In this article…
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.
Latest posts
The concept of identity resolution has emerged over the years as a strategic imperative among marketers and technology vendors. A report by Forrester contends that accurately establishing and maintaining customer identity is one of the most perplexing challenges facing marketers today. Customers have footprints in the offline and online worlds and tend to seamlessly transition across various channels and devices – presenting a unique challenge to truly understand who they are. But the ability to stitch these disparate components of information together means marketers can make better decisions and have more meaningful interactions with their customers. And for customers, this means an experience with personalized advertising content more likely to resonate with them. Why should marketers prioritize identity? The ability to accurately identify customers is the most basic prerequisite for marketing analytics, orchestration and execution. As such, it is becoming increasingly important for brands and marketers planning to link together disparate systems of audience insights and engagement to foster a more seamless and personalized omnichannel customer experience. For example, if an advertiser can identify a customer’s interests, as well as how that person prefers to consume information, then the advertiser can create and deliver messaging that will resonate with the customer. However, like most competitive differentiators, the mission critical components to accurately determine an identity reside within the suite of identity management tools at the marketers’ disposal and the expertise required for proper execution – a struggle for most marketers. But when properly implemented, a comprehensive customer identity strategy can be among a brand or marketer’s most valuable and proprietary assets. Where to begin with identity resolution? With the convergence of CRM platform data, cross-channel online touchpoints, offline record linkage management, probabilistic cross-device graphs, and data onboarding—evolving from point solutions to unified platforms—marketers are faced with an increasingly complex set of challenges in addressing and solving for customer identity management. To properly implement from the get go, and to avoid having to bolt on disparate technologies down the road, emerging industry trends and success stories suggest marketers need a neutral technology service provider that can provide each of these solutions via a single, unified platform. A vendor that can build a solid identity management foundation comprised of omni-channel targeting and attribution, cross-device resolution, online-offline linkage management, and data onboarding form the nexus of a cohesive identity strategy, built to last. Experian helps connect consumer identity As a trusted name in data and information services for more than 40 years, we are committed to privacy by design and the responsible usage and security of data. Whether you’re a brand, agency, or publisher, Experian has the wide-ranging toolset to help you put people at the heart of your business and make better marketing decisions. By harnessing the power of the sum of these parts, fusing both offline and online identifiers and attributes, Experian has established a leadership position in identity management. If you're ready to begin building your identity foundation, contact us and get started today! Learn more about why identity matters to marketers and consumers, here!
Partnership Yields Increased Match and Connectivity Rate Through Tapad Graph, acquired by Experian March 27, 2018 — New York, N.Y. — Tapad, part of Experian, is reinventing personalization for the modern marketer and today announced the impactful results of its strategic partnership with Flashtalking, the leading global independent platform for ad delivery, unification and insights. Flashtalking is one of Tapad's most engaged partners, using the Tapad Graph to unify cross-device engagement and identity-driven consumer behaviors for attribution modeling. The company leverages a unique identifier that, in conjunction with Tapad's Graph, provides robust multi-touch attribution solution for its clients. This partnership has resulted in above-industry match and bridge rates for Flashtalking and its customers. Overall, the Tapad Graph yielded a 71 percent match rate with 41 percent of converters engaging on multiple devices, highlighting the importance of cross-device measurement. Tapad’s identity solutions provide Flashtalking with a more holistic view of global engagement. Flashtalking marries ad server log file data with the Tapad Graph to connect all interactions in the consumer journey. This enables Flashtalking to provide more accurate and impactful cross-device attribution, which ultimately enables better optimization. These achievements have led to recognition of Tapad and Flashtalking’s work by the I-COM Global Forum for Marketing, Data and Measurement. “Tapad allows us to understand user engagement across devices and platforms at both the household or individual user level, which is extremely beneficial when providing marketers with true path to conversion and attribution,” said Steve Latham, global head of analytics at Flashtalking. “Since our relationship began, we’ve successfully leveraged Tapad data to provide more accurate, actionable insights that have helped numerous brands achieve substantial gains in media effectiveness.” Flashtalking client Michael Lamontagne, SVP of analytics and CRM at 22squared says “We are big believers in using cross-device insights to improve our campaigns. Flashtalking has been a strategic partner in the pursuit of that goal. By incorporating the Tapad Graph, Flashtalking delivers powerful insights into user engagement and media attribution across browsers and devices. Of equal importance, their bundled solution makes it easy and efficient, saving our team countless hours of busy work.” “Our ongoing work with Flashtalking has had a significant impact on the accomplishments we’ve achieved,” said Chris Feo, SVP, strategy & global partnerships at Tapad. “Being able to grow with a dedicated partner that leverages our proprietary technology in unique ways has helped us uncover the global impact our services can have on a business. We’re proud to drive success for Flashtalking’s clients on a global scale.” Contact us today
Digital Marketing Challenges Are you new to digital marketing? If you answered yes, then you may already know this is a complex world made up of cookies, pixels, attribution, and unique KPIs. With nearly 10 years of advertising experience, Experian is familiar with the challenges advertisers face as they prepare for their first digital marketing campaign. Those challenges include: determining a target audience, justifying data fees for targeting, sending a consistent message to every channel and measuring the success of a campaign. Of these challenges, creating an accurate, data-driven target audience and understanding the attribution process are the two most common. Coincidentally, these two challenges tend to be the most difficult to overcome and have the highest impact on a campaign’s success. 1.) Creating an accurate, data-driven target audience Understanding the basic demographics of your customer is the first step in the audience creation process. Your next step should be to understand your customers’ lifestyles, purchase behaviors, and current interests. By truly knowing your customer, you are then able to build out a multi-channel targeting strategy comprehensive of not only basic demographics and past behavioral data, but current behavioral trends that lead to individuals who are in market for a product or service. This reduces irrelevant marketing to individuals who may have the demographic characteristics, but are not yet in market. Overall, creating a relevant target audience saves media spend by focusing on targeting tactics that have a higher potential for success. 2.) Understanding the attribution process Now that you have your target audience, you need to determine how to measure the success of your campaign. Is your goal to increase online purchases? Drive store visits? Or, do you want your overall revenue to grow by a certain amount? Before launching your campaign, make sure you have a clear goal as well as a plan for measuring whether or not you meet your goal. Most digital marketers will judge the success of a campaign by online events, such as site visits, form completions, or online purchases. However, you may also want to measure offline metrics like phone calls or in person visits to a brick and mortar store. Offline metrics are essential to campaign performance, but are frequently over looked. Experian’s OmniActivation Strategic Services recommends having one clear goal that can be accurately measured. This ensures your campaign’s target audience and optimizations support the metric that will ultimately determine the success of your campaign.