Syndicated Brand Lift Measurement Protocol
A Proposal For a New Measurement Approach
Executive Summary
This document proposes a Syndicated Brand Lift Measurement Protocol based on a hybrid-AI (synthetic + human), impression level measurement solution. By leveraging machine learned models applied to campaign data (ad exposure, creative quality, media context), this approach predicts brand lift without relying on traditional, slow, and costly survey methods. The core objective is to provide an always on, scalable, and cost effective way to evaluate brand impact, delivering standardized metrics integrable with media optimization platforms. The protocol involves three key data sources (Exposure & Audience, Creative Effectiveness, Media Engagement & Quality) feeding a central predictive model, all operating within privacy preserving clean room environments. While acknowledging significant operational and technical challenges, this framework offers a path toward more timely, granular, and actionable brand measurement, ultimately enabling optimization for brand outcomes and establishing a potential industry standard.
Vision
A synthetic, impression level brand measurement solution that uses machine learned models to predict lift from campaign data. This eliminates the need for direct survey based input and enables always-on, scalable, and cost effective evaluation of brand impact.
Objectives
The objectives of this initiative are fourfold.
To eliminate dependence on human survey data for real-time brand lift.
To provide standardized, impression level metrics that can integrate with media optimization platforms.
To establish scalable data sources that leverage ad server logs, creative scoring, and normative engagement models.
To build a shared syndicated/human model architecture that evolves with industry collaboration.
Challenges with Current Brand Measurement Methods
Despite decades of investment and iteration, traditional brand measurement methods continue to face significant limitations.
Surveys remain the dominant methodology, but they introduce latency and cost. Recruiting participants, designing and fielding surveys, and analyzing results can take weeks, far too slow for the pace of modern, always-on campaigns. Survey responses are also prone to bias and sampling error, particularly when targeting niche or high value segments.
Ad tracking, typically reliant on cookies or pixels, has become increasingly fragile. Cross device identity is fragmented, signal loss is growing in privacy centric environments, and platform walled gardens restrict data sharing. These factors collectively reduce the fidelity of exposure data, making attribution and effect modeling more speculative.
Reporting infrastructures, often built on siloed or batch processed data, fail to provide marketers with actionable insight in real time. Legacy dashboards surface metrics that are lagging, aggregated, and often disconnected from brand performance. Meanwhile, campaign managers are forced to interpret success through proxies, like viewability, engagement or reach, and these proxies don't reveal whether brand perception has actually improved.
Together, these issues contribute to a system that is slow, opaque, and disconnected from the realities of media execution today. A more adaptive, data rich, and scalable alternative is urgently needed.
Foundations of Brand Lift Measurement
At its core, brand lift is driven by two primary forces: the effectiveness of the creative and the quality of the media environment in which it runs. These two components form the foundation of any serious brand impact assessment and should be treated as interdependent, yet distinctly measurable, pillars.
Creative effectiveness is widely theorized, and supported by a broad base of empirical evidence, to account for 70–80% of overall brand performance in advertising. This includes how well the creative captures attention, conveys the brand message, and evokes the desired emotional or cognitive response. Elements such as brand cues, storytelling, pacing, and audio visual quality all play a significant role. Creative quality may be perceived differently depending on the audience, cultural context, or format, but its core attributes remain consistent over time; it can vary by audience, cultural context, and even format. Yet when well executed, strong creative has the ability to significantly drive brand metrics such as recall, favorability, and purchase intent.
The second factor is media, which acts as a moderator of creative effectiveness. Media determines whether the right audiences are reached, at the right time, and in the right mindset. A compelling creative that appears in a low attention or cluttered environment may see its potential squandered. Conversely, a moderately effective ad placed in a high attention, premium context may outperform expectations. Metrics such as viewability, attention scores, dwell time, and engagement are all indicators of the quality of the media context. Furthermore, media also influences frequency and sequencing, two important dimensions that affect cumulative brand lift over time. Unlike creative, however, media quality is in constant flux. It is subject to an array of variables that can shift quickly and unpredictably, from programming choices and content adjacency to changes in platform management, ad policy, or even broader cultural and news cycles. The same publisher or placement may perform very differently from one week to the next, influenced by changes in user behavior, media narratives, or trending content. This volatility makes continuous media quality evaluation not just important but essential for accurate brand measurement.
Understanding brand lift, therefore, requires an approach that considers not just exposure but the dynamic interplay between what the consumer sees and where they see it. Any model that omits either side of this equation risks misattributing performance and failing to deliver actionable insights.
Connecting Foundations to Function: Summary
The prior section established that brand lift is primarily driven by the dual forces of creative quality and media context. Building on that foundation, the three core data sources introduced here, Exposure & Audience, Creative Effectiveness, and Media Engagement & Quality, form the basis for modeling and predicting brand lift in a scalable, synthetic framework. Each dataset represents a critical vector of influence and contributes to a holistic view of how brand perception is shaped across digital campaigns.
The Exposure & Audience dataset provides the who, what, where, and when of advertising delivery. It captures the essential metadata that anchors every impression and ensures the model can reconstruct campaign delivery patterns. The Creative Effectiveness dataset scores the intrinsic quality of the ad content, how well it is likely to perform in driving attention and emotional resonance, based on its structure, messaging, and design. The Media Engagement & Quality dataset adds critical context by evaluating the environment in which the creative appears, helping the model understand whether and how that environment enhances or suppresses effectiveness.
These datasets are not isolated. They operate in concert to reflect the real world conditions in which advertising succeeds or fails. When structured and integrated correctly, they provide the resolution, nuance, and predictive power needed to model brand lift without relying on surveys. They are the analytical scaffolding that supports an always-on measurement approach attuned to the realities of today’s media landscape.
Core Product Components
1. Exposure & Audience Data Source
The goal of this source is to capture impression level data that identifies where ads were delivered, to whom, and in what context. Inputs include ad server logs detailing publisher, placement, and creative IDs; impression metadata such as viewability, duration, fraud filtering; and estimated audience reach and frequency drawn from ad servers or clean room sources.
To be most effective, this pipeline can and should operate within clean room environments such as Google ADH, Amazon Marketing Cloud, or other privacy compliant data sharing infrastructures. It should normalize formats and data schemas across a variety of DSPs, SSPs, and publishers, which often differ in log structure, taxonomy, and granularity. Furthermore, it should support deterministic or probabilistic mapping of creative assets to impressions in near real time to preserve campaign fidelity.
Challenges in executing this source pipeline are multifaceted. First, access to ad server logs is increasingly restricted due to privacy regulations, data governance policies, and proprietary constraints from platforms. Second, creative ID resolution is inconsistent, many environments strip or obscure these identifiers, making linkage to creative scoring or metadata more difficult. Third, data fragmentation across programmatic, social, and direct buy environments leads to incomplete or duplicated exposure records. Fourth, time synchronization issues across logs (e.g., impression timestamps from different platforms) introduce noise into sequencing and frequency capping analysis. Finally, data refresh intervals and latency in clean room query environments may limit near real-time performance.
Fortunately, there is growing industry momentum to address many of these issues. Brands and agencies are already investing in infrastructure and standardization efforts aimed at improving data interoperability, asset tracking, and clean room practices. These initiatives, including centralized creative repositories, enhanced ad server integrations, and clean room schema alignment, can serve as valuable building blocks. Rather than reinventing the wheel, this measurement protocol should aim to align with and build upon the best practices emerging from those investments, accelerating adoption while reducing implementation friction.
In addition to aligning with these ongoing initiatives, it is ideal that the core brand lift measurement application runs natively within clean room environments themselves. By embedding the model directly within platforms such as Snowflake, leveraging Snowflake Native Apps or similar constructs, the solution can operate on sensitive, row level impression data without requiring data to be extracted or moved. This approach helps mitigate privacy and compliance challenges, reduces latency, and ensures compatibility with existing data governance protocols adopted by major brands, agencies, and media owners.
While this proposal is primarily framed with agency and brand use cases in mind, it is equally applicable to media sellers. In fact, media sellers may find implementation to be substantially more straightforward due to their direct ownership of the necessary impression, creative, and contextual data. This access reduces the technical and legal friction typically associated with integrating disparate data sources across multiple platforms. Because the internal data environments of media owners are often already structured for measurement, the complexities addressed in this document, such as data normalization and asset resolution, are less severe. That said, for the sake of broader applicability and to tackle the more complex scenario, this proposal focuses on the agency side implementation. Still, the framework and methodology are fully extensible to media sellers and can be readily adapted to support publisher led brand measurement solutions.
Addressing each of these technical, legal, and operational constraints is essential to ensure the Exposure & Audience data source is not only robust and scalable, but also compliant and interoperable across a fragmented ecosystem. These complexities cannot be solved in isolation; they require strategic alignment with the broader industry and the systems already in place within clean rooms and marketing infrastructure. Doing so will establish a strong foundation for consistent, impression level data capture that underpins reliable and scalable brand lift modeling.
2. Creative Effectiveness Data Source
The goal of this source is to assign creative quality scores using validated machine learned models to estimate expected brand effect. It draws from creative assets, video, display, native, synthetic scoring models such as Kantar LinkAI, RealEyes, and other emerging AI driven systems, as well as historical benchmarks derived from human tested creative studies.
In recent years, the efficacy of synthetic creative evaluation has improved dramatically. Modern models can accurately predict attention, recall, and even brand favorability lift using only the content and structural elements of the ad. These systems leverage deep neural networks trained on thousands of past campaigns, enabling them to recognize features and patterns strongly associated with brand outcomes. Many creative scoring solutions now demonstrate strong correlation with traditional human panels, often delivering comparable rankings with greater speed, scale, and consistency.
However, the biggest challenge is no longer methodological, it is operational. The infrastructure for applying these models across live campaigns in a standardized, scalable fashion is still lacking. There are inconsistencies in how creative assets are stored, identified, and shared across platforms and organizations. Real-time access to final creative versions is rare, and creative metadata is frequently missing, unstructured, or disconnected from impression level data. As a result, many campaigns are still evaluated using incomplete or outdated creative inputs.
Solving this problem will require executional discipline and collaboration. It is likely that meaningful progress will depend on deep partnerships with Agency Holding Companies and Creative Management Platforms. These entities play a central role in creative production and distribution workflows and are best positioned to ensure access to the right creative assets at the right time. Standardizing asset ID systems, an area where the IAB has recently made promising strides, can help address this fragmentation. Through initiatives like the IAB Tech Lab’s efforts to define a universal creative identifier, the industry is moving toward a shared framework for tracking, referencing, and scoring creative assets consistently across platforms. These developments create an encouraging foundation for interoperability and will be critical in enabling seamless integration of synthetic scoring into campaign workflows, ensuring timely ingestion into scoring models, and integrating creative quality scores back into planning and measurement environments will be critical. Without this operational foundation, the predictive power of synthetic creative evaluation cannot be fully realized.
Despite these challenges, the opportunity is significant. Synthetic creative quality scoring is not a theoretical capability, it is a proven asset waiting to be fully integrated into modern brand measurement. Doing so will close a major gap in understanding what drives brand performance and unlock new opportunities to optimize creative at scale.
3. Media Engagement & Quality Data Source
The Media Engagement & Quality data source is designed to quantify the environmental context in which advertising is delivered and assess how that context amplifies, or diminishes, the performance of creative assets. This dimension of the model is critical to understanding brand lift holistically, as it captures the nuances of platform dynamics, audience behavior, and content adjacencies that directly influence campaign effectiveness.
This is also where the most novel and high leverage synthetic modeling is likely to occur. While creative quality scoring has already achieved a high level of maturity, the variability and unpredictability of media environments present a far more complex challenge for prediction. Media engagement is deeply influenced by context, platform behavior, user mood, and cultural timing, all of which are fluid and rarely standardized. As such, modeling media quality will require innovative techniques in causal inference, time series analysis, and potentially even reinforcement learning to identify the latent structures that modulate performance over time. Success in this area will mark a significant leap forward in making synthetic brand lift measurement more adaptive and precise.
Inputs into this dataset typically include historical brand lift studies that can be matched to specific media environments, publisher and platform level engagement scores, and contextual metadata such as page content, ad clutter, screen size, and scroll velocity. Third party data providers such as IAS, TVision, Adelaide, and others have made it possible to normalize and standardize many of these variables across environments, offering a more stable and comparative foundation for measurement.
If we return to the foundational model of ad effectiveness, where brand lift is the result of creative quality moderated by media context, then the implications of accurate synthetic scoring become even more powerful. If we can reliably estimate a creative quality score, and if we accept that creative quality is stable over time, then any observed variation in performance across media environments can be attributed to the media context itself. In other words, once the creative signal is isolated and held constant, media quality scores can be derived by subtracting the predicted creative effect from the total observed brand impact. This approach unlocks the potential for empirical, impression level benchmarking of media effectiveness using only modeled data, provided the inputs are accurate and clean. Importantly, the resulting media quality score may be either positive or negative, meaning it can either enhance or detract from the overall brand performance of a campaign. In this way, media is not just a passive vessel for creative, it is an active participant that can amplify or dilute impact depending on the audience's engagement and the environment’s attentional dynamics.
To function properly, this data source must support normalization of creative impact across historical benchmarks to isolate media quality as a distinct driver. This enables the development of environment specific multipliers that can be used to tune brand lift projections based on where and how ads are delivered. For example, a 15 second video in a high attention CTV environment may carry more brand impact weight than the same ad in a skippable mobile pre-roll context. Additionally, this dataset must support the modeling of diminishing returns curves and optimal frequency thresholds, helping determine when repeated exposures transition from effective reinforcement to waste.
Key challenges stem from the inherently dynamic nature of media environments. Publisher layouts, ad loads, user behavior, and platform policies are constantly shifting. Media quality is not static; it is a moving target that can vary week to week based on everything from editorial changes and algorithm updates to shifts in audience sentiment and cultural trends. Normative benchmarks, therefore, must be continuously updated and contextualized.
There is also a likely concern about the long term viability of new human based brand lift studies needed to fuel these models. If a synthetic model succeeds one would expect budgets shifting and fewer campaigns being measured traditionally, shrinking the pool of fresh training data. To mitigate this, strategic partnerships with third party verification vendors and publishers will be essential, not only to gain ongoing access to proprietary engagement scores and attention signals, but to align on standards for how those signals are interpreted and applied.
As synthetic approaches become more reliable, the role of human data collection is likely to evolve rather than disappear. Rather than relying on broad, panel based studies to power measurement systems, human based research will increasingly shift toward more precise, targeted forms of data collection. This "thinner" signal, collected directly from media sellers through attention panels, experimental designs, or in-platform instrumentation, will be better suited for model training and validation. These targeted studies can serve as truth sets for calibration, improving the accuracy of synthetic models without requiring the scale or cost of traditional panels. As publishers grow more sophisticated in their measurement capabilities, they are well positioned to take on a more active role in providing this type of high resolution signal, shifting the source of validation from third party survey panels to the environments where campaigns are actually running.
At the same time, it is important to acknowledge a foundational challenge facing all synthetic measurement approaches: the data used to train models is not collected at census scale. Most normative datasets are built from a limited set of panel studies, opt-in user groups, or historical campaign records that reflect only a fraction of the full advertising landscape. As a result, the training data used to calibrate media quality scores may not fully represent the diversity of audiences, platforms, or creative executions currently in market. This creates a fundamental tension, models are being asked to predict census level outcomes using non-census data.
Compounding this issue is the age of much of the normative data in circulation. The media ecosystem is in constant flux, shaped by shifts in user behavior, platform algorithms, ad load policies, and cultural relevance. Older studies, even those just a few years old, can reflect outdated assumptions about media engagement or platform value. If used without contextualization or adjustment, these datasets risk introducing structural bias into the model.
To address this, the measurement framework must include mechanisms for dynamic updating, retraining, and validation. Synthetic models must be regularly recalibrated against fresh empirical signals, whether from controlled experiments, platform instrumentation, or human verified performance benchmarks. Doing so ensures the system evolves with the media environment it seeks to measure and maintains relevance over time.
There are a range of modeling techniques that can be applied to isolate media quality while still accounting for the compounding effects of reach and multi platform exposure. These include hierarchical modeling approaches, such as multilevel regression with post-stratification (MRP), which allow media level variables to be contextualized within broader audience level and creative level effects. Uplift modeling techniques and synthetic control methods can also be used to isolate marginal media impacts when multiple variables are in play. Furthermore, causal inference frameworks, especially those that use temporal ordering and counterfactual estimation, can help parse out whether brand lift is accumulating due to media quality or merely repeated exposure.
Importantly, these methods must reflect how media actually works in the real world. Consumers rarely see a campaign in a single channel or on a single platform. Therefore, the model must account for the additive, or even multiplicative, effects of sequential or simultaneous exposure across multiple sites. This means controlling for both intra-channel and inter-channel frequency effects and attributing lift appropriately. Sophisticated path modeling or media mix modeling logic, when layered with synthetic data inputs, can help make these estimates more precise and actionable.
Despite these challenges, the opportunity is substantial. Media context is the most under leveraged variable in the brand measurement stack. With the right data structure and modeling logic, this dataset can transform passive exposure logs into actionable, predictive inputs. By integrating these insights directly into the core model, marketers can begin to understand not just if an ad worked, but why it worked in a specific environment, enabling smarter planning, buying, and optimization decisions at scale.
While much of the value in media quality modeling stems from novel methods and diverse inputs, its effectiveness depends heavily on the ability to structure this data with precision. This data source is designed to generate normalized media quality indices that measure how different publishers and platforms moderate creative performance. It draws from a diverse range of inputs, historical brand lift studies matched with media data, publisher level engagement scores, contextual metadata such as page content and ad clutter, and supplemental attention or quality scores from third-party sources like IAS, TVision, and Adelaide.
However, turning this data into a stable, reliable model of media quality requires more than aggregation. The creative impact must be normalized across these historical studies to isolate the distinct influence of the media environment. Only by stripping out the creative contribution can media specific effects be accurately observed. From there, modeling must account for the complexity of cross platform combinations, frequency effects, and evolving audience behavior, calculating dynamic multipliers that reflect the unique ways media environments shape ad effectiveness over time.
This task is not without challenges. Publisher environments are inherently dynamic, influenced by rapid shifts in layout, ad density, content strategy, and user experience. Normative data is perishable, what reflected platform quality a year ago may be outdated today. Additionally, as synthetic models gain traction, the flow of new human based brand lift studies may dwindle, making it harder to replenish and recalibrate these benchmarks over time. Addressing these issues will be essential to maintaining accuracy and relevance in media quality scoring models.
Core Model Design
At the heart of the synthetic brand lift approach lies a compositional model that treats advertising impact as the result of creative strength, modulated by the quality of the media context and influenced by campaign specific delivery conditions. The central formula is:
Brand Lift Effect = Creative Quality Score × (Media Quality Score × Campaign Specific Multipliers).
This formulation enables impression level prediction of brand outcomes by breaking down the constituent parts of what makes an impression effective. The creative score captures the intrinsic potential of the ad to drive attention and recall; the media quality score adjusts that potential based on how conducive the environment is to brand engagement; and the campaign specific multipliers introduce granularity around timing, targeting, and specific audience effects/saturation.
To operationalize this model, each impression is scored in real time based on its associated creative asset, the media environment in which it was delivered, and context specific variables such as format, frequency, and sequencing. The system aggregates these impression level estimates upward to compute placement level, publisher level, and campaign level brand lift scores. These results can then be surfaced to marketers through dashboards, reporting tools, or optimization platforms.
A key feature of the model is its ability to perform impression level attribution, assigning incremental brand impact back to specific tactics or placements. This enables more precise budgeting decisions and unlocks a pathway to optimize not just for efficiency or viewability, but for actual brand outcomes.
The model must be modular enough to support a wide range of media types or formats, and flexible enough to evolve as new data becomes available. For example, in high attention environments like connected TV, the media quality score may carry more weight. In contrast, for lower attention environments like mobile banner ads, frequency multipliers or sequencing effects may be more significant. This adaptability is crucial to reflect the non-linear nature of brand building across platforms.
Tuning and calibration will be critical. Initial model weights may be set using historical brand lift studies and human evaluated creative scores, but these must be regularly recalibrated using ongoing validation signals from experimental designs, panel surveys, or observed market lift. Maintaining alignment between model predictions and observed outcomes ensures long term trust and utility.
The modeling methodology itself can vary based on available data and use case complexity. In simple contexts, a regression based scoring framework may suffice. In more complex, multi-touch environments, ensemble models, causal forests, or structural causal models may be more appropriate. What’s essential is not the specific technique but the transparency and interpretability of results, marketers must be able to understand and act on what the model tells them.
Ultimately, the core model is both a prediction engine and a decision support system. It transforms fragmented, high volume campaign data into clear signals of what’s working and why, serving as the foundation for a modern, scalable, and fully synthetic brand measurement solution.
If you’ve made it this far into this document - thanks! What follows below is a more technical and governance overview which you can skip if you’re not technically inclined. Feel free to jump to the section called Strategic Impact.
Technical Infrastructure
The successful deployment of synthetic brand lift measurement depends heavily on a robust and interoperable technical infrastructure, one that can handle the scale, sensitivity, and velocity of modern advertising data. This infrastructure ideally resides within clean room environments to ensure privacy safe computation. Platforms such as Snowflake, BigQuery, and Databricks are already widely used for data warehousing and analytics, and they are increasingly being adopted as environments where modeling applications can be natively deployed. Snowflake Native Apps, in particular, offer the ability to run code and inference directly on customer data without ever moving it, a critical capability in a privacy first landscape.
Model management must be equipped to support a continuous cycle of training, validation, and deployment. Historical brand lift studies and creative test datasets serve as the foundation for initial model training, while campaign data streamed in near real time allows for active refinement. MLOps frameworks should be embedded into the architecture, supporting model versioning, testing, deployment, and rollback. Monitoring infrastructure should be in place to track drift, anomalies, and divergence from expected outcomes.
Output delivery needs to be tightly integrated into the activation and reporting workflows marketers already use. This includes API endpoints that return impression level brand lift scores, batch pipelines that export aggregated lift metrics by publisher, placement, or tactic, and data feeds that can be merged into DSPs, CDPs, or BI tools like Looker and Tableau. Realtime integrations with tools such as The Trade Desk or Google's Display & Video 360 (DV360) would allow for brand outcomes to be optimized mid flight.
Moreover, the technical stack must be built to interface with the existing measurement ecosystem. This includes alignment with standard identity frameworks (such as UID2.0, ID5, or RampID), and seamless interoperability with attention providers and verification platforms. Companies like TVision, Adelaide, DoubleVerify, and Integral Ad Science are already supplying impression level engagement and quality metrics that can enrich brand lift modeling. Similarly, platforms such as VideoAmp, Samba TV, and iSpot offer exposure level TV and cross-screen data that can be used to validate or supplement synthetic estimates.
Finally, the system should be cloud native, horizontally scalable, and built to operate efficiently at massive data volumes. Ingesting and scoring billions of impressions daily requires a highly performant data pipeline that supports parallel processing, query optimization, and data partitioning across large scale environments, something not often done by market research companies. Without this scalability, the promise of synthetic, always on brand lift measurement cannot be realized.
Validation and Governance
While this proposal outlines a scalable, synthetic solution that could materially increase the value of brand measurement across the advertising ecosystem, it is not intended to replace traditional methods outright. Rather, it should be seen as a complementary system, one that sits on top of existing measurement approaches to expand their reach, frequency, and granularity. Synthetic brand lift offers a way to estimate brand outcomes at the impression level and in near real time, but its credibility and utility are deeply dependent on ongoing human validation.
Validation should therefore be handled with care and rigor. One effective strategy is to run parallel human survey measurements, which can be used to calibrate synthetic estimates and provide a benchmark for model accuracy. These parallel studies can be designed to focus on key campaign types, audience segments, or creative formats to ensure the model performs well across varied contexts. In addition, validation should include comparisons against holdout test/control designs wherever feasible, especially in environments with well structured media delivery.
Over time, a network of trusted calibration studies can serve as an evolving “truth layer” for the model, used to adjust weights, tune sensitivity, and track model drift. These studies don’t need to be frequent or large scale, but they do need to be smartly designed and regularly refreshed to ensure alignment with current market realities.
Governance must support transparency in how the model is built, trained, and maintained. This includes documentation of modeling assumptions, openness around training data sources, and third-party review where appropriate. Industry collaboration will be essential, especially if the goal is to move toward a syndicated model architecture that benefits the broader ecosystem. Participating stakeholders, brands, agencies, media sellers, and vendors, must align on how brand lift is defined, validated, and operationalized.
In this way, the synthetic measurement framework becomes not just a technical solution, but a collaborative effort to advance the science of brand effectiveness without compromising on rigor or trust.
Strategic Impact
This proposal directly addresses the long standing limitations of traditional brand lift measurement described earlier: high costs, latency, limited scalability, and an over reliance on survey based methods. By shifting to a synthetic, impression level framework, this approach fundamentally redefines what is possible in brand effectiveness measurement.
The immediate impact of this model is to unlock visibility into brand performance at a scale and speed that legacy systems cannot match. Instead of waiting weeks for post-campaign survey results, marketers can now access continuous, real time insight into what creative is working, where, and why. Costly and isolated research efforts can be replaced with a common infrastructure that is syndicated across brands, publishers, and platforms, turning what was once a bespoke measurement effort into a shared, adaptive utility.
This also enables true optimization. With granular brand lift scores tied to individual impressions, tactics, and media placements, marketers can start to optimize for brand outcomes just as they do for performance metrics like clicks and conversions. Brand building can move from strategic guesswork to operational precision.
Over the long term, this approach creates the foundation for an industry standard protocol, enabling consistent benchmarking across categories and establishing a shared outcome currency for evaluating media value. Brand lift can finally become a core KPI, integrated into media planning, buying, and attribution models alongside performance metrics.
This evolution won't eliminate the need for human validation, nor is it designed to. Instead, it raises the ceiling on what brand measurement can do, extending its reach, increasing its frequency, and grounding it in the same programmatic infrastructure that powers modern advertising. In doing so, it bridges the gap between what brand marketers want and what measurement systems have historically been able to deliver.
Secondary Applications
The data pipelines proposed herein offer the opportunity for secondary monetization by measurement firms. Potential applications include forecasting brand lift using planned media and creative inputs before campaigns launch, benchmarking publisher performance to support outcome based deal structuring, and identifying underperforming creatives in pre-launch QA. One of the most promising implications of this pre-campaign forecasting capability is the opportunity to create a new, predictive currency metric, one that reflects the expected brand impact of media environments before spend is committed. By modeling projected lift based on the combination of media context and creative quality, marketers and media sellers can align on performance expectations upfront.
This shifts the value conversation in fundamental ways. Instead of media being judged post hoc through outdated proxy metrics or isolated survey results, it can be evaluated in advance using standardized brand outcome forecasts. That allows media sellers to be held accountable for the one thing they truly control: the attentional and contextual environment in which ads appear. It also creates a more equitable measurement landscape, ensuring that a poor performing creative execution doesn’t unfairly penalize a high quality media placement.
Over time, this predictive currency could evolve into a foundational planning tool, shaping how media is priced, sold, and optimized. It opens the door for publishers to compete not just on reach or CPM, but on expected brand impact per impression, bringing brand outcomes into the core economic logic of digital media transactions.
Getting Started: A Blueprint for Implementation
While the long term vision of syndicated, always on synthetic brand lift measurement is ambitious, there are low friction ways to begin demonstrating value today. The simplest path forward involves partnering with a single publisher and a known advertiser, creating a controlled environment to validate the core model and operational workflow.
Start with a media partner that has strong engagement metrics and robust clean room infrastructure, such as a premium news publisher or connected TV platform. Choose an advertiser with a consistent creative strategy and a history of brand lift measurement. Ideally, the campaign should feature creative assets that have already been scored using AI based quality tools and be delivered across formats that are well instrumented for media attention signals.
Use this initial engagement to map impressions to creative IDs, extract attention and engagement signals from the publisher environment, and model brand lift synthetically using the proposed framework. To validate outcomes, run a parallel human based brand lift survey to benchmark the synthetic estimates.
This first test will create a microcosm of the larger ecosystem and help identify operational gaps, calibration opportunities, and integration challenges. From there, the framework can be expanded to additional publishers, advertisers, and platforms, scaling with confidence and a track record of proof.
This kind of pilot approach not only accelerates learning but helps socialize the methodology and build stakeholder trust through real world performance.
Final Thought
Synthetic brand lift measurement has the potential to unlock a new era of upper funnel accountability. By fusing creative diagnostics, media quality data, and programmatic infrastructure, we can create a system that is not only more efficient, but better aligned with how modern marketing actually works.
It’s time to build it.