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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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At this year’s Shoptalk, one thing was crystal clear: Retailers are no longer just competing on price or product—they’re competing on experience. And in that race, customer expectations are not just the starting line—they’re the finish line, too. Over three days of discussions, demos, and side conversations, Shoptalk 2025 delivered a fresh look at how brands and advertisers are adapting to an increasingly blended retail environment. The show spotlighted not just what's new in retail media and AdTech—but how the industry is rethinking the entire shopper journey. What we heard again and again on the ground was this: there is no one-size-fits-all playbook anymore. Every retailer is navigating their own unique mix of identity, data, tech, and consumer needs. The winners will be those who stay nimble while staying connected to what customers actually want. 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Learn more at www.openx.com. Collaboration solves programmatic challenges Could you share the story behind the partnership between OpenX and Experian and how this collaboration differs from typical data-provider/DSP or SSP relationships in the market? What unique challenges in programmatic advertising does this partnership solve? OpenX first partnered with Experian in 2019 when we were building the industry’s first data-driven supply-side curation platform. Being the only SSP with a proprietary people-based identity graph (further enriched by Experian) gives OpenX a unique set of capabilities that are only growing in value in the market. We are seeing retail media networks, large agency planning platforms, and indie and specialty shops lean into OpenX’s tools to match, activate, and measure people-based audiences through our robust curation platform and premium supply. Enhancing campaigns with data enrichment How does combining Experian's marketing data with OpenX's technology create tangible benefits for advertisers, agencies, and publishers? Last year, we expanded our partnership with Experian to enrich our digital IDs with Experian’s Digital Audiences, essentially making Experian data available directly to marketers across all OpenX supply and formats, including CTV. For marketers, this direct integration increases both match and activation rates. Meaning, not only do we match more of the starting audience universe to our system, we then provide more opportunities to identify and transact on those users in the bidstream. The result is greater reach for buyers even in previously unaddressable environments like Safari or mobile web – and publishers benefit from the increased addressability OpenX provides their supply. Delivering impactful inventory solutions OpenX has been enhancing its curation offerings beyond just providing curated marketplaces. Could you describe the strategic shift you’re making in how you package and deliver inventory? At OpenX, we have a broader and more dynamic view of curation. It’s not just about gathering data or bundling inventory; it’s about layering on identity-based precision, enabling the targeting of the right audiences with premium, brand-safe inventory for our clients. We saw the value of curating inventory and audiences on the supply side early on. We started by building capabilities for our own exchange and then found that our approach created tremendous value for data owners and marketers alike. Over the past five years, we’ve been continuously investing our curation platform capabilities to super serve those partners. As a result, we have what we think is by far the most robust and flexible platform in the market. We can match and integrate with any kind of data, curate supply at a granular level, activate audiences and help measure outcomes in multiple ways. We also provide turnkey integrations to third-party platforms. Balancing customization with scalability in deals There's often tension between customization and scalability when it comes to curated deals. How does OpenX strike the right balance to meet varied advertiser objectives while ensuring operational efficiency for publishers? Truthfully, we’re not finding that scale suffers with curation. We currently have 237 million monthly active users in our exchange that we can match and activate curated deals against. That’s a unique claim for an SSP, and we back it up with our identity graph. This directly benefits our publishers who see a 20% increase in overall bid density and a 118%+ increase in win rate for curated deals vs. open market. Data-driven curation done on the supply side offers efficiency and drives results for buyers, while publishers are able to activate their own first-party data programmatically, increase their monetization, and maximize the value of their inventory. As the industry continues to adapt to a privacy-first, consent-based ecosystem, data-driven curation will play a key part in ensuring both sides of the marketplace continue to thrive. Driving results with CTV curation Connected TV is arguably the most dynamic channel in programmatic right now. How do curation improvements accelerate more precise or outcome-based targeting in CTV environments? I want to take this a step further and say that biddable is the future of CTV. Not only does biddable enable advertisers to purchase closer to campaign activation, it gives buyers the option to curate deals, flexibility, addressability and ease of transacting at will. No minimums, no commitments. Our CTV strategy has been centered around combining flexibility, efficiency, and real-time optimization capabilities with access to premium, direct, glass-on-wall inventory. TV by OpenX, powers the direct activation of curated audiences at scale through data-driven, contextual, attention, and sustainability offerings. What does this mean for buyers? Advertisers can choose from any one of OpenX’s 250+ data partners, including Experian, to target an audience via CTV inventory using OpenX’s cross-platform identity graph. This setup allows buyers to increase scale and optimize toward their desired campaign outcomes via their preferred DSP. The focus on inventory quality and scale combined with advanced targeting curation provides a key driver of performance in CTV. Identity resolution for better CTV measurement In a channel as fragmented as CTV, measuring performance can be complex. What role does identity resolution play in better measurement and attribution? How do Experian's identity capabilities integrate within your platform to drive measurable outcomes? We talked about the value of audience targeting via curation above. Another critical driver is our ability to power true closed-loop measurement for advertisers or partners like retail media networks. OpenX is able to provide automated log-level reporting via BIDS, which includes exposed IDs from our proprietary ID graph back to our partners in near real time. This closed-loop attribution enables partners to measure real-world outcomes like ROAS, conversion rates and incrementality. Insights and learnings from data can then be used to make optimizations mid-campaign, to further improve performance. Measurement starts with having a strong foundation to identity resolution – which Experian helps us achieve. Tailoring audience strategies in the auto sector The automotive vertical demands highly specific audience insights—everything from in-market signals to lifestyle and aftermarket service and parts data. How does the Experian–OpenX partnership enhance audience strategies in auto? Experian’s deterministic data, combined with the OpenX identity graph, empowers buyers with identity tools to create targeted audience segments of likely auto intenders. For verticals that have high customer acquisition costs like auto, these insights are particularly valuable, as buyers often struggle to identify their audiences at scale in environments that drive campaign performance. Experian’s automotive data is one of our most requested audiences from buyers. We match Experian’s high-quality data directly to our platform, often leveraging Experian’s IDs, which leads to greater scale and fidelity. In addition, our platform can curate supply to a granular level to drive results for buyers. Complying with evolving privacy regulations With data privacy regulations multiplying—like GDPR, CCPA, and others—how does OpenX’s direct connection with Experian ensure responsible data usage and compliance? At OpenX, we don’t see privacy regulations as a challenge but rather an opportunity. Instead, it’s a key differentiator for us. We’ve had a strong focus on data and identity since 2017, and we believe that if you’re talking about these topics but not talking about privacy, you’re missing an important piece of the equation. Regardless of the environment — CTV, mobile, app, or web — in today’s privacy-focused world, success in data and identity is inseparable from a commitment to privacy. We support this obligation with dedicated leadership that helps our partners navigate evolving global regulations, including critical areas like child-directed content under new laws from Australia to Maryland. Thanks for the interview. Any recommendations for our readers if they want to learn more? To learn more about our solutions and partnership opportunities, visit the OpenX website or contact your Experian account representative to schedule your free match test. Contact us About our expert Brian Chisholm, Senior Vice President of Strategic Partnerships, OpenX Brian Chisholm is the Senior Vice President of Strategic Partnerships at OpenX, where he spearheads the curation, data, and identity efforts. He and his team have been instrumental in building out OpenX’s industry-leading curation platform and partnerships. With more than two decades of experience in digital media, Brian has developed partnerships that leverage and expand OpenX’s core technology assets and deliver material value for the company’s buyer, publisher, and platform partners. Before joining OpenX, Brian held senior roles at innovative startups and digital stalwarts, including Overture/Yahoo, SpotRunner, and Apptera. Latest posts