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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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Privacy-forward pharma media supports more informed healthcare journeys Effective pharma marketing depends on reaching patients, caregivers, and healthcare professionals (HCPs) with useful information in appropriate, privacy-forward ways. The goal is to make relevant education easier to find, support more informed conversations, and help brands understand how media contributes across the health journey. In this Ask the Expert session, Natalie Mancuso, SVP, Data Partnerships at DeepIntent, joins Sheila Wirick, who leads the health team at Experian, to discuss how to craft patient and HCP campaigns, privacy-safe activation workflows, connected TV (CTV), and measurement approaches built for pharma. Why should your media plan start with understanding the patient and provider The patient-and-provider journey should inform your media plan, as treatment decisions in pharma are increasingly informed, personal, and patient-driven. With more health information available than ever, patients are playing a more active role in understanding their options and participating in treatment conversations. As other industries become more customer-centric, pharma media planning needs to become more patient-centric. “We’ve got patients who are more informed, more involved in their treatment decisions than ever. The accessibility of data and information and the ways we can consume it with just AI alone is so vast that if we’re not arming people with the right information in real time, we’re missing the boat.”Natalie Mancuso, SVP, Data Partnerships This means media should be aligned to where a patient or provider is in the decision journey and what they need at that moment. Early on, media may play an educational role by building awareness of a condition, symptom, or treatment category. Later, it may help support more specific questions, treatment consideration, access, affordability, or adherence. The patient-provider conversation remains the key boundary. That exchange should stay private and clinically led. Media can play a valuable role before and after those moments by offering information that helps patients and providers feel more informed. How can brands connect patient and HCP messaging responsibly? It’s critical for agencies and brands to deliver relevant messaging to inform each stakeholder’s needs throughout the treatment journey, especially ahead of key clinical milestones. To execute this, brands must exercise privacy-forward activation, keeping patient and HCP identity, activation, and measurement paths separate, while using a common workflow layer to support the same brand goal. Experian helps pharma marketers do this by acting as an independent, privacy-forward identity, data, and workflow layer. We bring together high-fidelity matching, governed onboarding, and non-clinical consumer context, so patient and HCP engagement can be activated more accurately across channels and connected to reporting. The distinction is not only where each audience is reached. HCPs may be engaged in both professional and personal environments, but their needs are different from patients’. For example, an HCP may receive messaging about co-pay savings for eligible patients, while a patient may receive information about patient support programs. These are different messages for different audiences, but they ladder up to the same goal: informing both parties in a privacy-forward, relevant way. Hear from Natalie Mancuso in our 2026 State of advertising report In our 2026 State of advertising report, Natalie shares why identity serves as the connective infrastructure that links planning, activation, and measurement across connected TV (CTV), programmatic, commerce media, and walled garden environments. Download the report Watch Natalie’s Q&A What does privacy-forward activation look like in pharma? Pharma brands often start with fragmented patient and HCP signals. Privacy-forward activation turns those inputs into usable audiences by first confirming that the data can be used, then translating identity in controlled environments that protect personally identifiable information through tokenization or other methodologies. For DTC, that means building and matching audiences without relying on inferred health conditions from browsing behavior. For HCPs, it means connecting professional identity to verified sources in a way that supports activation and measurement. In a category shaped by HIPAA, state privacy laws, internal review, and consumer sensitivity, building privacy in from sourcing through activation is what makes responsible health media possible. How can CTV play a more useful role in pharma? CTV can play a more useful role in healthcare when it’s connected to a larger, privacy-forward plan and not treated as just an awareness channel. DeepIntent connects CTV and digital exposure data using identity signals, such as National Provider Identifiers (NPIs), hashed email addresses, IP addresses, device IDs, and household signals, where permitted and reviewed. That identity layer helps support sequencing and suppression across streaming, endemic, mobile, and digital settings. In practice, CTV can introduce targeted education or broad brand awareness, and later touchpoints can provide sequential information in the right channels. Instead of repeating the same message, brands can make pivots based on engagement and context. “For us, it starts with identity and not panels. When we’re resolving CTV and digital exposures to the same person, we’re leveraging signals so that every touchpoint across streaming, endemic, and mobile is recognized as the same individual, not model lookalikes.”Natalie Mancuso, SVP, Data Partnerships For pharma, campaign performance should not be measured by reach alone. A stronger approach looks at whether sequenced engagement contributes to meaningful downstream signals, including: Prescription activity Provider engagement Referral patterns Patient compliance Adherence signals, where measurement is permitted Why should messaging and reporting change in pharma? Pharma campaigns are journeys with changing information needs. Reporting should guide the campaign optimization. “If I’m launching into a market with a new class of drug, I’m asking what education needs to be put into the market – from both an HCP and a consumer standpoint – to build awareness?”Natalie Mancuso, SVP, Data Partnerships Clicks and website visits still have a place, but they can’t tell the full pharma story. DeepIntent processes data in real time, giving brands an up-to-date read on how media connects to campaign engagement. Campaign signals can help teams optimize audience logic, creative, channel mix, or sequencing during a live campaign. Heading Test for Media & Text Block Content Test for Media &Text Block Learn more Heading Test for Media & Text Block Content Test for Media &Text Block Learn more Heading Test for Media & Text Block Content Test for Media &Text Block Learn more Explore how privacy-safe pharma activation can make an impact Experian brings marketing data and identity expertise for health marketing and DeepIntent adds pharma-specific activation experience. Together, we help brands connect patient and HCP strategies responsibly, with privacy built in from the start. Watch the full video with our experts to hear Sheila Wirick and Natalie Mancuso discuss patient and HCP messaging, CTV, privacy-safe activation, and outcome measurement for pharma brands. About our experts Natalie Mancuso SVP, Data Partnerships, DeepIntent Natalie Mancuso is SVP, Data Partnerships at DeepIntent and a leader in health-focused AdTech and DSP strategy. With over 20 years of experience growing billion-dollar healthcare brands, she turns complex data into scalable, performance-driven solutions that keep marketers ahead of industry change. Sheila Wirick Director of Health Sales, Experian Sheila Wirick is the Director of Health Sales at Experian with more than 20 years of experience in data and analytics. As Director of Health Sales at Experian Marketing Services, she collaborates with healthcare, pharmaceutical, and nonprofit organizations to apply data and insights that enhance customer retention, brand awareness, and acquisition. She is committed to helping organizations use data to deliver measurable results and meaningful impact. FAQs What makes a pharma audience activation-ready? A pharma audience is activation-ready when patient or HCP inputs can be translated into usable audiences with the right accuracy, controls, interoperability, and destination readiness. For patient engagement, that may include privacy-safe cohort construction, caregiver logic, non-clinical enrichment and appropriate exclusion rules. For HCP engagement, it may include governed identity inputs such as NPI, specialty, practice location, professional address and other verified attributes where appropriate. How can pharma brands sequence messages across channels responsibly? Pharma brands can sequence messages responsibly by giving each channel a clear role and using audience logic to maintain continuity. CTV and out-of-home can support awareness, social can reinforce the message, search can capture active research and point-of-care or rep-triggered channels can support decision moments. The workflow should preserve separation between patient and HCP identity strategies. What should pharma marketers measure beyond clicks and site visits? Pharma marketers should measure whether media connects to outcomes such as audience quality, prescription activity, qualified HCP engagement, verified delivery, referral patterns, therapy starts or adherence signals where permitted. These outcomes often depend on partner-enabled workflows, clean rooms, and aggregated outputs rather than direct end-to-end tracking. How does Experian help pharma brands connect identity, activation and measurement? Experian acts as an independent privacy-first identity, data, and workflow layer for pharma marketers. We help brands bring together identity, high-fidelity matching, governed onboarding, non-clinical consumer context, and partner interoperability so patient and HCP engagement can be activated more accurately across channels and connected more cleanly to partner-enabled measurement. Latest posts
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