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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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Media is changing and the sell-side is stepping boldly into the identity jungle—a dense and complex environment where privacy regulations, evolving signals, and advertiser expectations make every step an adventure. It’s not about survival; it’s about navigation. Experian’s identity solutions offer sell-side players like connected TV (CTV) publishers, supply-side platforms (SSPs), and open web publishers a roadmap to deliver rich consumer insights and build addressable audiences. Here’s how different stakeholders are navigating the landscape—and why having the right sherpa makes all the difference. CTV publishers: Turning anonymous viewers into addressable audiences The surge in CTV viewing, fueled by the shift from linear TV to digital streaming, has made it a critical channel for marketers—but navigating the identity jungle isn’t the same for every platform. For major players like Netflix, Hulu, and Max, where users log in to access content, the challenge isn’t identifying viewers but enriching their profiles. By layering behavioral and purchase data onto these profiles, platforms can go far beyond insights on media habits to create highly attractive audience segments for marketers to target. For free ad-supported streaming TV (FAST) platforms like Tubi, where logins aren’t required to watch content, the jungle is denser. These platforms have unknown viewers they can’t identify, which limits their ability to know who the customer is and reach them with relevant ads. By utilizing identity solutions, FAST platforms can turn unknown users into addressable audiences, resolving viewership at the household or individual level. This transformation allows for personalized, relevant ads that increase engagement, boost inventory value, and unlock new revenue opportunities. How Experian can help Imagine a CTV platform struggling with anonymous viewers on its FAST channels, where users tune in without logging in. Using Experian’s household-level data, the platform can convert these anonymous sessions into known, addressable audiences. This allows for personalized, precisely targeted ads that boost viewer engagement and significantly increase ad inventory value. For platforms with logged-in users, Experian takes it further by enriching profiles with behavioral and purchase data. This deeper understanding enables even more precise ad targeting, stronger advertiser demand, driving higher CPMs, and ultimately greater revenue growth. With Experian, CTV publishers turn anonymity into opportunity and build meaningful connections across their audience. SSPs: Delivering premium audiences across channels SSPs are under pressure to differentiate themselves in a competitive marketplace. The days of simply aggregating inventory are gone; today, SSPs must prove their worth by delivering premium value to advertisers and publishers. Addressability is a cornerstone of this strategy. By combining demographic and behavioral data with offline and digital identifiers, SSPs can build and deliver high-quality audiences across various channels. At the same time, supply path optimization (SPO) is taking center stage. SPO acts as a machete in the underbrush, clearing out unnecessary intermediaries and reducing costs while creating direct, transparent pathways to premium, brand-safe inventory. When paired with identity data, SSPs can offer buyers precisely targeted audiences, more premium inventory and a streamlined supply path. How Experian can help Imagine an SSP striving to stand out in a crowded market by delivering premium value to advertisers and publishers. Experian’s Digital Graph and Marketing Attributes empowers SSPs to enhance addressability and audience insights by combining digital identifiers with demographic and behavioral data. This enriched understanding of an audience leads to greater reach for the buy side and higher revenue for publishers. Additionally, these capabilities enable SSPs to form exclusive inventory partnerships, positioning them as go-to sources for high-value audiences. With Experian’s solutions, SSPs can differentiate themselves by delivering superior targeting, deeper audience understanding, and streamlined supply paths that drive measurable results for advertisers and publishers alike. Open web publishers: Promoting addressability and audience understanding For open web publishers, programmatic advertising has created opportunities—and challenges. Inventory commoditization makes it difficult to stand out and often leads to suppressed CPMs. To compete, publishers need data and identity solutions that enable them to differentiate their inventory and reveal the true value of their audience. Similar to FAST publishers, the jungle for open web publishers often starts with anonymous visitors. Recognizing and identifying all their users allows publishers to present advertisers with rich audience insights that lead to more efficiently targeted ads. Publishers are now equipped to fight commoditization and maximize revenue potential. How Experian can help Imagine an open web publisher striving to deliver more value to advertisers in a crowded programmatic landscape. Experian’s identity solutions help publishers turn anonymous traffic into addressable audiences, enabling them to understand their visitors and provide richer audience insights. This allows advertisers to target ads more effectively, increasing engagement and driving higher ad revenue. With the ability to recognize their visitors and offer actionable data, publishers can break free from commoditization. Experian empowers publishers to maximize their inventory’s value and help marketers drive results. Turning identity challenges into a strategic advantage The identity jungle can feel daunting, but for those willing to explore its opportunities, the rewards are immense. Sell-side players—CTV publishers, SSPs, and open web publishers—have the tools to not just navigate but thrive in this dense and dynamic ecosystem. By embracing data-driven strategies and identity solutions, they can uncover new paths to audience engagement, inventory value, and revenue growth. Get started today Read our companion article to learn how the buy-side is approaching data and identity challenges. Read now Contact us Latest posts

In a perfect world, we’d all have a single, go-to grocery store that carried everything on our shopping list – fresh produce, gourmet coffee beans, rare spices, and maybe even that special-grade olive oil, right alongside our wholesale bulk purchases at unbeatable prices. It would be convenient and efficient, and it’d save a lot of driving around town. The changing data marketplace: From one-stop shop to specialized selection For a long time, data buyers enjoyed something similar in their world: a small set of large-scale data marketplaces that offered a wide array of audiences, making it easy to load up on whatever you needed in one place. Not only are there fewer places to pick everything up, but new factors like privacy and signal deprecation are placing a spotlight on quality and addressability. Just as our dinner plans are growing more ambitious insofar as we want health, flavor, value, and convenience all in one place – so are our data strategies. Instead of a single steak-and-potatoes meal, today’s data marketplace operators might be cooking up a complex menu of campaigns. As a result, data buyers are beginning to shop around. Some still rely on large-scale marketplaces for familiar staples, but now they have reasons to explore other options. Some are turning to providers known for offering top-tier, transparently sourced segments. Others are focusing on specialty providers that excel in one area. A more selective approach to data buying In this environment, choosing where to “shop” for data is becoming more deliberate and selective. Data buyers aren’t just thinking about broad scale; they’re looking to prioritize quality, durability, data privacy, and differentiation. They need to place higher value on data marketplaces that can maintain audience addressability over time, despite signal loss. Sometimes, that means accepting a smaller assortment in exchange for tighter vetting and more reliable targeting. Other times it means mixing and matching – stopping by one marketplace for premium segments and another for cost-friendly, wide-reaching data sets. Either way, they can benefit from having more choices. Experian’s marketplace: A trusted source for high-quality data Experian’s vetted and curated blend of data partners and vertically-aligned audiences offers a trusted specialty store for data buyers. Experian’s marketplace, powered by identity graphs that include 126 million households, 250 million individuals, and 4 billion active digital IDs, enables partner audiences to be easily activated and maintain high addressability across display, mobile, and connected TV (CTV) channels. In particular, Experian’s marketplace provides: Enhanced addressability and match rates All audiences delivered from the marketplace benefit from our best-in-class offline and digital identity graphs, which ensure addressability across all channels like display, mobile, and CTV. 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If you're interested in learning more about Experian's marketplace or becoming an active buyer or seller in our marketplace, please contact us. Contact us Latest posts

Conventional TV advertising campaigns have historically relied on general audience metrics like impressions and ratings to measure outcomes. These metrics can help marketers understand how many people have seen an ad, but they don’t reveal its real-world impact, which leaves a gap between ad exposure and results. Outcome-based TV measurement bridges this gap and helps marketers tie ad spending directly to their business goals. Instead of counting eyeballs alone, TV measurement zeroes in on what viewers do after seeing an ad — whether signing up for a service, visiting your website, or purchasing a product. TV ad measurement helps marketers adjust campaigns based on clear, trackable outcomes rather than guesswork. Let’s talk about how marketers can get started with outcome-based TV measurement and start experiencing tangible results. Why outcome-based TV measurement matters Outcome-based measurement indicates a massive shift in how marketers evaluate TV advertising success. 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Marketers can deliver the most value by adjusting TV ad spending to maximize desired results: Sales goals: Identify which ads and platforms directly influence purchases to ensure TV ad spend contributes to revenue growth. Customer engagement: Link actions like website visits or app downloads to TV campaigns and refine messaging to deepen audience connections. Desired outcomes: Align ad spend with goals like consumer awareness or repeat purchases to allocate resources effectively for measurable success. Reducing wasted spend on ineffective channels Outcome-based TV measurement allows you to pinpoint which networks, times, or programs drive the most engagement and conversion. When you know your underperforming channels, you can reallocate budgets to those with a higher ROI and avoid waste. Core metrics in outcome-based TV measurement The effective implementation of outcome-based measurement requires advanced TV advertising analytics and tracking metrics that shed light on TV ad performance. Incremental lift This metric measures the increase in desired actions and business results — like purchases or site visits — that can be attributed directly to a TV campaign. Incremental lift quantifies your campaign’s impact and separates organic activity from the results your ads have driven. Let’s say a meal kit service experiences a 20% lift in subscriptions within a single week of running TV ads compared to a week without ads. They’d want to be able to isolate the impact of their ad from their organic growth so they can determine if the growth is actually a result of the TV ads or another effort. Attribution and conversions Attribution links TV ad exposure to specific customer actions, such as newsletter sign-ups and product purchases. Conversion data helps marketers understand the whole customer journey to optimize messaging, targeting, and channel mix to improve conversion rates. A retailer that knows 50% of TV ad viewers visit its e-commerce site within 36 hours of exposure could use that information to adjust the timing of its retargeting and align with site visit spikes. Audience segmentation for targeted measurement Outcome-based measurement breaks down performance across target demographics and allows for granular audience segmentation so TV ads resonate with the right audiences. For example, if a luxury brand saw better TV ad performance with high-earning Millennials, they’d want to refine their campaign messaging based on this group’s habits and preferences. Customer journey tracking Knowing how viewers move from awareness to conversion is critical. Outcome-based TV measurement helps you track the customer journey by pinpointing touchpoints where engagement happens and tying these to your TV campaigns. If a fitness brand found that TV campaigns drive app downloads, it could combine app analytics and TV exposure data to find out when most of their conversions happen after ad exposure and create follow-up messaging for that window of time. Integrating these insights with other marketing channels allows you to fine-tune your messaging, channel mix, and audience targeting to drive better outcomes and deliver more personalized customer experiences. Lifetime value (LTV) Beyond immediate conversions, outcome-based TV ad measurement helps brands identify which TV campaigns attract high-value customers with long-term revenue potential. If a financial institution ran a TV ad campaign centered on its new credit card, for instance, it could use LTV to track new cardholders and determine whether ads occurring during financial news airtime produced customers with higher average annual spend compared to other segments. How outcome-based TV measurement works Outcome-based measurement is a data-driven process that involves collecting, analyzing, and applying insights to improve TV ad performance. 1. Collect data When someone sees your TV ad, they might take action, like downloading your app or buying something. Outcome-based TV measurement begins by tracking these actions and gathering data from various sources, such as: TV viewership CRM Digital engagement Purchase behavior Cross-platform interactions And more Data integration with digital platforms Combining TV data with insights from platforms like social media or website analytics creates a more unified view of campaign performance. This integration powers easier retargeting and better alignment between digital and TV advertising strategies. Some marketers enhance this integration further using artificial intelligence (AI) to streamline data coordination and ensure campaigns are optimized for effectiveness and ROI. 2. Connect the dots Next, marketers need to find out which actions were influenced by TV ads. It’s important to ask questions like these as you work to connect the dots: Did website traffic spike right after the ad aired? Did the ad viewers match the people who signed up for the service or made a purchase? You can link TV exposure to real-world behaviors with tools and identifiers like hashed emails, device IDs, surveys, and privacy-safe data-matching techniques. 3. Analyze the data Then, the data needs to be analyzed for patterns like these: Which TV ads or time slots drove the most engagement? Did certain customer groups respond better than others? Was there a noticeable lift in sales or signups after the ad campaign? This step can help you uncover what’s working and what’s not. Role of advanced analytics and machine learning The data analysis required in this process can be overwhelming, time-consuming, and risky without the right tools. Fortunately, advanced analytics and fast, effective artificial intelligence tools can process large amounts of data from digital platforms, TV viewership, and customer interactions in less time to reveal accurate, actionable insights and patterns. They can also predict which audiences, messages, and channels will be most profitable so campaigns can adapt in real time, whether by reallocating spend to higher-performing channels or refining audience targeting. 4. Turn insights into action Once you have your data-derived insights, you can tweak your campaign in a number of ways, whether you decide to: Adjust your ads: If one message works better than another, lean into it. Refine your targeting: Focus on the audience segments most likely to act. Optimize your spend: Invest in channels or times that deliver the best return. For example, if you see that ads during prime time lead to more purchases than morning slots, you can shift your budget accordingly. This type of knowledge can be used to continuously improve your campaigns. Each time you run a new ad, you measure again, building on past insights to make your outcome-based TV advertising even smarter. Applications of outcome-based TV measurement Outcome-based TV measurement has wide-ranging applications across industries. Here’s how it’s helping businesses link TV ad exposure to real-world actions and optimize campaigns for better results. E-commerce and retail: Retailers can track how TV ads influence purchases and use those insights to refine their assets and target specific customer groups. A clothing retailer may track how well a TV ad boosts online traffic and in-store purchases. For instance, if a seasonal sale commercial correlates with a spike in website visits or mobile app downloads, the brand can refine its ad placement to focus on the most responsive demographics. Automotive: Automakers use outcome-based TV measurement insights to determine how ads drive dealership visits, test drives, or inquiries. A car manufacturer could analyze whether TV spots featuring a new vehicle increase traffic to its dealership locator or car configuration tool online. Healthcare: Pharmaceutical companies could assess whether TV spots lead to increased prescription fills, or a health provider could test how ads promoting flu shots result in appointment bookings through its website or app. If any messages resonate more with families, the provider can create similar campaigns for the future. How Experian enhances outcome-based TV measurement Experian has recently partnered with EDO, an outcomes-based measurement provider, to offer more granular TV measurement across platforms. Our identity resolution and matching capabilities enhance EDO’s IdentitySpine™ solution with rich consumer data, including age, gender, and household income, all in a privacy-centric way. Integrating these demographic attributes is helping advertisers achieve more precise audience insights and connect their first-party data to actionable outcomes. As a result of this collaboration, brands, agencies, and networks can optimize their TV campaigns by identifying which ads drive the most decisive engagement among specific audience segments. We’re improving accuracy, targeting, and more so advertisers can maximize the performance of their CTV strategies. Get in touch with Experian’s TV experts If you’re ready to take your data-driven TV advertising strategies to the next level, connect with our team. We combine advanced data and identity solutions as well as strong industry collaborations to help brands optimize their TV campaigns. Whether you're navigating traditional or advanced TV formats, our expertise ensures your efforts deliver maximum impact. Connect with us today to drive engagement, connect with audiences, and achieve better ROI. Let’s transform the way you measure success on TV. Reach out to our TV experts Contact us Latest posts