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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.
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In our Ask the Expert Series, we interview leaders from our partner organizations who are helping lead their brands to new heights in AdTech. Today’s interview is with Allison Dewey (Director, Data & Curation) at 33Across. Navigating complexity in digital advertising Digital advertising is more fragmented and privacy-constrained than ever. How is 33Across helping marketers cut through that complexity to drive real outcomes, and what makes your approach distinct? Reaching audiences without compromising scale or performance is one of the toughest challenges for marketers. Users consume content across multiple devices and channels, making it difficult for marketers to identify and accurately target them with the right message. 33Across segments leverage AI-powered contextual and behavioral signals across privacy-safe environments to help marketers accurately identify audiences, whether they’re streaming content on their connected TV or researching products on their mobile device. What sets us apart is that we don’t just identify valuable audiences; we help marketers also target cookieless inventory and power it with real-time signals. Marketplace integration milestones What excites you most about bringing 33Across audiences into Experian’s data marketplace? We’re excited to bring 33Across audiences into Experian’s data marketplace because it connects our unique segments with a powerful data ecosystem that marketers already trust. Buyers looking to activate audiences that are both privacy-safe and performant continue to tap into the Experian data marketplace for high-quality, high-performing data. We offer a wide range of audience verticals, including B2B, demographic, retail purchase data, interest and intent, and political data. In addition, we offer the ability to create custom segments across verticals. Our intent-based audiences, built from contextual and engagement signals, help buyers reach consumers on CTV, desktop, or mobile devices with scale. Being part of Experian’s data marketplace accelerates access to these audiences, drives better ROI, and helps brands future-proof their strategies today. Retail demand signals Retail brands are racing toward privacy-safe, first-party data. Which 33Across retail datasets or segments are experiencing the highest demand, and what makes them a must-have? Retail marketers are leaning into contextual and behavioral intent signals to complement their first-party data strategies. At 33Across, we’re seeing high demand for segments tied to shopping intent, including in-market consumers browsing for categories like fashion, home goods, electronics, and health & wellness. What makes these segments essential is their real-time nature – they can capture consumer interest as it happens. For retail brands looking to expand their reach while respecting privacy, our segments offer scalable, actionable intent that drives results. B2B without cookies Reaching real B2B decision-makers at scale is tough with or without signals. How does 33Across deliver both precision and reach in this environment? B2B marketing often struggles with balancing scale and specificity. 33Across addresses this by combining contextual precision with AI-modeled behavioral signals; this segment approach reaches professionals actively engaging with relevant content and topics, even in environments where IDs are unavailable. Marketers gain access to more signals and, in turn, better reach from 33Across’ unique publisher integrations and audience curation built from machine learning and AI. We surface intent through content consumption patterns and contextual engagement, unlocking valuable, privacy-safe signals at scale. Allowing B2B marketers to reach real decision-makers in a signal-sparse world. Use cases With retail, B2B, and beyond, can you share an example of how brands in these verticals are utilizing your audiences? Top brands that have a user-focused approach use 33Across audiences to drive scale; performance. These brands enable our segments to precisely reach the right users across devices and increase conversion rates; brand awareness. By reaching the right users, brands have higher conversion rates and increase campaign efficiency. Supply path innovation As identifiers disappear, advertisers are looking for scalable, privacy-safe ways to reach real people. How is 33Across helping unlock more addressable inventory and drive performance? By combining contextual, semantic, and engagement-based signals, we deliver intent-based targeting that performs across CTV, display and video. Higher addressability helps marketers not only extend their reach but also deliver personalized messaging across digital channels in a privacy-compliant way. Contact us FAQs How can advertisers reach audiences without traditional identifiers? By using contextual and engagement-based signals, advertisers can target consumers across CTV, mobile, and desktop in a privacy compliant way, even as identifiers become less available. What audience segments are most in demand for retail marketers? Segments tied to shopping intent, such as consumers browsing fashion, electronics, or health products, are highly sought after because they capture real time interest and drive results. How can B2B marketers find decision-makers without cookies? Combining content engagement patterns with machine learning allows marketers to reach professionals actively engaging with relevant topics, even in environments where IDs are unavailable. What makes privacy safe audience targeting effective? Privacy safe targeting uses real time contextual and behavioral signals to deliver relevant messaging across devices and channels without compromising consumer trust. How can real-time intent signals drive demand? Real time intent signals allow advertisers to capture consumer interest as it happens, helping demand side platforms and brands deliver timely, relevant ads that increase engagement and drive conversions across devices like CTV, mobile, and desktop. About our expert Allison Dewey, Director of Data and Curation, 33Across Allison Dewey is the Director of Data & Curation at 33Across, where she oversees data partnerships, integrations, and supply-side curation. With a deep expertise in audience targeting and signal optimization, Allison plays a key role in connecting data into the programmatic world. Allison holds a Bachelor's degree in Psychology from Bates College. About 33Across Rooted in over 15 years of data expertise, 33Across harnesses signals to enrich and expand marketers’ audiences and reach them wherever they consume content. 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