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
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
2023 has been an unprecedented year for AdTech. With new challenges arising every day — from ongoing signal loss to changing consumer privacy regulations– staying on top of trends is more important than ever before. To help you stay informed on successful tactics from across our ecosystem we’ve compiled a list of our top five 2023 AdTech blog posts. Keep reading to learn about the top themes from CES and Cannes this year, why contextual targeting is so popular, and more – so you can remain competitive in today's rapidly changing environment. #5 CES 2023: Clean rooms, activation, and more Experian attended CES in January to kick off the AdTech year, connecting with partners, clients, and industry leaders to understand their challenges and goals. Discussions were focused on clean rooms and digital activation to address data deprecation and consumer privacy. Clean rooms have the potential to standardize data and address interoperability issues. Digital activation was predicted to increase significantly in 2023, with more focus on demand-side, video, and supply-side platforms. Did digital activation increase in 2023? Download our 2024 Digital audience trends and predictions report to find out. Read our full 2023 CES recap here. Meet with the Experian team at CES 2024 CES is around the corner, and we are looking forward to the opportunity to connect with you in our suite at the Aria Hotel & Casino in Las Vegas, January 9-12. There is no better place to connect and discuss the latest industry trends and innovations. Let’s start the conversation! Book a meeting with our team #4 Experian identity resolution now available in AWS Clean Rooms Earlier this year, Experian announced that our identity resolution solution is now available in AWS Clean Rooms. Through this new partnership, customers can pair Experian's identity capabilities with AWS Clean Rooms to safely collaborate and access deeper insights without exposing sensitive data. Check out the full announcement to hear from Kalyani Koppisetti, Principal Partner Solution Architect at AWS, Matt Miller, Business Development Principal at AWS, and Tyler Middleton, Sr. Partner Marketing Manager at Experian. #3 Four key themes from Cannes Lions 2023 At Cannes Lions 2023, discussions revolved around four main themes: Signal loss: Experts discussed the importance of adapting paid media strategies to align with consumer behaviors. Collaboration: Discussions highlighted the need for industry players to collaborate and focus on solutions that benefit all stakeholders in the ecosystem. Personalization: This was a key topic and emphasized the growing significance of tailored content, data-driven insights, and first-party data solutions for advertisers adapting to cookie deprecation and the evolving consumer privacy landscape. Balancing AI and creativity: Attendees explored how to balance AI capabilities and nurture creativity while maintaining a human touch. Cannes 2024 will be here before we know it and Experian will be there. To get ready, check out this post by Tyler Middleton, Sr. Partner Marketing Manager, to hear from a first-time Cannes attendee, #2 Three key insights from our 2023 Holiday spending report Experian's annual Holiday spending trends and insights report analyzed recent trends, consumer spending habits, and anticipated what was to come in the 2023 holiday shopping season. This blog post covered three key insights from our report: Consumers are starting their holiday shopping earlier, particularly with online sales. Online sales have been increasing year-over-year, surpassing in-store sales. Spending during the 2022 holiday season was lower than in previous years but is expected to be on par with what was seen in 2023. For advice from our experts and access to all of our predictions for this year’s holiday shopping season, download our 2023 Holiday spending trends and insights report today. #1 How contextual ad targeting addresses signal loss Our most viewed blog post of 2023 was How contextual ad targeting addresses signal loss. Marketers are seeking new solutions due to signal loss caused by the phasing out of third-party cookies. Contextual ad targeting offers a way to combine contextual signals with machine learning for more accurate targeting. Experian's Jason Andersen and Yieldmo's Alex Johnston discuss the challenges of signal loss, addressability, the importance of good creative, and tips for digital ad success in this blog post. By understanding contextual advertising, marketers can create powerful and effective campaigns to reach target audiences. Subscribe to our newsletter Sign up for our email newsletter to receive our latest blog posts, product and partnership announcements, thought leadership, and more straight to your inbox. Subscribe Latest posts
In this article… Why AI and CTV are a great match The rising popularity of CTV How is AI already being used in CTV? Demand-side platforms Artificial intelligence (AI) and connected TV (CTV) have a perfect synergy that’s revolutionizing how advertisers connect with their audiences. CTV serves as a medium for streaming content, while AI acts as a sophisticated technology that improves the performance of CTV advertising campaigns. The integration of these two technologies has paved the way for advertisers to reach their target audience more effectively, making CTV advertising a powerful and efficient tool. In this blog post, we’ll dive into how these technologies work together — and why you should jump on board with AI for CTV advertising if you haven’t already. Why AI and CTV are a great match CTV and AI are transforming how advertisers connect with their audiences and improving the performance of their advertising campaigns in the CTV space. They work together to make advertising smarter and more enjoyable for everyone involved. AI uses sophisticated computer programs to analyze and understand data, while CTV refers to the streaming services that consumers use at home. But what makes them a great match in advertising? AI uses data to determine which TV ads are most exciting and relevant to certain people, and it can even adjust ads in real time to ensure viewers are always getting the most personalized experience. AI can provide suggestions to viewers based on previously watched content to help them find what they’d enjoy watching next. To sum it up, AI allows for: Precise targeting: AI uses data to determine which TV ads are most exciting and relevant to certain people. Personalization: AI can adjust ads in real time to ensure viewers are always getting the most personalized experience. Effective ad insertion: AI can provide suggestions to viewers based on previously watched content to help them find what they’d enjoy watching next. CTV facilitates these AI-driven strategies for enhanced user engagement and satisfaction. The rising popularity of CTV CTV has become increasingly popular as people change the way they watch TV. Instead of the traditional approach, more viewers are now choosing CTV platforms for their entertainment. One of the main reasons for this shift is that CTV offers greater flexibility and lets viewers watch content at their convenience. The ability to skip ads on many CTV platforms also improves the experience. CTV offers a great opportunity to interact with your target audience in a more engaging way. CTV allows for highly targeted advertising capabilities so you can reach specific demographics and households with tailored messages. Additionally, CTV provides valuable data insights that enable you to measure campaign effectiveness accurately. If you haven’t embraced this advertising channel yet, you may be missing out on a growing and engaged audience. Here are three reasons you should add CTV to your advertising strategy. Global video ad impressions As a global platform, CTV has the unique ability to reach audiences worldwide. Unlike traditional TV, CTV transcends geographical boundaries and brings marketers a global audience, which makes it an ideal channel for global ad campaigns. No matter your target audience, they’re consuming content on CTV. In fact, a recent study showed that 51% of global video ad impressions came from CTV in 2022. This abundance of global video ad impressions generates vast amounts of data, which AI can process in real time to help you make data-driven decisions and optimize your campaigns for diverse international audiences. AI can analyze viewer data from various regions, identify audience preferences and behaviors across borders, and tailor ad content accordingly. These data analysis capabilities ensure your ads get in front of the right viewers. Viewers prefer ad-supported CTV In 2020, the viewing time of ad-supported CTV surged by 55% while subscription video on demand decreased by 30%, according to TVision Insights. Viewers have a well-established preference for ad-supported CTV due, in part, to cost-effective access to premium content. Viewers are more engaged and less resistant to ads, as AI tailors ad content to viewer preferences and behavior to enhance ad relevance. AI-powered insights can also aid in viewer retention and help you optimize your CTV campaigns. By accommodating viewers’ preference for ad-supported CTV and harnessing AI to improve the ad experience, you’re more likely to be successful in your marketing efforts. CTV outpaces mobile and desktop for digital video viewing eMarketer recently reported that U.S. adults spend 7.5+ hours each day on CTV — more than half of their digital video viewing time. Comparatively, they only spend 37.5% of their viewing time on mobile and 10% on desktops and laptops. These statistics demonstrate that CTV has become the preferred platform for digital video consumption, as viewers enjoy larger screens with superior quality for an immersive experience. It's important to note that AI is an essential CTV marketing tool, as it allows for precise targeting and content optimization. By utilizing AI on CTV, you can take advantage of this trend and deliver more engaging and effective campaigns to a growing and engaged audience. How is AI already being used in CTV? CTV has been integrated with AI across various facets and has revolutionized the television landscape. Here's a look at how AI is already shaping the CTV experience: Generative AI ads Generative AI ads are taking CTV personalization to a whole new level. These innovative ads are customized versions of the same CTV ad to suit individual viewers. Some AI tools can generate several versions of the same CTV ad — swapping the actor’s clothing and voiceover elements like store locations, local deals, promo codes, and more — and can create up to thousands of personalized iterations in just a few seconds. Such capabilities are a game-changing approach to connecting with your audience. Next, we dive into the advantages and impact of generative AI ads, and explore their transformative role in CTV advertising. Contextual ads vs personal data Generative AI ads use personal data, such as viewing history and demographics, to create highly personalized ad experiences. This sets them apart from contextual ads, which rely solely on the content being viewed. Using AI to harness this data, you can move beyond traditional contextual targeting and ensure your ads connect with viewers on a more individualized level. Generative AI ads can be used to A/B test Generative AI ads are not just about personalization; they also open the door to A/B testing. Being able to create several versions of one ad quickly allows you to experiment with various ad elements, such as messaging, visuals, and calls to action, to identify what works best for different segments of your audience and drives the best performance. This flexibility is especially valuable for refining ad campaigns and maximizing their impact. What’s next for AI-generated ads like this? The potential of AI-generated ads is exciting. As AI technologies constantly advance, we can expect even more personalized and automated CTV advertising. It’s a good idea to keep up with the latest AI-driven innovations to create more effective ad campaigns in the fast-evolving CTV space. The possibilities are endless, and you’ll likely find the most success when you embrace AI in CTV advertising. Optimize streaming quality AI helps viewers enjoy more seamless CTV experiences. By assessing network speed and user preferences, AI optimizes video quality in real time to reduce buffering interruptions. For instance, streaming platforms use AI to adjust video settings based on a user's connection speed. This guarantees an uninterrupted and enjoyable viewing experience. Review content for compliance AI also has a part to play in quality assurance and compliance management. It assesses content alignment with technical parameters and moderates compliance with local age restrictions and privacy regulations. This means AI can identify and filter out unsuitable content to provide a safer and more enjoyable viewing environment for audiences while safeguarding brands from association with undesirable material. Voice command AI-powered voice command technology is increasingly used to control CTV viewing. This technology is embedded in streaming devices and smart TVs and allows viewers to interact with their CTV content through voice-activated commands. This personalizes the viewing experience and improves convenience, as it eliminates the need for remote controls. CTV-integrated voice assistants like Google Assistant, Amazon Alexa, Apple Siri, and Samsung Bixby offer a more human-like interaction with the television, allowing users to give commands and receive tailored responses. Content recommendations AI can offer content recommendations that provide viewers a more personalized and engaging experience. Major over-the-top (OTT) services like Netflix, Hulu, and Amazon Prime use AI-driven data analysis to deliver tailored content suggestions to their audiences. By analyzing user habits in detail, AI can recommend content based on factors such as actors, genres, reviews, and countries of origin. This personalized approach helps viewers discover content that matches their preferences and enhances their viewing experience. Advertising Programmatic ad buying, driven by AI, automatically matches ad placements to specific audience segments based on behavioral patterns. It improves ad delivery by moving away from gross rating points (GRP) to more intelligent and targeted placements. This benefits marketers by ensuring ads are seen by the right people at the right time. It’s also cost-effective for publishers, as it maximizes the sale of ad spots to suitable buyers. Automatic content recognition (ACR) technology, which AI powers, is integrated into smart TVs and streaming devices to improve ad relevance. It provides contextual targeting and extends the reach of ads across multiple devices. For example, platforms like Roku use ACR data to display ads to viewers who haven’t seen them on traditional TV. Similarly, Samba TV retargets mobile users based on IP address and aligns their viewing habits with their smart TVs. Demand-side platforms CTV advertising relies heavily on demand-side platforms (DSPs) to efficiently manage and optimize ad campaigns. These platforms use machine learning and AI in several important ways: Using machine learning and AI to address data fragmentation Data is abundant but fragmented when it comes to CTV advertising. DSPs are flooded with a massive amount of data, including information about households, viewer behavior, and viewing patterns. This data is far too much for manual analysis to handle effectively, which is where AI comes in. By integrating machine learning algorithms into DSPs, AI can harmonize this fragmented data and provide valuable insights and a holistic view of your audience. AI can process zettabytes of data in real time, which streamlines the decision-making process and empowers you to compete quickly for limited CTV impression opportunities. Predicting advertising outcomes with AI AI is quickly changing the way we predict and optimize advertising outcomes. TV buying and optimization platforms are now using AI to improve ad performance. With machine learning, these platforms can anticipate which ad creatives will produce the best results based on various non-creative factors. These include the context of the ad, the audience's profiles, the time of day it is displayed, and the frequency of the ad display. By relying on AI to make these predictions, you can make sure your campaigns are highly optimized for success and deliver more relevant, compelling ads to viewers. Optimizing generative ads AI is also driving optimization in generative ads. These personalized versions of the same CTV ad can be tailored to suit individual viewers. By utilizing AI-driven analytics, DSPs can process extensive amounts of data in real time and optimize generative ads to ensure they align with viewers' preferences and behaviors. This level of personalization is a game-changer in CTV advertising that boosts engagement and delivers content that truly resonates with the audience. Add AI to your CTV strategy today Integrating AI into your CTV strategy can help you stay competitive and ensure your ad campaigns are effective and engaging. At Experian, we’re ready to help you elevate your CTV advertising and implement AI as part of your strategy. Our solutions, such as Consumer View and Consumer Sync, provide valuable audience insights, enhance targeting capabilities, and optimize engagement on TV. Plus, our partnerships with leading media marketing solutions can help you achieve greater success through effective advanced television advertising. As you incorporate AI into your CTV strategy, you’ll be able to make more data-driven decisions, deliver more relevant content, and reach the right audience at the right time. Explore Experian's TV solutions and empower your CTV advertising with AI today. Start exploring Latest posts
The digital advertising landscape is undergoing a significant transformation with the gradual deprecation of third-party cookies. This shift necessitates the adoption of new strategies for audience targeting and data management. In our next Ask the Expert segment, we explore this evolution, discussing new strategies for audience targeting and data management in a world without cookies. We’re joined by industry leaders, Gabe Richman, Senior Director of Data Partnerships at The Trade Desk, and Chris Feo, Experian’s SVP of Sales & Partnership who spotlight The Trade Desk’s innovative approach to navigating a cookieless future. Tune in to our Q&A below to learn more about these topics and learn how the collaborative efforts of Experian and The Trade Desk offer a glimpse into the future of advertising. Cookieless IDs are the new face of identity in advertising Traditional tracking and targeting techniques are being replaced by more advanced and privacy-conscious methods. Unified I.D. 2.0 (UID2), led by The Trade Desk, exemplifies this shift, offering a new identifier based on encrypted email addresses or phone numbers. This approach not only caters to the evolving privacy regulations but also places greater control in the hands of consumers. UID2’s design fundamentally differs from cookies since it is rooted in transparency and consumer consent. UID2: A catalyst for industry-wide adoption UID2’s journey reflects a rising industry-wide recognition of its value across the industry. The adoption of UID2 by major publishers, demand-side platforms, and advertisers indicates a shift toward more sustainable and consumer-friendly approaches to identity in advertising. This is particularly evident in areas like connected TV (CTV), where UID2 is rapidly becoming a currency and standard. “The purpose of UID2 is not only to create a better ID for advertisers and publishers to achieve their objectives, but also to benefit the consumers. Unlike cookies, UID2 provides transparency and control to the consumers for the first time.”gabe richman, sr. director, data partnerships, the trade desk How Experian and The Trade Desk work together The partnership between The Trade Desk and Experian goes beyond adapting to the absence of cookies. Our joint efforts highlight a commitment to developing solutions that cater to advertiser’s needs while respecting consumer privacy, a balancing act becoming increasingly crucial in today’s digital ecosystem. The Trade Desk's emphasis on UID2 as a foundational element in the open web, campaign design, and activation is a testament to the potential of new identifiers in enhancing advertising efficacy. Similarly, our ability to utilize these identifiers to deliver detailed audience insights offers advertisers a powerful tool to remain effective in a post-cookie world. Experian’s role in the adoption of UID2 Experian's integration strategies have played a critical role in diversifying the applications of UID2. By partnering with The Trade Desk, we help broaden the reach and effectiveness of UID2 across various advertising channels. In terms of reach – by incorporating a prominent cookieless ID, we further amplify the reach of UID2. The increased adoption of this new ID allows the digital ecosystem the ability to interact using an alternative identifier, thereby broadening the potential audience. In terms of effectiveness – we help advertisers serve relevant ads to the right audiences, ensuring the relevance of the ads and control over their frequency. Targeting with Geo-Indexed audiences The Trade Desk works with Experian to ingest and host our syndicated audiences. This partnership gives The Trade Desk’s clients access to over 2,400 syndicated audiences that span across eight verticals. This includes access to our new Geo-Indexed audiences that allow brands to reach consumers and households based on geographic regions that over-index for a common set of attributes, ultimately offering brands a targeting solution that prioritizes both consumer privacy and accuracy. What a future beyond cookies looks like Looking ahead, the focus in advertising is not solely on replacing cookies but on a broader evolution of the industry. This includes continuing to apply machine learning technologies, like artificial intelligence (AI), to enhance ad personalization and effectiveness. The interplay between creative content, audience insights, and privacy-compliant targeting will become increasingly important as the industry evolves. As cookies become a thing of the past, the initiatives spearheaded by The Trade Desk and Experian will likely set the tone for the next era of digital advertising, and emerging solutions like UID2 are leading the way. Watch the full Q&A Visit our Ask the Expert content hub to watch Gabe and Chris' full conversation about cookieless advertising. In their conversation, Gabe and Chris share more about UID2, consumer transparency, and the importance of consumer data for targeted advertising. Watch now About our experts Gabe Richman, Sr. Director, Data Partnerships, The Trade Desk Gabe Richman is the Senior Director of Data Partnerships at The Trade Desk where he focuses on global identity strategy and platform partnerships as well as driving UID2 and EUID adoption across the broader ecosystem. Prior to joining The Trade Desk in 2021, Gabe held various roles in AdTech at HealthVerity, Wunderkind and LiveRamp. For the last decade Gabe has taken pride in helping advertisers and platforms alike demystify the complex identity landscape and embrace the change needed to preserve the open internet. Gabe is a graduate of the University of Maryland and resides in Los Angeles. Chris Feo, SVP, Sales & Partnerships, Experian As SVP of Sales & Partnerships, Chris has over a decade of experience across identity, data, and programmatic. Chris joined Experian during the Tapad acquisition in November 2020. He joined Tapad with less than 10 employees and has been part of the executive team through both the Telenor and Experian acquisitions. He’s an active advisor, board member, and investor within the AdTech ecosystem. Outside of work, he’s a die-hard golfer, frequent traveler, and husband to his wife, two dogs, and two goats! Latest posts