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How the AI revolution is transforming the future of commerce

Published: September 10, 2024 by Experian Marketing Services

The widespread adoption of AI in commerce

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 sets us apart is that we don’t just identify valuable audiences; we help marketers also target cookieless inventory and power it with real-time signals. Marketplace integration milestones What excites you most about bringing 33Across audiences into Experian’s data marketplace? We’re excited to bring 33Across audiences into Experian’s data marketplace because it connects our unique segments with a powerful data ecosystem that marketers already trust. Buyers looking to activate audiences that are both privacy-safe and performant continue to tap into the Experian data marketplace for high-quality, high-performing data. We offer a wide range of audience verticals, including B2B, demographic, retail purchase data, interest and intent, and political data. In addition, we offer the ability to create custom segments across verticals.  Our intent-based audiences, built from contextual and engagement signals, help buyers reach consumers on CTV, desktop, or mobile devices with scale.  Being part of Experian’s data marketplace accelerates access to these audiences, drives better ROI, and helps brands future-proof their strategies today. Retail demand signals Retail brands are racing toward privacy-safe, first-party data. Which 33Across retail datasets or segments are experiencing the highest demand, and what makes them a must-have?  Retail marketers are leaning into contextual and behavioral intent signals to complement their first-party data strategies. At 33Across, we’re seeing high demand for segments tied to shopping intent, including in-market consumers browsing for categories like fashion, home goods, electronics, and health & wellness. What makes these segments essential is their real-time nature – they can capture consumer interest as it happens. For retail brands looking to expand their reach while respecting privacy, our segments offer scalable, actionable intent that drives results. B2B without cookies Reaching real B2B decision-makers at scale is tough with or without signals. How does 33Across deliver both precision and reach in this environment?  B2B marketing often struggles with balancing scale and specificity. 33Across addresses this by combining contextual precision with AI-modeled behavioral signals; this segment approach reaches professionals actively engaging with relevant content and topics, even in environments where IDs are unavailable. Marketers gain access to more signals and, in turn, better reach from 33Across’ unique publisher integrations and audience curation built from machine learning and AI. We surface intent through content consumption patterns and contextual engagement, unlocking valuable, privacy-safe signals at scale. Allowing B2B marketers to reach real decision-makers in a signal-sparse world.  Use cases With retail, B2B, and beyond, can you share an example of how brands in these verticals are utilizing your audiences? Top brands that have a user-focused approach use 33Across audiences to drive scale; performance. These brands enable our segments to precisely reach the right users across devices and increase conversion rates; brand awareness. By reaching the right users, brands have higher conversion rates and increase campaign efficiency. Supply path innovation As identifiers disappear, advertisers are looking for scalable, privacy-safe ways to reach real people. How is 33Across helping unlock more addressable inventory and drive performance? By combining contextual, semantic, and engagement-based signals, we deliver intent-based targeting that performs across CTV, display and video. Higher addressability helps marketers not only extend their reach but also deliver personalized messaging across digital channels in a privacy-compliant way.  Contact us About our expert Allison Dewey Director of Data and Curation, 33Across Allison Dewey is the Director of Data & Curation at 33Across, where she oversees data partnerships, integrations, and supply-side curation. With a deep expertise in audience targeting and signal optimization, Allison plays a key role in connecting data into the programmatic world. Allison holds a Bachelor's degree in Psychology from Bates College. About 33Across Rooted in over 15 years of data expertise, 33Across harnesses signals to enrich and expand marketers’ audiences and reach them wherever they consume content. Built from over 300 billion proprietary data signals, we apply machine learning and AI to create over 1,500 B2C and B2B segments using privacy-first principles to reach audiences.   Cookieless targeting FAQs How can advertisers reach audiences without traditional identifiers?  By using contextual and engagement-based signals, advertisers can target consumers across CTV, mobile, and desktop in a privacy compliant way, even as identifiers become less available.  What audience segments are most in demand for retail marketers?  Segments tied to shopping intent, such as consumers browsing fashion, electronics, or health products, are highly sought after because they capture real time interest and drive results.  How can B2B marketers find decision-makers without cookies?  Combining content engagement patterns with machine learning allows marketers to reach professionals actively engaging with relevant topics, even in environments where IDs are unavailable.  What makes privacy safe audience targeting effective?  Privacy safe targeting uses real time contextual and behavioral signals to deliver relevant messaging across devices and channels without compromising consumer trust.  How can real-time intent signals drive demand?  Real time intent signals allow advertisers to capture consumer interest as it happens, helping demand side platforms and brands deliver timely, relevant ads that increase engagement and drive conversions across devices like CTV, mobile, and desktop.  Latest posts

Oct 22,2025 by Experian Marketing Services

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