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
Media is changing and the sell-side is stepping boldly into the identity jungle—a dense and complex environment where privacy regulations, evolving signals, and advertiser expectations make every step an adventure. It’s not about survival; it’s about navigation. Experian’s identity solutions offer sell-side players like connected TV (CTV) publishers, supply-side platforms (SSPs), and open web publishers a roadmap to deliver rich consumer insights and build addressable audiences. Here’s how different stakeholders are navigating the landscape—and why having the right sherpa makes all the difference. CTV publishers: Turning anonymous viewers into addressable audiences The surge in CTV viewing, fueled by the shift from linear TV to digital streaming, has made it a critical channel for marketers—but navigating the identity jungle isn’t the same for every platform. For major players like Netflix, Hulu, and Max, where users log in to access content, the challenge isn’t identifying viewers but enriching their profiles. By layering behavioral and purchase data onto these profiles, platforms can go far beyond insights on media habits to create highly attractive audience segments for marketers to target. For free ad-supported streaming TV (FAST) platforms like Tubi, where logins aren’t required to watch content, the jungle is denser. These platforms have unknown viewers they can’t identify, which limits their ability to know who the customer is and reach them with relevant ads. By utilizing identity solutions, FAST platforms can turn unknown users into addressable audiences, resolving viewership at the household or individual level. This transformation allows for personalized, relevant ads that increase engagement, boost inventory value, and unlock new revenue opportunities. How Experian can help Imagine a CTV platform struggling with anonymous viewers on its FAST channels, where users tune in without logging in. Using Experian’s household-level data, the platform can convert these anonymous sessions into known, addressable audiences. This allows for personalized, precisely targeted ads that boost viewer engagement and significantly increase ad inventory value. For platforms with logged-in users, Experian takes it further by enriching profiles with behavioral and purchase data. This deeper understanding enables even more precise ad targeting, stronger advertiser demand, driving higher CPMs, and ultimately greater revenue growth. With Experian, CTV publishers turn anonymity into opportunity and build meaningful connections across their audience. SSPs: Delivering premium audiences across channels SSPs are under pressure to differentiate themselves in a competitive marketplace. The days of simply aggregating inventory are gone; today, SSPs must prove their worth by delivering premium value to advertisers and publishers. Addressability is a cornerstone of this strategy. By combining demographic and behavioral data with offline and digital identifiers, SSPs can build and deliver high-quality audiences across various channels. At the same time, supply path optimization (SPO) is taking center stage. SPO acts as a machete in the underbrush, clearing out unnecessary intermediaries and reducing costs while creating direct, transparent pathways to premium, brand-safe inventory. When paired with identity data, SSPs can offer buyers precisely targeted audiences, more premium inventory and a streamlined supply path. How Experian can help Imagine an SSP striving to stand out in a crowded market by delivering premium value to advertisers and publishers. Experian’s Digital Graph and Marketing Attributes empowers SSPs to enhance addressability and audience insights by combining digital identifiers with demographic and behavioral data. This enriched understanding of an audience leads to greater reach for the buy side and higher revenue for publishers. Additionally, these capabilities enable SSPs to form exclusive inventory partnerships, positioning them as go-to sources for high-value audiences. With Experian’s solutions, SSPs can differentiate themselves by delivering superior targeting, deeper audience understanding, and streamlined supply paths that drive measurable results for advertisers and publishers alike. Open web publishers: Promoting addressability and audience understanding For open web publishers, programmatic advertising has created opportunities—and challenges. Inventory commoditization makes it difficult to stand out and often leads to suppressed CPMs. To compete, publishers need data and identity solutions that enable them to differentiate their inventory and reveal the true value of their audience. Similar to FAST publishers, the jungle for open web publishers often starts with anonymous visitors. Recognizing and identifying all their users allows publishers to present advertisers with rich audience insights that lead to more efficiently targeted ads. Publishers are now equipped to fight commoditization and maximize revenue potential. How Experian can help Imagine an open web publisher striving to deliver more value to advertisers in a crowded programmatic landscape. Experian’s identity solutions help publishers turn anonymous traffic into addressable audiences, enabling them to understand their visitors and provide richer audience insights. This allows advertisers to target ads more effectively, increasing engagement and driving higher ad revenue. With the ability to recognize their visitors and offer actionable data, publishers can break free from commoditization. Experian empowers publishers to maximize their inventory’s value and help marketers drive results. Turning identity challenges into a strategic advantage The identity jungle can feel daunting, but for those willing to explore its opportunities, the rewards are immense. Sell-side players—CTV publishers, SSPs, and open web publishers—have the tools to not just navigate but thrive in this dense and dynamic ecosystem. By embracing data-driven strategies and identity solutions, they can uncover new paths to audience engagement, inventory value, and revenue growth. Get started today Read our companion article to learn how the buy-side is approaching data and identity challenges. Read now Contact us Latest posts
In a perfect world, we’d all have a single, go-to grocery store that carried everything on our shopping list – fresh produce, gourmet coffee beans, rare spices, and maybe even that special-grade olive oil, right alongside our wholesale bulk purchases at unbeatable prices. It would be convenient and efficient, and it’d save a lot of driving around town. The changing data marketplace: From one-stop shop to specialized selection For a long time, data buyers enjoyed something similar in their world: a small set of large-scale data marketplaces that offered a wide array of audiences, making it easy to load up on whatever you needed in one place. Not only are there fewer places to pick everything up, but new factors like privacy and signal deprecation are placing a spotlight on quality and addressability. Just as our dinner plans are growing more ambitious insofar as we want health, flavor, value, and convenience all in one place – so are our data strategies. Instead of a single steak-and-potatoes meal, today’s data marketplace operators might be cooking up a complex menu of campaigns. As a result, data buyers are beginning to shop around. Some still rely on large-scale marketplaces for familiar staples, but now they have reasons to explore other options. Some are turning to providers known for offering top-tier, transparently sourced segments. Others are focusing on specialty providers that excel in one area. A more selective approach to data buying In this environment, choosing where to “shop” for data is becoming more deliberate and selective. Data buyers aren’t just thinking about broad scale; they’re looking to prioritize quality, durability, data privacy, and differentiation. They need to place higher value on data marketplaces that can maintain audience addressability over time, despite signal loss. Sometimes, that means accepting a smaller assortment in exchange for tighter vetting and more reliable targeting. Other times it means mixing and matching – stopping by one marketplace for premium segments and another for cost-friendly, wide-reaching data sets. Either way, they can benefit from having more choices. Experian’s marketplace: A trusted source for high-quality data Experian’s vetted and curated blend of data partners and vertically-aligned audiences offers a trusted specialty store for data buyers. Experian’s marketplace, powered by identity graphs that include 126 million households, 250 million individuals, and 4 billion active digital IDs, enables partner audiences to be easily activated and maintain high addressability across display, mobile, and connected TV (CTV) channels. In particular, Experian’s marketplace provides: Enhanced addressability and match rates All audiences delivered from the marketplace benefit from our best-in-class offline and digital identity graphs, which ensure addressability across all channels like display, mobile, and CTV. Unlike other data marketplaces, Experian ensures all identifiers associated with an audience have been active and are targetable, improving the accuracy of audience planning. Audience diversity and scale Access a broad range of audiences across top verticals from our partner audiences, which can be combined with one another and with 2,400+ Experian Audiences. The ability to join audiences across data providers ensures that buyers can build the perfect audience for the campaign. Trusted compliance and oversight With decades of experience, Experian is a trusted expert in data compliance. Our rigorous data partner review ensures available audiences comply with all federal, state, and local consumer privacy regulations. The future of data marketplaces: Precision and flexibility matter The evolution of data marketplaces reflects the industry's shifting priorities. Data buyers seek specificity, reliability, and adaptability to align with their diverse campaign needs. The best data strategy, much like the best grocery run, isn’t about grabbing everything in one place – it’s about carefully selecting the right ingredients to create the perfect recipe for success. This shift underscores the importance of flexibility and precision as data buyers navigate a landscape shaped by privacy regulations, signal loss, and evolving consumer expectations. As data marketplaces adapt to meet these demands, they are redefining what it means to deliver value. Experian’s marketplace enables buyers to strike the perfect balance between reach and quality by offering enhanced match rates, precise audience planning, and seamless distribution. In this new era, data buyers have the tools and options to craft campaigns that are impactful and aligned with the increasingly selective and privacy-conscious digital landscape. The key is recognizing that today’s data strategy is about utilizing the strengths of many to create a cohesive and effective whole. If you're interested in learning more about Experian's marketplace or becoming an active buyer or seller in our marketplace, please contact us. Contact us Latest posts
Conventional TV advertising campaigns have historically relied on general audience metrics like impressions and ratings to measure outcomes. These metrics can help marketers understand how many people have seen an ad, but they don’t reveal its real-world impact, which leaves a gap between ad exposure and results. Outcome-based TV measurement bridges this gap and helps marketers tie ad spending directly to their business goals. Instead of counting eyeballs alone, TV measurement zeroes in on what viewers do after seeing an ad — whether signing up for a service, visiting your website, or purchasing a product. TV ad measurement helps marketers adjust campaigns based on clear, trackable outcomes rather than guesswork. Let’s talk about how marketers can get started with outcome-based TV measurement and start experiencing tangible results. Why outcome-based TV measurement matters Outcome-based measurement indicates a massive shift in how marketers evaluate TV advertising success. As a principal analyst at Forrester explained, the industry is about to “move into a whole different world" where multiple metrics are tailored to advertisers’ unique goals, such as sales, store traffic, or web engagement. This shift is driven by improved tools for tracking TV outcomes, which help justify spending and clarify ROI. With TV measurement, you can see how your campaigns impact aspects of your marketing like sales and engagement. Aligning TV ad spend with business goals Every business has distinct objectives. Outcome-based measurement ties your marketing efforts to business goals and enables smarter decisions, campaign optimization, and ROI improvements. Whether you're a B2C brand wanting immediate sales or a B2B organization looking to drive website traffic, this method provides the insights needed for strategic decision-making. Marketers can deliver the most value by adjusting TV ad spending to maximize desired results: Sales goals: Identify which ads and platforms directly influence purchases to ensure TV ad spend contributes to revenue growth. Customer engagement: Link actions like website visits or app downloads to TV campaigns and refine messaging to deepen audience connections. Desired outcomes: Align ad spend with goals like consumer awareness or repeat purchases to allocate resources effectively for measurable success. Reducing wasted spend on ineffective channels Outcome-based TV measurement allows you to pinpoint which networks, times, or programs drive the most engagement and conversion. When you know your underperforming channels, you can reallocate budgets to those with a higher ROI and avoid waste. Core metrics in outcome-based TV measurement The effective implementation of outcome-based measurement requires advanced TV advertising analytics and tracking metrics that shed light on TV ad performance. Incremental lift This metric measures the increase in desired actions and business results — like purchases or site visits — that can be attributed directly to a TV campaign. Incremental lift quantifies your campaign’s impact and separates organic activity from the results your ads have driven. Let’s say a meal kit service experiences a 20% lift in subscriptions within a single week of running TV ads compared to a week without ads. They’d want to be able to isolate the impact of their ad from their organic growth so they can determine if the growth is actually a result of the TV ads or another effort. Attribution and conversions Attribution links TV ad exposure to specific customer actions, such as newsletter sign-ups and product purchases. Conversion data helps marketers understand the whole customer journey to optimize messaging, targeting, and channel mix to improve conversion rates. A retailer that knows 50% of TV ad viewers visit its e-commerce site within 36 hours of exposure could use that information to adjust the timing of its retargeting and align with site visit spikes. Audience segmentation for targeted measurement Outcome-based measurement breaks down performance across target demographics and allows for granular audience segmentation so TV ads resonate with the right audiences. For example, if a luxury brand saw better TV ad performance with high-earning Millennials, they’d want to refine their campaign messaging based on this group’s habits and preferences. Customer journey tracking Knowing how viewers move from awareness to conversion is critical. Outcome-based TV measurement helps you track the customer journey by pinpointing touchpoints where engagement happens and tying these to your TV campaigns. If a fitness brand found that TV campaigns drive app downloads, it could combine app analytics and TV exposure data to find out when most of their conversions happen after ad exposure and create follow-up messaging for that window of time. Integrating these insights with other marketing channels allows you to fine-tune your messaging, channel mix, and audience targeting to drive better outcomes and deliver more personalized customer experiences. Lifetime value (LTV) Beyond immediate conversions, outcome-based TV ad measurement helps brands identify which TV campaigns attract high-value customers with long-term revenue potential. If a financial institution ran a TV ad campaign centered on its new credit card, for instance, it could use LTV to track new cardholders and determine whether ads occurring during financial news airtime produced customers with higher average annual spend compared to other segments. How outcome-based TV measurement works Outcome-based measurement is a data-driven process that involves collecting, analyzing, and applying insights to improve TV ad performance. 1. Collect data When someone sees your TV ad, they might take action, like downloading your app or buying something. Outcome-based TV measurement begins by tracking these actions and gathering data from various sources, such as: TV viewership CRM Digital engagement Purchase behavior Cross-platform interactions And more Data integration with digital platforms Combining TV data with insights from platforms like social media or website analytics creates a more unified view of campaign performance. This integration powers easier retargeting and better alignment between digital and TV advertising strategies. Some marketers enhance this integration further using artificial intelligence (AI) to streamline data coordination and ensure campaigns are optimized for effectiveness and ROI. 2. Connect the dots Next, marketers need to find out which actions were influenced by TV ads. It’s important to ask questions like these as you work to connect the dots: Did website traffic spike right after the ad aired? Did the ad viewers match the people who signed up for the service or made a purchase? You can link TV exposure to real-world behaviors with tools and identifiers like hashed emails, device IDs, surveys, and privacy-safe data-matching techniques. 3. Analyze the data Then, the data needs to be analyzed for patterns like these: Which TV ads or time slots drove the most engagement? Did certain customer groups respond better than others? Was there a noticeable lift in sales or signups after the ad campaign? This step can help you uncover what’s working and what’s not. Role of advanced analytics and machine learning The data analysis required in this process can be overwhelming, time-consuming, and risky without the right tools. Fortunately, advanced analytics and fast, effective artificial intelligence tools can process large amounts of data from digital platforms, TV viewership, and customer interactions in less time to reveal accurate, actionable insights and patterns. They can also predict which audiences, messages, and channels will be most profitable so campaigns can adapt in real time, whether by reallocating spend to higher-performing channels or refining audience targeting. 4. Turn insights into action Once you have your data-derived insights, you can tweak your campaign in a number of ways, whether you decide to: Adjust your ads: If one message works better than another, lean into it. Refine your targeting: Focus on the audience segments most likely to act. Optimize your spend: Invest in channels or times that deliver the best return. For example, if you see that ads during prime time lead to more purchases than morning slots, you can shift your budget accordingly. This type of knowledge can be used to continuously improve your campaigns. Each time you run a new ad, you measure again, building on past insights to make your outcome-based TV advertising even smarter. Applications of outcome-based TV measurement Outcome-based TV measurement has wide-ranging applications across industries. Here’s how it’s helping businesses link TV ad exposure to real-world actions and optimize campaigns for better results. E-commerce and retail: Retailers can track how TV ads influence purchases and use those insights to refine their assets and target specific customer groups. A clothing retailer may track how well a TV ad boosts online traffic and in-store purchases. For instance, if a seasonal sale commercial correlates with a spike in website visits or mobile app downloads, the brand can refine its ad placement to focus on the most responsive demographics. Automotive: Automakers use outcome-based TV measurement insights to determine how ads drive dealership visits, test drives, or inquiries. A car manufacturer could analyze whether TV spots featuring a new vehicle increase traffic to its dealership locator or car configuration tool online. Healthcare: Pharmaceutical companies could assess whether TV spots lead to increased prescription fills, or a health provider could test how ads promoting flu shots result in appointment bookings through its website or app. If any messages resonate more with families, the provider can create similar campaigns for the future. How Experian enhances outcome-based TV measurement Experian has recently partnered with EDO, an outcomes-based measurement provider, to offer more granular TV measurement across platforms. Our identity resolution and matching capabilities enhance EDO’s IdentitySpine™ solution with rich consumer data, including age, gender, and household income, all in a privacy-centric way. Integrating these demographic attributes is helping advertisers achieve more precise audience insights and connect their first-party data to actionable outcomes. As a result of this collaboration, brands, agencies, and networks can optimize their TV campaigns by identifying which ads drive the most decisive engagement among specific audience segments. We’re improving accuracy, targeting, and more so advertisers can maximize the performance of their CTV strategies. Get in touch with Experian’s TV experts If you’re ready to take your data-driven TV advertising strategies to the next level, connect with our team. We combine advanced data and identity solutions as well as strong industry collaborations to help brands optimize their TV campaigns. Whether you're navigating traditional or advanced TV formats, our expertise ensures your efforts deliver maximum impact. Connect with us today to drive engagement, connect with audiences, and achieve better ROI. Let’s transform the way you measure success on TV. Reach out to our TV experts Contact us Latest posts