ANA Industry Benchmark
Methodology
Methodology
ANA Benchmark Methodology
and TrueKPI Framework
​
The ANA Programmatic Transparency Benchmark provides marketers with comprehensive, data-driven insights into programmatic media buying through a range of standardized cost and quality metrics and industry trends over time. Built on the reconciliation of impression log-level data (LLD) provided by the suppliers of participating marketers, the ANA Industry Benchmark is a step towards data symmetry, allowing marketers to evaluate and compare their own supply chain metrics to industry indicators and make informed decisions to get better returns on their programmatic investments.
​
1. Access to the ANA Industry Benchmark
​
The ANA Industry Benchmark findings are available to registered users of the Fiducia platform who have completed the Benchmark Registration Form and comply with the requirements to access the Benchmark.
​
The platform offers comprehensive access to the latest findings and long-term trends for key performance indicators, including the TrueCPM Index, TrueAdSpend Index, Cost Waterfall, and a wide range of detailed benchmark metrics. These include median values across participating marketers, with distributions presented in quartiles.
Data can be segmented by time period, media environment (CTV, Web, Mobile, In-App), and marketplace type (Open Marketplaces [OMP], Private Marketplaces [PMP]).
The benchmark data is collected and reconciled daily via the Fiducia Data Intelligence Platform on behalf of participating marketers, who can also monitor their own data in near real-time (depending on data availability and contractual terms). For the Industry Benchmark, this data is anonymized and aggregated at the end of every quarter, while advertisers can get a daily read of their own data on daily basis.
​
Quarterly benchmark reports are published five to six weeks after each quarter concludes. These reports highlight both positive and negative trends, assess optimization opportunities by comparing CPM to TrueCPM, and include simulations of the TrueCPM Opportunity — demonstrating the potential performance gains from optimization.
​
2. Data Access, Harmonization and Matching
​
Industry Participation
Following the 2023 ANA Programmatic Transparency Study, the ANA Industry Benchmark was launched in Q1 2024 as a permanent transparency initiative. Since launch, over 70 marketers have expressed interest in contributing data. Despite the availability of LLD, supplier resistance has made access to LLD feeds challenging in some cases — even when those suppliers utilize the same data themselves. As of now, 39 marketers are actively enrolled and are either already contributing or in the process of onboarding. Contributions include programmatic LLD feeds from delivery and verification platforms such as DSPs, SSPs, and ad verification vendors.
Compliance with Requirements
All data feeds are validated against TAG TrustNet Requirements, as defined in the TAG Certification for Transparency (CFT) guidelines. These requirements specify which data fields are mandatory or recommended to support both the Industry Benchmark and individual Advertiser Benchmarks. Data availability constraints — particularly when suppliers omit or restrict access to key fields — can limit:
​
-
The completeness of aggregated data used in the Industry Benchmark
-
The availability of specific metrics at the advertiser level
Such limitations can reduce the depth and precision of benchmarking insights for both industry-wide and individual analyses.
LLD Requirements
Participating advertisers are required to provide impression LLD, including costs, quality metrics, and delivery details for each ad transaction. LLD offers the most granular view of the programmatic supply chain, capturing key attributes of each ad impression — such as transaction pricing, verification outcomes, and delivery confirmation — enabling accurate benchmarking and transparency.
LLD Delivery Requirements
In accordance with TAG TrustNet Requirements and the industry standards established by the EU Digital Markets Act (DMA) for gatekeepers, suppliers are expected to deliver LLD updates to marketers:
-
Every 24 hours (ongoing cadence)
-
Within 48 hours of each impression event
-
At no cost to the marketer
-
Without restrictions on how the marketer may use the data, including sharing with third parties
Data must be transmitted using secure delivery methods, such as:
-
Amazon S3 buckets
-
Google BigQuery or Cloud Storage (GCP)
-
SFTP
When applied, these delivery standards ensure marketers to get timely, unrestricted access to granular data, enabling transparency, compliance, and advanced analytics. These requirements should be included as standard clauses in the agreements marketers have with all their suppliers.
3. Data Processing and Integration
Unified Data Integration
The Fiducia platform standardizes and integrates LLD from multiple sources by mapping disparate formats and field names into a common taxonomy — a necessary step given the lack of industry-wide LLD standards.
Custom-built data connectors for each provider enable the ingestion, normalization, and reconciliation of LLD across the programmatic ecosystem. This results in unified impression-level records, which serve as the foundation for accurate benchmarking. The suppliers for which an LLD connector has been developed by Fiducia are listed in the TAG TrustNet LLD Register which is published and updated quarterly together with the Benchmark.
Automated data processing supports multiple reconciliation pathways:
-
DSP-Only Analysis
Direct analysis of transaction-level data from demand-side platforms (DSPs). -
DSP + Ad Verification Matching
Uses unique impression identifiers (e.g. imp_id) shared between DSPs and ad verification vendors via key-value pairs embedded in verification tags. -
DSP + SSP Matching
Leverages OpenRTB impression IDs present in both DSP and SSP LLD.
If SSP LLD or identifiers are unavailable, estimated SSP fee rates are applied based on periodic SSP disclosures. These reports must include average take rates segmented by media environment and marketplace type. -
DSP + Data Exchange Matching
Matches DSP LLD to third-party datasets using common identifiers such as site/domain, Seller ID, and publisher metadata. -
The Fiducia Data Exchange expands transparency by integrating external datasets related but not limited to:
-
Made-for-advertising (MFA) classification
-
Brand safety and suitability
-
Carbon emissions
-
Publisher data integrity
-
ESG scoring
-
Metadata and Dimensions
Once reconciled, data is enriched with comprehensive metadata and dimensions, including:
-
Timestamps
-
DSP/SSP IDs
-
Environment and media type
-
Marketplace classification
-
Device type
-
App/Domain IDs
-
Seller IDs
-
Impression quality scores
-
Cost and fee data
This structured, normalized dataset enables deep-dive analytics and the generation of industry and advertiser benchmark metrics.
4. Benchmark Data Intelligence
Data Anonymization and Aggregation
The Fiducia platform anonymizes and aggregates LLD from each participating marketer’s suppliers, in accordance with applicable data usage permissions. This process preserves the confidentiality of individual marketers and their suppliers, while enabling the generation of robust, industry-wide benchmark insights based on reconciled and normalized data.
Multi-Dimensional Segmentation
With the launch of the online version of the Benchmark in Q2 2024, users can access benchmark metrics segmented across multiple dimensions:
​
-
Time Periods: Quarterly
-
Environments: All, CTV, Mobile In-App, Web, Web + Mobile In-App
-
Marketplaces: All, Open Marketplace, Private Marketplace
​
This multi-dimensional segmentation enables more granular analysis compared to earlier reports, which were based on aggregate-level data only reflecting total ad spending metrics.
To maintain clarity and consistency, historical findings from previous reports are available on the platform, but segmentation is only supported starting with the Q2 2025 benchmark findings.
​
Median-Based Analysis
The Benchmark is designed to provide advertisers with a clear, reliable view of industry benchmarks by leveraging robust statistical methods. Median values and quartiles are used for all key metrics. Medians are prioritized over averages as they are less affected by extreme outliers — such as unusually high CPMs — ensuring that results provide representative market trends.
To further enhance insight, the benchmark divides all individual participating advertisers' data points into quartiles, splitting the full range of results into four equal segments. This allows users to see not just the median, but also the spread and distribution above and below that midpoint.
In the user interface, these quartiles are visually represented with a color-coded legend. For metrics where high or low values clearly indicate better or worse outcomes, quartiles are ordered from best to worst from left to right and labeled as:
-
Top quartile / Green (better)
-
Above median / Yellow
-
Below median / Orange
-
Bottom quartile / Red (worst)
For metrics where the value interpretation is subjective, quartiles are simply shown by their position in the distribution with a visual in blue.
Each data point in these calculations represents a unique value for each participating marketer, reflected in the distribution by quartiles with values for the lows, the highs and the median. This structure allows marketers to visualize their position in the industry distribution and identify specific areas requiring their attention.
Interpretation Guidelines
-
Top Quartile: Reflects industry-leading performance, indicating strong optimization, robust quality controls, and effective supply chain management. These practices should be sustained or scaled where possible.
-
Above Median: Indicates better-than-average performance with room to progress toward top-quartile benchmarks through targeted improvements in key areas.
-
Median: Represents typical industry performance, serving as a baseline for market conditions and common challenges in optimization and efficiency.
-
Below Median: Highlights performance gaps relative to industry norms, pointing to opportunities for improvement via refined strategies, quality assurance, and supply path optimization.
-
Bottom Quartile: Signals critical underperformance, warranting a comprehensive review of campaign strategy, supplier mix, quality controls, and overall execution.
Eligibility Criteria for Segment Inclusion
To maintain statistical significance, advertisers are excluded from distribution calculations in any market segment where they do not meet the minimum qualifying ad spending threshold of one-thousand dollars. Market segments are defined by combinations of environment and marketplace type (e.g., CTV–Private Marketplace). In addition to the spend threshold, inclusion in a segment requires the presence of all required quality signals across relevant data sources to ensure metric accuracy and reliability.
Distribution Validation
To ensure statistical integrity, benchmark metric distributions are disabled in the platform user interface when fewer than four qualifying advertisers are present in a given segment. This safeguard prevents the display of skewed or unreliable data due to insufficient sample size.
Double-Counting Prevention
To avoid duplicate attribution, the Cost Waterfall metrics are calculated using a sequential, left-to-right approach, applying average values across advertisers. For example, if an impression is flagged as IVT (Invalid Traffic), it is excluded from subsequent categories such as non-viewable, non-measurable, or MFA.
This approach differs from the individual benchmark metrics, which are calculated:
-
Using total ad spending as the basis
-
Showing median values and quartile distributions across all participating marketers
As a result, Cost Waterfall values are not directly comparable to the medians shown in the detailed benchmark findings due to the different aggregation and attribution logic.
5. TrueKPI Framework
Originally introduced in the 2023 ANA Programmatic Transparency Study, the TrueKPI Framework is a measurement methodology designed to evaluate the cost-efficiency and quality of programmatic ad impressions using log-level data (LLD).
The core components of the TrueKPI Framework are:
TrueImpression
A TrueImpression is an ad impression that meets the following quality and verification criteria:
-
Matched – Successfully reconciled between DSP and ad verification sources.
-
Non-IVT – Not identified as invalid traffic (IVT) or bot activity.
-
Measurable for viewability – Eligible for viewability measurement.
-
Viewable – Meets MRC standards (≥50 percent pixels in-view for ≥1 second for display, ≥2 seconds for video).
Note: MFA (Made for Advertising) is not included in the TrueImpression definition due to its subjective nature. However, it is accounted for in the Cost Waterfall using a classification based on Deepsee.io data, given its measurable impact on media productivity.
TrueAdSpend
-
Buy-Side (DSP): Total ad spending of the marketer attributed to TrueImpressions.
-
Sell-Side (Seller): Part of the total ad spending reaching publishers for TrueImpressions after deducting transaction costs and losses of media productivity, as reflected in the Cost Waterfall.
CPM
Standard Cost per Thousand Impressions billed to the advertiser or agency.
TrueCPM
Cost per Thousand TrueImpressions, reflecting the actual cost of impressions that meet all TrueImpression criteria. It accounts for total ad spending relative to high-quality, validated impressions.
​​
TrueCPM Opportunity
The TrueCPM Opportunity is a realistic simulation of the TrueCPM optimization marketers can reasonably expect to achieve by implementing an optimization plan using impression LLD and following ANA recommendations. The simulation analyzes unique combinations of Campaign ID, SSP, Seller ID, and Publisher for each participating marketer and aggregates the results for the Benchmark. Here are the details of the simulation methodology:
a. Ad spending reallocation
For each participating advertiser and Campaign ID:
-
Ranking of all unique SSP, Seller ID, and Publisher combinations by TrueCPM.
-
Identifying the lowest-performing combinations that collectively account for approximately 33 percent of the marketer’s total ad spending.
-
Simulating reallocation of this 33 percent of spend to the top-performing combinations (those representing the remaining ~66 percent of spend with the highest TrueCPM).
-
Recalculating total impressions and TrueImpressions, assuming the CPM and the share of TrueImpressions for top-performing combinations remain constant
b. Calculate TrueCPM metrics
-
Compute the current TrueCPM as: TrueCPM = Total Ad spending ÷ Sum of TrueImpressions x 1000.
-
Compute the optimized TrueCPM as: Optimized TrueCPM = Total Ad spending ÷ Sum of Optimized TrueImpressions x 1000.
c. Determine TrueCPM Opportunity
-
Calculate the percentage improvement as: TrueCPM Opportunity = (TrueCPM – Optimized TrueCPM) ÷ TrueCPM.
This approach evaluates the potential decrease of TrueCPM achievable through the reallocation of ad spending to higher-quality impressions and serves as a directional indicator to support strategic decision-making.
​​
6. Benchmark Indices
Buidling on the TrueKPI Framework, the Benchmark indices were introduced, as part of the Q1 2025 findings, as key industry metrics to watch to track the effectiveness of programmatic investments. The following indices serve as key industry indicators:
TrueAdSpend Index
Measures supply chain productivity by evaluating the share of ad spending that is allocated to verified TrueImpressions and that is going to media, as reflected in the Cost Waterfall. The TrueAdSpend Index is equivalent to TrueAdSpend (Seller), which is calculated by deducting transaction costs and loss of media productivity costs from total ad spending.
TrueCPM Index
The TrueCPM Index highlights the relationship between the cost and quality of impressions marketers get in return for their investments. It reflects the difference, or delta, between the CPM paid for all impressions and the TrueCPM paid for TrueImpressions relative to total ad spending. This delta can be improved by increasing the number of TrueImpressions matching quality requirements while keeping the costs under control. In the unrealistic scenario that hundred percent of impressions match the quality requirements, the TrueCPM Index would go to zero.
​
7. Cost Waterfall
​
The Cost Waterfall offers a comprehensive view of the programmatic media investment flow, starting with hundred percent of ad spending. Using matched DSP and ad verification data, transaction costs and media quality losses are deducted sequentially, to get to the TrueAdSpend — the portion of total ad spending reaching sellers for TrueImpressions. Each stage in the waterfall represents the ad spending weighted average value across all participating marketers, providing a standardized, market-wide benchmark of supply chain efficiency and media quality.
The categories include:
Transaction Costs
-
DSP Platform Costs
Fees charged by demand-side platforms for technology access and media buying services. -
DSP Data & Other Costs
Includes costs for audience data, additional platform features, and third-party services. -
SSP Platform Costs
Fees from supply-side platforms for inventory access and transaction facilitation.
​
Media Productivity Losses
-
IVT ad spending
Spend attributed to impressions identified as invalid traffic or bot activity by verification tools. -
Non-Measurable ad spending
Spend on impressions marked as not measurable for viewability. -
Non-Viewable ad spending
Spend on measurable impressions that fail MRC viewability standards (e.g., <50 percent in-view for <1 second). -
MFA ad spending
Spend on inventory classified as Made for Advertising (MFA), based on Deepsee.io’s definition and categorization.
​
8. Detailed Benchmark Metrics
a. Transaction Cost Metrics
Transaction Costs: The total fees from DSPs and SSPs represents the cumulative cost of accessing programmatic technology, services, and inventory. Top quartile performance indicates excellent cost efficiency maximizing working media budget, while bottom quartile performance may reflect investment in premium services requiring proportional value demonstration through enhanced targeting, optimization, or exclusive inventory access.
DSP Platform Costs: Covers basic programmatic buying technology, bid execution, and standard optimization capabilities. Top quartile performance indicates cost-efficient access to core functionality, while bottom quartile fees may reflect enterprise-grade platforms with advanced features, enhanced data integration, or premium support requiring justification through improved campaign outcomes.
DSP Data Costs: Covers audience targeting segments, pre-bid segments and data enrichment services. Leftmost quartile performance may indicate efficient first-party data use or broad targeting strategies, while rightmost quartile suggests heavy investment in premium third-party data requiring evaluation based on measurable improvements in targeting accuracy, audience quality, and conversion rates.
DSP Other Costs: Includes additional features like creative optimization, lookalike modeling, device graphs, advanced attribution, or dedicated account management. Leftmost quartile indicates focused use of core capabilities, while rightmost quartile reflects comprehensive utilization of premium services requiring justified returns through measurable campaign improvements.
SSP Costs: Represents investment in supply-side technology, inventory access, and curation services. Top quartile performance indicates efficient supply path management and strong partner negotiation, while bottom quartile fees may reflect partnerships with premium SSPs offering exclusive inventory, advanced curation algorithms, or comprehensive brand safety controls.
Sellers Revenue: The portion received by sellers (publishers, SSPs, or ad networks), calculated as ad spending minus Transaction Costs, also known as Media Cost. Top quartile performance indicates higher advertiser spend flow-through to sellers with efficient supply chains, while bottom quartile performance shows significant DSP and SSP fee consumption requiring clear value justification.
b. Media Productivity and Quality Metrics
Loss of Media Productivity: The percentage of total ad spending on impressions not qualifying as TrueImpressions represents overall campaign waste across quality factors. Top quartile performance indicates excellent optimization across quality dimensions, while bottom quartile performance signals significant waste requiring enhanced quality controls or may reflect inherent challenges in emerging inventory types with developing measurement standards.
IVT (Invalid Traffic): Percentage of total ad spending on impressions not delivered to human users according to ad verification tools. Top quartile performance indicates excellent fraud prevention through filtering and quality verification partnerships, while bottom quartile performance suggests opportunities for enhanced fraud protection through better pre-bid filtering, advanced verification tools, or improved supply source selection.
Non-Measurable: Percentage of total ad spending on impressions not measurable for viewability indicates measurement implementation effectiveness and publisher partnerships. Top quartile performance demonstrates excellent measurement capability through proper technical implementation and quality publisher relationships, while bottom quartile performance may indicate technical issues or access to emerging inventory types with developing measurement standards.
Non-Viewable: Percentage of ad spending on measurable impressions not meeting MRC viewability standards reflects placement optimization effectiveness. Top quartile performance indicates excellent viewability management through bid optimization and quality inventory selection, while bottom quartile performance suggests wasted spend on below-the-fold or hidden placements providing no brand exposure value.
MFA (Made for Advertising): Percent of ad spending on websites with high ad density and low-quality content according to DeepSee data (deepsee.io). Top quartile performance indicates excellent supply quality controls avoiding MFA websites, while bottom quartile performance suggests significant waste on sites designed primarily for ad revenue rather than user value.
TrueAdSpend (Buy-side): Percentage of total tracked ad spending (without deducting transaction costs) directed to TrueImpressions measures overall campaign effectiveness across quality dimensions. Top quartile performance indicates excellent campaign quality management, while bottom quartile performance suggests significant improvement opportunities through better supply source selection and enhanced quality controls.
c. Advanced Cost and Efficiency Metrics
CPM: Cost paid per thousand impressions calculated against matched DSP and Ad Verification platform spend measures basic media pricing efficiency. CPM interpretation varies significantly based on targeting precision, inventory quality, format types, and competitive dynamics, with quartile distribution showing market pricing ranges.
TrueCPM: Cost paid per thousand TrueImpressions as calculated against matched DSP and Ad Verification platform spend. TrueCPM combines cost and quality to quantify effective pricing for quality impressions, with interpretation varying based on targeting precision, inventory quality, format types, and competitive dynamics.
TrueCPM Delta: The difference between TrueCPM and CPM, which represents additional investment per thousand impressions required to achieve quality standards, highlighting additional costs for non-True impressions relative to standard CPM pricing. Top quartile performance indicates minimal quality premiums suggesting excellent baseline quality or efficient optimization, while bottom quartile performance may reflect significant improvement opportunities.
TrueCPM Index: Reflects percentage premium paid for quality impressions compared to standard CPM, calculated as (TrueCPM − CPM) ÷ TrueCPM. Top quartile performance suggests efficient quality buying with minimal premiums, while bottom quartile performance indicates significant quality premiums potentially reducible through better supply source selection.
TrueImpressions: Impressions meeting comprehensive quality requirements including DSP and verification data matching, non-IVT status, measurability, and MRC viewability standards. Top quartile performance indicates excellent campaign quality management, while bottom quartile performance suggests significant improvement opportunities through enhanced supply source selection and quality controls.
d. Supply Chain and Inventory Metrics
SSPs: Number of SSPs receiving tracked ad spending indicates supply path strategy and inventory access approach breadth. Optimal SSP count varies based on campaign scale, geographic reach, inventory requirements, and efficiency versus access balance. Leftmost quartile may indicate focused partnerships prioritizing efficiency and direct relationships, while rightmost quartile suggests opportunities to reduce SSP partners for improved supply path efficiency.
​
Domains and Apps: Number of domains and applications where ads appeared reflects campaign reach strategy and the balance between scale and quality control. Domain reach interpretation depends on campaign objectives, targeting precision, brand safety requirements, and desired balance between broad exposure and controlled environments. Leftmost quartile may indicate focused targeting or stringent brand safety controls, while rightmost quartile may indicate excessive long-tail spending with sub-optimal quality.
​
Non-Direct Supply Path: Percent of ad spending on domains and apps not purchased directly by the SSP based on ads.txt and sellers.json reconciliation indicates supply chain complexity where indirect supply typically signals excessive intermediaries and value loss. Leftmost quartile indicates strong direct supply relationships prioritizing efficiency and transparency, while rightmost quartile suggests reliance on reseller chains potentially compromising cost efficiency and supply chain control.
​
Private Marketplace: Percent of ad spending allocated to PMP deals reflects investment in premium, curated inventory providing enhanced control, transparency, and high-quality placement access. PMP allocation should align with brand safety requirements, performance objectives, inventory exclusivity needs, and premium publisher relationship value. Leftmost quartile reflects open exchange efficiency focus with rigorous quality controls, while rightmost quartile indicates heavy PMP investment requiring clear justification through TrueCPM comparisons.
e. Data Exchange Metrics
Brand Risk: Assesses the likelihood of reputational harm posed by a webpage categorized as high or medium risk, including the exposure to hate speech, graphic violence, or adult content (source: Mobian). Mobian applies a multimodal artificial intelligence (AI) framework designed to interpret and classify digital content with contextual accuracy. By combining inputs from video, text, image, and audio, the system extracts rich semantic signals such as high, medium and low risk levels across formats and platforms. Top quartile performance indicates a low percentage of spending going to ads associated to high and medium risk content, while the bottom quartile suggests a higher exposure to content likely to cause reputational harm to brands associated with it, requiring enhanced supply quality controls.
​
CO2ePM: Kilograms of CO2 emissions per thousand impressions (source: Good-Loop) measures environmental efficiency of digital advertising campaigns, reflecting the ICT sector's contribution to global greenhouse gas emissions through servers, networks, and digital devices supporting programmatic processes. Top quartile performance indicates excellent sustainable advertising through optimized supply paths and environmentally conscious inventory selection, while bottom quartile performance suggests environmental optimization opportunities through improved supply chain efficiency.
CO2e/$: Kilograms of CO2 emissions per dollar of ad spending (source: Good-Loop) provides cost-efficiency perspective on campaign environmental impact within the digital advertising ecosystem's carbon contribution. Top quartile performance shows excellent carbon efficiency per dollar invested indicating sustainable media practices, while bottom quartile performance may indicate opportunities for sustainable optimization through better supply chain efficiency and environmentally conscious inventory selection.
​
Data Integrity Index Score: Ad-spend-weighted average of Data Integrity Index scores for publisher domains (0-100 range, source: Compliant) measures website commitment to data protection and privacy regulation compliance. Evaluation factors include data collection practices, user consent mechanisms, privacy policy transparency, and security measures. Top quartile performance indicates sophisticated publisher vetting supporting user information safeguarding, while bottom quartile performance suggests exposure to questionable data practices requiring enhanced supply quality controls.
​
ESG Score: Ad-spend-weighted average of publisher domain scores across Environmental, Social, and Governance metrics (0-100 range, source: TheGoodNet) covering content, advertising products, and corporate behavior. Top quartile performance indicates excellent alignment with responsible media practices and sophisticated publisher selection supporting brand values and corporate responsibility commitments, while bottom quartile performance suggests opportunities for enhanced ESG alignment through improved publisher vetting and supply chain responsibility measures.
ESG Risk Media: Percent of web ad spend on sites with ESG scores at or below 30 (source: TheGoodNet) indicating poor ESG performance across all categories including brand suitability concerns potentially conflicting with corporate sustainability and social responsibility commitments. Top quartile performance demonstrates excellent brand safety and values alignment through sophisticated quality controls and responsible media selection, while bottom quartile performance suggests significant ESG risk exposure requiring enhanced risk management through improved supply chain vetting.
​
Sentiment Analysis: Evaluates the emotional tone and contextual suitability of a page as positive, neutral, or negative (source: Mobian). Mobian applies a multimodal artificial intelligence (AI) framework designed to interpret and classify digital content with contextual accuracy. By combining inputs from video, text, image, and audio, the system extracts rich semantic signals such as positive, neutral or negative sentiment across formats and platforms. Top quartile performance indicates a low percentage of ads associated to content with negative sentiment, while the bottom quartile suggests a higher exposure to content expressing disapproval or negative evaluations.
9. Methodology Validation and Continuous Improvement
Data Validation Protocols
A multi-layered validation process ensures data integrity, including:
-
Compliance with TAG TrustNet requirements
-
Verification of supplier certification status
-
Statistical validation of benchmark distributions to ensure robustness and reliability
Industry Collaboration
The methodology is continuously shaped in partnership with industry stakeholders, ensuring it remains relevant, accurate, and aligned with real-world marketing and media-buying practices.
Continuous Enhancement
Ongoing reviews integrate industry feedback, technological advancements, and market evolution to improve the framework and maintain the accuracy and usefulness of the benchmark over time.
​
This rigorous, adaptive methodology ensures that participating marketers receive trusted, actionable intelligence — empowering them to compare their own supply chain metrics against industry benchmarks and uncover opportunities for programmatic investment optimization.