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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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2022 was a year of adjustment. Consumers adjusted to a post-pandemic world and returned to pre-pandemic shopping behaviors. Consumers adjusted their budgets as the price of goods skyrocketed, as a result of high inflation. To combat inflation, the U.S. Federal Reserve adjusted interest rates. This further restricted consumer buying power. The AdTech ecosystem also experienced adjustments. Google adjusted the date of cookie deprecation. Federal legislation forced technology companies to adjust their consumer privacy practices. Marketers and advertisers adjusted how they address interoperability issues by investing in clean room solutions. This year of adjustment makes it harder to predict where consumers will spend and how marketers should plan their digital audience strategies. What will 2023 bring to AdTech? Download our 2023 AdTech trends and predictions report to access our forecast to help you plan for 2023. Our report will answer: How has digital activation changed over the last four years? What are the top advertising platforms? Which digital audiences are advertisers buying? Do digital audience strategies vary by vertical? Our AdTech trends forecast In 2023, digital activation will increase. Digital audience activation continues to grow at a significant rate despite market shocks like the pandemic, inflation, and higher interest rates. Given the current economic uncertainty, we predict that marketers will look toward tried and true channels where they are confident they will have quality audiences, inventory, and be able to drive ROI. What will digital activation look like in 2023? Between 2018-2021, digital audience activation increased annually by 46%. Using projected 2022 results, between 2018-2022, it will increase annually by 34%. We anticipate continued growth in 2023. Top advertising platforms in 2023 2023 will see increased digital activation, but which platforms will advertisers use to serve their ads? Advertisers will shift their focus to demand-side, video, and supply-side platforms. Social media platforms will continue to experience volatility. Advertisers will place bigger bets on the combination of addressable and CTV. Our report will also reveal which platforms are creating a path toward a post-cookie future and where data-sharing relationships will become the strongest. The most popular advertiser audiences trending now in AdTech Which digital audiences are advertisers buying? Demographics Modeled Lifestyles Behavioral Custom Audiences Traditional targeting methods like Demographics and Modeled Lifestyles are the baseline of many marketing strategies. We predict that we will continue to see marketers activate against these data sets. Digital audience strategies by vertical Digital audience strategies vary by vertical. Download our report to uncover the digital audiences purchased by advertisers in the following industries: Financial Services Health Retail & CPG Technology & Communication Download our new 2025 Digital trends and predictions report Marketers, agencies, and platforms are facing new challenges as privacy regulations evolve, AI technology advances, and consumer behaviors shift. Our latest report highlights actionable strategies for navigating these changes and improving how you connect with audiences, measure impact, and deliver results. What you’ll learn Navigating signal loss: Explore the rise of alternative IDs and contextual targeting as privacy regulations and signal loss reshape data-driven advertising. Connected TV (CTV): Understand the growth of connected TV (CTV), the importance of frequency capping, and strategies for effective audience activation. Omnichannel campaigns: Learn how marketers are moving from channel-specific strategies to audience-led omnichannel campaigns that tell a more cohesive story. Retail media networks: Learn how retail media networks (RMNs) are capitalizing on enriched first-party data to learn more about their customers and reach them across on-site and off-site inventory. Curation: Examine how curation is transforming programmatic campaigns by combining audience, contextual, and supply chain signals to deliver premium inventory packages that maximize addressability, efficiency, and performance. Download now Get in touch

Viewers shift between streaming services, live TV, and on-demand content across multiple devices, making it harder to know exactly who sees your message. Instead of wondering if your ads are reaching the right viewers, it's important to have a clearer understanding of viewing behaviors so you can focus your efforts on the audiences that matter most to your campaign. Experian has collaborated with The Advertising Research Foundation (ARF) to create new opportunities for marketers. By combining data from the ARF’s DASH (Device and Account Sharing) study with Experian Marketing Data, we’ve developed a new way for you to understand and reach modern TV viewers. Instead of estimating who might see your message, you gain a clearer view of viewing behavior and can align activation with the audiences that matter to your campaign. What is the DASH study? The DASH study, developed by the ARF together with the National Opinion Research Center (NORC) and seven industry sponsors, including Experian, provides a detailed picture of how American households consume TV and digital media. This research offers an unbiased and accurate view of media habits, measuring everything from device usage to streaming account sharing. When this viewership data is combined with Experian Marketing Data, it allows for the creation of unique audience segments. These segments are built on real-world media and device usage, providing a more accurate representation of how people watch, share, and engage with TV content. This combination of identity and connectivity helps marketers understand exactly how people engage with media, technology, and their favorite brands. “Television viewing behavior has undergone a massive transformation, making it challenging for advertisers to reach their target audience and optimize frequency. These audiences give advertisers invaluable tools for managing their campaigns in an increasingly fragmented environment.”Doug McLennan, Director of Product Management How do DASH audiences help? By using the DASH study, Experian developed TV audience segments that reflect how people truly interact with content. These audiences provide the insights you need to align your campaigns with actual media consumption habits, helping you reach viewers with more relevant messages. This approach moves beyond basic demographics. It allows you to connect with people based on specific behaviors, such as co-viewing, screen preferences, or household streaming habits. The result is a more focused and efficient advertising strategy that delivers better outcomes. “DASH has established itself as a reliable and unbiased calibration set, a “true North”, for media measurement. Our collaboration with Experian puts the power and precision of DASH in the hands of marketers and advertisers as well.” Paul Donato, Chief Research Officer Which audience segments help you target viewers more effectively? These audience segments make it possible to find specific types of viewers and align your marketing campaigns with their media usage. Whether you’re connecting with people who are receptive to ads, households that enjoy shows together, or individuals who are frequent streamers, you can approach campaigns with greater accuracy and confidence. We’re pleased to introduce these segments and continue our partnership with the ARF, creating new opportunities to help you build effective connections with your target audiences. Explore some of our key audience segments: Ad Acceptors: Viewers who are more open to watching advertisements. Ad Avoiders: People who actively try to skip or block ads. Co-Watchers: Households where multiple people view content together. Solo Watchers: Individuals who typically watch TV by themselves. Paid TV High Spenders: Households that subscribe to multiple paid TV or streaming services. Large Screen Viewers: People who primarily watch content on large television screens. Small Screen Viewers: Individuals who prefer watching on smaller devices like tablets or phones. How can I use these audiences? Experian’s DASH audiences are available in your demand-side platform (DSP) of choice, ready for activation across all offline and online channels. This easy access means you can build more effective campaigns without changing your existing workflow. Using these segments, you can manage your advertising with greater confidence. You gain the tools needed to navigate the fragmented media environment and ensure your campaigns are seen by the right people. This targeted approach helps you maximize the impact of your marketing efforts and achieve your goals. Strengthen your TV planning and activation with DASH audiences. Are you ready to connect with your audience in a more meaningful way? FAQs What is the DASH study? The DASH study, developed by ARF, provides an unbiased view of how American households consume TV and digital media. It measures device usage, streaming habits, and account sharing to create a detailed picture of media consumption. How does Experian help advertisers understand media habits? Experian combines its Marketing Data with insights from studies like DASH to create audience segments based on real-world behaviors. This allows advertisers to align their campaigns with how people actually consume content. What types of audiences can I target with these segments? Audience segments include Ad Acceptors, Co-Watchers, Solo Watchers, Paid TV High Spenders, and Large or Small Screen Viewers, enabling precise targeting based on viewing habits. Why is understanding viewing behavior important for advertisers? 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Advertisers continue to increase their spending across addressable TV, connected TV (CTV), and digital. According to IAB's "2021 Video Ad Spend and 2022 Outlook" report, digital video ad spending is expected to increase by 26% to $49.2 billion in 2022. Understanding who consumers are and how to best reach them in their preferred channel is becoming more complex. Damian Amitin and Colleen Dawe discuss how a seamless identity strategy can address the complexity of the emerging TV space. The evolution of identity resolution Around ten years ago, the idea of digital “identity resolution” or “Device Graphs” was born. This idea connected cookies and MAIDs to understand when many IDs were the same person or household. In more recent years, our industry began to connect that initial understanding to the CTV ecosystem. But, a large part of the TV ecosystem existed in silos, like first and third-party audience data, and the growing advanced TV market. The goal of identity resolution has always been to understand the consumer better. To achieve more accurate targeting and measurement in the CTV ecosystem, we must incorporate the following: What we know about the household and consumer from an ID perspective Who the consumer is as it relates to audience data, as well as the wealth of first-party data in the advanced TV space We know the cookie is a flawed way to collect data. While Google delayed the deprecation of third-party cookies, there are other challenges that we face right now. Such as the glaring gap in Safari traffic and the Identifier for Advertisers (IDFA) turning to “opt-in." Understanding consumer behavior across devices and platforms continues to challenge marketers and publishers. These challenges are creating the need to find more stable identifiers. Though the cookie remains valuable, it has an uncertain future. This has led advertisers to place bigger bets on the combination of addressable and CTV. The overlap in addressable and CTV data leads to fragmentation Personally identifiable information (PII) makes up the majority of addressable TV households' data. Part of the attraction to CTV is that their IDs remain universal, persistent, and stable. Analysts project that CTV ad spending will hit $23B in 2023. Consumers now have an average of 4.7 streaming subscriptions per household. It’s no surprise then, that Disney+, HBO, and Netflix released or announced ad-supported tiers. Addressable TV and CTV are often thought of as distinct markets across the industry. But, in the context of identity, we should look at them through the same lens. Millions of households still consume TV and video content via a set-top box or through apps on CTVs. This is in addition to what they consume on their laptops, tablets, and phones. Of the top 11 cable and satellite providers, 65 million U.S. households still have a box in their homes. On the other hand, approximately 96 million U.S. households have at least one or more Smart TVs and streaming services. With about 126 million total U.S. TV households, that’s a lot of overlap. There are still significant numbers of both addressable and CTV homes. How can we address fragmented TV consumption? Through a holistic and comprehensive approach to identity. An approach that captures addressable TV, CTV, and digital identifiers. An approach that captures all audience attributes inside of a single identity graph. This is the ideal approach for publishers, AdTech vendors, and brands. Discover how to unlock holistic identity How can we achieve a holistic identity? Through a three-pillared approach: First-party data onboarding Digital identifiers Consumer data First-party data onboarding Bringing offline data from a brand’s consumers is very valuable due to the quality of the data. Because the data is being collected right from the source, you know it’s accurate. It provides the foundation you can build your identity strategy from. Digital identifiers Once you create a foundation with first-party data, you need to connect it. Either with an internal or licensed digital ID graph. Then you can understand the connections between all devices within the household. Consumer data After you know which devices tie to a single consumer, you'll want to act on that knowledge. The next step is to partner with a data provider that can help you understand your consumers. Establishing this partnership will help improve targeting, measurement, and the customer experience. To achieve a well-rounded customer view tomorrow, we need to start today The three-pillared approach bridges the gap between the offline and online worlds. This provides a well-rounded view of customers and audiences. However, the ability to tie these aspects of identity together still presents several challenges. To achieve the three-pillared approach today, you need to use many vendors and fragmented data sources. Often with conflicting data. As we look forward, the tools to do this are becoming more advanced and unified. The players in our ecosystem should adopt a seamless identity strategy. One that provides a privacy-safe yet full-picture solution. That means capturing and unifying all devices within a household. While also understanding the consumer behaviors and profiles behind those devices. As TV becomes more sophisticated, our data and services will enable you to unlock a holistic identity. Chris Feo, SVP of Advanced TV and Platforms, spoke with Broadcasting & Cable about how our data powers measurement, audience insights, and results for businesses within the TV space. "As more and more companies enter the general TV space, whether you're a publisher, an advertiser or anyone in between that's doing measurement, insights, analytics, our data or our services will play a role in some part of that value exchange." – Chris Feo, SVP of Advanced TV and Platforms, Experian Marketing Services Keep up with your customers and their data Once we create an informed identity strategy, we can begin to understand the makeup of each household and the individuals within. In this new world, personalizing the experience for an audience is key. Where do they prefer to spend their time? What type of content are they most engaged in? Only then can we as an industry provide an optimal experience for each consumer. All while driving greater ROI for advertisers and publishers. Are you ready to know more about your customers than ever before? Let's get to work together to achieve your marketing goals. Contact us to learn how we can connect the complex dots of identity resolution. About our experts Damian Amitin, VP of Enterprise Partnerships, Experian Marketing Services Damian Amitin is the VP of Enterprise Partnerships and joined Experian during the Tapad acquisition in November 2020. Damian is a senior sales and partnerships executive, specializing in the identity resolution and marketing data ecosystem. Damian helps brands, publishers, and technology vendors enable enhanced ID resolution through The Experian/Tapad platform to attain a 360 view of the customer across targeting analytics, attribution, and personalization. Colleen Dawe, Senior Account Executive, Experian Marketing Services Colleen Dawe is a Senior Account Executive on the Advanced TV Team within Experian Marketing Services. With 15 years of experience working within the television ecosystem, Colleen works with clients to bring the value and expertise of Experian to support their objectives in the areas of data, identity, activation, and measurement. Get in touch