Ad Fraud Prevention across Programmatic advertising landscape in the USA

Programmatic has become the powerhouse behind ad placements in the U.S. However, alongside its growth, ad fraud has also skyrocketed, posing a significant challenge for brands trying to make the most of their digital ad spending. Ad fraud costs U.S. advertisers billions each year, and the automated process makes it increasingly difficult to detect and prevent.  

According to mFilterIt reports, bot traffic remains one of the biggest culprits, making up an estimated 20-25% of programmatic ad impressions. Meanwhile, the ANA report says low-quality “made-for-advertising” (MFA) sites account for roughly 10-15% of programmatic spending, delivering little value as they exist solely to serve ads on poorly engaging or irrelevant content. Even the burgeoning Connected TV (CTV) sector isn’t immune, with up to 17% of CTV programmatic impressions deemed fraudulent. Ad fraud solution with integrated brand safety is the key to optimizing programmatic ad campaigns. 

Are you aware of fraudulent or invalid traffic in your programmatic ad campaigns? The need for full-funnel protection with AI, ML-driven solutions, and real-time analysis, has never been greater. 

Why is programmatic a black box? 

Programmatic advertising is often described as a “black box” due to the complex, largely automated processes that operate behind the scenes, making it challenging for advertisers to see where their ads end up, how budgets are spent, and the actual impact of their campaigns. 

• Massive Volume Makes It Hard to Track: Massive volume of ad impressions served daily, programmatic operates on an enormous scale, making it difficult to monitor each ad placement. Ads are distributed across thousands of websites, apps, and devices, creating a web of tough placements to trace without advanced tools.  

• Viewability metrics Alone aren’t Enough. They don’t tell the full story. Many advertisers focus on viewability, but an ad might be “viewable” but still appear in an irrelevant or low-quality context, or worse, on a fraudulent site. Viewability is just the tip of the iceberg in determining if an ad placement is valuable. 

• CPM is not the true reflection of ad performance: A go-to metric in programmatic, Cost-per-thousand (CPM) doesn’t necessarily reflect ad performance. As they do not show the genuine engagement generated or conversions. Moving beyond CPM for ad performance measurement and focusing on true performance metrics gives a clearer picture of campaign effectiveness.  

• Transaction-wise analysis is a major problem: Transaction-wise analysis can reveal which sources drive real value versus those that don’t. Programmatic transactions happen in milliseconds, and each one is an opportunity to optimize or lose value. Advertisers may be paying for impressions that don’t deliver ROI, amplifying the “black box” problem. 

• Need for AI & ML Tools for Better Transparency: Artificial intelligence (AI) and machine learning (ML) tools can help analyze data at scale and are essential for breaking open the black box. Track ad placements in real time and identify patterns of low-quality or fraudulent traffic. Dig deeper into their programmatic campaigns, ensuring ads appear in relevant, brand-safe environments and that budgets are spent effectively. 

Here are some additional reasons to consider programmatic advertising “Black box” 

1. Fraudulent Traffic and Ad Waste: Click fraud, bot traffic, and ad stacking are prevalent in programmatic advertising. Without adequate validation, advertisers might pay for impressions that never reach a genuine audience. 
2. Limited Performance Metrics: Programmatic platforms may restrict access to granular performance metrics, offering only top-level KPIs. This limits advertisers’ ability to make informed decisions and optimize campaigns effectively. 
3. Complex Supply Chain: Programmatic advertising involves multiple intermediaries, including supply-side platforms (SSPs), demand-side platforms (DSPs), ad exchanges, and data management platforms (DMPs). Each layer takes a portion of the advertising budget, but transparency on costs and value added by each is often missing. 
4. Lack of Transparent Data: Advertisers frequently lack access to the complete data trail across the ad supply chain. This makes it hard to track where ads are being served, who is viewing them, and whether they are reaching the intended audience. 
5. Data Discrepancies Across Platforms: Advertisers often face data inconsistencies between platforms (e.g., discrepancies between ad platforms and analytics tools). These inconsistencies make it challenging to evaluate ROI accurately.   
6. High Reliance on Algorithms: Programmatic platforms rely heavily on machine learning algorithms for targeting and bidding. These algorithms operate as “black boxes,” with little transparency into how they make decisions or optimize for specific goals. 
7. Brand Safety and Ad Placement Risks: Without control over ad placements, brands risk their ads appearing alongside inappropriate or harmful content, which can damage brand reputation. Solutions to ensure brand safety often come with additional costs. 
8. Lack of Accountability: With limited insight into who is responsible for performance issues within the ecosystem, advertisers struggle to hold intermediaries accountable for missed KPIs or wasted ad spending. 

A clearer view into the “black box” of programmatic advertising with mFIlterIt enables more transparent, performance-driven campaigns that truly optimize ad spending. 

The Issue in Programmatic Advertising  

Programmatic advertising faces a range of issues that can dilute campaign effectiveness and drain ad budgets. That includes:  

• Fraudulent Impressions: Bots or non-human traffic generate false ad views leading to fraudulent interactions, inflate metrics without engaging real customers, waste Ad spending and skew data analytics. Advanced fraud detection tools such as mFilterIt can effectively distinguish between legitimate users and malicious bot traffic. 

• Made-for-Advertising (MFA) Sites: MFA sites are created solely to host ads, with minimal, often low-quality content. These sites prioritize ad placement over user experience, resulting in low engagement and poor ad performance. Identifying and avoiding MFA sites helps advertisers focus on genuine, content-rich environments where ads are more likely to reach and engage real users. 

• Identify and weed out traffic from Invalid Geographies: Programmatic campaigns sometimes face issues with ads being served in locations outside the target audience’s region. Invalid geographies, where ads might not be relevant, can dilute the effectiveness of a campaign and waste budget on non-converting audiences. Geotargeting and refined audience filters can help ensure ads reach relevant locations. 

• Frequency Capping: Without effective frequency capping, programmatic ads may be shown to the same user excessively, leading to ad fatigue and negative brand perception. Setting optimal frequency limits ensures users aren’t overwhelmed with ads, improving both engagement and brand reputation. 

• Attention Metrics to track engagement: Traditional metrics like CPM and viewability don’t always capture the true intensity of the ad results. Attention metrics—which track user engagement, such as time spent on the ad or interactions with ad elements—provide a clearer picture of ad effectiveness. These insights help advertisers understand not just if an ad was viewed, but if it held the audience’s attention and encouraged further action. 

Case Study: Programmatic Ad Optimization  

A global leader in the energy sector running a large-scale programmatic advertising campaign to expand its digital reach and drive brand visibility across various platforms. However, with the shift towards programmatic, they encountered significant brand safety and ad placement issues that risked their reputation and ad effectiveness. 

The key challenges for the brand Programmatic Ad Campaign  

The brand was running the audio and display (banner) campaign to boost visibility & audience reach on platforms like DCM & DV360. The reach and engagement metrics they were experiencing were low and did not seem commensurate with their digital spending. 

• Brand Unsafe Placements: Programmatic ads were appearing alongside inappropriate or irrelevant content, which could harm the brand’s reputation. This lack of alignment with brand-safe environments undermined the credibility and aimed to maintain as a trusted energy provider. 

• Ad Stuffing Placement: Multiple instances of ad stuffing were observed, where multiple ads were crowded within a single placement. This not only diluted their brand message but also led to budget wastage, as these placements did not generate meaningful user engagement.  

• Unsafe App Placement: Ads appeared in apps that did not align with the brand’s values or target audience. This misalignment was likely due to a lack of stringent filtering in the ad placement process, which allowed the ads to be shown in low-quality or irrelevant mobile apps. 

On the DoubleClick Campaign Manager (DCM) platform, the campaign faced 11% invalid traffic, with breakdowns revealing traffic from invalid geographies, Data Center Hosting (DCH), IP bots repeat, and low intent users. Over on Google Display & Video (DV) 360, invalid traffic was even more prominent, reaching 38%. This included 22.90% from IP bots repeat, 11.61% from invalid geographies, 3.77% from VPN proxy, and 0.18% classified as low-intent users. 

Brand safety issues were also notable, with 16% of impressions in an app identified as flagged for unsafe placements, potentially exposing the brand to inappropriate content.  

Additionally, ad stuffing was identified as an issue in a single ad placement, where 38% of impressions showed multiple ads crowded in single slots, reducing engagement and wasting budget. 

How they tackled these challenges with mFilterIt  

Improved Placement Strategy with Ad Fraud Prevention: The programmatic strategy was refined to ensure ads appeared in high-quality, well-spaced placements, preventing ad stuffing and ad stacking. By focusing on premium ad placements, Chevron ensured that each ad had the intended impact without the budget being wasted on oversaturated slots.  

Enhanced Brand Safety Filters: Deployed advanced AI and machine learning to analyze page content and assess ad placement quality in real-time. This significantly reduced instances of ads being displayed alongside inappropriate or brand-damaging content. 

Blacklisting all the traffic anomalies resulted in significant improvement in their traffic and ultimately resulted in improved conversion rates of 5% and increased ROI on digital spending by 14%.    

The updated filters led to a reduction in unsafe ad placements, effectively protecting brand reputation. By eliminating ad stuffing and focusing on quality placements, they achieved an increase in engagement rates, with ads more prominently displayed to relevant audiences. With reduced budget wastage on low-quality placements, they also saw an improvement in ad spend efficiency, maximizing the impact of each advertising dollar. 

Their experience underscores the importance of stringent brand safety measures and strategic ad placement in programmatic advertising. By refining its approach, they not only safeguarded its brand but also optimized ad performance, setting a benchmark for effective programmatic advertising in the energy sector. 

Need For Full-funnel protection 

Comprehensive brand protection is needed to safeguard brand reputation in the digital ecosystem along with optimizing ad campaigns and ad fraud prevention.  

Pre-bid not enough: Most of our competitors only focus on pre-bid which is not enough, need robust post-bid verification. In the current ad ecosystem, effective post-bid checks are essential to ensure that genuine impressions are served. Leading FMCG and BFSI brands are now moving toward more comprehensive ad traffic validation and brand safety with mFilterIt, as our solutions are industry-agnostic and cover multiple layers of verification. 

The superiority of the Post-bid approach: 

• Deterministic Checks: These checks use fixed identifiers (e.g., IP addresses, device IDs) to distinguish known bots or suspicious sources from legitimate users. We also look for patterns, such as repeated impressions from the same IP address in a short span, which can indicate invalid traffic. 

• Heuristic Checks: We identify non-human behavior by analyzing patterns, such as repeated browsing actions, identical click times, and clustered impressions. This helps flag bot-like traffic that follows unnatural behaviors. 

• Behavioral Checks: Human users generally exhibit varied and unpredictable behaviors, while bots perform repetitive actions. We track user engagement metrics, like time on the page, scrolling, and clicking patterns, to distinguish real users from automated scripts or bots. 

Integrated, Industry-Agnostic Brand Protection 

Full-funnel protection, covering both pre-bid and post-bid stages, is vital in today’s programmatic ecosystem to achieve effective ad campaigns and robust brand safety. mFilterIt industry-agnostic solutions provide deep visibility into ad traffic, identifying fraudulent or low-quality impressions before they impact performance. By adopting a multi-layered post-bid approach, advertisers can secure ad placements, enhance campaign effectiveness, and ensure every advertising dollar is spent on authentic, high-quality impressions. 

Conclusion 

For optimizing complex and fast-paced programmatic advertising, mFilterIt stands out as the go-to solution for tackling ad fraud and ensuring brand safety. As ad fraud continues to rise, costing U.S. advertisers billions annually, a reliable and comprehensive approach to ad protection has become essential. With our full-funnel protection, covering both pre-bid and post-bid verification, mFilterIt provides robust, AI and ML-powered tools that give advertisers the transparency and control needed to navigate the “black box” of programmatic advertising. 

Our solutions go beyond basic viewability metrics to deliver multi-layered post-bid analysis, which includes deterministic, heuristic, and behavioral checks that differentiate real users from bots, flag invalid traffic sources, and prevent ads from appearing in low-quality or irrelevant placements helping brands, optimize their digital ad spend with industry-agnostic tools that maximize reach and engagement in safe, relevant environments. 

mFilterIt provides a clearer, safer, and more impactful programmatic advertising experience for advertisers and can transform their programmatic strategy from uncertain to effective, protecting their brand reputation and securing every advertising dollar spent. 

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