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
Privacy-forward pharma media supports more informed healthcare journeys Effective pharma marketing depends on reaching patients, caregivers, and healthcare professionals (HCPs) with useful information in appropriate, privacy-forward ways. The goal is to make relevant education easier to find, support more informed conversations, and help brands understand how media contributes across the health journey. In this Ask the Expert session, Natalie Mancuso, SVP, Data Partnerships at DeepIntent, joins Sheila Wirick, who leads the health team at Experian, to discuss how to craft patient and HCP campaigns, privacy-safe activation workflows, connected TV (CTV), and measurement approaches built for pharma. Why should your media plan start with understanding the patient and provider The patient-and-provider journey should inform your media plan, as treatment decisions in pharma are increasingly informed, personal, and patient-driven. With more health information available than ever, patients are playing a more active role in understanding their options and participating in treatment conversations. As other industries become more customer-centric, pharma media planning needs to become more patient-centric. “We’ve got patients who are more informed, more involved in their treatment decisions than ever. The accessibility of data and information and the ways we can consume it with just AI alone is so vast that if we’re not arming people with the right information in real time, we’re missing the boat.”Natalie Mancuso, SVP, Data Partnerships This means media should be aligned to where a patient or provider is in the decision journey and what they need at that moment. Early on, media may play an educational role by building awareness of a condition, symptom, or treatment category. Later, it may help support more specific questions, treatment consideration, access, affordability, or adherence. The patient-provider conversation remains the key boundary. That exchange should stay private and clinically led. Media can play a valuable role before and after those moments by offering information that helps patients and providers feel more informed. How can brands connect patient and HCP messaging responsibly? It’s critical for agencies and brands to deliver relevant messaging to inform each stakeholder’s needs throughout the treatment journey, especially ahead of key clinical milestones. To execute this, brands must exercise privacy-forward activation, keeping patient and HCP identity, activation, and measurement paths separate, while using a common workflow layer to support the same brand goal. Experian helps pharma marketers do this by acting as an independent, privacy-forward identity, data, and workflow layer. We bring together high-fidelity matching, governed onboarding, and non-clinical consumer context, so patient and HCP engagement can be activated more accurately across channels and connected to reporting. The distinction is not only where each audience is reached. HCPs may be engaged in both professional and personal environments, but their needs are different from patients’. For example, an HCP may receive messaging about co-pay savings for eligible patients, while a patient may receive information about patient support programs. These are different messages for different audiences, but they ladder up to the same goal: informing both parties in a privacy-forward, relevant way. Hear from Natalie Mancuso in our 2026 State of advertising report In our 2026 State of advertising report, Natalie shares why identity serves as the connective infrastructure that links planning, activation, and measurement across connected TV (CTV), programmatic, commerce media, and walled garden environments. Download the report Watch Natalie’s Q&A What does privacy-forward activation look like in pharma? Pharma brands often start with fragmented patient and HCP signals. Privacy-forward activation turns those inputs into usable audiences by first confirming that the data can be used, then translating identity in controlled environments that protect personally identifiable information through tokenization or other methodologies. For DTC, that means building and matching audiences without relying on inferred health conditions from browsing behavior. For HCPs, it means connecting professional identity to verified sources in a way that supports activation and measurement. In a category shaped by HIPAA, state privacy laws, internal review, and consumer sensitivity, building privacy in from sourcing through activation is what makes responsible health media possible. How can CTV play a more useful role in pharma? CTV can play a more useful role in healthcare when it’s connected to a larger, privacy-forward plan and not treated as just an awareness channel. DeepIntent connects CTV and digital exposure data using identity signals, such as National Provider Identifiers (NPIs), hashed email addresses, IP addresses, device IDs, and household signals, where permitted and reviewed. That identity layer helps support sequencing and suppression across streaming, endemic, mobile, and digital settings. In practice, CTV can introduce targeted education or broad brand awareness, and later touchpoints can provide sequential information in the right channels. Instead of repeating the same message, brands can make pivots based on engagement and context. “For us, it starts with identity and not panels. When we’re resolving CTV and digital exposures to the same person, we’re leveraging signals so that every touchpoint across streaming, endemic, and mobile is recognized as the same individual, not model lookalikes.”Natalie Mancuso, SVP, Data Partnerships For pharma, campaign performance should not be measured by reach alone. A stronger approach looks at whether sequenced engagement contributes to meaningful downstream signals, including: Prescription activity Provider engagement Referral patterns Patient compliance Adherence signals, where measurement is permitted Why should messaging and reporting change in pharma? Pharma campaigns are journeys with changing information needs. Reporting should guide the campaign optimization. “If I’m launching into a market with a new class of drug, I’m asking what education needs to be put into the market – from both an HCP and a consumer standpoint – to build awareness?”Natalie Mancuso, SVP, Data Partnerships Clicks and website visits still have a place, but they can’t tell the full pharma story. DeepIntent processes data in real time, giving brands an up-to-date read on how media connects to campaign engagement. Campaign signals can help teams optimize audience logic, creative, channel mix, or sequencing during a live campaign. Heading Test for Media & Text Block Content Test for Media &Text Block Learn more Heading Test for Media & Text Block Content Test for Media &Text Block Learn more Heading Test for Media & Text Block Content Test for Media &Text Block Learn more Explore how privacy-safe pharma activation can make an impact Experian brings marketing data and identity expertise for health marketing and DeepIntent adds pharma-specific activation experience. Together, we help brands connect patient and HCP strategies responsibly, with privacy built in from the start. Watch the full video with our experts to hear Sheila Wirick and Natalie Mancuso discuss patient and HCP messaging, CTV, privacy-safe activation, and outcome measurement for pharma brands. About our experts Natalie Mancuso SVP, Data Partnerships, DeepIntent Natalie Mancuso is SVP, Data Partnerships at DeepIntent and a leader in health-focused AdTech and DSP strategy. With over 20 years of experience growing billion-dollar healthcare brands, she turns complex data into scalable, performance-driven solutions that keep marketers ahead of industry change. Sheila Wirick Director of Health Sales, Experian Sheila Wirick is the Director of Health Sales at Experian with more than 20 years of experience in data and analytics. As Director of Health Sales at Experian Marketing Services, she collaborates with healthcare, pharmaceutical, and nonprofit organizations to apply data and insights that enhance customer retention, brand awareness, and acquisition. She is committed to helping organizations use data to deliver measurable results and meaningful impact. FAQs What makes a pharma audience activation-ready? A pharma audience is activation-ready when patient or HCP inputs can be translated into usable audiences with the right accuracy, controls, interoperability, and destination readiness. For patient engagement, that may include privacy-safe cohort construction, caregiver logic, non-clinical enrichment and appropriate exclusion rules. For HCP engagement, it may include governed identity inputs such as NPI, specialty, practice location, professional address and other verified attributes where appropriate. How can pharma brands sequence messages across channels responsibly? Pharma brands can sequence messages responsibly by giving each channel a clear role and using audience logic to maintain continuity. CTV and out-of-home can support awareness, social can reinforce the message, search can capture active research and point-of-care or rep-triggered channels can support decision moments. The workflow should preserve separation between patient and HCP identity strategies. What should pharma marketers measure beyond clicks and site visits? Pharma marketers should measure whether media connects to outcomes such as audience quality, prescription activity, qualified HCP engagement, verified delivery, referral patterns, therapy starts or adherence signals where permitted. These outcomes often depend on partner-enabled workflows, clean rooms, and aggregated outputs rather than direct end-to-end tracking. How does Experian help pharma brands connect identity, activation and measurement? Experian acts as an independent privacy-first identity, data, and workflow layer for pharma marketers. We help brands bring together identity, high-fidelity matching, governed onboarding, non-clinical consumer context, and partner interoperability so patient and HCP engagement can be activated more accurately across channels and connected more cleanly to partner-enabled measurement. Latest posts
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Use case #1: Identifying customer spending potential to boost growth for a retail chain Objective Solution Results The solution By syncing its cookies with the Digital Graph, the DSP gained access to related identifiers, including: This expanded identity universe gave the DSP a unified view of individuals and households, making it possible to connect impressions to conversions across devices and channels. With each weekly refresh, attribution models stayed accurate and up to date, turning fragmented signals into proof of performance. Results Within weeks, the DSP saw measurable improvements