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
Over the last few months, Experian has released new syndicated audiences to most major platforms supporting retail and travel. In this blog post, we’ll highlight some of these new audiences and how they can be used with other data from Experian to build the perfect audience to reach your customers and prospects. What separates Experian's syndicated audiences Experian’s 2,400+ syndicated audiences are available directly on over 30 leading television, social, programmatic advertising platforms, and directly within Audigent for activation within private marketplaces (PMPs). Reach consumers based on who they are, where they live, and their household makeup. Experian ranked #1 in accuracy by Truthset for key demographic attributes. Access to unique audiences through Experian’s Partner Audiences available on Experian’s data marketplace, within Audigent for activation in PMPs and directly on platforms like DirectTV, Dish, Magnite, OpenAP, and The Trade Desk. Household Expenditure audiences We’ve created new predictive audiences to help retailers reach consumers across 35 categories likely to spend within that category. A few categories include Apparel, DIY, Health, and more. With the launch of these new audiences, we will retire our existing Household Consumer Expenditure, Online and Retail category audiences in the November Digital Master update. Who these audiences are for Our Household Expenditure audiences use data from multiple sources, providing brands with highly accurate purchase predictions and data that scales for digital execution. Household Expenditure audiences are an excellent solution for brands with new product lines or where targeting based on historical purchases lack signal brands seek. Building data from multiple data sources helps ensure high performance and accuracy and can illuminate trends in consumer shopping patterns. These trends can be used to help predict future shopping behaviors. How to refine our Household Expenditure audiences To refine your audience, you can combine this data with Experian’s demographic and household expenditure audiences to ensure you are reaching consumers. For example, suppose you’re an apparel brand launching a new line aimed toward women over the age of 40. In that case, you can use Experian’s demographic data to reach those women and layer in our household expenditure purchase predictor segment for women's apparel to reach their new target audience. Mobile Location audiences We’ve expanded our location database to include more locations and points of interest. With this new data, we could strengthen our existing mobile location audiences to broaden the reach, improve accuracy, and increase performance. We’ve created 11 new mobile location audiences with our new dataset that supports the retail and travel verticals. These new audiences include new shopping behaviors, including high-income and high-end shoppers and travel and entertainment behaviors, including visiting sporting arenas like MLB, NBA, NFL, and university stadiums. Who these audiences are for These audiences are for brands that want to reach consumers based on their location behaviors. Often valid for retail, travel, and entertainment brands, Mobile Location audiences provide brands with highly accurate data that shows previous intent and interest in critical locations. How to refine our Mobile Location audiences To refine your audience, you can combine your Mobile Location audience with Lifestyle and Interest data. For example, if you are creating an advertising campaign for a hotel near a university stadium for the largest game in the season, you could combine university stadium visitors with sports enthusiasts and in-market for travel to find consumers most likely to be interested in your campaign and staying at the hotel. Purchase-Based Transaction audiences For use cases where predictive audiences aren’t the best fit to reach the right consumer, such as targeting consumer’s historical purchases, we’ve created new purchase-based transaction audiences that utilize opt-in consumer transaction data across 29 retail categories, including apparel, home, lifestyle, health, food and beverage, and more. Who these audiences are for These audiences are a perfect fit for brands trying to reach consumers based on previous purchases. These audiences can be broken out by their spending patterns – frequency of purchase and high spenders – and their response to advertising, including direct mail, email, inserts, and digital. How to refine our Purchase-Based Transaction audiences Combine these new audiences with Mosaic to fine-tune your audience based on their purchasing and lifestyle patterns. Suppose you are a brand with a new line of home décor products launching and will utilize influencers to endorse your product line. In that case, you can use Experian’s purchase-based transaction audiences for high spenders in home décor and layer our Mosaic audience Influenced by Influencers to find consumers who are most likely to purchase and trust an influencer. We can help you discover and activate your perfect audience Our audiences are available in most major data and execution platforms. Visit our partner page for more information. Don’t see our audiences on your platform of choice? We can help you build and activate an Experian audience on the platform of your choice. Contact us Latest posts
The AdTech industry is buzzing with discussions about cookie deprecation and effective strategies to tackle it. One of the commonly suggested solutions is the utilization of clean rooms alongside responsibly sourced first-party data. Above all else, the industry recognizes the importance of respecting consumer data and complying with all privacy laws. Additionally, the industry acknowledges the need for a change in our historical practices. This shift benefits everyone involved, as consumer data is more secure than ever. Tremendous investments have been made to ensure the utmost security of consumer information. Clean rooms are one of the tools that enable companies to use data securely, ensuring the content that you see is as relevant as possible. Two ways the AdTech industry is addressing cookie deprecation The days of sending data directly to partners for usage or for using only third-party data for marketing efforts are gone. Now, the emphasis is on responsibly collecting first-party data and using clean rooms to enrich first-party data to enhance marketing efforts. First-party data The industry is starting to lean into first-party data gained through transparent means. This valuable information provides organizations with deeper insights into their customers, allowing for more personalized and effective interactions. By embracing the power of first-party data, either on its own or enriched via partner collaboration, you can cultivate stronger relationships, build trust, and deliver tailored experiences that resonate with your customers on a deeper level. Clean rooms Many data lakes and warehouses offer this service, ensuring their clients can not only store their data with them but can connect it with other partners in a secure environment and extract more information through the combined data sets versus their data on its own. Brands and their partners recognize that they need to work together, and a clean room provides a secure environment to share their first-party data without exposing their sensitive data to their partner. So, while we're losing third-party cookies, brands and partners can still get value from first-party data by using a clean room to generate audience insights, segmentation strategies, personalized experiences and offers, media plans, and measurement and attribution. Three ways data clean rooms can improve Data clean rooms are a great way to facilitate data collaboration while ensuring sensitive data is not exposed. Data clean rooms are not yet easy to use nor are they inexpensive. They require investment, both financially and resource allocation-wise, and you are not guaranteed to yield great match results. Let’s dive into three areas for data clean room improvement. High cost According to the IAB's State of Data 2023, nearly two-thirds of data clean room users spent at least $200K on the technology in 2022. In addition, one-third of data clean room users expect the price of data clean rooms to rise in 2023. The high cost of this solution can make it inaccessible to smaller companies in the advertising space. Resource intensive Nearly half of the companies using data clean rooms have a team of six or more dedicated to the technology, according to the IAB’s State of Data 2023, while nearly a third of companies using data clean rooms have 11 or more employees focused on the technology. Data clean rooms are not turnkey solutions. Inefficient matching Even if companies are using clean rooms does not mean that they are automatically going to achieve great success. Identity fragmentation, data hygiene, and differing identifiers can suppress client match rates in clean rooms, leading to significant investment and a lackluster output. How to get the most return on your clean room investment The finish line for data collaboration in clean rooms is not just having a relationship with a clean room. Instead, you should incorporate an identity resolution solution in your clean room. By adding an identity solution to your clean room, you can: Resolve and match all your identity data, regardless of the identity data that you or your partner have, giving you a larger data foundation to analyze. Generate more valuable insights and information, leading to a better experience for your customers. Join data sets to create smarter activation and targeting strategies and produce more holistic measurement. Experian can help you get started with identity resolution and data clean rooms If you are investing in data clean rooms, that means you are committed to the best in data practices. Experian recommends going the extra step and that you also invest in finding an identity resolution solution. By doing this, you can see better match rates. Experian offers this capability and has existing relationships with three clean room partners, Amazon Web Services, InfoSum, and Snowflake. In addition to collaborating in clean rooms, we offer collaboration in two other secure environments. Contact us today to discuss how we enable identity resolution in clean rooms or to chat about our other collaboration capabilities. Get in touch Latest posts
Ongoing signal loss is driving marketers, agencies, and platforms to turn to supply-side advertising. By using first-party data from publishers and platforms, supply-side advertising has the potential to deliver high-quality audience and context for more effective ad targeting. The supply-side refers to the publishers and platforms that sell advertising inventory. These companies have access to first-party data about their users, which can be used to target ads more effectively. By tapping into supply-side advertising, you can overcome the challenges of signal loss and target ads more effectively. To shed light on this topic, we hosted a panel discussion at Cannes, featuring industry leaders from Audigent, Captify, Newsweek, Pubmatic, Truthset, and Experian. In this blog post, we'll explore how partnerships between supply-side channels and publishers are working to enhance advertising opportunities while balancing the need for transparency and control in programmatic ad buying. Shift toward supply-side advertising Traditionally, the demand-side dominated the programmatic media buying chain due to an abundance of supply. However, with the emergence of finite data and its interpretation, collaboration between supply-side technology companies and publishers is required to redefine these economics. It's no longer sufficient for the demand-side to blindly negotiate prices based on limited knowledge. Marketers can still define their target audience, but effective communication is key. This presents an opportunity for premium journalistic outlets to guide the industry's understanding of how data from the supply-side impacts media buying economics in the future. "Supply-side technology partnerships with publishers are now in a position to shape the economics of programmatic media buying as there is a finite amount of data. It’s crucial for supply-side technology companies to collaborate with publishers to shape these new economics. This presents an opportunity for premium journalistic outlets to provide guidance on how data from the supply-side can affect the future of media buying." matthew papa, svp, business & corporate development, captify Democratizing data from the supply-side Cookies haven't brought significant benefits to premium publishers. They mainly serve to retarget users from sites like The Wall Street Journal to advertising sites. This approach primarily serves the purpose of generating revenue. The elimination of third-party cookies presents an opportunity for premium publishers to shift this dynamic. By using their knowledge of first-party audiences, and using identifiers like Experian's LUID, publishers can own and understand their audience data, which can then be modeled. Here’s how publishers can win Establishing a connection with consumers and emphasizing the value exchange is essential to building trust. Determining what incentives and benefits consumers find meaningful will be crucial in gaining their opt-in. With consumers The Apple tracking transparency initiative, specifically the deprecation of IDFA signals, had significant implications for mobile app developers. Overnight, opt-in rates plummeted, causing a drastic decline in iOS ad monetization. To combat this, developers focused on demonstrating the value exchange to consumers—better ad experiences and personalized content. By articulating the benefits over a couple of years, opt-in rates increased from 10-15% to 30-40%. The key takeaway is the need to effectively communicate the value exchange to consumers. With partners Trust plays a crucial role in planning your first-party data strategy. Publishers, advertisers, and data partners highly value their proprietary data. However, there are concerns about how it's used, mishandled, or leaked in the ecosystem. Building trust between partners is essential. It's important to work with trustworthy partners who are agnostic, committed to innovative solutions, and globally oriented. These partners can help navigate the complexities of laws and regulations. Choosing the right partners is crucial in a world where first-party data is a key asset. "Power is shifting toward brands that have strong relationships with customers and possess first-party data. As the ownership of customer data becomes more important, it is crucial to establish a first-party data strategy to better serve customers and adapt to changing market dynamics."chip russo, president, truthset Balance probabilistic and deterministic data Focus on building trust with consumers and collaborating with reliable companies to share data. However, it's important to remember that achieving a 100% opt-in rate is unlikely. The cookie, which has become omnipresent, requires us to shift our strategic thinking. We need to consider both deterministic and probabilistic approaches instead of viewing them as mutually exclusive. The landscape will be fragmented, with some consumers opting in and others not. "Probabilistic and predictive audience data holds immense potential. With the power of AI, we can expect enhanced performance and efficacy in media campaigns. At Audigent, we firmly believe that this data will outperform deterministic data, making it an integral part of our strategy." drew stein, ceo, audigent Premium content Trust plays a crucial role in leading to premium content. By placing trust in the best media brands, data, and technology partners, we can expect to see improvements in media, journalism, and advertising. This shift may have a direct impact on the long tail of free natural resources, making it more challenging for them to thrive. However, this change is ultimately beneficial since it promotes higher-quality media experiences overall. "The homepage surface is making a comeback in the publishing industry, proving its value in establishing a direct connection with readers. While we acknowledge the importance of technology partnerships for addressability and identity, our core competency as a publisher remains outstanding journalism that captures and engages great audiences." kevin gentzel, cco, newsweek Watch our Cannes panel for more on supply-side advertising We hosted a panel in Cannes that covered supply-side advertising. Check out the full recording below to hear what leaders from Audigent, Captify, Newsweek, Pubmatic, Truthset, and Experian had to say. Watch now Check out more Cannes content: Our key takeaways from Cannes Lions 2023 Insights from a first-time attendee Four new marketing strategies for 2023 Exploring the opportunities in streaming TV advertising The future of identity in cookieless advertising Follow us on LinkedIn or sign up for our email newsletter for more informative content on the latest industry insights and data-driven marketing. Contact us today Latest posts