great tactic to use when you want a viewer to take a specific action later – but shoppable ads can help motivate a user to act now, and new formats can really simplify things. Shoppable can feel out of range for most – the top players in this space own major e-comm storefronts and then tie them back into their own demand-side platforms (DSPs), content, and streaming devices. For the rest of us, dipping our toes in slowly through simple and cheap solutions like QR codes can connect audiences right to a web experience from their TVs, or intermediate solutions like interactive video ads. Users love to play around with fun on-screen experiences, and there’s a whole spectrum of crawl/walk/run options available. Trust and governance Which shared guardrails—brand safety, fraud control, and frequency management- are essential to unlocking sustainable, scaled investment in CTV? I’ve always thought of CTV inventory analogously to high-end watches – if you buy from the source or a well-known, reputable dealer, you’re probably buying the real thing. But that fancy timepiece the guy was selling outside the bar, that you swore looked real? Probably not. Untrusted resellers and too-good-to-be-true pricing might mean you’re running ads on a screen at a lonely gas station at 3 am to an audience of no one, and that\’s not even the worst case scenario. Good relationships and deep pockets can solve brand safety and fraud issues, but not every advertiser is going to have those resources. Brand safety and fraud prevention can reduce workload and help distinguish the good stuff from the growing pool of gray area, arguably, CTV inventory that isn’t what was promised to a customer. Outsourcing this trust also goes a long way, with buyers knowing you’re not grading your own homework. Frequency management is equally as important. Once you have your audience and your good supply, it’s important not to abuse a single household just because they meet your targeting criteria. Reach is your best friend with CTV. Data and audience strategy How do Experian’s syndicated audiences provide a consistent, scalable foundation for planning, activation, and measurement across CTV and digital, and what outcomes are clients seeing? We love to talk about how Experian’s data is such an integral part of so much of streaming’s architecture, and the fact that it’s built on deterministic datasets means you’re getting scaled audiences built on knowledge rather than best guesses. That means a lot when working across multiple channels, privacy-safe environments, and households with an ever-growing number of connected devices. Our customers are always delighted at how precise targeting can be, especially in the B2B/B2C space – and knowing the size of those audiences helps them to understand how budget transforms into reach in a more predictable way. It’s confidence-inspiring to point to a new audience and tell your client that these are their future customers. The best part is showing them the outcomes reporting – we consistently see a massive spike in site traffic and engagement on days when a new Experian syndicated audience is launched! Contact us FAQs Why is identity resolution important in CTV? Identity resolution ensures marketers can connect devices and maintain audience accuracy. Experian\’s identity solutions help reduce data loss and improve audience forecasts, making campaigns more effective. How can marketers find the right audiences on CTV? With viewer attention spread across platforms, marketers need tools that offer both broad and detailed targeting. Experian\’s syndicated audiences provide reliable, scalable data to help identify and reach high-value audiences across channels. How can creative strategies improve CTV campaigns? Techniques like sequential messaging and shoppable ads keep viewers engaged and encourage action. Simple tools like QR codes or interactive video ads can connect audiences to web experiences directly from their TVs. How do DSPs benefit from strong identity frameworks in CTV? Strong identity frameworks help DSPs maintain accurate targeting and audience reach, even in privacy-focused environments. By connecting devices to household IDs, solutions like Experian’s Digital Graph ensure DSPs can deliver relevant ads and measure performance effectively across channels. About our expert George Castrissiades, General Manager of Connected TV, AdRoll George leads the CTV go-to-market strategy at NextRoll, driving rapid growth and adoption of the channel for both B2B and B2C customers. With a track record of building and scaling CTV solutions, he is focused on developing a strategic playbook that accelerates success in the evolving digital advertising landscape. Before joining NextRoll, George spearheaded CTV product innovation at iSpot.tv and held leadership roles in product and operations at YouTube, VICE Media, Crackle, Roku, and Innovid. His expertise spans product development, monetization, and market expansion. About AdRoll AdRoll is a connected advertising platform built for growth-minded marketers. With powerful AI, flexible campaign tools, and seamless integrations, AdRoll helps mid-sized businesses turn complexity into clarity and clicks into customers. The AdRoll platform delivers full-funnel performance through multi-channel advertising, audience insights, and cross-channel attribution, supporting marketers across industries including ecommerce, technology, financial services, education, and more. For B2B teams, AdRoll ABM extends these capabilities with account-based precision, multi-touch campaigns, and real-time buyer intelligence. 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