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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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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 Ben Smith, VP of Product, Data Products at Infillion. Adapting to signal loss What does the Experian–Infillion integration mean for advertisers looking to reach audiences as signals fade? As cookies and mobile identifiers disappear, brands need a new way to find and reach their audiences. The Experian integration strengthens Infillion’s XGraph, a cookieless, interoperable identity graph that supports all major ID frameworks, unifying people and households across devices with privacy compliance, by providing a stronger identity foundation with household- and person-level data. This allows us to connect the dots deterministically and compliantly across devices and channels, including connected TV (CTV). The result is better match rates on your first-party data, more scalable reach in cookieless environments, and more effective frequency management across every screen. Connecting audiences across channels How does Experian’s Digital Graph strengthen Infillion’s ability to deliver addressable media across channels like CTV and mobile? Experian strengthens the household spine of XGraph, which means we can accurately connect CTV impressions to the people and devices in that home – then extend those connections to mobile and web. This lets us plan, activate, and measure campaigns at the right level: household for CTV, and person or device for mobile and web. The outcome is smarter reach, less waste from over-frequency, and campaigns that truly work together across channels. The value of earned attention Infillion has long championed “guaranteed attention” in advertising. How does that philosophy translate into measurable outcomes for brands? Our engagement formats, such as TrueX, are based on a simple principle: attention should be earned, not forced. Viewers choose to engage with the ad and complete an action, which means every impression represents real, voluntary attention rather than passive exposure. Because of that, we consistently see stronger completion rates, deeper engagement, and clearer downstream results – like lower acquisition costs, improved on-site behavior, and measurable brand lift. To take that a step further, we measure attention through UpLift, our real-time brand lift tool. UpLift helps quantify how exposure to a campaign influences awareness, consideration, or purchase intent, providing a more complete picture of how earned attention translates into business impact. Creative innovation and location insights Beyond identity resolution, what are some of Infillion’s capabilities, like advanced creative formats or location-based insights, that set you apart in the market? One key area is location intelligence, which combines privacy-safe geospatial insights with location-based targeting through our proprietary geofencing technology. This allows us to build custom, data-driven campaigns that connect media exposure to real-world outcomes – like store visits and dwell time – measured through Arrival, our in-house footfall attribution product. We also build custom audiences using a mix of zero-party survey data, first-party location-based segments, and bespoke audience builds aligned to each advertiser’s specific strategy. Then there’s creative innovation, which is a major differentiator for us. Our high-impact formats go beyond static display, such as interactive video units that let viewers explore products through hotspots or carousels, rich-media ads that feature polls, quizzes, dynamic distance, or gamified elements, and immersive experiences that encourage active participation rather than passive viewing. These creative formats not only capture attention but also generate deeper engagement and stronger performance for a variety of KPIs. Future ready media strategies How does Infillion’s ID-agnostic approach help brands future-proof their media strategies amid ongoing privacy and tech changes? We don’t put all our eggs in one basket. XGraph securely unifies multiple durable identifiers alongside our proprietary TrueX supply to strengthen CTV household reach. This agnostic design allows us to adapt as platforms, regulations, and browsers evolve – so you can preserve reach and measurement capabilities without getting locked into a single ID or losing coverage when the next signal deprecates. Raising the bar for media accountability Looking ahead, how is Infillion evolving its platform to meet the next wave of challenges in audience engagement and media accountability? From an engagement standpoint, we’re expanding our ability to support the full customer journey, offering ad experiences that move seamlessly from awareness to consideration to conversion. That includes smarter creative that adapts to context, intelligent targeting and retargeting informed by real data, and formats designed to drive measurable outcomes rather than just impressions. When it comes to accountability, we’re ensuring that measurement is both flexible and credible. In addition to our proprietary tools, we partner with leading third-party measurement providers to validate results and give advertisers confidence that their investment is truly performing. Within our DSP, we emphasize full transparency and log-level data access, ensuring advertisers can see exactly what’s happening on every impression. All of this builds toward the next era of agentic media buying – one enabled by our MCP suite and modular, component-based tools. This evolution brings greater accountability and next-generation audience engagement to an increasingly automated, intelligent media landscape. Our goal is to help brands connect more meaningfully with audiences while holding every impression – and every outcome – to a higher standard of transparency and effectiveness. Driving impact across the funnel What is a success story or use cases that demonstrate the impact of the Experian–Infillion integration? We recently partnered with a national veterans’ organization to raise awareness of its programs for injured or ill veterans and their families. Using the Experian integration, we combined persistent household- and person-level identifiers with cross-device activation to reach veteran and donor audiences more precisely across CTV, display, and rich media. The campaign achieved standout results – industry-leading engagement rates, a 99% video completion rate, and measurable lifts in both brand awareness (3.6 % increase) and donation consideration (13.7% lift). It’s a clear example of how stronger identity and smarter activation can drive meaningful outcomes across the full funnel. Contact us Identity resolution FAQs Why is identity resolution critical for CTV and cross-channel campaigns? Identity resolution ensures accurate connections between devices, households, and individuals. Experian's Offline Identity Resolution and Digital Graph strengthen these connections for improved targeting and consistent measurement across CTV, mobile, and web. What strategies help address the loss of cookies and mobile IDs? Solutions like Experian's Digital Graph enable brands to connect first-party data to household and person-level identifiers, ensuring scalable reach and compliant audience targeting legacy signals fade. How can engagement translate into measurable results? Focusing on earned attention (where audiences actively choose to engage) leads to stronger completion rates, improves on-site behavior, and drives measurable increases in brand awareness and consideration. What makes cross-channel targeting more effective? By linking CTV impressions to households and extending those connections to mobile and web, Experian's identity solutions ensure campaigns work together seamlessly, reducing over-frequency and improving overall reach. About our expert Ben Smith VP Product, Data Products, Infillion Ben Smith leads Infillion’s Data Products organization, delivering identity, audience, and measurement solutions across the platform. Previously, he was CEO and co-founder of Fysical, a location intelligence startup acquired by Infillion in 2019. About Infillion Infillion is the first fully composable advertising platform, built to solve the challenges of complexity, fragmentation, and opacity in the digital media ecosystem. With MediaMath at its core, Infillion’s modular approach enables advertisers to seamlessly integrate or independently deploy key components—including demand, data, creative, and supply. This flexibility allows brands, agencies, commerce and retail media networks, and resellers to create tailored, high-performance solutions without the constraints of traditional, all-or-nothing legacy systems. Latest posts
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