Contextual ad targeting paves the way for new opportunities
Advertisers and marketers are always looking for ways to remain competitive in the current digital landscape. The challenge of signal loss continues to prompt marketers to rethink their current and future strategies. With many major browsers phasing out support for third-party cookies due to privacy and data security concerns, marketers will need to find new ways to identify and reach their target audience. Contextual ad targeting offers an innovative solution; a way to combine contextual signals with machine learning to engage with your consumers more deeply through highly targeted accuracy. Contextual advertising can help you reach your desired audiences amidst signal loss – but what exactly is contextual advertising, and how can it help optimize digital ad success?
In a Q&A with our experts, Jason Andersen, Senior Director of Strategic Initiatives and Partner Solutions with Experian, and Alex Johnston, Principal Product Manager with Yieldmo, they explore:
- The challenges causing marketers to rethink their current strategies
- How contextual advertising addresses signal loss
- Why addressability is more important than ever
- Why good creative is still integral in digital marketing
- Tips for digital ad success
By understanding what contextual advertising can offer, you’ll be on the path toward creating powerful, effective campaigns that will engage your target audiences.
Check out Jason and Alex’s full conversation from our webinar, “Making the Most of Your Digital Ad Budget With Contextual Advertising and Audience Insights” by reading below. Or watch the full webinar recording now!
Macro impacts affecting marketers
How important is it for digital marketers to stay informed about the changes coming to third-party cookies, and what challenges do you see signal loss creating?
Jason: Marketers must stay informed to succeed as the digital marketing landscape continuously evolves. Third-party cookies have already been eliminated from Firefox, Safari, and other browsers, while Chrome has held out. It’s just a matter of time before Chrome eliminates them too. Being proactive now by predicting potential impacts will be essential for maintaining growth when the third-party cookie finally disappears.
Alex: Jason, I think you nailed it. Third-party cookie loss is already a reality. As regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) take effect, more than 50% of exchange traffic lacks associated identifiers. This means that marketers have to think differently about how they reach their audiences in an environment with fewer data points available for targeting purposes. It’s no longer something to consider at some point down the line – it’s here now!
Also, as third-party cookies become more limited, reaching users online is becoming increasingly complex and competitive. Without access to as much data, the CPMs (cost per thousand impressions) that advertisers must pay are skyrocketing because everyone is trying to bid on those same valuable consumers. It’s essential for businesses desiring success in digital advertising now more than ever before.
Contextual ad targeting: A solution for signal loss
How does contextual ad targeting help digital marketers find new ways to reach and engage with consumers? What can you share about some new strategies that have modernized marketing, such as machine learning and Artificial Intelligence (AI)?
Jason: We’re taking contextual marketing to the next level with advanced machine learning. We are unlocking new insights from data beyond what a single page can tell us about users. As third-party cookies go away, alternative identifiers are coming to market, like RampID and UID2. These are going to be particularly important for marketers to be able to utilize.
As cookie syncing becomes outdated, marketers will have to look for alternative methods to reach their target audiences. It’s essential to look beyond cookie-reliant solutions and use other options available regarding advertising.
Alex: I think, as Jason alluded to, there’s a renaissance in contextual advertising over the last couple of years. If I were to break this down, there are three core drivers:
- The loss of identity signals. It’s forcing us to change, and we must look elsewhere and figure out how to reach our audiences differently.
- There have been considerable advances in our ability to store and operate across a set of contextual signals far more extensive than anything we’ve ever worked with in the past and in far more granular ways. That’s a huge deal because when it comes to machine learning, the power and the impact of those machine learning models are entirely based on how extensive and granular the data set is that you can collect. Machine learning can pull together critical contextual signals and figure out which constellations, or which combinations of those signals, are most predictive and valuable to a given advertiser.
- We can tailor machine learning models to individual advertisers using all those signals and find patterns across those in ways that were previously impractical or unfeasible. The transformation is occurring because of our ability to capture much more granular data, operate across it, and then build models that work for advertisers.
Addressability: Connect your campaigns to consumers
How does advanced contextual targeting help marketers reach non-addressable audiences?
Jason: Advanced contextual targeting allows us to take a set of known data (identity) and draw inferences from it with all the other signals we see across the bitstream. It’s taking that small seed set of either, customers that transacted with you before that you have an identity for, or customers that match whom you’re looking for. We can use that as a seed set to train these new contextual models. We can now look at making the unknown known or the unaddressable addressable. So, it’s not addressable in an identity sense, it is addressable in a contextual or an advanced contextual sense that’s made available to us, and we can derive great insight from it.
One of the terms I like to use is contextual indexing. This is where we take a set of users we know something about. So, I may know the identity of a particular group of households, and I can look at how those households index against any of the rich data sets available to us in any data marketplace, for example, the data Yieldmo has. We can look at how that data indexes to those known users to find patterns in that data and then extrapolate from that. Now we can go out and find users surfing on any of the other sites that traditionally don’t have that identifier for that user or don’t at that moment in time and start to be able to advertise to them based on the contextually indexed data.
Historically, we’ve done some contextual ad targeting based on geo-contextual, and this is when people wanted to do one to one marketing, and geo-contextual outperformed the one to one. But marketers weren’t ready for alternatives to one to one yet. We want marketers to start testing these solutions. Advertisers must start trying them, learning how they work, and learn how to optimize them because they are based on a feedback loop, and they’re only going to get better with feedback.
Alex: Jason, you described that perfectly. I think the exciting opportunity for many people in the industry is figuring out how to reach your known audience in a non-addressable space, that is based on environmental and non-identity based signals, that helps your campaign perform. Your known audience are people that are already converting – those who like your products and services and are engaged with your ads. Machine learning advancements allow you to take your small sample audience and uncover those patterns in the non-addressable space.
It’s also worth noting that in this world in which we are using seed audiences, or you are using your performing audiences to build non-addressable counterpart targeting campaigns, having high-quality, privacy-resilient data sets becomes incredibly important. In many cases, companies like Experian, who have high quality, deep rich training data, are well positioned to support advertisers in building those extension audiences. As we see the industry evolve, we’re going to see some significant changes in terms of the types of, and ways in which, companies offer data, and make that available to advertisers for training their models or supporting validation and measurement of those models.
Jason: Addressable users, the new identity-based users, are critical to marketers’ performance initiatives. They’re essential to training the models we’re building with contextual advertising. Together, addressable users and contextual advertising are a powerful combination. It’s not just one in isolation. It’s not just using advanced contextual, and it’s not just using the new identifiers. It’s using a combination to meet your performance needs.
It’s imperative to start thinking about how you can begin building your seed audiences. What can you start learning from, and how do you put contextual into play today? You are looking to build off a known set and build a more advanced model. These can be specialized models based on your data. You can hone in and create a customized model for your customer type, their profile, and how they transact. It’s a greenfield opportunity, and we’re super excited about the future of advanced contextual targeting.
Turn great creative into measurable data points
Why does good creative still play an integral part in digital advertising success?
Jason: Good creative has always been meaningful. It’s vital in getting people to click on your ad and transact. But it’s becoming increasingly important in this new world that we’re talking about, this advanced contextual world. The more signal that we can get coming into these models, the better. Good creative in the proper ad format that you can test and learn from is paramount. It comes back to that feedback loop. We can use that as another signal in this equation to develop and refine the right set of audiences for your targeting needs.
Alex: If you imagine within the broader context of identity and signal loss, creative and ad format becomes incredibly powerful signals in understanding how different audiences interact with and engage with different creative. In the case of the formats that serve on the Yieldmo exchange, we’re collecting data every 200 milliseconds around how individual users are engaging with those ads. Interaction data like the user scrolling back or the number of pixel seconds they stay on the screen, fills this critical gap between video completes and clicks. Clicks are sparse and down the funnel, and views and completes are up the funnel. All those attention and creative engagement type metrics occupy the sweet spot where they’re super prevalent, and you can collect them and understand how different audiences engage with your ads. That data lets you build powerful models because they predict all kinds of other downstream actions.
Throughout my career, I learned that designing or tailoring your creative to different audience groups is one of the best ways to improve performance. We ran many lift studies with analysis to understand how you can tailor creative customized for individual audiences. That capability and the ability to do that on an identity basis is starting to deteriorate. The ability to do that using a sample of data or using a smaller set of users, either where you’re inferring characteristics or you’re looking at the identity that does exist in a smaller group, becomes powerful for being able to customize your creative to tell the right story to the right audience. When you layer together all the interaction data collected at the creative level on top of all the contextual and environmental signals, you can build powerful models. Whether those are driving proxy metrics, or downstream outcomes, puts us in a powerful position to respond to the broader loss of identity that we’ve relied on for so many years.
Our recommendations for marketers for 2023 and beyond
Do you have recommendations for marketers building out their yearly strategies or a campaign strategy?
Jason: Be proactive and start testing and learning these new solutions. I mentioned addressability and being in the right place at the right time. That’s easier in today’s third-party cookie world. But as traditional identity is further constricted, you will have these first-party solutions that will not be at scale, so you’re less likely to find your user at the scale you want. It would be best if you thought about how to reach that user at the right place at the right time. They may not be seen from an identity basis. They might not be at the right place at the right time when you were delivering or trying to deliver an ad. But you increase your chance of reaching them by building these advanced contextual targeting audiences using this privacy-safe seed ‘opted-in’ user set; this is a way to cast that wider net and achieve targeted scale.
Alex: Build your seed lists, test your formats with different audiences, and understand what’s resonating with whom. Take advantage of some of the pretty remarkable advances in machine learning that are allowing us, really, for the first time to fully uncork the potential and the opportunity with contextual in a way that we’ve never done before.
Jason: At the end of the day, it’s making the unaddressable addressable. So, it’s a complementary strategy; having that addressable piece will feed the models. But also, that addressable piece still needs to be identity-based, addressable still needs to be part of your overall marketing strategy, and you need to complement it with other strategies like advanced contextual targeting. The two of them together are super complimentary. They learn from each other, and it’s a cyclical loop. Now is the time to take advantage and start testing and understanding how these solutions work.
We can help you get started with contextual ad targeting
Contextual advertising can help you stay ahead of the curve, identify your target audience, and continue to drive conversions despite signal loss. We’ve partnered with Yieldmo to help make sure that your marketing campaigns are reaching the right target audiences on the platforms that are most relevant. To get started with contextual ad targeting to reach the right audience at the right time and drive conversions, contact our marketing professionals. Let’s get to work, together.
Find the right marketing mix in 2023
Check out our webinar, “Find the right marketing mix with rising consumer expectations.” Guest speaker, Nikhil Lai, Senior Analyst from Forrester Research, joins Experian experts Erin Haselkorn, and Eden Wilbur. We discuss:
- New data on the complexity and uncertainty facing marketers
- Consumer trends for 2023
- Recommendations on finding the right channel mix and the right consumers
Get in touch
About our experts
Jason Andersen, Senior Director, Strategic Initiatives and Partner Solutions, Experian
Jason Andersen heads Strategic Initiatives and Partner Enablement for Experian Marketing Services. He focuses on addressability and activation in digital marketing and working with partners to solve signal loss. Jason has worked in digital advertising for 15+ years, spanning roles from operations and product to strategy and partnerships.
Alex Johnston, Principal Product Manager, Yieldmo
Alex Johnston is the Principal Product Manager at Yieldmo, overseeing the Machine Learning and Optimization products. Before joining Yieldmo, Alex spent 13 years at Google, where he led the Reach & Audience Planning and Measurement products, overseeing a 10X increase in revenue. During his time, he launched numerous ad products, including YouTube’s Google Preferred offering. To learn more about Yieldmo, visit www.Yieldmo.com.
Latest posts
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