Executive Summary
In 2025, digital advertising fraud has escalated to an industrial scale, siphoning tens of billions of ad dollars annually. Recent estimates suggest that up to 22% of global online ad spend, about $84 billion in 2023, was wasted on fake or non-human traffic. Nearly half of all web traffic now comes from bots rather than real people, meaning a huge portion of online ads never reach legitimate consumers. For marketers and agencies, this epidemic of bot-driven advertising fraud not only drains budgets but also distorts performance metrics and erodes trust in digital campaigns.
For years, the digital advertising industry has leaned on metrics like viewability and click-through rate (CTR) as pillars for ad success, assuming these were measured by human engagement. However, fraudsters have been able to keep up; sophisticated bots can mimic human behavior to achieve 100% “viewability” and even high CTRs on fraudulent impressions. As a result, these once-standard KPIs have become largely unreliable indicators of real impact. Essentially, a campaign can look great on paper yet deliver very little real ROI if its “engagement” came from bots instead of actual prospects.
This whitepaper outlines a more effective, validated alternative: El Toro’s IP-based targeting coupled with conversion-driven attribution. Instead of chasing superficial metrics, this method delivers ads directly to known household IP addresses, ensuring real human exposure, and even measures success by actual conversions and sales lift. By focusing on outcomes that fraud cannot fake such as verified purchases and revenue lift, and by cutting off exposure to fraudulent traffic sources, El Toro’s solution dramatically improves transparency and return on ad spend. The result is a fraud-resistant, performance-driven advertising strategy that gives marketers confidence that their investments are driving real results. This is a critical shift amid 2025’s rampant advertising fraud.
Advertising Fraud
Bot farms, & online scammers. Have you wondered who is actually viewing and clicking on your ads? You’re not alone. Digital advertising is plagued by fraudulent and non-human traffic that takes billions of dollars from marketers each year. In 2025, combatting digital advertising fraud has become more critical than ever, as new data shows the problem has only grown. In this report, we’ll define what counts as fraudulent or non-human traffic, quantify the size of the advertising fraud problem with 2023–2025 data, explain why common metrics like viewability and CTR are flawed measures of success, and highlight how El Toro’s IP-based targeting is a proven solution to reduce fraud and boost return on ad spend (ROAS) through verified conversions.
The Growing Scope of Digital Advertising Fraud
Digital advertising fraud has reached an industrial scale in recent years. Estimates vary, but all paint a troubling picture:
As mentioned, global marketers lost roughly $84 billion to advertising fraud in 2023, which is about 22% of all online ad spending. By 2028, that loss is expected to soar to ~$170 billion if current trends continue. Even more conservative analyses still suggest around 12% of digital ad spend was wasted by fraud in 2023. In short, tens of billions of advertising dollars are being wasted every year.
The majority of this fraud comes from fake, non-human traffic. Recent research shows that nearly half of all internet traffic is generated by bots rather than real people. In 2023, automated programs (bots) accounted for 49.6% of web traffic, the highest level ever recorded. Malicious “bad bots” (automated software programs designed to perform harmful or illegal actions online, often mimicking human behavior to evade detection) made up about 32% of all traffic globally (and 35% in the US) last year, while human traffic fell to barely half. In other words, the web is now as much machine-driven as human-driven, and advertisers are paying the price for this fake traffic.
Different sources indicate that the share of fraudulent or invalid ad traffic is in the 15% to 45% range, depending on varying definitions. For example, an analysis by the Association of National Advertisers (ANA) in 2023 found that 15% of programmatic ad spend was wasted on invalid or low-quality placements (such as spoofed websites made just for ads). On the higher end, studies of certain programmatic exchanges have found 50% or more of ad impressions to be non-human or fraudulent in nature. The World Federation of Advertisers (WFA) warned that advertising fraud could exceed $50 billion by 2025, making it the second-largest source of criminal income behind drug trafficking. No matter which figure you look at, the consensus is that a huge chunk of online advertising is wasted on fraud.
These updated statistics confirm what many advertisers have long suspected, a significant portion of their ad budgets never reach real, human audiences. To understand how this happens, we first need to distinguish between “non-human” traffic and other forms of fraudulent traffic, and why they’re so prevalent in digital advertising.
Fraudulent vs. Non-Human Traffic: What’s the Difference?
Digital advertising fraud comes in various forms, but a key distinction is between non-human traffic (bots) and human-driven fraudulent traffic. Both ultimately steal ad impressions and budget, but they operate differently:
Non-Human (Bot) Traffic
“Non-human” traffic refers to visits and clicks generated by automated programs or bots. These software robots can mimic human browsing behavior, loading pages, clicking ads, watching videos, all without an actual person behind the screen. Bots run on networks of infected computers or servers (botnets) or as standalone scripts, and they come in different levels of sophistication: from simple scripts that reload pages, to advanced bots that can move the mouse, generate fake keystrokes, and evade detection. Some key points about bot traffic:
Not all bots are bad, search engine crawlers and uptime monitors are legitimate “good” bots that aren’t meant to inflate ads. However, malicious bots are built explicitly to distort online activity. They can fake page views, clicks, form fills, and even “behavior” on site. Their goal is often to generate ad impressions and clicks that look human but are completely fake, in order to defraud advertisers out of CPM or CPC ad spend.
Malicious bots engage in tactics like spam clicks, ad clickers, and “viewability busters.” They load ads in hidden windows or out-of-view iframes, or cycle through a list of sites to create the impression of a human-user browsing. Some bots are programmed to scroll, hover, or randomly click to defeat basic fraud filters. They are increasingly evasive, according to Imperva’s 2024 Bad Bot report, 66% of bad bots are classified as “evasive”, meaning they can avoid detection by imitating human patterns or randomizing their behavior. With the rise of AI, generative AI is being leveraged to improve bots, making them even harder to spot.
Every bot-generated impression or click drains ad budgets while providing zero real exposure or sales opportunity. Bots also skew campaign metrics, they can inflate click-through rates and even “conversion” events (e.g. form submissions), leading advertisers to believe a campaign is doing well when in reality they’re garnering fake engagements. Essentially, bots produce false signals that corrupt performance data and steal ad spend. With one-third or more of all ad traffic now coming from bots, this is a massive source of fraud.
Human-Driven Fraudulent Traffic
Not all fraud is non-human. There is also fraudulent traffic generated by real humans with deceptive intentions. In these schemes, a human user is actually viewing or clicking, but in a context that makes the engagement fraudulent. Common examples include:
These are operations where low-paid workers are hired to click on ads or visit websites repeatedly. A click farm might employ dozens or hundreds of people (or use banks of mobile devices) to systematically generate clicks/impressions for ad campaigns, giving the illusion of real engagement. Humans are completing these actions, therefore, each impression can pass as “viewable” and each click as legitimate, but none of it represents genuine consumer interest, it’s paid fraud.
This is when fraudsters misrepresent low-quality or fake websites as premium publishers in ad exchanges. For instance, a scam site might masquerade as a reputable news site in the programmatic auction, causing advertisers to bid high prices thinking they are buying a top-tier placement. In reality, the ad runs on a fraudulent site (often loaded with bots or incented traffic) and the scammer pockets the difference.
Here, multiple ads are layered on top of each other in a single ad placement or shrunk to a nearly invisible size (1×1 pixel). A real user might visit the page, but dozens of ads load “underneath” what they see, meaning the user never actually views them. Advertisers still get charged for those stacked or pixel-sized impressions. This is fraudulent (the ads aren’t truly viewable to the user) yet can pass basic viewability checks in some cases.
So-called “Made-for-Advertising” (MFA) websites are another human fraud vector. These are low-quality sites built solely to attract traffic (often by clickbait or arbitrage) and serve ads. A human may end up on the site (via a misleading link or ad), but the content is worthless and the site’s sole purpose is to rack up ad impressions. In 2023, an ANA study found advertisers wasted about $13 billion (15% of programmatic spend) on MFA sites that deliver little real value, which is effectively a legal but unethical form of fraud.
Why does human-driven fraud exist? It earns money. Both the site owners and the intermediaries can profit from high volumes of ad impressions and clicks that have no sales intent. Given that a human is in the loop, this kind of fraud is harder to detect, the user behavior looks “real” (it is real, just not valuable). Whether via bots or willing humans, fraudulent traffic schemes misrepresent internet audiences and steal from advertisers. The result is that advertisers are often paying for phantom viewers, either robots or people with no interest in their product.
Why Traditional Metrics Fail to Catch Advertising Fraud
Given the onslaught of fake traffic, the digital ad industry has tried various countermeasures. One widely adopted approach was to introduce new metrics to gauge ad quality, for example, “viewability”, and filter out obvious fraud. Unfortunately, sophisticated fraudsters have outsmarted many of these metrics, rendering them largely ineffective as safeguards.
Viewability as a Metric Doesn’t Work As Intended
“Viewability” was introduced as a metric to determine whether an ad was actually seen by a human user. The common standard defines an ad impression as “viewable” if at least 50% of the ad’s pixels are in view on the screen for at least 1 second for display ads or 2 seconds for video. The idea was that if an ad never appeared on a user’s screen (e.g. it was below the fold or in a hidden tab), it shouldn’t count. Advertisers started demanding high viewability rates, and publishers/ad-tech vendors endorsed viewability improvements as fraud countermeasures.
The problem? Bots can fake viewability. Fraudulent bot traffic is often specifically designed to meet or beat viewability criteria. For example, a bot can scroll a page to ensure an ad enters the viewport for the required time, or simulate mouse movements that keep the ad in focus. Sophisticated bots replicate human behavior so well that they can achieve 100% “viewability” on ads, all while no real person ever saw the ad.
Even human fraudsters can game viewability. If someone is running a network of dummy websites for ad arbitrage, they know how the viewability metrics work and can design their pages to trigger a “viewable” impression (for instance, loading an ad in a visible area for the required time) without delivering any meaningful viewer attention. In other words, meeting the technical definition of “viewable” doesn’t guarantee a human actually paid attention, or that a human was even present at all.
Because bots by their nature mimic human actions, any metric that relies on observable user behavior (impression in view, click, time on page, etc.) can be manipulated. Viewability, Click-Through Rate (CTR), bounce rate, all of these can be artificially inflated. In fact, malicious bots are often coded specifically to boost these metrics because higher CTRs or viewability make a campaign look successful. The result is inflated campaign reports and wasted budgets.
False Positives and Misleading KPIs
Over-reliance on such metrics has led to false confidence. A campaign might show an impressive CTR or a high percentage of “viewable” impressions, but still be a failure in real terms because those interactions were fake. For example:
If 30–40% of clicks are coming from bots or click farms, the campaign’s CTR might look great. But those clicks will not translate into sales or tangible leads. Advertisers often scratch their heads wondering why a high-click campaign drove no ROI, the answer is often fraudulent clicks.
Similarly, bots can mimic a user browsing a site, scrolling and clicking around, thus low bounce rate or decent time on site, yet none of those visits are potential customers. Traditional web analytics might flag such traffic as “engaged users” when it’s actually automated behavior.
A campaign might achieve, say, 70% viewability according to Moat or IAS (ad verification services). But if a large share of those “viewable” impressions were on fabricated sites or in environments specifically designed to trick the viewability metric, it’s a faulty victory. A viewable impression is not the same as a valuable impression.
The bottom line: Metrics like viewability and CTR, originally intended as quality filters, have been co-opted by fraudsters. They are no longer reliable indicators of true campaign success. As the fraud problem has grown, it has become clear that the only metrics that truly matter are those tied to actual business outcomes, namely, conversions, sales, and revenue. El Toro recognized this early on and built a solution around real results rather than easily gamed proxies.
Quantifying the Advertising Fraud Problem – 2025 Edition
How much of the internet advertising environment is fake? One way to grasp the magnitude is to compare the theoretical legitimate ad inventory (what real humans could reasonably consume) with the actual volume of ad impressions available. El Toro performed this analysis in the late 2010s, and we update it here with recent figures:
Approximately 311–312 million Americans are online as of 2024 (about 94% of the population). Not everyone is active every day, but for a rough estimate, let’s assume on any given day the majority of these users are browsing the web.
This is hard to measure but using available data we can approximate. Back in 2012, ComScore found the average U.S. internet user was served ~1,707 display ads per month, roughly 57 ads per day. Internet usage has risen sharply since then. U.S. adults now spend over 7 hours online per day on average, up from ~4 hours in 2012, and in tandem, digital ad spend has exploded. A conservative estimate today might be around 90–100 ads per person per day on average, given the growth of mobile and social media advertising. We’ll use ~95 ads/day as an updated figure for 2023.
Using these numbers, we can calculate a ballpark for how many ad impressions should be available daily in the U.S. if every ad were shown to a real person:
Theoretical daily ad impressions (U.S.) = Number of internet users × avg. ads per user per day.
That gives: 312 million × 95 ≈ 29.6 billion impressions per day (around 30 billion). This is an upper-bound, assuming everyone online sees a typical amount of ads.
Now, compare that to the actual volume of ad impressions being recorded and sold in the advertising market:
- Major ad platforms serve far more than 30 billion ads per day. For instance, Google, which held ~42% of the digital ad market a few years ago, was estimated to deliver about 30 billion ad impressions per day on its platforms in 2017. With that, the total ad impressions across all exchanges and sites in 2017 was roughly 71 billion per day in the U.S.
- Fast forward to today: programmatic advertising has only grown. Over 90% of U.S. digital display ads are now bought programmatically, and U.S. programmatic digital display ad spend reached $156.8 billion in 2024, about five times the spend of 2014. Even if we assume ad prices (CPMs) have risen, the sheer increase in spending implies that actual ad impressions available per day are well above 70 billion now, perhaps even 80–100+ billion per day in the U.S. market since more ads are being served across mobile apps, video, CTV, etc.
To put this together, consider the theoretical vs. actual comparison in simpler terms:
| Metric | 2015 | 2025 |
|---|---|---|
| Internet Users with Online Access (population) | ≈284 million users InternetLiveStats (2015) |
≈322 million users DataReportal – Digital 2025 |
| Avg. Display Ads Served per User per Day | ≈50 ads/day Comscore (2016) | ≈350 ads/day Frictionless Commerce (2023) |
| Theoretical Daily Ad Impressions (Users × Ads/Day) | ≈14 billion per day Comscore (2016) | ≈100 billion per day (est.) Statista/Frictionless Analysis |
| Actual Daily Ad Impressions Observed (approx.) | ≈7 billion/day (viewable) Comscore | ≈70 billion/day (viewable) Statista – Ad Viewability (2023) |
| Fraudulent/Non-Human Impressions (volume & %) | ≈1.6 billion/day (≈11%) White Ops & ANA (2015) | ≈8 billion/day (≈8%) White Ops & ANA (2019) |
Sources: ComScore Ad Metrix (via VRM Intel, TechCrunch) for 2015 ad exposure and impression data;
Even allowing for rough estimates, the gap is staggering. In 2017, El Toro’s calculations suggested that upwards of 70% of all ad impressions could be fraudulent or non-human. In 2025, despite industry efforts, a similar math exercise still indicates that a majority of available impressions have no real people behind them. In short, we’re seeing far more ad opportunities than there are human eyeballs to view them.
This surplus is explainable only by widespread fake traffic generation. In fact, if ~50% of web traffic is bots, then it follows that at least ~50% of ad impressions are also bot-generated. Some experts put the figure even higher, especially in open programmatic exchanges where fraudulent inventory (from bots, spoofed sites, etc.) is most rampant.
Recent estimates of global advertising fraud range from about 10% on the low end (in well-protected channels) to over 30% in more vulnerable channels. Our theoretical vs. actual exercise indicates the upper bound could be 60%+ when considering all forms of invalid traffic and waste.
The Cost in Dollars and Cents
What does this mean in monetary terms? Earlier we noted the $84 billion global loss in 2023. Drilling down further:
- In 2016, digital display ad spending in the U.S. was about $32 billion. Using the ~70% fraud rate from that time, roughly $22+ billion of that spend was wasted.
- Today, with programmatic spend in the U.S. around $157 billion and fraud still affecting an estimated 15–30%+ of it, one could easily be looking at tens of billions of dollars in waste annually in the U.S. alone. A 2023 ANA study found that anti-fraud initiatives (like ads.txt and traffic analysis) savedS. advertisers $10.8 billion that would have been lost to fraud, indicating the scale of the threat without countermeasures.
- Globally, as shown in the chart above, we’re on track for over $100 billion per year being lost to advertising fraud within a couple of years. To put that in perspective, that’s more than the GDP of many countries, or in business terms, equivalent to the entire annual ad budgets of the world’s top 10 advertisers combined.
This is not just a minor nuisance; it’s a crisis. Every marketing dollar wasted on a fake impression is a dollar that could have driven real growth if spent better. That’s why combatting digital advertising fraud has become a top priority for advertisers, agencies, as well as ad-tech providers. It’s also why El Toro took a fundamentally different approach to digital advertising from the start, to ensure ads are served to actual humans and linked to verifiable outcomes.
The Cost in Dollars and Cents
The advertising industry hasn’t been sitting idle. Over the past several years, multiple initiatives have launched to curb advertising fraud and improve transparency. These include:
The IAB Tech Lab introduced ads.txt (Authorized Digital Sellers) in 2017, a simple text file that publishers host on their domain listing who is allowed to sell their ad inventory. This was meant to counter domain spoofing, if an exchange sees an offering not listed in the site’s ads.txt, it should be flagged as fraudulent. Ads.txt (and its mobile app counterpart, app-ads.txt) has seen broad adoption. This has indeed reduced spoofing of premium publisher domains (one study credited such standards with a 92% reduction in potential losses due to domain spoofing in 2023).
Industry groups formed the Trustworthy Accountability Group (TAG) which issues certifications to ad tech companies that adhere to anti-fraud guidelines and undergo audits. Buying through TAG-certified channels can lower fraud risk. TAG claims its programs have significantly cut down fraud for participating companies, leading to billions in savings.
Companies like Integral Ad Science (IAS), DoubleVerify, Moat, and Oracle Data Cloud provide verification services that detect invalid traffic (IVT) and filter known data-center bots, identify ad stacking, measure viewability, etc. Advertisers often use these services to screen out impressions deemed fraudulent after the fact (and demand credits/refunds). The major programmatic platforms such as Google and Facebook also refund advertisers for any invalid clicks they detect. For example, in 2017 Google issued refunds for ads run on sites with fake traffic, and such make-goods have become more common as fraud detection improves.
As a response to rampant fraud in the open ad exchanges, many advertisers shifted to Programmatic Direct or Private Marketplaces (PMPs), buying inventory from known publishers or via invite-only auctions. These curated environments, often with ads.txt compliance and publisher verification, tend to have lower fraud rates than the open exchanges. The trade-off is higher cost and limited reach, but many find the quality worth it.
These efforts have indeed made a dent in certain types of fraud. Domain spoofing has become harder due to ads.txt. Obvious bot traffic is often caught by verification vendors. However, significant gaps remain:
It mainly addresses one vector, fake domains. It “does not solve bot-related fraud”, as one anti-fraud firm noted, bots can still visit real sites and rack up fake impressions legitimately. Ads.txt also can be implemented incorrectly or not at all on smaller sites, and fraudsters have adapted by hijacking user devices (malware) to generate valid-looking traffic on real sites.
As detection gets better, fraud operations tweak their methods. For instance, when data-center (server) traffic started being filtered out, bots moved to residential IPs (via malware-infected devices) to appear more human. When viewability became a focus, bots ensured ads were viewable. There’s an arms race where criminals always seek the next loophole or weak link in the supply chain.
Even with all measures, the global fraud loss number keeps climbing because the overall digital spend is growing and fraudsters are taking a piece of that growth. New platforms like Connected TV (CTV) and OTT video have even seen fraud spikes (e.g., fake CTV apps generating phantom streams) before countermeasures catch up. Wherever money flows, fraud attempts follow.
The takeaway is that while industry initiatives are necessary and useful, they often address symptoms or isolated tactics rather than uprooting the fundamental incentive problem. Many solutions are reactive, they identify and filter invalid traffic after it occurs, or restrict buying to sources deemed “safer.” These are partial remedies. What’s needed is a more proactive approach that prevents fraudulent impressions from being served in the first place and focuses on outcomes that fraudsters cannot fake. This is exactly where El Toro’s methodology comes in.
El Toro’s IP Targeting Solution: A Different Approach to Combat Fraud
El Toro was founded on a simple but revolutionary idea: deliver digital ads with the same precision and verifiability as direct mail. Instead of spraying ads across the web and hoping real people see them, El Toro’s platform targets specific known households directly by their IP addresses. This model inherently cuts out much of the fraud prevalent in programmatic advertising. Here’s how El Toro’s IP-based targeting works and why it’s so effective:
- Start with a Known Audience (Physical Addresses): El Toro’s products begin by utilizing a list of physical street addresses for their target audience. These could be past customers, qualified leads, or households filtered by demographics and geography. The key is that these are real, verified locations tied to actual individuals (similar to a mailing list). For example, an auto dealership might supply a list of 10,000 customer addresses due for a lease renewal or service.
- IP Mapping to Match Digital IDs: Using a patented algorithm and vast proprietary databases, El Toro maps those street addresses to the household IP addresses associated with them. An IP address is like the digital “mailbox” of a home network. El Toro’s technology can determine with a high degree of accuracy which IP belongs to which physical residence. This mapping is continually updated to account for IP changes. The result is a highly curated list of IPs that correspond to the client’s target households.
- Direct Ad Delivery to Targeted IPs: Once the campaign begins, El Toro bids on ad impressions only when it detects one of the target IP addresses in the ad exchange. In practice, this means if someone in a target household is browsing the web or using an app that serves ads, and an ad slot becomes available, El Toro will purchase that impression and serve the client’s ad to that specific household. No third-party cookies, no behavioral profiles, no broad audience guesses, it’s a one-to-one marketing approach. If the target IP isn’t present, El Toro won’t bother bidding, which greatly reduces waste.
- Avoiding the Fraud Hubs: Because El Toro’s system is IP-driven and location-specific, it naturally bypasses a lot of the fraudulent inventory. The platform effectively ignores placements that are not tied to a known target’s presence. This means El Toro doesn’t randomly serve ads on sketchy long-tail sites or to suspicious users, it serves only when a known good IP (i.e., a real household) is on the page. Moreover, El Toro’s tech actively screens for malware-infected traffic and bots. If an IP shows signs of being part of a botnet (e.g., known virus signatures or abnormal activity patterns), it can be filtered out. By foregoing cookie-based targeting, El Toro also avoids chasing bots that generate fake cookies or browsing histories. In short, the system sticks to engaging genuine users at known addresses.
- One Ad, One Household – True 1:1 Marketing: El Toro’s IP targeting creates an advertising scenario similar to personal outreach. Just as a direct mail piece goes to a specific address, an El Toro-served ad is delivered to a specific household’s devices. This drastically contrasts with the typical programmatic model where you target large audience segments that inevitably hit a mix of real and fake users. With El Toro, if “123 Main St.” is on the target list, only that household (123 Main St.) will see the ads. This precision means nearly zero impressions wasted on non-targets or non-humans. El Toro’s clients have effectively removed the “unknown” middleman audience from the equation by directly selecting who should see their message.
- Patented and Proven: This IP mapping and targeting process is patented technology that El Toro pioneered. It has been refined over years, now analyzing tens of billions of impressions daily. As of 2025, El Toro was analyzing 100–120 billion display impressions daily at the exchange level, covering about 91% of the programmatic inventory except the walled gardens. By analyzing this firehose of data and only acting on the slice relevant to target IPs, El Toro has demonstrated it can drastically reduce exposure to fraud while still scaling campaigns to reach millions of households. Essentially, El Toro reinvented the ad delivery model to solve fraud, rather than trying to patch the existing broken model.
Focusing on What Matters: Real Conversions and ROAS
Targeting real households is half the battle, you also need to measure success in a way that actively combats fraud instead of catering to it. This is where El Toro’s approach to attribution and ROAS (Return on Ad Spend) comes in, completely sidestepping unreliable metrics like clicks or viewability.
El Toro’s mantra is “metrics can be abused, but conversions cannot.” Rather than counting clicks or impressions as a sign of success, El Toro measures tangible outcomes: sales, leads, and revenue lift. Here’s how the process works:
After an El Toro campaign, the advertiser provides El Toro with a list of actual conversions that occurred during the campaign period. Importantly, these conversions are tied to customer identities or addresses (for example, a list of purchasers and their mailing addresses, or new sign-ups and their address info). El Toro then matches those conversion addresses against the original target list. If a conversion came from a household that was targeted with ads, it’s counted as an influenced conversion. For instance, if John Doe at 123 Anywhere Lane was on the target list and John Doe made a purchase, that’s a direct match, proving the ad campaign likely influenced that purchase. This is a 1-to-1 attribution that is highly transparent. There’s no guesswork or probabilistic modeling, it’s literally matching two lists of names/addresses to see overlap.
El Toro often recommends holding out a control group from the targeting list. Say you have 100,000 households to target; you might deliberately not serve ads to 10,000 of them (randomly selected). After the campaign, you compare the conversion rate of the exposed group vs. the holdout group. If the targeted group purchased at a higher rate, that difference is the incremental lift caused by the ads. This is crucial because it accounts for the fact that some people would have bought anyway. By measuring lift, El Toro can tell exactly how much extra sales volume the campaign generated, beyond what would have happened naturally. This goes far beyond simplistic metrics, it’s actual business impact.
With the revenue from matched conversions and the campaign cost, El Toro calculates Return on Ad Spend (ROAS) for the campaign. For example, if the campaign cost $50,000 and the matched conversions from the targeted group yielded $200,000 in sales (above the control group baseline), the ROAS is 4:1 (or 400%). This reflects true return, not inflated clicks. El Toro is able to naturally provide this level of accountability, something virtually unheard of in standard programmatic advertising where connecting an impression to an offline sale is often considered impossible.
Think about the incentives: a bot cannot go buy a product with a credit card or sign a contract for a service in the real world. So any conversion that shows up in the matchback analysis is, by definition, a real human outcome. Bots might click your ad a thousand times, but they will never show up in the sales ledger. By shifting the success metric to conversions/sales, El Toro makes all the typical fraud tactics (fake clicks, fake form fills, etc.) irrelevant. They don’t even track clicks as a primary KPI, so a bot clicking doesn’t register as “success”. This removes the motivation for bots to target El Toro campaigns at all, there’s no cookie to hijack, no conversion pixel to fool (since the conversion is verified offline). In effect, El Toro’s system rewards only genuine performance and thus starves fraudulent traffic of any value. If an impression doesn’t lead to a conversion, it doesn’t matter in the final analysis. No one else in AdTech was offering this level of end-to-end transparency and accountability when El Toro introduced it, and it remains a cornerstone of why the platform works.
By closing the loop, from a targeted household impression to an actual purchase, El Toro has made waves throughout the advertising industry. Advertisers using this approach don’t have to rely on CTR or viewability at all. In fact, many El Toro clients have come to see how misleading their intermediary metrics were. When real sales lift is revealed, it’s not uncommon to find that campaigns with poor click rates can produce great ROI given that the few clicks were from real consumers, whereas campaigns that looked good on clicks delivered no lift, because bots were clicking. El Toro’s methodology exposes those truths and ensures that every ad dollar is held accountable for producing real results.
Conclusion: A Blueprint for a Fraud-Fighting Digital Advertising Future
Digital advertising fraud is often treated as an unfortunate “cost of doing business” in online advertising, a tax marketers must grudgingly pay in the pursuit of scale. However, it doesn’t have to be this way. The 2025 landscape of combatting digital advertising fraud shows us two diverging paths:
On one path, the industry keeps layering on patches, new metrics, blacklists, anti-fraud tools all to try and retroactively catch bad actors. This path has had some successes, but as we’ve seen, the fraud problem is still huge and continually evolving. It’s like fighting a hydra: cut off one fraudulent scheme, and two new ones spring up, whether it’s bots abusing CTV, or fraudsters exploiting programmatic loopholes. Traditional players are often stuck reacting to the latest scheme, and as long as ad money flows freely with few immediate consequences for fraud, the incentive for criminals remains high.
On the other path, companies like El Toro are reimagining the model entirely, removing the hiding spaces that fraud depends on. By targeting known audiences directly and measuring actual conversions, El Toro essentially locks the back door that fraudsters have been sneaking through. When you can tie your ad spend to real people and real sales, there’s nowhere for fraudulent traffic to hide. It simply gets filtered out by design.
The results speak for themselves. Brands using El Toro’s IP targeting have dramatically reduced wasted impressions and seen their ROAS improve because their ads hit the intended targets and drive tangible outcomes. It’s a refreshingly transparent approach: you know exactly who you targeted, and you find out exactly who bought. Everything in between, all the murky supply chain and dubious impressions, is bypassed.
El Toro’s success is a proof-of-concept that the industry can defeat advertising fraud by refusing to participate in it. By choosing not to follow the herd into cookie-based retargeting and opaque programmatic auctions, El Toro opened up a “blue ocean” of advertising that fights fraud at its core. Now others in AdTech are starting to take note, and we’re seeing a shift toward more accountable media buying.
In summary, combatting digital advertising fraud in 2025 requires bold action and innovative thinking. It’s not enough to keep tweaking metrics or trusting algorithms that can be duped. Advertisers must demand solutions that fundamentally align with their goals, reaching real customers and driving real results. El Toro’s IP targeting and conversion-based attribution is one such solution, and it has proven that you can heavily reduce fraud while boosting performance.
Every advertiser should ask themselves: “How much of my budget is secretly feeding bots and fake websites? And what would it mean for my business if I could reclaim that waste and invest it in genuine customers?” The answers could very well be the difference between a campaign that merely looks good on a dashboard and one that truly delivers to the bottom line.
In the fight against digital advertising fraud, the leaders will be those willing to break from the status quo and implement strategies that make fraud economically unviable. El Toro has chosen to lead that fight, and the invitation is open for others to join. It’s time to stop accepting fraud as inevitable and start demanding advertising that is honest, accountable, and effective. Together, by adopting fraud-fighting methodologies and holding our media to higher standards, we can finally put an end to the vast waste and ensure that every online ad has the opportunity to reach a real human being. Click here to connect with El Toro today to learn how IP-based and device-based targeting can help your campaigns combat fraud and deliver real results.