Advertising measurement is rapidly evolving, especially with the rise of new digital channels like Connected TV (CTV) and streaming. Understanding true ad value requires moving beyond traditional attribution models, which often fall short in today's complex media ecosystem. As advertisers seek to prove the genuine impact of their investments, incrementality testing has emerged as a crucial approach. This testing methodology helps uncover the direct, causal effect of advertising campaigns. Keep reading to learn more about designing incremental lift tests for CTV and streaming ad spend.
Understanding Incrementality in the Modern Advertising Landscape
Incrementality testing is a vital evolution in advertising measurement. This is particularly true with the rapid growth of new digital channels like Connected TV and streaming. Traditional attribution models often fail to capture the true, isolated value of ad spend in a complex, multi-touch media landscape.
What is Incremental Lift and Why Does it Matter?
Incremental lift measures the direct, causal impact an advertisement has on a desired action, beyond what would've occurred naturally. It differs significantly from observed lift, which simply reflects all conversions seen after an ad exposure. Advertisers need to look beyond last-touch attribution to truly understand if their ad spend is driving new outcomes.
This is especially important for new and emerging channels like CTV and streaming, where measurement can be complex. Determining true incremental lift helps advertisers prove their campaigns aren't just reaching people, but actively influencing behavior. It provides a clearer picture of an ad's effectiveness.
The Limitations of Traditional Attribution for CTV and Streaming
Common attribution models, such as last-click, first-click, or linear models, often have shortcomings when applied to CTV and streaming. These models frequently misattribute conversions or fail to capture the holistic influence of upper-funnel media. They struggle to account for consumers' multi-device viewing habits.
Challenges like the cookieless environment on CTV and the household-level nature of viewing further complicate traditional attribution. Platform-reported conversions rarely align with site-side analytics or actual sales performance. This is due to inherently biased attribution models that credit themselves for conversions.
Why CTV and Streaming Demand Specialized Incrementality Testing
CTV and streaming advertising have unique characteristics that necessitate a dedicated approach to incrementality testing. These channels differ significantly from other digital platforms, presenting specific measurement challenges. Understanding these distinctions is key to proving true ad impact, especially for agencies with over a decade of experience in ad placements.
The Unique Challenges of Measuring CTV and Streaming Impact
Measuring CTV and streaming ads comes with specific difficulties. These include cross-device viewing, shared household devices, and limited direct click-throughs. Tying impressions to individual user actions in a privacy-safe manner also poses a significant challenge.
These factors make traditional digital attribution methods less effective for these platforms. CTV is unique in the digital advertising domain because consumers can't click on TVs, making incrementality testing especially important for going beyond last-click attribution. Nearly half, or 48%, of advertisers cite measuring incremental reach as their top challenge as they navigate the Connected TV landscape.
Proving True ROI Beyond Standard Metrics
Proving incremental ROI is important for justifying increased investment in CTV and streaming advertising. Many brands operate with limited visibility on measurement, even in a rapidly growing $30 billion connected TV advertising market, with 70% of US consumers streaming content across multiple platforms. Demonstrating true lift helps advertisers optimize budgets and allocate resources more effectively.
It also helps gain confidence in their media strategy, especially when new channels compete with established ones for marketing spend. By showing a direct, positive impact, incrementality testing allows for more informed budget decisions. This ensures that every dollar spent is working to its fullest potential.
Key Methodologies for Designing Robust Incremental Lift Tests
This section outlines the primary approaches and practical steps for setting up effective incrementality tests for CTV and streaming. It details how to isolate the true impact of these campaigns. Selecting the right methodology is important for obtaining accurate and actionable results.
Control vs. Exposed Group Design
The fundamental principle of A/B testing for incrementality involves creating a control group that doesn't see the ads and an exposed group that does. This allows for a direct comparison of behavior between those who received the ad treatment and those who didn't. Ensuring these groups are statistically similar is paramount for valid results.
Random assignment is important for minimizing bias and ensuring the groups are comparable in all relevant aspects. The control group should represent a minimum of 10% of the total test and control reach for valid incrementality testing. This A/B testing methodology, the same used in clinical trials for vaccines and life-saving drugs, offers exceptional rigor for determining incremental lift in CTV advertising.
Geo-Targeted Lift Testing
Geo-targeted tests involve exposing different geographic regions to varying levels of ad spend or different campaign treatments. Advertisers can select comparable geo-regions based on demographics, historical performance, and market characteristics. One region might receive the full ad campaign, while another serves as a control with no or reduced ad exposure.
Analyzing performance differentials between these regions helps infer incremental lift. This method requires careful consideration of potential confounding factors, such as local events or competitive activity, which could impact results. By isolating the geographic impact, advertisers can gain insights into how their CTV and streaming campaigns influence specific markets. Shinola ran a zip-code-level geo-matched market test that showed a 14.3% incremental lift in conversions, revealing that Facebook had been underreporting campaign performance by a massive 413%.
Ghost Ads and PSA Testing
The concept of "ghost ads" or Public Service Announcement (PSA) testing involves filling ad placements with non-promotional content for the control group. This method is particularly useful in environments where ads can't be entirely removed, ensuring both groups receive similar media exposure time. PSA testing helps control for media placement bias, as both groups occupy the same ad slots.
It provides a cleaner read on the impact of ad creative and frequency. This method is recommended for those new to A/B testing in CTV, as it simplifies the comparison process. It allows advertisers to confidently attribute any performance difference to the actual campaign content, rather than simply the presence or absence of an ad.
Matched Market Testing
Matched market testing involves identifying similar markets based on historical performance and demographics. One market is then designated as the test market, where the campaign runs, while the other serves as the control market, with no campaign activity. This approach aims to minimize external variables by comparing two areas that would likely perform similarly under normal circumstances.
Criteria for market matching include population size, income levels, past sales trends, and media consumption habits. After the test period, analysts compare the performance metrics, such as sales or website visits, between the test and control markets to determine incremental lift. This method is effective for measuring broader campaign impacts in a real-world setting.
Executing and Analyzing Your Incrementality Test
This section guides advertisers through the practical considerations for running incrementality tests on CTV and streaming platforms. It also covers interpreting the data to derive actionable insights. Careful execution and analysis are key to maximizing the value of these tests.
Establishing Clear Hypotheses and Key Performance Indicators (KPIs)
Before starting any incrementality test, defining clear, testable hypotheses is important. These hypotheses should outline the expected outcome of the campaign. For example, a hypothesis might be that CTV ads will incrementally increase website visits by 10% among exposed households.
Selecting relevant KPIs that align with campaign objectives is also important. These could include website visits, conversions, app installs, brand recall, or unique user reach. These metrics must be accurately measurable to effectively gauge incremental impact and validate the initial hypotheses.
Data Collection and Measurement Best Practices
Robust data collection strategies specific to CTV and streaming are essential, considering their unique challenges. This includes the need for consistent tracking across all channels and integrating various data sources. Data accuracy and completeness are paramount for drawing reliable conclusions.
Advertisers must also consider privacy and data compliance, such as CCPA and GDPR, when collecting and utilizing data. Securely linking impression data from CTV platforms with conversion data from websites or apps is crucial. This often requires advanced identity resolution techniques to attribute actions to household exposures while respecting user privacy.
Considerations for Implementing Incrementality Tests
While highly effective, incrementality testing requires careful planning and can be a significant investment in time and resources. Advertisers should consider their budget, campaign goals, and existing data infrastructure when deciding on the scale and complexity of their tests. For instance, accurately segmenting truly comparable control and exposed groups across fragmented CTV platforms can be challenging.
Harmonizing data from disparate CTV platforms with site-side analytics and CRM systems often requires advanced data engineering. Additionally, ensuring tests are free from external biases like seasonality, competitive activity, or other ongoing marketing efforts adds another layer of complexity. These factors underscore the value of partnering with experienced experts.
Statistical Significance and Interpreting Results
Statistical significance is an important concept in incrementality testing, indicating whether observed differences between groups are likely due to the advertising campaign or just random chance. It's important for drawing valid conclusions and avoiding misleading interpretations. The incrementality calculation formula is: (Test Conversion Rate – Control Conversion Rate) / (Test Conversion Rate) = Incrementality percentage.
When analyzing results, advertisers compare the performance of control versus exposed groups to identify true incremental lift versus random fluctuations. It's important to avoid making decisions based on insignificant results, as they may not represent a genuine effect. The need for accurate CTV attribution is more urgent than ever, as nearly 50% of US marketers cite attribution and measurement as a top investment priority in 2025.
Integrating Incrementality into a Modern Attribution Strategy
Incrementality testing is a core component of "Attribution Modernization." These tests provide direct causal insights, and they should complement other attribution models and data sources. This approach helps build a holistic understanding of media effectiveness.
Only 32% of global marketers currently report measuring their media spending holistically across both digital and traditional channels. This highlights why a modern attribution strategy needs to integrate incrementality insights with broader models like multi-touch attribution or AI-driven forecasting. This creates a more complete view of how all marketing efforts contribute to business goals, including the role of first-party data in a privacy-first world.
Optimizing Future Ad Spend with Incremental Insights
This section focuses on how insights gained from incrementality tests can be leveraged to refine and optimize future CTV and streaming ad campaigns. The goal is to maximize efficiency and ROI. These insights provide a data-driven path to continuous improvement.
Iterative Testing and Campaign Refinement
Incrementality testing is an ongoing process, not a one-time event. Initial test results should inform subsequent campaign adjustments. This includes refining targeting, adjusting creative, or modifying bidding strategies.
The goal is to continuously improve ad spend effectiveness through a cycle of testing, learning, and optimization. Each test provides new hypotheses to explore. This iterative approach ensures that CTV and streaming campaigns become more efficient and impactful over time.
Scaling Successful Strategies
Once incremental lift has been proven, advertisers can confidently scale campaigns on CTV and streaming channels. This involves strategies for expanding reach, increasing budget, and applying learnings to similar audiences or markets. The rapid expansion of CTV ad spend, which grew from $20.3 billion in 2023 to $23.6 billion in 2024 (a 16% growth) and is projected to reach $26.6 billion in 2025, demonstrates the channel's potential for continued growth.
Successful incrementality tests provide the data-backed confidence needed to invest more heavily in effective approaches. Scaling intelligently means replicating what works and continuously monitoring for sustained incremental gains. This ensures that increased investment translates into proportional returns.
Integrating Incrementality into Budget Allocation
Incremental lift data enables more informed decisions about budget allocation across different media channels. It helps justify investments in new channels like CTV and streaming, especially when they demonstrate a strong, proven impact. Around 23%, or about $20 billion, of online ad budgets are wasted each year, highlighting the need for proper spend optimization through incrementality testing.
This data allows advertisers to optimize the overall marketing mix for better returns and greater overall efficiency. By understanding the true incremental value of each channel, businesses can shift resources to where they generate the most impact. This leads to more effective and accountable marketing strategies.
Unlock the True Potential of Your CTV & Streaming Ad Spend with Mynt Agency
Incrementality testing is a fundamental tool for understanding the true ROI and making data-driven decisions in the complex CTV and streaming advertising landscape. It helps advertisers move beyond surface-level metrics to uncover the genuine impact of their campaigns, ensuring every dollar spent works harder. These insights are essential for optimizing budgets and proving the value of innovative media strategies.
At Mynt Agency, we understand the intricacies of CTV and streaming measurement. Our expertise in designing, executing, and analyzing robust incremental lift tests provides clear, actionable insights for your campaigns. We help you confidently navigate the modern advertising ecosystem and make informed decisions that drive measurable results.
Contact us today for more information, and let us develop an advertising strategy that not only meets your goals but exceeds them.
