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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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With the impending deprecation of third-party cookies, marketers find themselves at the crossroads of innovation and adaptation. As we bid farewell to this identifier, the emphasis shifts to forging deeper connections, understanding customer needs, and navigating the marketing landscape with data-driven precision. At Experian, we stand as your trusted partner, committed to guiding you through this transition. In this blog post, we'll explore: How third-party cookie deprecation is impacting digital advertising Six alternatives to third-party cookies and where they fall short How Experian can help you navigate a cookieless world Four ways third-party cookie deprecation is impacting digital advertising Third-party cookie deprecation is causing significant challenges within the AdTech industry, manifesting in four key areas: Reach: Advertisers and demand-side platforms (DSPs) will face difficulties in reaching their target customers due to the absence of third-party cookies. Understanding audiences: Advertisers will find it challenging to understand the demographics and behaviors of their customer base without third-party cookies. Similarly, publishers are struggling to identify their audiences accurately, resulting in less addressable and appealing inventory. Measurement: Measurement providers may encounter obstacles in accurately assessing the effectiveness of advertising campaigns. Additionally, DSPs are finding it hard to measure the impact of their ads without the assistance of third-party cookies. Matching: Data providers may experience challenges in matching users with the appropriate audience segments, leading to difficulties in delivering targeted advertising. Six alternatives to third-party cookies As the deadline approaches for Google's removal of third-party cookies from Chrome by the end of 2024, marketers are scrambling to discover alternative methods for delivering effective advertising. Fortunately, various alternatives are emerging. However, the abundance of options can create confusion rather than clarity. Which alternatives are worth considering? Here are six compelling alternatives to third-party cookies: 1. First-party data Acquiring consented first-party data directly from users is becoming increasingly vital as it can lay the groundwork for more precise targeting. 2. Universal IDs Alternative identifiers like The Trade Desk's UID2 and ID5’s Universal ID are becoming increasingly important, offering the ability to maintain a comprehensive consumer view across channels and platforms, leading to enhanced personalization and addressability across various channels, even in cookieless environments. 3. Identity graphs As browser-based IDs shift and digital signals decline, the need for an identity graph grows, with companies adopting a "graph-of-graph" strategy by combining their own robust first-party data with licensed identity graphs, as highlighted in recent announcements by industry giants such as Disney, VideoAmp, and Magnite. 4. Contextual targeting Contextual targeting aligns publisher content with relevant ads, ensuring ad delivery based on content rather than individual identifiers. This privacy-respecting approach is less dependent on third-party cookies, providing effective audience activation. 5. Data collaboration In a cookieless world, it becomes more difficult for companies to "communicate" with one another. We expect to see more pick up of data collaboration in the market, using addressable IDs and identity resolution to power connectivity between partners and their data sets. 6. Google Privacy Sandbox The primary goal of Google’s Privacy Sandbox is to continue to deliver valuable consumer information that yields relevant marketing and media strategies, while protecting a user’s privacy. How these alternatives to cookies fall short While it's promising to see numerous alternatives to cookies emerging, it's essential to recognize that each alternative has its limitations and is not a perfect one-to-one replacement for third-party cookies. Let’s review the shortcomings of these alternatives, and then we’ll walk through how Experian can help you navigate these alternatives to cookies. 1. First-party data First-party data, which is data directly collected from your users with their consent, is highly valuable. However, you will likely face limitations in terms of the number of consumers in your database, the identifiers linking them, and the insights into their demographics and behaviors. To overcome these limitations, it's essential to expand both the quantity and quality of your first-party data. 2. Universal IDs Universal identifiers are valuable for tracking users across different devices and websites. However, no single universal identifier has enough reach to fully replace third-party cookies. Universal IDs are most effective in terms of scaling, when they are combined with other universal identifiers or alternative addressable identifiers. 3. Identity graph Identity graphs excel at connecting digital audiences. However, establishing an identity graph from scratch is a significant accomplishment, demanding expertise, financial resources, and more. 4. Contextual targeting Contextual targeting and advertising aim to place your ads next to relevant content. However, there's a risk that your ads might appear alongside misaligned content, reaching audiences who are uninterested or unintended. 5. Data collaboration Data collaboration is beneficial for enhancing your consumer data and informing your strategies. However, it can introduce potential data security risks, if not done in the right framework, and may lead to subpar matching results due to issues like data hygiene or discrepancies in identifiers. 6. Google Privacy Sandbox Google’s Privacy Sandbox aims to balance effective advertising with consumer privacy and data security. However, it lacks transparency and has yet to prove its effectiveness, raising concerns about whether it meets industry standards. How Experian can help you navigate a cookieless world As an industry innovator and leader in data and identity, we've developed solutions to address the challenges posed by the shift away from third-party cookies. Our products are designed to adapt to these changes and ensure your success. We've anticipated industry shifts and proactively prepared our offerings to support you through this transition. Below we outline how our products are ready to support you through the transition away from third-party cookies. Graph The Experian Graph facilitates connectivity without relying on cookies. Our Graph helps ensure connectivity by supporting a variety of addressable identifiers, not limited to but including universal IDs, like Unified ID 2.0 (UID2) and ID5's universal ID. Whether you have first-party data or not, our Graph can be used to expand the reach of your first-party data or provide you with access to the full scope of our Graph's 126 million households and 250 million individuals. Activity Feed Supported by our Graph, Activity Feed can help you deliver digital connectivity and resolution in a cookieless environment. Activity Feed can resolve disparate activity to a single, consumer profile. It can expand the quantity of addressable identifiers associated with your first-party consumers. Additionally, Activity Feed, by joining disparate activity and identifiers, provides clearer insights, more addressable targets, and more holistic measurement. Our Marketing Attributes and Audiences In a cookieless environment, our Marketing Attributes and Audiences provide valuable information and insights about who your consumers are, like their demographics, shopping patterns, and more, to facilitate more informed decision-making. You can use our Marketing Attributes and Audiences to enrich your first-party data, giving you crucial insights into your customers so you can make informed, strategic decisions. They can be matched to universal identifiers, expanding their utility. Additionally, our Marketing Attributes and Audiences are sourced from non-cookie dependent offline and digital sources, ensuring they are unimpacted by third-party cookie deprecation. Collaboration While third-party cookies have primarily served to connect data in the industry, many companies are turning to data collaboration in lieu of having third-party cookies. In doing so, they can connect data with key partners, which they can use to make better media decisions. Experian Collaboration helps make data collaborations better, powering higher match rates by using the various identifiers supported in our offline and digital graphs. Through our current support of collaboration in three environments, within Experian, through crosswalks, and in clean rooms, such as AWS, InfoSum, and Snowflake, we ensure that you only share the data you intend to share, while the sensitive information remains secure. This way, your partner and you can focus on how to use the data to benefit you and not on anything else. Get started with alternatives to third-party cookies today While many view the deprecation of third-party cookies as disruptive, we see it as an opportunity for the industry to embrace a new era of advertising while prioritizing consumer privacy. Achieving this balance is crucial, and Experian's solutions are here to help you navigate it effectively. As the AdTech industry gravitates toward a few tactics to effectively advertise in the cookieless future, Experian is here to understand your core needs and recommend products that will help. In a rapidly evolving marketing landscape, Experian stands as your trusted partner, offering expertise in data-driven and identity solutions. Connect with our team to seamlessly transition into these alternatives to third-party cookies, ensuring your marketing strategies remain effective, privacy-compliant, and focused on meaningful connections. Get started today Latest posts

In the ever-evolving and soon-to-be cookieless advertising world, brands and agencies need help finding and maintaining access to accurate and comprehensive marketing data that enables them to reach the audience segments that matter most. Flashtalking by Mediaocean and Experian have a collaboration in place to do just that. Through this partnership, Experian’s more than 2,400 syndicated audiences are available for activation within the Flashtalking platform and its Social Ads Manager. “There are a lot of audience segments and data disappearing within the advertising industry right now because of the deprecation of third-party cookies, but Experian’s syndicated audiences are built for this new privacy-first world. Through this partnership, Flashtalking's clients gain access to some of the industry's most actionable on-the-shelf and custom audience capabilities for activation and targeting across the publishers and social platforms that matter most. 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