At A Glance
Addressable advertising helps you reach addressable audiences with relevant messages across digital, TV, and streaming. As signals fragment across browsers, apps, and platforms, AI customer segmentation and privacy-first identity separate guesswork from accuracy. Experian brings trusted data, identity resolution, and activation partnerships together so you can connect with people in ways that feel relevant, respectful, and measurable.In this article…
A decade ago, you could buy media by broad categories and call it a day. But today, your audience lives in a curated world. They watch what they want, skip what they don’t, and expect what they see to match their interests. Research shows that when ads are tailored to households, people pay more attention, stay engaged longer, and are more likely to remember your ads.
That shift in expectations is why addressable advertising continues to grow. It’s a practical response to how media works today, with audiences moving fluidly across platforms, streaming spread across services, and measurement spanning screens and environments. Under these conditions, reaching the right people depends on clarity, not approximation.
Artificial intelligence (AI) strengthens that clarity. When applied responsibly, AI helps connect signals, deepen audience understanding, and deliver relevant messages while protecting consumer data. The result is advertising that feels more human, not less.
What is addressable advertising?
Addressable advertising is the ability to deliver personalized ads to specific individuals or households and measure results using privacy-safe data and identity. It works across digital, connected TV (CTV), linear TV, and over-the-top (OTT) streaming and relies on strong identity resolution and accurate data inputs to ensure your audience definitions remain consistent across channels and over time.
Benefits of addressable advertising
Addressable advertising changes how advertising performs by delivering messages to defined audiences, reducing wasted impressions, and making results simpler to measure.
| Benefit | What it means for you |
| Clarity | Reach the right audience with the personalized messages they want, instead of hoping the right people are watching |
| Efficiency | Avoid wasted impressions by focusing spend where interest already exists |
| Higher ROI | Improve conversion by delivering messages that feel relevant |
| Omnichannel consistency | Carry the same message across digital and TV without starting over |
| Measurable impact | Connect exposure to actions so performance is clear |
| Privacy and compliance | Activate audiences responsibly using privacy-safe data, clear governance, and compliant practices |
These are some of the reasons that addressable advertising has moved from a niche tactic to a core strategy. When audiences are clear, identity is connected, and measurement is built in, advertising becomes relevant, accountable, and easy to improve over time.
Addressable advertising vs. traditional advertising
Unlike traditional advertising, addressable advertising doesn’t depend on broad exposure or assumptions. It’s personalized by design and measurable by default, making it possible to connect ad exposure to outcomes. Another distinction is in how addressable delivers advertising to audiences and how performance is measured.
| Traditional media buys | Addressable advertising buys |
| You pay for broad reach | You pay for relevant reach to defined audiences |
| Ads run by placement or program | Ads are delivered to known households or individuals |
| Personalization is limited | Personalization is built into delivery |
| Measurement indicates trends, not who actually acted | Measurement connects exposure to actions by linking ads to defined audiences across channels |
But before you can activate addressable advertising, you need to understand who you’re actually trying to reach.
What is an addressable audience?
An addressable audience is a group of people you can identify and reach using data-based targeting. In other words, they’re not anonymous “maybe” viewers. They’re a defined audience you can activate across channels.
Here’s what typically builds addressable audiences:
| Factor | What it is | Why it matters |
| First-party data | Data from your own relationships (site activity, app activity, CRM, emails, purchases) | It’s your most direct view of existing customers and prospects |
| Third-party household and individual data | Demographic, behavioral, lifestyle, interest, and intent attributes from trusted providers | It fills gaps so your audience definitions don’t collapse when your own data is limited |
| Identity resolution | A privacy-first way to match people across devices, households, and channels | It improves accuracy so you don’t over-message the same people or miss them entirely |
| Contextual signals | Page-level, content, or viewing context where ads appear | It reinforces relevance in the moment and complements addressable targeting when identity signals are limited |
How Experian helps with addressable audiences
Experian helps you build and activate addressable audiences at scale without losing accuracy or trust. With more than 3,500 syndicated audiences available, you can activate consistently across 200+ destinations — including social platforms like Meta and Pinterest, TV and programmatic environments, and private marketplaces (PMPs) through Audigent.
That means reaching people based on who they are, where they live, and their household makeup, using data governed with care. Our approach is built on accuracy first, which is why Experian data is ranked #1 in accuracy by Truthset for key demographic attributes.
And when standard customer segments aren’t enough, Experian Partner Audiences expand what’s possible. These unique audiences are available through Experian’s data marketplace, within Audigent for PMP activation, and directly on platforms like DIRECTV, Dish, Magnite, OpenAP, and The Trade Desk.
The evolution of addressability and why it matters more than ever
As the media ecosystem shifts, reaching people across browsers, apps, CTV, and streaming platforms has become more complex. Signals are fragmenting everywhere as expectations for relevant, personalized experiences continue to rise, while reliable identifiers become increasingly challenging to access.
In response, addressability is shifting from a channel-specific tactic to an identity-driven approach to reach and measure defined audiences across screens.
That evolution puts new pressure on performance. Marketing budgets require accuracy and accountability, which means targeting must deliver measurable reach and outcomes you can trust.
At the same time, the growth of CTV and streaming is expanding addressable TV opportunities. As CTV inventory grows, so does the need for cross-channel, identity-based activation that works consistently and supports reach, frequency, and measurement in one connected view. That’s why identity has become the foundation for making addressable advertising work today.
When to apply addressable advertising
You don’t need addressable for everything, but it shines when you need your spend to go farther with accurate targeting and resonant messaging.
| Scenario | Why addressable helps |
| Product launches and seasonal pushes | Reach people who are more likely to care without flooding everyone else |
| High-consideration purchases (auto, travel, financial services) | Focus on likely intent and suppress audiences that don’t fit |
| Cross-channel campaigns (digital, TV, mobile) | Keep messaging consistent across screens |
| When using first-party data with AI | Use AI customer segmentation to scale responsibly and improve performance without sacrificing accuracy |
| Regulated categories | Rely on compliant data practices and clearer controls for regulated industries |
Addressable advertising is one way to put relevance and respect into practice — but it shouldn’t be the only time these principles apply. Marketers are expected to be thoughtful about who they reach, how often they show up, and how data is used across every channel. Addressable simply makes it easier to live up to that standard when accuracy, accountability, and scale matter most.
Addressable advertising and third-party data
There’s a common misconception that third-party data is no longer useful, but what’s really changed is the environment around it.
In the early days of digital advertising, third-party data often felt like the Wild West. Today, modern third-party data is more transparent, better governed, and held to far higher standards with:
- Clear data sourcing
- Documented consent practices
- Regular quality audits
- Strict limits on how data can be used
Used responsibly, third-party data plays a critical role in addressable advertising by complementing your first-party data and keeping audience strategies flexible as signals change.
Benefits of third-party data
When paired with identity resolution, high-quality third-party data helps you:
- Fill first-party gaps: Add demographic, behavioral, and interest-based insight when your own data is limited.
- Expand prospecting: Reach new audiences through modeling and lookalike expansion.
- Enrich segmentation: Combine household, behavioral, and interest signals to tailor creative, offers, and messaging to interests for more accurate and personalized activation.
- Support cross-channel addressability: Maintain consistent audience reach across devices and channels even as individual signals change.
Why work with Experian for your data needs?
At Experian, we approach third-party data with the belief that trust comes first. Our data is privacy-compliant, ethically sourced, and governed by strict standards so you can use it confidently.
Accuracy matters just as much. Our identity and data-quality framework verifies that the data behind your audiences holds up in the real world — a key reason Experian is ranked #1 by Truthset for key demographic attributes.
And because addressable advertising only delivers value when audiences move seamlessly from planning to activation, our audiences are interoperable by design. You can activate them across digital, social, and CTV platforms without rebuilding or reformatting your strategy for each channel.
How AI is redefining customer segmentation
Addressable advertising depends on audiences that stay accurate as people move across devices, platforms, and moments. Traditional segmentation built on static rules and snapshots in time can’t keep up with that reality.
AI customer segmentation analyzes massive sets of household and individual data (such as intent, household demographics, purchase behavior, and content consumption) to identify patterns, predict intent, and group people into addressable audiences.
As the AI advertising ecosystem continues to mature, reflected in industry frameworks like the LUMA AI Lumascape, segmentation and identity have become foundational layers rather than standalone tools. Those audiences update as conditions change, so they stay relevant instead of aging out.
Here’s how AI-driven segmentation supports addressable advertising.
| What AI enables | Why it matters |
| Predictive, intent-based audiences | Analyze behavioral and transactional data to group people based on likely next actions |
| Broader audience availability | As more data signals are incorporated responsibly, AI makes it possible to support a wider range of addressable audience options without sacrificing accuracy |
| Deeper insights from data | Discover what people care about, how intent is forming, and which signals are most important with larger, more diverse data sets |
| Real-time audience updates | Keep segments aligned as behaviors change, not weeks later |
| Higher accuracy, less guesswork | Rely on data-driven patterns for decision-making instead of assumptions |
| Ongoing optimization | Refine audiences throughout the campaign lifecycle as performance signals come in |
We’ve used machine learning and analytics for decades to support responsible segmentation — balancing performance with privacy and transparency.
That foundation now supports addressable advertising that adapts in real time while staying grounded in trust.
Addressable TV: Targeting in the streaming era
TV has become an addressable channel powered by data and identity resolution. CTV and OTT streaming are booming, while linear TV continues to decline, reshaping how people watch and how advertising works alongside it. For the first time, CTV spending is expected to outpace traditional TV ad spending in 2028, reaching $46.89 billion and signaling that addressable TV is now central to the media mix.
With CTV and OTT platforms, advertising can now be delivered at the household level. That means two homes watching the same show can see different ads based on who lives there and what they like. This is what makes addressable TV possible.
Benefits of addressable TV
As streaming inventory continues to grow, addressable TV creates new ways to bring relevance and accountability to a channel once defined by broad exposure. Experian links identity data across streaming, linear, and digital platforms to help you manage frequency, attribution, and household-level insights in one connected view.
Addressable TV also raises the bar. To manage reach, frequency, and measurement across streaming and linear environments, addressable TV depends on identity resolution that connects households across screens.
Here’s how addressable TV helps you when identity is in place.
| What addressable TV enables | Why it matters |
| Household-level targeting | Deliver messages that reflect who’s watching, not just what’s on |
| Frequency control across screens | Reduce overexposure and improve viewer experience |
| Cross-channel measurement and attribution | Connect TV exposure to digital actions, site visits, and conversions |
| More efficient use of TV spend | Bring accuracy, accountability, and outcome-based insight to premium inventory and improve reach of streaming-first, harder-to-reach viewer segments |
Ultimately, addressable TV isn’t a replacement for linear TV, but it is an evolution. As streaming becomes the default viewing experience, the ability to engage TV audiences with the same care and clarity as digital is essential.
Use cases for addressable advertising
Addressable advertising works across industries because it adapts to how people make decisions. The examples below are illustrative scenarios that show how addressable audiences, identity resolution, and AI-driven segmentation can come together in practice using Experian solutions.
Retail: Seasonal promotions
A home décor retailer could use identity resolution and AI-driven segmentation to build addressable audiences, such as holiday decorators and recent movers, who are more likely to engage during peak seasonal periods.
Campaigns could then be activated across CTV, display, and social, helping the retailer stay visible across screens while tailoring creative to seasonal intent.
Automotive: In-market car buyers
An auto brand might identify consumers nearing lease expiration using automotive-specific data tied to household and individual attributes.
By suppressing current owners, the brand could avoid wasted impressions and activate addressable audiences across OTT and mobile to reach likely buyers during active consideration.
Financial services: Credit card launch
For a new credit card launch, a national bank could use modeled financial segments to reach credit-qualified prospects.
Addressable digital advertising campaigns could apply frequency controls and personalized messaging, balancing reach with relevance while seamlessly measuring response.
Streaming media: New subscriber growth
A streaming platform looking to grow subscriptions could use an identity graph to exclude current subscribers.
Likely viewers could then be targeted across CTV based on content preferences and viewing behavior, keeping spend focused on net-new growth.
Media and entertainment: Audience expansion for a new release
Ahead of a new release, a film studio could use behavioral and lifestyle data to identify likely moviegoers and fans of similar franchises.
Addressable campaigns across CTV and digital video could help drive awareness and opening weekend attendance.
Travel: High-value traveler acquisition
A travel brand could use travel propensity data and household-level demographics to identify frequent flyers and family vacation planners.
Personalized offers could then be activated across display, social, and programmatic channels to increase bookings while keeping spend focused on higher-value travelers.
How Experian enables more effective addressable campaigns
Addressable advertising is most effective when identity, data, and activation are connected from the start.
Experian brings trusted household and individual data, privacy-first identity resolution, and broad activation partnerships together so you can move from audience insights to activation with minimal friction. Here’s how that comes to life across our core offerings.
Identity resolution with Consumer Sync
Consumer Sync connects devices, emails, digital identifiers, and offline data into a single, privacy-safe identity foundation. This connection helps your audiences stay consistent across streaming, linear TV, mobile, and digital despite changing signals.
Audience insight and segmentation with Consumer View
Consumer View supports clear segmentation, prospecting, and enrichment across industries. It combines demographic, behavioral, and interest-based data to help you build accurate, intent-driven audiences that reflect real people, not assumptions. Data is continuously updated and governed for accuracy.
Omnichannel activation with Audience Engine
Audience Engine enables direct activation of Experian audiences across CTV, digital, social, and programmatic platforms. It supports suppression, frequency management, and cross-channel consistency to keep messaging aligned and exposure controlled.
More efficient media through curation and Curated Deals
Curation combines data, identity, and inventory through Experian Curated Deals. These deal IDs, available off-the-shelf or privately, make it easier to activate high-quality audiences and premium inventory in the platforms you already use without custom setup.
AI-enhanced segmentation and optimization
Our AI-enhanced models analyze large data sets to create and refresh addressable audiences in real time, supporting intent-based targeting and ongoing optimization throughout the campaign lifecycle. These models work seamlessly with demand-side platforms (DSPs), ad platforms, and data clean rooms, so audience insights flow directly into activation and measurement without added complexity.
Seamless integration with your ecosystem
As an advertiser, you want addressable advertising to fit naturally into how you already plan and buy media. That’s why integration matters as much as insight.
Experian integrates with leading DSPs, ad platforms, and data clean rooms, so you can activate addressable audiences in the environments you already use without reworking your strategy or adding complexity. This approach helps you:
- Build and activate addressable audiences: Reach the people you want with accuracy and respect.
- Activate across channels: Keep messaging consistent across digital, TV, and streaming.
- Optimize with data ranked #1 in accuracy by Truthset: Improve performance using the industry’s most reliable data.
When identity, data, AI, and activation come together, addressable advertising does what it’s supposed to do: deliver relevance naturally, measure impact clearly, and give you confidence in every decision along the way.
That’s the foundation for campaigns people want to engage with.
Start creating campaigns audiences want to see
Experian can help you apply addressable advertising in ways that respect consumers, perform across channels, and stand up to real-world measurement.
Connect with our experts today to explore how addressable audiences, AI-driven segmentation, and identity-powered activation can work together in support of your goals.
FAQs about addressable advertising
Addressable data-driven advertising involves delivering personalized ads to specific individuals or households using privacy-safe data and identity.
An addressable audience is a defined group of consumers you can identify and reach based on known household or individual attributes.
Advertising becomes addressable when it’s possible to identify the audience by linking devices and households to people through identity graphs. This allows you to measure ad performance at the audience level and provide more personalized advertising.
Addressable advertising isn’t just for TV; it also works across digital, mobile, streaming, and social channels.
AI improves addressable advertising by analyzing large data sets to predict intent, build more accurate audiences, boost performance over time, and improve your ability to find and build your audiences.
Yes — identity resolution and first-party data are key to cookieless addressability.
Experian supports addressable advertising by providing trusted consumer data, privacy-centric identity resolution, and curated audience segments that activate across CTV, digital, mobile, and streaming platforms.
Latest posts
After a six-month beta period, collaboration in Snowflake Data Clean Rooms using Experian's offline or digital graph is now generally available for all clients. As part of this, Experian is excited to announce that Experian's identity graph will be integrated into Snowflake's Data Clean Rooms. With the growing importance of data privacy and marketing efficiency, this partnership builds off of Experian's previously-announced integration into Snowflake's AI Data Cloud for Media. Adding Experian's identity graph to Snowflake Data Clean Rooms helps advertisers, advertising platforms, and measurement partners work more effectively. Built upon Experian’s rich offline and digital identity foundation, with support for various identifiers across platforms, collaboration in Snowflake Data Clean Rooms helps clients maximize the value of their data and meet the diverse needs of modern business: Collaborate with partners for richer data insights Achieve higher match rates Improve audience building Produce more accurate and complete reports Ensure data privacy Seamless integration of AdTech and MarTech platforms Regardless of the identifier type you are looking to collaborate on, Experian has the identity data in Snowflake Data Clean Rooms to support you and your partner. This leads to higher match rates and more resolved data for you to use to benefit your media initiatives. "Integrating Experian's identity graph into Snowflake Data Clean Rooms marks a transformative leap for digital marketing. This collaboration empowers advertisers, programmatic platforms, and measurement partners with unparalleled accuracy, privacy, and efficiency. Together, we are excited to provide innovative solutions to meet the evolving needs of our clients."Kamakshi Sivaramakrishnan, Head of Data Clean Rooms at Snowflake The Experian and Snowflake partnership showcases how collaboration can enhance scalability and cost-efficiency. Data clean rooms provide a secure environment where multiple parties can share, join, and analyze their data assets without leaving the clean room or exposing the underlying data. By integrating Experian's identity graph within Snowflake's secure platform businesses of all sizes can receive advanced data collaboration and identity tools without the high costs usually involved. The integration prioritizes consumer privacy and data security. Backed by Experian’s Global Data Principles, Experian's deep roots in data protection and security provide customers with the most trusted way to share data and protect consumer privacy. With Experian's graph in Snowflake Data Clean Rooms, customers will get a solution that respects customer consent, safeguards sensitive data, and ensures that processing occurs with the utmost respect for user confidentiality and preferences. Further, Snowflake Data Clean Rooms uses advanced methods to preserve privacy, such as differential privacy and secure computations on encrypted data, enabling data security and integrity. Together, these methods prevent unauthorized access by keeping sensitive data within the secure confines of the cleanroom on a strict, collaboration-to-collaboration basis. The collaboration between Experian and Snowflake significantly enhances data matching and identity resolution within the Snowflake Data Cleanroom. Experian’s identity solution uses digital identifiers like hashed emails, MAIDs, and CTV IDs and offline identifiers like name and address. This allows advertisers to reach more consumers and enrich their data. Marketers can easily use their first-party data in the cleanroom, and with Experian's Graph, they get higher match rates for more accurate targeting and campaign measurement. The continued partnership between Snowflake and Experian provide advertisers, platforms, and measurement providers a secure and effective way to collaborate. This sets the stage for continued innovation in programmatic advertising, ensuring that our solutions evolve in step with our clients' needs. If you're not utilizing clean rooms for collaboration but have advanced identity needs, you can license our Graph and seamlessly integrate it into your Snowflake account. Reach out to our team to learn more Latest posts
With U.S. brands expected to invest over $28 billion in connected TV (CTV) in 2024, balancing linear TV and CTV is now a top priority. Advertisers need to integrate these platforms as the TV landscape evolves to reach audiences with various viewing habits. A successful strategy requires both linear and CTV approaches to effectively reach audiences at scale. We interviewed experts from Comcast Advertising, Disney, Fox, Samsung Ads, Snowflake, and others to gain insights on the evolving landscape of linear and CTV. In our video, they discuss audience fragmentation, data-driven targeting, measurement challenges, and more. Watch now to hear their perspectives. Five considerations for connecting with linear TV and CTV audiences 1. Adapt to audience fragmentation With consumers' rapid shift toward streaming, it's easy to overlook the enduring significance of linear TV, which still commands a large portion of viewership. According to Jamie Power of the Walt Disney Company, roughly half of the current ad supply remains linear, highlighting the need for brands to adapt their strategies to target traditional TV viewers and cord-cutters. As streaming continues to rise, ensuring your strategy integrates both CTV and linear TV is crucial for reaching the full spectrum of audiences. "I don't think that we thought the world would shift so quickly to streaming, but it's not always just all about streaming; there's still such a massive audience in linear."Jamie Power, Disney 2. Combine linear TV’s reach with CTV’s precision Blending the reach of linear TV with the granular targeting capabilities of CTV allows advertisers to engage both broad and niche audiences. Data is critical in understanding audience behavior across these platforms, enabling brands to create highly relevant campaigns tailored to specific audience segments. This strategic use of data enhances engagement and ensures that the right viewers see advertising campaigns. "The future of TV is really around managing the fragmentation of audiences and making sure that you can reach those audiences addressably wherever they're watching TV."Carmela Fournier, Comcast Advertising 3. Manage frequency across platforms Cross-platform campaigns require managing ad frequency to avoid oversaturation while ensuring adequate exposure. With a variety of offline and digital IDs resolved to consumers, our Digital and Offline Graphs can help maintain consistent messaging across linear TV and CTV. This approach allows advertisers to strike the right balance, preventing ad fatigue and delivering the right audience reach for campaign impact. "You've got to make sure that you're not reaching the same homes too many times, that you're reaching everybody the right amount of times."Justin Rosen, Ampersand 4. Focus on consistent measurement Linear TV and CTV offer different data granularities, necessitating tailored approaches for accurate cross-platform campaign measurement. Bridging these data gaps requires advanced tools that streamline reporting for both mediums. As the industry moves toward consistent measurement standards, advertisers must adopt solutions that provide a comprehensive view of campaign performance, enabling them to optimize their cross-platform efforts. "Where I think there are pitfalls are with the measurement piece, it's highly fragmented, there's more work to be done, we're not necessarily unified in terms of a consistent approach to measurement."April Weeks, Basis 5. Align with shifts in audience behavior The success of cross-platform campaigns hinges on staying agile and responsive to shifting audience preferences. As CTV adoption grows, advertisers must proactively adjust their strategies to align with how viewers engage across linear and streaming platforms. Ideas include: Regularly updating creative Adjusting the media mix Utilizing real-time data insights to ensure campaigns remain relevant "At Fox we were a traditional linear company, and essentially what we're trying to do is merge the reach and the scale of TV as well as the reach and the scale of all the cord-cutters and cord-nevers that Tubi possesses." Darren Sherriff – FoxDarren Sherriff, Fox As streaming TV rapidly changes, brands must stay ahead of trends and shifts in consumer behavior to tap into CTV's growing potential. By focusing on these opportunities, advertisers can blend linear TV and CTV, ensuring their campaigns reach audiences wherever they watch. Connect with Experian's TV experts As a trusted leader in data and identity services, Experian offers the expertise to help you succeed in television marketing. With our strong partnerships with key players in the TV industry, we provide access to unique marketing opportunities. Learn how Experian’s data and identity solutions can deliver outstanding results in advanced TV advertising. Partner with us today to enhance your marketing strategies using our Consumer View and Consumer Sync solutions. Connect with our TV experts Contact us Latest posts
In this article…Understanding the AI revolution in commerceFour benefits of the AI revolution coming to commerceFuture trends and predictionsChart the future of commerce with Experian 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. Contact us Latest posts