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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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In our Ask the Expert Series, we interview leaders from our partner organizations who are helping lead their brands to new heights in AdTech. Today’s interview is with Paul Zovighian, VP of Marketplaces at Index Exchange. Sell-side activation vs. buy-side packaging What’s fundamentally changed with sell-side decisioning, and how does it now diverge from traditional buy-side packaging? Sell-side decisioning is programmatic’s next major evolution – one that redefines how intelligence enters the transaction. Advances in infrastructure and computing power now allow supply-side platforms(SSPs) to act in the crucial pre-bid moment, enriching impressions with context, quality, and data before they reach the buy side. This isn’t just about efficiency; it’s about unlocking new value. Smarter requests mean buyers see only the most relevant opportunities, while publishers gain recognition for the true worth of their audiences and environments. We’re still at the beginning of this shift. Many players still package inventory without engaging in real pre-bid intelligence. As the market matures, the companies that evolve toward sell-side decisioning will be the ones to set the pace for programmatic’s future. Economic shifts with scaled curation As curation scales, what economic levers shift for both publishers and buyers, and how do those shifts influence deal structure and media planning? As curation scales, one of the most powerful levers is data. It’s the industry’s most valuable asset, and on Index it keeps its full worth. We don’t take a platform cut or add hidden fees, so data partners benefit from the clearest, most efficient economics in the market. Data vendors gain confidence that their economics aren’t eroded by a platform tax. For publishers, this means stronger yield and more ad spend flowing directly into working media. When data retains its full value, it enhances how impressions are packaged, priced, and differentiated—driving more competition for quality inventory and more opportunities for revenue. For buyers, it means compressed supply paths and total transparency – they know exactly what they’re paying for. With no intermediaries and full transparency into economics, buyers gain a clearer view of where their budgets go and the confidence that their investments reach real audiences in trusted environments. They benefit from cleaner supply chains, better performance, and more meaningful alignment between spend and outcome. The result is a healthier marketplace where both sides benefit from efficiency, fairness, and scale. Moving decisions upstream for value What decisions historically made in DSPs should now move upstream to publishers or SSPs to unlock more value, and which should remain buy-side? Decisioning is no longer confined to demand-side platforms(DSPs). We can enrich impressions by applying intelligence — via data, algorithms, creative technology, and more, before they even reach the buy side — adding context, filtering out low-quality supply, and expanding audience discovery. This isn’t about shifting roles; DSPs remain critical for campaign strategy, optimization, and budget allocation. The sell side simply ensures every bid request is smarter from the start, creating more value for all parties. In doing so, we also alleviate pressure on DSPs — enabling more comprehensive data discovery by searching for signals at the top of the funnel, prior to optimization. That means DSPs can focus on what they do best, supported by a cleaner, more transparent supply path. Index Marketplaces use cases explained Index Marketplaces is designed to enable the strength of our partners, and Experian brings one of the broadest sets of demographic and audience insights in the industry. That scale enables a wide variety of applications, from more precise audience activation to deeper measurement and analytics. What’s different on the sell side is how those insights are applied. By activating Experian’s syndicated audiences directly at the point of decision, their value is realized in real time and across the full scale of the open internet. Buyers gain a clearer path to relevant audiences, and publishers benefit from stronger alignment between data and media. It’s an approach that ensures partners like Experian can maximize the impact of their assets while helping the market move toward more intelligent, performance-driven activation. Identity signals with stronger privacy For identity partners like Experian, what’s the right way to bring audience, context, and propensity signals into sell-side activation? The beauty of sell-side decisioning is that it reduces the hops in how identity signals are applied. Without it, IDs have to travel through multiple platforms, creating extra handoffs and additional risks of data loss or leakage. With sell-side decisioning, those signals are obfuscated under a deal ID and applied directly at the point of decision. That means audience, context, and propensity data are activated securely, without ever leaving the sell-side environment. For partners like Experian, it’s the cleanest path to value: fewer hops, stronger privacy protection, and clearer economics for everyone in the chain. Contact us FAQs What is sell-side decisioning, and why is it important? Sell-side decisioning allows publishers to add intelligence, like audience data and context, before ad impressions are sent to buyers. This makes the process more efficient and ensures advertisers see only the most relevant opportunities. How does sell-side decisioning differ from traditional buy-side packaging? Traditional buy-side packaging happens after impressions are sent to demand-side platforms (DSPs). Sell-side decisioning moves some of that intelligence upstream, enriching impressions earlier and reducing inefficiencies. What does "curation" mean in this context, and how does it benefit publishers and advertisers? Curation refers to the process of organizing and enriching ad inventory with data and context. For publishers, it leads to better yield and more ad spend going directly to their media. For advertisers, it means clearer, more transparent supply paths. How does sell-side decisioning improve privacy? By applying audience and identity signals directly on the sell side, data stays within a secure environment. This reduces the number of platforms handling sensitive information, lowering the risk of data loss or leakage. What role does Experian play in sell-side decisioning? Experian provides demographic and audience insights that are activated directly at the point of decision. This helps advertisers reach the right audiences more effectively while ensuring publishers can maximize the value of their inventory. Why is moving decisioning upstream beneficial for DSPs? When publishers and SSPs handle some decisioning earlier, DSPs can focus on campaign strategy and optimization. This creates a cleaner, more efficient process for everyone involved. What is a deal ID, and how does it enhance privacy? A deal ID is a unique identifier used in programmatic advertising to bundle audience and context signals securely. It ensures data is applied without being exposed or shared across multiple platforms. About our expert Paul Zovighian, VP of Marketplaces, Index Exchange Paul Zovighian carries over a decade of industry expertise, stemming from his analytics and optimization roots to his current post as VP, Marketplaces, where he is focused on the commercial activation of Index’s newest product, Index Marketplaces. Previously, in his role as VP of corporate development, Paul led Index’s first-ever business acquisition. In his spare time, he enjoys long walks on the beach and befriending cats in NYC’s thriving bodega community. About Index Exchange Index Exchange is a global advertising supply-side platform enabling media owners to maximize the value of their content on any screen. They’re a proud industry pioneer with over 20 years of experience connecting leading experience makers with the world’s largest brands to ensure a quality experience for consumers. Latest posts

As artificial intelligence (AI), connected TV (CTV), and data collaboration continue to advance, advertisers are discovering new ways to meet audiences where they are; on their terms and in their spaces. These innovations are creating opportunities to deliver more personalized, impactful campaigns that were unimaginable just a few years ago. At Cannes Lions 2025, we sat down with industry leaders from Butler Till, Comcast Advertising, Index Exchange, IQVIA Digital, Optable, PMG, Samsung Ads, and Sports Innovation Lab. From reimagining the living room experience to using AI in practice for better outcomes, here’s what we learned about the trends driving advertising forward. 1. CTV turns living rooms into active spaces CTV has turned the living room into a hub of interaction, discovery, and commerce. Younger audiences are using their TVs like mobile devices; streaming, learning, and even controlling their homes. This shift is creating new opportunities for advertisers to deliver relevant, personalized experiences where audiences are already engaged. With premium content and interactive tools, the living room is no longer just a passive space, it’s where attention meets action, and where brands can connect with audiences in meaningful ways. How Experian helps With Experian, advertisers can connect first-party data with CTV IDs, ensuring accurate and measurable targeting while maintaining a privacy-first approach. That means brands reach viewers with messages that feel personal, without losing trust. “We surveyed 1,000 smart TV owners and found that younger audiences are using their TVs like mobile devices. Two-thirds use them for social media, 40% for self-improvement like Coursera or TED Talks, and 25% for interactivity; controlling appliances or home temperatures. Interactivity with connected TVs is skyrocketing.”Justin Evans 2. Creators build stronger connections with audiences Creators are no longer limited to social media; they are now a driving force in CTV. Creator led programming is capturing attention and driving post view actions, offering advertisers a unique way to connect with passionate, engaged audiences. By thinking of creators as “micro networks” with built in communities, advertisers can meet fans where they already gather and deliver authentic, impactful messages that resonate. How Experian helps Experian helps advertisers tap into the creator economy by identifying topical audiences that align with influencer niches—like food, travel, gaming and entertainment—and activating them across the open web. Through Audigent’s integration with DV360, brands can pair Experian's expansive audience targeting capabilities with Audigent's Curated Deals to reach engaged viewers in creator-led environments. This approach ensures ads appear where audiences are most receptive, enhancing relevance and performance. “The creator economy is moving into TV. It’s incredible to see social influencers, once dominant on platforms, now creating high quality content for streaming, networks, and more.”Gina Whelehan 3. Data collaboration that drives better results Advertisers rely on data to reach the right audiences, but privacy concerns are reshaping how it’s collected, shared, and used. Data collaboration enables brands to combine multiple data sets (like first-party data and syndicated audiences) to improve planning, activation, and measurement. While privacy remains a priority, the focus is on creating actionable insights that drive better results and build trust with consumers. By focusing on consented, privacy safe identity solutions, advertisers can achieve better outcomes while respecting consumer privacy; a win-win for brands and audiences alike. How Experian helps Experian’s privacy-first approach ensures that all data activation occurs with compliance and consent. By maintaining high match rates, offering flexible collaboration options (including clean rooms, first-party data onboarding, and syndicated audiences) and adhering to transparent methodologies, Experian facilitates seamless collaboration between brands, publishers, and platforms. This helps build trust and strengthen long-term connections with audiences. “The area we’re most excited about is identity resolution on the publisher side. Publishers can reinsert signal and create better results for advertisers. This wasn’t always well-articulated, but today we have case studies proving publishers can help improve outcomes.” Vlad Stesin 4. Optimizing supply paths for better outcomes Supply path optimization (SPO) helps advertisers improve campaign efficiency by increasing viewability and reducing waste. Supply-side decisioning builds on this by identifying the audiences advertisers want to reach, the content those audiences consume, and the publishers with the most relevant inventory. Together, these strategies create a more intelligent and efficient ecosystem, ensuring ads are delivered in the right context, to the right people, on the right platforms. How Experian helps Experian’s data solutions, including both Experian’s and Audigent’s contextual and identity capabilities, are available across sell-side (SSPs) and buy-side (DSPs) platforms, enabling smarter decision-making throughout the media supply chain. Audigent’s direct integrations with publishers provide an unfiltered view into available inventory, offering deeper insights that inform campaign optimization. These insights can be activated in real time and transacted within advertisers’ existing buying platforms. By powering real-time intelligence across the ecosystem, from advertisers to DSPs, SSPs, and publishers, Experian and Audigent help drive better outcomes, more efficient media spend, and greater value for all participants. “Sell-side decisioning activates the intelligence of the exchange, along with partners like Experian, to optimize auctions in real time. This helps pre-decision buys that flow to the DSPs, making the buying process smarter, more efficient, and ultimately driving better value for marketers and publishers.” Mike McNeeley 5. AI that streamlines agency workflows AI is a practical tool that agencies are using to streamline workflows and deliver better results. From planning and pacing to creative iteration, AI is helping teams move faster and smarter. In fact, 67% of global marketing and communications professionals now use AI for content creation frequently or all the time, underscoring its role in modern workflows. The key is to think of AI as a navigator, not a replacement. It handles repetitive tasks, freeing up teams to focus on strategy and creativity, while enabling faster tests, fewer dead ends, and better client clarity. How Experian helps Experian uses AI and machine learning to deliver highly personalized marketing solutions. In our Digital Graph, advanced clustering algorithms analyze household and individual device connections, improving targeting and measurement accuracy. We also use AI powered audience recommendations to create tailored audience solutions for clients. Our contextual data models, powered by Audigent’s contextual engine, further improve this process by analyzing bidstream traffic in real time, ensuring audiences are aligned with the most relevant inventory. “We’ve extended our platform with Marketplace, which lets us integrate third-party partners, new tech, and data seamlessly into activation. Clients are asking for this level of innovation, especially with the speed at which AI is evolving and transforming what’s possible in marketing.”Sam Bloom Connecting the dots: Data, creativity, and outcomes The common thread across these insights is how we connect with audiences, collaborate on data, and create meaningful outcomes. By reimagining the living room experience and utilizing AI and creator-led programming, brands are embracing innovation. How Experian helps Experian helps you build privacy-first identity foundations, collaborate seamlessly, optimize supply paths, streamline with AI, and connect through creators. Let's start a conversation FAQs What is CTV, and why does it matter now? CTV brings premium, interactive streaming to the largest screen at home, allowing brands to reach engaged viewers with measurable, personalized experiences. What is data collaboration, and how does it stay privacy-first? It’s the consented, secure use of first-party and partner data (often via clean rooms) to improve planning, activation, and measurement without exposing raw consumer data. What do “SPO” and sell-side decisioning actually do? SPO streamlines the path from advertiser to publisher, reducing waste and improving quality. Sell-side decisioning adds real-time intelligence to the exchange, delivering the proper context and audience more efficiently. How are creators changing TV advertising? Creator-led programming functions like “micro networks” with built-in communities, helping brands show up where fans are already engaged and ready to act. How are living rooms becoming “active spaces”? Viewers use TVs like mobile devices, discovering content, learning, shopping, and interacting; advertisers can meet their intent and drive post-view actions. Latest posts

Demand-side platforms (DSPs) are more than just technology providers, they’re strategic partners, helping marketers answer the key question: “How should I spend my media budget?” A leading DSP struggled to attribute consumer actions across digital channels such as connected TV (CTV) and display. Without connecting impressions to conversions, they risked losing client trust and ROI proof. With Experian’s Digital Graph, they resolved 84% of IDs and increased match rates, strengthening attribution and client confidence. The challenge A leading DSP had trouble showing which ads drove results across CTV, display, and digital. Without linking ad views to conversions, they couldn’t prove ROI. The missing piece was attribution. They needed to show which channels drove conversions, but without strong identity resolution, it was hard to connect CTV ads to website activity. See how Experian is shaping addressability in CTV What is Experian's Digital Graph? Built from trillions of real-time data points and updated weekly, Experian’s Digital Graph connects billions of identifiers across devices and households, such as cookies, mobile ad IDs (MAIDs), CTV IDs, IP addresses, universal IDs, and more. It gives DSPs a reliable foundation by linking these identifiers back to households and individuals, improving DSPs' ability to offer attribution by better connecting impressions to conversions. Learn more about our Digital Graph here What makes the Digital Graph unique is its scale and freshness. It ingests trillions of signals in real time and delivers updates weekly. That consistency matters: it gives DSPs confidence that they’re working with the most accurate view of digital identity. AI and machine learning (ML) are core to how we maintain that level of accuracy. Our models use sophisticated clustering algorithms to analyze device connections at both household and individual levels. By evaluating data points such as timestamps, IP addresses, user agents, cookie IDs, and device identifiers, these algorithms create precise device groupings that enhance targeting and measurement accuracy. The models are continuously refined, ensuring our clients can better understand consumer behaviors within households and activate more effective, personalized marketing. Think of it like connecting puzzle pieces scattered across devices and channels. On their own, each piece doesn’t say much. Together, they reveal the full picture of who saw an ad, engaged, and converted, and which ads performed best. Watch the video The solution By syncing its cookies with the Digital Graph, the DSP gained access to related identifiers, including: MAIDs CTV IDs IP addresses Experian cookies This expanded identity universe gave the DSP a unified view of individuals and households, making it possible to connect impressions to conversions across devices and channels. With each weekly refresh, attribution models stayed accurate and up to date, turning fragmented signals into proof of performance. Results Within weeks, the DSP saw measurable improvements: 84% of IDs synced 9% increase in match rates With a stronger foundation of digital identifiers, the DSP matched more MAIDs, CTV IDs, and IP addresses to conversions. This allowed them to show clients exactly which ads and channels drove ROI, transforming impression reports into actionable proof of performance and strengthening client trust. See how MiQ strengthened their Identity Spine with Experian's Digital Graph Why attribution matters now Attribution has never been more critical. With signals fading and marketing budgets under pressure, DSPs need reliable data to prove performance. Experian’s Digital Graph takes a multi-ID, always-on approach, refreshed weekly with trillions of signals. This delivers consistency and accuracy that single-point, stale-ID solutions can’t match. For this DSP, that meant transforming attribution from guesswork into clear proof, strengthening client trust, and proving ROI across channels. Download the full case study Connect with us today to see how our Digital Graph can help you maximize advertiser trust and ROI. Ready to strengthen your approach to attribution? FAQs What is Experian’s Digital Graph? Experian’s Digital Graph is a privacy-conscious identity resolution solution built from trillions of real-time data points, refreshed weekly, that links identifiers like cookies, MAIDs, CTV IDs, Unified I.D. 2.0 (UID2), ID5 IDs and IP addresses to households and individuals. How does Experian’s Digital Graph improve attribution? Experian’s Digital Graph improves attribution by connecting impressions to conversions across devices and channels, giving DSPs a clearer view of which ads and channels drove results. What makes Experian’s Digital Graph different from other solutions? While many platforms rely on single, static IDs, Experian’s Digital Graph uses a multi-ID, always-on approach with weekly refreshed, ensuring accuracy even as signals shift. What results can DSPs expect when using Experian’s Digital Graph? When you use Experian’s Digital Graph, you can expect higher match rates, more synced IDs, clearer attribution models, and stronger proof of ROI for your clients. Because Experian’s Digital Graph serves as the backbone of the industry, it also helps DSPs maximize the scale and reach they can deliver to advertisers. Is Experian’s Digital Graph privacy-compliant? Yes. Experian’s Digital Graph is designed with privacy in mind, ensuring compliance while still delivering accurate attribution insights. Latest posts