At A Glance
AI learns what your data teaches in 2025, "good data" in AI means accurate, current, consented, and easy to connect, or your models lose relevance and your investments underperform. Experian is leading this next era of responsible data where trust, transparency, and innovation come together to make marketing more human, not less.What makes data “good” in the age of AI?
In AI-driven marketing, data quality now defines success. “Good data” in AI isn’t about volume; it’s about the balance of accuracy, freshness, consent, and interoperability. As algorithms guide decisions, they must learn from data that’s both accurate and ethical.
At Experian, we believe good data must meet four conditions:
This is the data AI can trust and the data that keeps marketing relevant, predictive, and privacy-first.
Why does data accuracy matter more than ever?
AI models are only as intelligent as their inputs. Incomplete or inconsistent data leads to bad predictions and wasted spend. As the industry moves toward agentic advertising, where autonomous systems handle campaign buying and optimization, data accuracy becomes even more critical. If your ad server or audience data is flawed, these new AI agents will simply automate bad decisions faster.
Experian applies rigorous quality filters and conflict resolution rules to ensure our data is both deterministic and accurate. Deterministic signals alone don’t guarantee accuracy; they must be verified, deduplicated, and contextualized. Our identity resolution process anchors every attribute to real people, giving brands and platforms the confidence that every insight stems from truth, not noise.

Our data is ranked #1 in accuracy by Truthset, giving our clients confidence that every decision they make is backed by the industry’s most reliable insights.
Just because it is deterministic, doesn’t mean it’s highly accurate. You still need to refine and validate your data to make sure it tells a consistent story. You need to anchor your data around real people.
Why does AI need fresh data?
Outdated data can’t predict tomorrow’s behavior. AI thrives on recency.
At Experian, our audiences are refreshed continuously to mirror real-world signals, from purchase intent to media habits, so every campaign reflects what’s happening now, not six months ago.
And we don’t just advocate for fresh data, we rely on it ourselves. Our own AI-powered models, used across our audience and identity platforms, are continuously retrained on the most current, consented signals. This allows us to see firsthand how freshness drives better accuracy, faster optimization cycles, and more relevant outcomes.
But freshness alone isn’t enough. With predictive insights, our models go beyond describing the past. They forecast behaviors, fill gaps with inferred attributes, and recommend next-best audiences, helping you anticipate opportunity before it happens.
Fresh and predictive data means you’re reaching people in the moment that matters and shaping what comes next. With AI, that’s what defines performance.
How do consent and governance build trust in AI?
Responsible AI starts with responsible data. With 20 U.S. states now enforcing privacy laws, data compliance isn’t optional, it’s operational.
At Experian, privacy and compliance are built in. Every data signal, attribute, audience, and partner goes through our rigorous review process to meet federal, state, and local consumer privacy laws. With decades of experience in highly regulated industries, we’ve built processes that emphasize risk mitigation, transparency, and accountability.

Governance isn’t just about regulation, it’s also about innovation done right. We drive transparent and responsible innovation through safe, modular experimentation, from generative applications to agentic workflows. By balancing bold ideas with ethical guardrails and staying ahead of evolving legislation, we ensure our innovations protect consumers, brands, and the broader ecosystem while moving the industry forward responsibly.
Compliance and governance aren’t just boxes to check; they’re the foundation that gives AI its license to operate.
How does interoperability enable AI’s full potential?
AI delivers its best insights when data connects seamlessly across fragmented environments. Our signal-agnostic identity spine allows data to move securely between platforms (connected TV, retail media networks, and demand-side platforms) without losing context or compliance.

Interoperability isn’t just about moving data between systems; it’s about connecting insights across them. When signals connect across environments, AI gains a more complete view of the customer journey revealing true behavior patterns, intent signals, and cross-channel impact that would otherwise remain hidden.
This unified perspective allows AI to connect insights in real time, improving predictions, performance, and personalization while protecting privacy.
Where do AI and human oversight meet?
AI can make marketing more predictive, but people make it meaningful. At Experian, our technology brings identity, insight, and generative intelligence together so brands, agencies, and platforms can reach the right people with relevance, respect, and simplicity.

Our AI-powered models surface connections, recommend audiences, and uncover insights that would take humans months to find. But our experts shape the process, crafting the right inputs, ensuring data quality, reviewing model outputs, and refining recommendations based on industry knowledge and client goals. It’s this partnership between advanced AI and experienced people that turns predictions into actionable, trustworthy solutions.
What “good data” looks like in action
“Good data” becomes most powerful when it’s put to work. At Experian, our marketing data and identity solutions help brands and their partners connect accurate, consented, and interoperable data across the ecosystem, turning insight into measurable outcomes.
When Windstar Cruises and their agency partner MMGY set out to connect digital media spend to real-world bookings, they turned to Experian’s marketing data and identity solutions to close the attribution loop. By deploying pixels across digital placements and using Experian’s identity graph to connect ad exposure data with reservation records, we created a closed-loop attribution system that revealed the full traveler journey, from impression to confirmed booking.
The results were clear: 6,500+ bookings directly tied to digital campaigns, representing more than $20 million in revenue, with a 13:1 ROAS and $236 average cost per booking. Attributed audiences booked $500 higher on average, and MMGY’s Terminal audience segments powered by Experian data achieved a 28:1 ROAS.
This collaboration shows that responsible, high-quality data and AI-driven insights don’t just tell a better story; they deliver measurable business performance.
Why the future of AI depends on “good” data
The next phase of AI-driven marketing won’t be defined by who has the most data, but by who has the best. Leaders will:
AI success starts with good data. And good data starts with Experian, where accuracy, privacy, and purpose come together to make marketing more human, not less.
Partner with Experian for AI you can trust
About the author

Budi Tanzi, VP, Product, Experian
Budi Tanzi is the Vice President of Product at Experian Marketing Services, overseeing all identity products. Prior to joining Experian, Budi worked at various stakeholders of the AdTech ecosystem, such as Tapad, Sizmek, and StrikeAd. During his career, he held leadership roles in both Product Management and Solution Engineering. Budi has been living in New York for almost 11 years and enjoys being outdoors as well as sailing around NYC whenever possible.
FAQs
At Experian, we define “good data” as the balance of accuracy, consent, freshness, and interoperability. We apply rigorous governance, validation, and cleansing across every signal to ensure that AI systems learn from real-time behaviors, not assumptions. This approach turns data into a foundation for reliable, ethical, and high-performing intelligence.
Experian ensures AI-ready data accuracy through advanced cleansing, conflict resolution, and human anchoring. Experian ensures AI models rely on verified, high-quality inputs. Experian’s data is ranked #1 in accuracy by Truthset.
Yes, Experian can help brands stay compliant with privacy laws. Experian’s privacy-first governance framework integrates ongoing audits, legal oversight, and consent management to ensure compliance with all federal, state, and global privacy laws. Compliance isn’t an afterthought; it’s embedded in every step of our data lifecycle.
Experian makes AI more human by pairing innovation with human oversight to ensure AI helps marketers understand people, not just profiles. At Experian, we believe the future of marketing is intelligent, respectful, and human-centered. AI has long been part of how we help brands connect identity, behavior, and context to deliver personalization that balances privacy with performance. Our AI-powered solutions combine predictive insight, real-time intelligence, and responsible automation to make every interaction more relevant and ethical.
Marketers can activate Experian’s high-quality data directly in Experian’s Audience Engine, or on-the-shelf of our platform partners where Experian Audiences are ready to activate. Built on trusted identity data and enhanced with partner insights, it’s where accuracy meets accessibility, helping brands power campaigns with confidence across every channel.
Latest posts

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! Watch 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. Contact us today 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 Watch now Get in touch About our experts Jason Andersen, Senior Director, Strategic Initiatives and Partner Solutions, ExperianJason 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, YieldmoAlex 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 2022, Google began changing the availability of the information available in User-Agent strings across their Chromium browsers. The change is to use the set of HTTP request header fields called Client Hints. Through this process, a server can request, and if approved by the client, receive information that would have been previously freely available in the User-Agent string. This change is likely to have an impact on publishers across the open web that may use User-Agent information today. To explain what this change means, how it will impact the AdTech industry, and what you can do to prepare, we spoke with Nate West, our Director of Product. What is the difference between User-Agents and Client Hints? A User-Agent (UA) is a string, or line of text, that identifies information about a web server’s browser and operating system. For example, it can indicate if a device is on Safari on a Mac or Chrome on Windows. Here is an example UA string from a Mac laptop running Chrome: To limit the passive fingerprinting of users, Google is reducing components of the UA strings in their Chromium browsers and introducing Client Hints. When there is a trusted relationship between first-party domain owners and third-party servers, Client Hints can be used to share the same data. This transition began in early 2022 with bigger expected changes beginning in February 2023. You can see in the above example, Chrome/109.0.0.0, where browser version information is already no longer available from the UA string on this desktop Chrome browser. How can you use User-Agent device attributes today? UA string information can be used for a variety of reasons. It is a component in web servers that has been available for decades. In the AdTech space, it can be used in various ad targeting use cases. It can be used by publishers to better understand their audience. The shift to limit access and information shared is to prevent nefarious usage of the data. What are the benefits of Client Hints? By using Client Hints, a domain owner, or publisher, can manage access to data activity that occurs on their web properties. Having that control may be advantageous. The format of the information shared is also cleaner than parsing a string from User-Agents. Although, given that Client Hints are not the norm across all browsers, a long-term solution may be needed to manage UA strings and Client Hints. An advantage of capturing and sharing Client Hint information is to be prepared and understand if there is any impact to your systems and processes. This will help with the currently planned transition by Google, but also should the full UA string become further restricted. Who will be impacted by this change? Publishers across the open web should lean in to understand this change and any potential impact to them. The programmatic ecosystem supporting real-time bidding (RTB) needs to continue pushing for adoption of OpenRTB 2.6, which supports the passing of client hint information in place of data from UA strings. What is Google’s timeline for implementing Client Hints? Source: Google Do businesses have to implement Client Hints? What happens if they don’t? Not capturing and sharing with trusted partners can impact capabilities in place today. Given Chromium browsers account for a sizable portion of web traffic, the impact will vary for each publisher and tech company in the ecosystem. I would assess how UA strings are in use today, where you may have security concerns or not, and look to get more information on how to maintain data sharing with trusted partners. We can help you adopt Client Hints Reach out to our Customer Success team at tapadcustomersuccess@experian.com to explore the best options to handle the User-Agent changes and implement Client Hints. As leaders in the AdTech space, we’re here to help you successfully make this transition. Together we can review the options available to put you and your team on the best path forward. Get in touch About our expert Nate West, Director of Product Nate West joined Experian in 2022 as the Director of Product for our identity graph. Nate focuses on making sure our partners maintain and grow identity resolution solutions today in an ever-changing future state. He has over a decade of experience working for media organizations and AdTech platforms. Latest posts

Up next in our Ask the Expert series, Ben Rothke, Senior Information Security Manager, reviews two certifications that should be part of your information security strategy: Service Organization Control (SOC) 2 Type 2 and International Organization for Standardization (ISO) 27001. Tapad, a part of Experian, is 27001 and SOC 2 Type 2 compliant. Two information security certifications you can trust Seals from Good Housekeeping and Underwriters Laboratories give consumers confidence that they can trust the product that they’re buying. For IT solutions or service providers, what, or who can you turn to for that seal of approval? There are many equivalent third-party attestations you can use. But which should you trust? The International Organization for Standardization (ISO) 27001 The American Institute of Certified Public Accountants (AICPA) System and Organization Controls (SOC) International Organization for Standardization (ISO) 27001 is an international standard for information security from the ISO. ISO 27001 is globally acknowledged and sets requirements for controls, maintenance, and certification of an information security management system (ISMS). This international standard provides organizations with a framework to identify, manage and reduce risks related to the security of information System and Organization Controls (SOC) The SOC, as defined by the AICPA, is a set of audit reports. SOC reports, like 27001 certificates, are used by service organizations to give their customers the confidence they have adequate information security controls in place to protect the data that they handle. SOC 2 is an assessment of controls at a service organization regarding security, availability, processing integrity, confidentiality, and privacy. The purpose of the report is to provide extensive information and assurance to a broad range of users about the controls at a service organization that are relevant to the security, availability, and processing integrity of the systems that process user data, as well as the confidentiality and privacy of the information processed by these systems. Why ISO 27001 and SOC 2 are important The value of these third-party attestations is two-fold: Organizations can show they have passed an independent external audit Third-party attestations save organizations the time of having to do their own audits In addition to 27001 and SOC 2 Type 2 compliance, we are also certified with ISO 27017 and 27018, which are add-ons to 27001 that are specific to cloud computing. We take the security and privacy of our customers’ data as seriously as they do. Every cloud service provider (CSP) has a responsibility matrix that details what security and privacy tasks they are responsible for and which ones the customer is responsible for. Any cloud customer that needs to be made aware of what their security tasks are is putting themselves at risk. So, when you want to engage a CSP, ask them for their attestations. They worked hard for them and will be proud to share their compliance. We’re powered by decades of setting standards in marketing services At Experian, we’re a privacy-first business. We’re highly focused on respecting people, their data, and their privacy. We continue to show our dedication to information security by completing these security audits every year. The constant changes to data compliance regulations can be challenging to navigate, but you don’t have to do it alone. Contact us today. We will be your guide so you can ethically and confidently reach your customers. Contact us today Contact us today About our expert Ben Rothke, Senior Information Security Manager Ben Rothke, CISSP, CISA, is a Senior Information Security Manager at Tapad, a part of Experian. He has over 25 years of industry experience in information systems security and privacy. His areas of expertise are in risk management and mitigation, security and privacy regulatory issues, cryptography, and security policy development. Ben is the author of Computer Security – 20 Things Every Employee Should Know (McGraw-Hill), and writes security and privacy book reviews for the RSA Conference Blog and Security Management magazine. Latest posts







