Blog - mfilterit

What is Invalid Traffic and How Does It Impact Your Ad Campaign Performance?

Leave a Comment / Blog, Ad Fraud / mFilterIt Experts

Are you proactively analyzing the ad traffic of your campaigns? Is it really coming from genuine users or just being generated by bots? Yes, a significant portion of traffic that makes your ad campaigns seem successful could be invalid traffic. According to mFilterIt’s analysis featured in FICCI Report 2025, invalid traffic contributes to as much as 30–50% of activity across digital channels, directly distorting performance metrics and draining ad budgets. This means the performance you see on dashboards may not always reflect real user intent. Instead, it could be influenced by automated systems, proxies, or manipulated interactions that inflate impressions, clicks, and even conversions. In this blog, we break down what invalid traffic really is, why it’s increasing, differences between general invalid traffic and sophisticated invalid traffic, and how you can identify and mitigate its impact to ensure your campaigns deliver genuine results. What is Invalid Traffic? Why is it Increasing Rapidly? Invalid traffic simply means ad activity that doesn’t come from real users but still shows up as genuine impressions, clicks, or visits. This happens when bots or automated systems interact with ads, making it look like people are engaging when they actually aren’t. Over time, this traffic has become more advanced and harder to spot. Bots now easily mimic real user behaviour, such as browsing pages, scrolling, or clicking on ads. Moreover, developments in technology, AI usage, and advertising infrastructure also contribute to this. Here’s how: AI is making bots smarter Earlier, bots were easy to detect because they behaved like machines. Today, AI-powered bots can scroll, pause, click, and even mimic browsing patterns. Some can simulate entire user journeys, making fake engagement look real in analytics tools. The ad ecosystem has become more complex Modern advertising runs through multiple layers and channels, DSPs, SSPs, ad exchanges, networks, and resellers. This fragmentation creates blind spots, making it easier for low-quality or fraudulent traffic to enter without being noticed. Cheap infrastructure fuels large-scale ad fraud Server farms allow fraudsters to generate massive volumes of ad traffic at very low cost. What once required physical devices can now be scaled instantly using virtual environments. Limited transparency and visibility Limited transparency and visibility across the digital ecosystem make it harder for advertisers to verify traffic quality. With restricted access to detailed user-level data, identifying whether engagement is coming from real users or sophisticated invalid traffic becomes more challenging. As long as advertisers pay based on clicks or impressions, there’s always a chance for misuse. Fraudsters take advantage of this by generating invalid clicks or views to earn money, especially when proper checks are not in place. Therefore, detecting invalid traffic has become more important than ever. Invalid traffic is generally classified into two main types: General Invalid Traffic (GIVT) and Sophisticated Invalid Traffic (SIVT). Two Major Types of Invalid Traffic Invalid traffic is broadly classified into two categories based on how complex the fraud technique is. What is General Invalid Traffic (GIVT)? It refers to non‑human or automated interactions that inflate ad metrics, caused by easily identifiable bots, spiders, or crawlers. These bots typically do not attempt to mimic real human behavior. They’re not malicious in intent but can distort campaign reporting and waste ad spend due to their automated nature. Because the patterns are predictable, platforms and verification tools can often identify and block this traffic using ad fraud detection techniques. Here’s what we observed in one of the campaigns . VPN and proxy traffic contributed 12.4% of total activity, indicating that a significant portion of traffic was not genuinely coming from real users. Several visits appeared to come from genuine mobile users but traced back to VPN and proxy networks, that were being used to hide the real user’s location. A deeper analysis showed that these IP addresses were linked to data center hosting providers (DCH) instead of real user networks. What is Sophisticated Invalid Traffic (SIVT)? Sophisticated Invalid Traffic (SIVT) refers to advanced forms of fraudulent or non-genuine traffic that are designed to look like real user activity. Unlike basic invalid traffic, these methods use automation, scripts, or manipulated devices to mimic real behavior, making them harder to detect with simple filters. Here are some sophisticated invalid traffic techniques we have observed in various campaign analysis: Sample Observation 1: In this case, the sophisticated invalid traffic technique that is being used is pop-under activity. It occurs when a website opens in the background instead of the active browser tab, meaning the user does not actually see or interact with the page. This is what we observed: the page loaded behind the main window and showed no real user interaction, indicating artificially generated visits. This type of activity is used to inflate traffic numbers without genuine engagement, and here, pop-under traffic contributed about 6.1% of the total traffic. Sample Observation 2: This shows a case of device spoofing, where traffic pretends to come from a real mobile device. Normally, smartphones support touch actions like tapping, scrolling, or pinch-zooming. However, in the above data, some devices marked as mobile showed “Not Standard” touch support, meaning these normal mobile features were missing. This suggests that the devices were not real smartphones but simulated environments or automated systems pretending to be mobile users. In this analysis, device spoofing made up about 2.7% of the traffic, indicating automated activity trying to appear like real user interactions. Sample Observation 3: Server farm-driven activity contributed 3.2% of total traffic, highlighting the presence of sophisticated, non-human interactions. In this case, traffic appeared to come from mobile devices across different sources. We noticed very high hardware concurrency values (192 and 96) from devices shown as mobile. Hardware concurrency simply means how many tasks a device can handle at the same time. Normal mobile phones can only run a limited number of tasks, so numbers this high are unusual for real users. This suggests that the traffic is likely coming from multiple channels, like bot farms, proxy-based execution, and automated browsers instead of real mobile devices. These systems are

What is Invalid Traffic and How Does It Impact Your Ad Campaign Performance? Read More »

5,000+ LPG Booking Scam Accounts Detected: How Fraudsters Exploits Essential Services During Crisis

Blog, Brand Protection / mFilterIt Experts

Consumer behaviour shifts rapidly during times of uncertainty. People seek immediacy, convenience, and assurance, especially when it comes to essential services. Unfortunately, this urgency is exactly what fraudsters capitalize on. Our fraud detection experts identify various types of scams day in and day out. And what we have witnessed recently is not just another wave of financial fraud, but a shift in how fraud embeds into everyday customer journeys. We have identified scams related to LPG gas bookings due to the shortage, considering the critical situation outside. Fraudsters are leveraging a sophisticated mix of fake websites, phishing scams, social media posts, and messaging platforms to appear legitimate. This is a critical inflection point. Because, unlike traditional investment scams that rely on greed or high-return promises, these scams exploit need and necessity. They target consumers in moments of urgency, when trust is assumed without verification. How Fraudsters Operate Through LPG Booking Scams? Even scams during national events and crises like war situations, pandemic waves, and natural disasters, etc. follow a structured model designed to guide users from discovery to payments. Here’s how LPG booking scam works: Fake messages creating urgency and panic Fraudsters begin by creating panic-driven messaging such as “gas connection will be disconnected” or “limited stock available.” These messages are often linked to ongoing situations like shortages or supply concerns. The objective is to push users into taking quick action without verification. Creation of digital assets like fake websites, social media handles, etc. Once attention is captured, users are directed to fake platforms. These include lookalike booking websites, misleading landing pages, or sponsored links that closely resemble legitimate services. Their purpose is to create a convincing first impression and reduce suspicion. Bulk messaging-based attacks Fraudsters also rely heavily on messaging-based platforms like WhatsApp, Telegram, SMS, etc. to circulate fake booking links, payment links, and even KYC update requests, often framed as urgent actions that need immediate attention. This coordinated, multi-channel presence creates repeated exposure, making the scam appear more legitimate and increasing the likelihood of user interaction without proper verification. Customer care impersonation and call-based fraud In many cases, fraudsters directly interact with users by posing as customer support representatives or gas agency officials. They guide users step-by-step, using scripted conversations to build trust and create a sense of legitimacy. UPI fraud and unauthorized transactions Once trust is established, users are asked to make payments through UPI or bank transfers. These payment details are not linked to official entities, and funds are often routed through multiple mule accounts, making tracking and recovery difficult. Disappearance after payment and no service delivery After the transaction is completed, fraudsters cut off communication. The websites, phone numbers, or links used during the interaction become inactive, leaving users without any confirmation, service, or refund. Why The Rise in Digital Scams Demands a Broader Industry Response? Digital scams or investment scams are not just platform problems, nor solely regulatory challenges. But an ecosystem issue. Brands, digital platforms, regulators, and brand protection technology providers need to collectively rethink how trust is built and protected online. Because the question is no longer “Is this ad or website safe? It is now “Is this interaction authentic?” And that requires a fundamental shift from reactive moderation to proactive intelligence and continuous monitoring across the entire digital landscape. How mFilterIt Helps Identify Digital Threats Using OSINT Technology To tackle such evolving digital scams, the approach needs to go beyond surface-level fraud detection. Here’s how mFilterIt’s brand protection solution helps: Continuous monitoring across digital ecosystems including websites, search engines, social media platforms, and app environments, etc. to detect fake booking websites, phishing links, brand impersonation fraud, and malicious APK distributions. Identifies patterns and anomalies such as sudden spikes in crisis-related keywords (e.g., “urgent booking”), coordinated campaigns, and emerging fraud narratives linked to real-world events. Tracks misuse of brand names, government schemes, and crisis-driven messaging, helping uncover fake ads, sponsored scam campaigns, and misleading promotions designed to build trust. Analyzes social media and messaging platforms to detect fake posts, viral scam creatives, and coordinated disinformation campaigns, including how such content is amplified across networks. Combines AI-led intelligence with human validation to verify critical elements such as payment instruments, UPI IDs, bank accounts, and other suspicious activity signals. Maps complete fraud infrastructure, including linked domains, payment handles, phone numbers, and email IDs, to connect multiple fraudulent assets to a single organized network. Moreover, once fraud is detected. The brand protection solution also helps in: Reporting fraudulent assets to platforms, hosting providers, and domain registrars for takedown. Shares structured intelligence with law enforcement and relevant authorities, including fraud URLs, payment details, and evidence to support investigation. Enables faster identification and blocking of mule accounts, fraudulent payment channels, reducing financial loss and limiting further spread. Issues early warnings and alerts to help prevent large-scale victimization before such scams escalate. Conclusion: Staying Ahead of Evolving Digital and Investment Scams The rise of LPG booking scams highlights a larger shift in how fraud operates today, moving beyond traditional formats and embedding itself into everyday consumer journeys. As fraud becomes more contextual, fast-moving, and harder to detect, relying on reactive measures is no longer enough. Addressing this requires continuous monitoring, deeper intelligence, and faster action to identify and disrupt fraud networks before they scale. To stay ahead of evolving scams, start monitoring and taking action before fraud reaches your consumers. Get in touch with our brand protection experts today. FAQs What is an LPG booking scam? An LPG booking scam is a type of digital fraud where scammers impersonate gas service providers using fake websites, phishing links, or messages to trick users into making payments or sharing sensitive information. These scams often appear during high-demand situations or supply concerns. How does an LPG booking scam work? Fraudsters create urgency through fake messages, redirect users to lookalike booking websites, build trust via calls or messages, and then collect payments through UPI or bank transfers. Once the payment is made, they disappear without delivering any service. What is brand

5,000+ LPG Booking Scam Accounts Detected: How Fraudsters Exploits Essential Services During Crisis Read More »

How Fraudsters Bypass MMP Detection

Blog, Ad Fraud / mFilterIt Experts

Mobile Measurement Partners (MMPs) have long been the industry’s first line of defence against mobile ad fraud. Through SDK integrations and last-click attribution, they have helped brands track installs and flag suspicious activity based on known patterns such as: Unusual install spikes Abnormal click-to-install times Repetitive device IDs While this works well for obvious fraud, the challenge today is far more sophisticated. Fraudsters now mimic normal user behaviour, making fraudulent traffic look genuine. In many cases, they have effectively reverse-engineered MMP detection logic and learned how to stay within acceptable thresholds. By carefully blending different traffic types in calculated proportions, bad actors are able to pass MMP checks and continue draining campaign budgets unnoticed. This is why the common concern today is clear: MMPs catch obvious fraud but often miss blended fraud. In this blog, we cover: Why MMPs struggle to catch blended traffic How mixed traffic gets a green signal in campaigns How brands can protect themselves beyond basic MMP checks Why MMPs Struggle to Catch Blended Traffic MMPs are designed to detect fraud using known red flags such as unusual click-to-install times, or repetitive user behavior. But today’s fraudsters have become smarter. Instead of sending clearly fake traffic, they mix fraudulent activity with genuine users so that nothing looks suspicious at first glance. This makes the traffic appear legitimate on the MMP dashboard, while budgets continue to get quietly drained in the background. Bot Traffic – Hiding Behind Volume Bots generate large volumes of clicks and fake installs, creating an illusion of strong campaign activity. When this fake traffic is mixed with real users, the overall data starts to look normal. Click and install ratios are high where one click is followed by one install hence time patterns seem balanced, device IDs appear varied, and nothing stands out as an obvious anomaly. Because MMPs are typically built to detect extreme outliers, this blended fraud often slips through unnoticed. Last-Click Attribution – Stealing Credit for Real Installs In fraud tactics like click spamming and click injection, fraudsters either flood the system with fake clicks or place a click just before a real user completes an install. This helps them hijack last-click attribution and steal credit for a conversion that should go to a genuine source. Since the install itself is real, the MMP often treats it as genuine. The fraud happens at the click stage, which many surface-level detection models fail to catch effectively. Incentivised Traffic – Real People, Misleading Results This is one of the hardest forms of fraud to detect because it involves real people. Users are paid or rewarded to install an app, so all the signals look human; real IP addresses, normal device behavior and natural session activity. To an MMP, this traffic appears completely clean. The problem usually becomes visible only later, when retention and engagement suddenly drop after the campaign budget has already been spent. Read in detail about device fraud How the Data Exposes the Evasion, MMPs Cannot Detect The data below highlights findings from a campaign ran between Sept–Oct 2025, where bot traffic was mixed with organic installs, making it difficult for MMPs to separate real activity from fraudulent traffic. Here’s what the data shows: The conversion rate gap is the clearest proof of hidden invalid traffic: The top source, publisher 1, shows conversion rate falling from 0.24% to 0.10% after bot traffic is removed, meaning nearly 58% of the apparent performance was artificial uplift. Massive click volume is creating a false sense of scale: Publisher 8 delivered 816M clicks, but its clean CVR drops to just 0.02%, huge activity on paper, but almost no genuine conversion value. Strong reported CVR can still hide severe bot contamination: Publisher 11 appears to be a top-performing source with 0.63% reported CVR, but once bots are removed it drops to 0.18%, with 72% bot share, indicating invalid traffic driving the most performance. Bot-heavy traffic is not an outlier – it is widespread: 7 out of 10 visible publishers show bot share above 60%, including sources like publisher 5 (68%), publisher 9 (70%), and publisher 10 (70%), despite all of them marked as clean by MMP. Even mid-volume sources show inflated performance: Publisher 7 drops from 0.20% to 0.08% CVR, while 61% of its traffic is bot, showing that inflation is not limited to only the largest traffic sources. The most dangerous fraud isn’t what MMPs catch, it’s what they don’t. Understanding the evasion tactics is the first step to building detection that actually keeps up. How Brands Can Protect Themselves from Mixed Traffic Beyond Basic MMP Checks MMPs should be treated as the first layer, not the only layer. An independent layer of validation through mobile ad fraud solution like mFilterIt’s empowers brands against mixed traffic, catching sophisticated tactics that MMPs miss – Recover stolen conversions through full click-path validation: Move beyond last-click attribution to validate the complete user journey from first touch to install so click hijacking and attribution theft are identified before they drain budgets. Pinpoint fraud sources with publisher-level transparency: Gain granular visibility down to publisher, sub-publisher, placement, and traffic source level to isolate hidden fraud pockets and eliminate waste at the source. Improve acquisition quality by validating real user intent: Go beyond app install counts and measure retention, session depth, registrations, and purchase signals to separate genuine users from low-intent or incentivised traffic. Surface blended fraud early with CVR and traffic anomaly detection: Monitor conversion gaps, abnormal click bursts, and sudden traffic mix shifts to detect bot-driven or manipulated traffic before it impacts performance metrics. Conclusion Blended fraud is changing the way brands need to think about campaign validation. What once appeared as a reliable defence layer is now being challenged by fraud tactics that are far more calculated and harder to spot. This makes it critical for brands to look beyond standard MMP signals and adopt deeper monitoring frameworks like mFilterIt’s Valid8 that can uncover hidden manipulation across clicks, installs, and post-install behaviour. In a high-investment digital ecosystem, protecting media spends is no longer just about catching obvious fraud, it is about identifying the traffic that is intentionally built to look real. FAQs How do fraudsters bypass MMP detection? They mix fake traffic with real user activity, making fraud look genuine and harder for MMPs to detect. Why do MMPs miss blended fraud? Because blended fraud mimics normal user behaviour and stays within acceptable detection thresholds. What are click injection and click spamming? These are fraud tactics that use fake clicks to steal credit for genuine app installs. How can brands detect

How Fraudsters Bypass MMP Detection Read More »

The $1 Billion Wake-Up Call: FTC Compliance Risks in Affiliate Marketing Explained

Blog, Affiliate / mFilterIt Experts

The Federal Trade Commission (FTC) is one of the most important regulatory bodies in the U.S., playing a critical role in protecting consumers and preserving trust in the marketplace. For brands, it sets the standards for fair advertising, transparent communication, and ethical business practices, helping prevent deceptive marketing and unfair trade conduct. Hence, non-compliance with FTC guidelines does not just slow growth, it directly impacts credibility, customer trust, and long-term business sustainability. Amazon learned this firsthand, paying $1 billion in civil penalties for misleading customers. For brands running affiliate programs in the U.S., this is not a distant cautionary tale, FTC compliance is the current reality, one that has been playing out for years and shows no sign of slowing down. (Source) FTC boundaries are clearly defined and crossing them, even unintentionally, carries serious financial and legal consequences. Affiliate partnerships remain a powerful growth lever, but their scale introduces complexity that is easy to underestimate. Violations rarely announce themselves. They surface as subtle lapses when some partners mislead your organic traffic, avoid ad disclosures, and claim attribution for what was already headed to you. By the time a complaint is raised or enforcement begins, the damage is done. In 2026, compliance cannot be an afterthought. It must be built into how affiliate marketing programs are managed, monitored and scaled from day one. In this blog, we break down: Common FTC compliance risks in affiliate programs Real-world cases of non-compliance The direct business impact of FTC violations How brands can stay compliant without disrupting affiliate-driven growth Common FTC Compliance Risks in Affiliate Marketing FTC compliance requirements are strict, and when marketing practices fall under direct government regulation, violations can become costly. Below are some of the most common FTC compliance risks in affiliate programs that brands must avoid staying aligned with FTC guidelines- Missing or Unclear Disclosure of Affiliate Relationships One of the most common compliance issues in affiliate programs is the lack of disclosure of partner relationships. When influencers or partners promote a brand’s products, they receive an incentive for purchases made through their links. Hence, while promoting, they must clearly disclose this relationship so customers are aware that their purchase will monetarily benefit the partner or influencer. Such disclosures can be made by using #ad or similar tags placed at the beginning or before the fold. Misleading Claims by Influencers & Publishers While promoting your brand, affiliates may make bold claims that don’t align with your brand messaging to drive conversions. For example, a partner might advertise heavy discounts that don’t actually exist, misleading users into visiting the site. In the process, they drop a cookie – so any future purchase by that user gets attributed to them. Similarly, claiming a product is “guaranteed” without any such promise from the brand can mislead customers. In both cases, the affiliate benefits, but the brand is left exposed to compliance violations and legal risk. Unauthorized Bidding on Brand & Restricted Keywords FTC guidelines emphasize that affiliates must not promote a brand in a way that makes users believe they are interacting with the brand’s official website. However, some partners bid on branded keywords and rank above the official site in search results, redirecting organic traffic. This not only misleads users but also forces brands to pay twice for the same traffic, once to acquire it and again as affiliate commission. If left unchecked, it drives up marketing costs and creates clear compliance and brand protection risks. Fake Reviews & Undisclosed Incentivised Ratings Fake reviews are widespread across social media and platforms like Google, often used to artificially build trust and influence purchase decisions. In many cases, these reviews are incentivized users are offered discounts, cashback, or freebies in exchange for posting positive feedback, without any disclosure. For example, a partner may run a campaign asking users to leave a 5-star review for a product in return for a coupon, creating a false perception of quality and popularity. To a potential customer, this appears as genuine validation, when in reality it is manipulated. FTC guidelines strictly prohibit such practices. If affiliates or partners engage in creating or promoting fake or undisclosed incentivized reviews, the brand itself is held accountable, exposing it to regulatory action, financial penalties, and loss of consumer trust. Inaccurate Pricing, Hidden Terms & Misleading Offer Pages Some affiliates promote exaggerated discounts or offers that do not actually exist. Others hide key conditions such as subscription commitments, additional fees, or eligibility restrictions in fine print. These practices create a misleading experience for consumers who click expecting one offer but encounter different terms later. The FTC considers such deceptive advertising practices a violation, especially when important details are not clearly disclosed upfront. Impact of FTC Violations in Affiliate Programs on Brands The impact of non-compliance in affiliate programs is huge, causing brands both financial and reputational damage. Let’s have a look on the direct business impacts a brand faces due to FTC non-compliance – Consumer refunds – Businesses can be required to compensate or refund customers affected by misleading or deceptive promotions. Legal and investigation costs – FTC investigations often involve expensive legal proceedings, internal audits, and compliance reviews. Mandatory compliance programs – Brands may be required to implement stricter monitoring, training, and reporting systems to prevent future violations. Operational restrictions – Regulators may force changes to marketing practices, advertising claims, or affiliate partnerships. Real-World Use Cases of Non-Compliance in FTC Following real-world cases of FTC non-compliance clearly demonstrate the seriousness and enforcement power of these regulations – Amazon Prime Case The Federal Trade Commission imposed one of its largest penalties on Amazon for misleading customers about its Amazon Prime membership. The company agreed to pay $1 billion in civil penalties after it was found that some users were enrolled in Prime without clear consent and faced difficulties when trying to cancel their subscriptions. In addition to the penalty, about $1.5 billion was allocated for refunds to customers who were unintentionally signed up or discouraged from cancelling their memberships. (Source) Fortnite / Epic Games Case The FTC also fined Epic Games, the creator of Fortnite, $520 million for violating children’s privacy and using design tactics that led players to make unintended in-game purchases. According to the FTC, the game’s interface made it easy for users, especially children, to accidentally spend money without clear consent. Along with the financial

The $1 Billion Wake-Up Call: FTC Compliance Risks in Affiliate Marketing Explained Read More »

If Your Brand Safety Strategy Is Global-Only, You’re Already Exposed in India

Blog, Brand Safety & Suitability / mFilterIt Experts

When a media team says, ‘we’re running a pan-India video campaign,’ they’re treating India as a single content environment. But it isn’t. India is a diverse market. Millions of content pieces go live on various video platforms every day. Each operating in a different language, shaped by different cultural norms, with entirely different definitions of what is acceptable, sensitive, or harmful. This is exactly where the risk begins. As an advertiser running programmatic ad campaigns across video platforms, you assume your targeting and exclusions are doing their job. But even as you read this, your ads could be appearing next to content you would never consciously choose to appear beside. A vashikaran tutorial. A graphic crime reconstruction video. Content in a language your brand safety tool cannot read. And the most concerning part? Traditional brand safety tools and platform filters don’t show you this side of reality. To help you identify this gap before it turns into a brand risk, this blog breaks down: What unsafe ad placements actually look like in India’s content ecosystem Why your current brand safety tools are missing them What India-ready brand safety requires What changes for your brand when you get it right Let’s start with what’s actually happening to your ads. What Content Ad Placements Are Your Ads Actually Running Next To? You didn’t choose these ad placements. But your brand is on them. When advertisers try to tap onto wider audiences through ad campaigns, they approve creatives, audiences, and budgets. They almost never see where their ads actually land. Here are the placement categories that brands in India often appear next to, without knowing it. Placement 1: Occult, Black Magic & Superstition Content What is it? Channels dedicated to vashikaran rituals, black magic spells, tantric practices, and superstition-based content. These videos use occult imagery, ritual settings, and fear-based messaging to attract millions of views across regional markets. Why it’s unsafe? These channels are algorithmically treated as general interest content. Platform classifiers read the title and tags, and often miss what the content actually depicts. A brand’s ad plays in the middle of a black magic ritual video, not because anything went wrong technically, but because nothing flagged it. Brand Impact For FMCG, BFSI, or any brand built on consumer trust, appearing next to content that promotes supernatural harm, fear, and occult practice directly contradicts the brand’s credibility. Viewers in these markets don’t separate the ad from the content. If your brand appeared there, it’s perceived as endorsing it. Placement 2: Made for Kids & Cartoon Content What is it? Children’s cartoon channels or animated content, that are classified by platforms as “Made for Kids.” These channels attract massive viewership across markets and are frequently part of broad run-of-network campaigns. Why is it unsuitable? “Made for Kids” content limits ad personalization, meaning your ad is reaching an unintended, non-converting audience. More critically, a brand running campaign for financial products, or adult-oriented services appearing on children’s content creates an immediate brand suitability issue. Brand Impact Every impression served on such content is a direct drain on campaign budget with zero return, no conversion intent, no brand recall, and no audience value. You end up paying for reach that does nothing for your brand. Placement 3: Adult & Sexually Suggestive Content What is it? Adult fashion content or OTT platform trailers that appear as standard video inventory across platforms. These videos carry no explicit adult content warning but contain visually suggestive material such as nudity, intimate couple scenarios, and adult-oriented fashion that platforms routinely monetize as general content. Why it’s unsafe? The problem here is a categorization gap. This content doesn’t meet the threshold for explicit adult content, so it doesn’t get flagged. But it is still considered under a brand-unsafe zone for most advertisers, particularly family-facing categories. Brand Impact When a trusted brand ad appears mid-roll on adult suggestive content, the viewer’s perception shifts immediately. The brand is no longer seen as careful or credible. For brands that spend heavily on trust-building, the reputational cost of a single misplaced impression, when screenshotted and shared, far exceeds the media value of the placement. Placement 4: Regional or Vernacular Content What is it? Regional language videos, in Bengali, Gujarati, and other vernacular languages, that depict weapons and armed violence in dramatized formats or normalize illegal gambling and Satta culture as regional entertainment. Why is it unsafe? Such content never triggers standard brand safety filters. A Bengali crime thriller and a Gujarati Satta video look identical to a global classifier; both are regional language videos with no English tags to read. Traditional brand safety tools cannot identify the content category, the cultural context, or the risk the placement carries. Brand Impact A brand appearing next to a video depicting a man pointing a gun at a woman, or next to content that normalizes illegal gambling, signals a complete lack of campaign oversight. For any brand associated with responsibility and trust, these placements directly undermine the credibility being built through every other brand touchpoint. Why Traditional Brand Safety Tools Miss Seeing Irrelevant or Brand-Unsafe Ad Placements? Here are the reasons why basic brand safety platform filters and tools fail to identify brand unsafe ad placements: Can’t read regional languages Most tools scan video titles and tags to check for unsafe content. If a vashikaran video is titled entirely in Hindi script with no English text, the tool finds nothing to read, marks it as safe, and your ad runs. Loses accuracy in translation Some tools translate regional content into English before checking it. But slang, coded phrases, and culturally loaded words don’t translate accurately. By the time the tool reads it, harmful content looks completely harmless. Moreover, some phrases or words that might appear to be safe in one region can be controversial or unsafe in another. Relies only on metadata, titles, and tags for classification of content Tools that only read titles and descriptions miss what is actually inside the video. A video titled “family entertainment”

If Your Brand Safety Strategy Is Global-Only, You’re Already Exposed in India Read More »

Brand Safety & Suitability: You Might Be Unknowingly Showing Ads to Kids – Know How

Blog, Brand Safety & Suitability / mFilterIt Experts

Yes, you read it right. Your ads often get placed next to kids’ content. And the concerning part? Your brand safety filters don’t identify these placements as unsafe, because technically they aren’t. What they are, however, are brand-unsuitable placements. It is a systemic gap in how traditional brand safety tools classify content and demographic targeting that affects more campaigns than most advertisers realize. In this blog, we break down exactly how this happens through real examples, why your current filters and targeting settings may not be enough, and what it actually takes to ensure your ads are brand safe and brand suitable, in every sense of the word. Brand Safety & Brand Suitability: They Are Not The Same Thing Before we get into how this happens, it’s important to understand two terms that are often used interchangeably but mean very different. Brand Safety Brand safety is about making sure your ad doesn’t appear next to content that is harmful, offensive, or controversial — violence, hate speech, adult content, or extremist material. Most advertisers today are aware of brand safety and have some level of filtering in place for it. Brand Suitability This goes one level deeper. Brand suitability is not just about avoiding inappropriate placements. It’s about making sure the content environment your ad appears in actually fits in the environment, context, and sentiment of your brand, your product, and the audience you’re trying to reach. Here is a simple example. A children’s cartoon is not unsafe content. It carries no violence, no hate speech, nothing harmful. But if you are a premium financial brand targeting urban professionals between 30 and 50, running your ad before that cartoon is a suitability failure, not a safety failure. The content is perfectly fine. The contextual ad targeting is completely wrong. This is the gap most advertisers are not solving for. Why Do Basic Brand Safety Tools Fail to Identify the Brand Suitability Issues? Here are the two major reasons why your ads keep appearing besides irrelevant content ad placements. The Content Identification Problem Platforms and traditional brand safety tool do their classification work by reading what is written about a video – the title, the description, the tags, and other metadata. They do not actually watch the video. What’s written about a piece of content and what’s actually inside it can be two entirely different things. A video can be tagged as a cartoon and correctly land in a kids’ content category, but still carry themes, visuals, or emotional tones that are completely at odds with what that label suggests. The Audience Targeting Problem When an advertiser sets up a campaign targeting adults, say, 25 to 45 year olds, the platform uses data signals like age registered in Gmail accounts, browsing behavior, and declared gender to identifyand reach that profile. The platform then delivers the ad to that account. Technically, it has done exactly what it was asked to do. But here is what no one identifies; the platform has absolutely no way of knowing who is physically sitting in front of the screen at that moment. The account belongs to an adult; however, the viewer watching the screen at that moment could be a child. And this can only be prevented if the content is identified not just based on title and tags, but based on content, context, sentiment, as well as frame-level video analysis. How mFilterIt’s Brand Safety Solution Solves Brand Suitability Issues? mFilterIt’s brand suitability and brand safety solution, PACE, addresses both the content gap and the visibility gap that traditional tools leave behind. Here’s how it works and what it delivers for advertisers. Analyses videos frame by frame including visuals, audio, on-screen text, sentiment, and scene context to understand what a video actually contains, not just what it is labelled as. Classifies placements against GARM brand safety categories with risk levels as high, medium, and low, so exclusions are precise and not over-blocking safe inventory. Operates in real time, detecting and blocking unsuitable placements before your ad impression is served, not after the budget is already spent. Builds a curated whitelist of videos that are not just safe but genuinely suitable for your specific brand, audience, and campaign sentiment. Identifies regional and vernacular content with language-specific ML models built for the diverse market, where over a billion hours of regional content is consumed every month. The result: Your ad runs in the right context, in front of the right audience, every time. Conclusion Brand safety keeps your ad out of harmful environments. On the other hand, brand suitability makes sure it lands in the right one. Both matter. And right now, most advertisers are only solving either one or none. So, before you take another campaign live, ask this: Is the content my ad is running next to reinforcing my brand or quietly working against it? The brands that get this right aren’t just the ones with genuine results. They’re the ones who know, with precision and confidence, exactly where their ads are landing. To learn more about how we can help, Get in touch with our experts today. FAQ,s What is the difference between brand safety and brand suitability? Brand safety avoids harmful or inappropriate content, while brand suitability aligns ad placements with a brand’s values, tone, and target audience for better contextual relevance. Why is brand suitability important for branding campaigns? Brand suitability ensures ads appear in relevant environments that match brand values, improving audience perception, engagement, and overall campaign effectiveness. How can brands ensure safe and suitable ad placements? Brands can use content filters, whitelist/blacklist strategies, and verification tools to control where ads appear and ensure alignment with brand guidelines. What tools help monitor brand safety in digital advertising? Brand safety tools analyze content, detect risks, and provide real-time insights. They help advertisers avoid unsafe placements and maintain control over ad environments. How do contextual targeting strategies support brand suitability? Contextual targeting places ads based on content relevance, ensuring alignment with brand messaging and improving engagement without relying on user data.

Brand Safety & Suitability: You Might Be Unknowingly Showing Ads to Kids – Know How Read More »

$14.8B at Stake: Will You Let Cookie Hijacking Slip Through?

Blog, Ad Fraud / mFilterIt Experts

The U.S. affiliate marketing industry is entering a new phase of scale. It is crossing the $10 billion mark for the first time, up from $9.1 billion in 2023, and projected to reach $14.8 billion by 2028. With giants like Amazon, Walmart, and Target running large, complex affiliate programs, the stakes have never been higher. But as the channel grows, so does the race to claim commissions (sometimes wrongfully) which can also look like this-imagine a shopper comes directly to your website, ready to buy but yet somehow, a third-party partner ends up taking creadit for that sale. Many brands are already trying to tackle it, but the growing sophistication of the tactic makes it increasingly difficult to control. In this blog, we dive into a sophisticated tactic known as cookie hijacking where affiliate cookies are secretly inserted into a user’s browser to claim credit for organic traffic, ultimately stealing conversions that rightfully belong to the brand. Behind the Scenes of Cookie Hijacking: 3 Tactics You Might Be Missing Here are three common ways affiliates cause cookie stealing to hijack organic traffic: Extensions Injecting Cookie Affiliates driving sales by influencing real customers is ideal but them stealing is not. One common way an affiliate program experiences this issue is through cookie hijacking. While analyzing a leading global e-commerce platform, we found that many users were directly visiting the site to make purchases. However, some had browser extensions installed (like coupon or deal tools) that silently triggered affiliate links in the background, without any click or user consent. As a result, when the purchase was completed, the system attributed it to an affiliate. Since most tracking follows a “last click wins” model, the affiliate whose cookie was dropped last received the credit, despite having no real influence on the sale. Auto-redirect with Affiliate Tag Another way of cookie hijacking that we noticed in the same brand’s use case was, the page as when users were redirected to brand’s website. If a user is browsing normally and visits a random page (this could be a shady site, popup, or even hidden script). The page quickly redirected them to brand’s site and in a split-second redirect, an affiliate cookie is dropped silently. When that user makes purchase, the credit is given to the affiliate as system sees the cookie. Forced cookie from an external site A user visits a completely unrelated website, not your brand’s. In the background, that site quietly drops an affiliate cookie without the user clicking anything or showing any intent. Sometimes, the user is even redirected to your website, making it look like a normal visit. Later, when they make a purchase, the affiliate gets credit, simply because their cookie was already placed earlier. How Can You Safeguard Your Brand From Cookie Hijacking Cookie manipulation is a growing risk for U.S. brands, especially those running large-scale affiliate programs. As partner ecosystems expand, having clearer visibility becomes essential to avoid affiliate fraud and protect genuine performance. Legacy, surface-level tools can highlight obvious issues, but the real question is whether they can keep up with increasingly sophisticated fraud tactics. In most cases, they fall short. And for U.S. brands running high-stakes affiliate programs , uncertainty isn’t something they can afford. With a more advanced, third-party approach like mFilterIt’s, renowned brand are already bringing more transparency to their affiliate marketing programs. Here’s how it empowers brands- Launch instantly, stay in control – No integrations needed, just immediate visibility into your affiliate ecosystem Gain complete transparency – Always-on scanning ensures you see every leakage, not just the obvious ones Expand your risk coverage – Protect your brand from both known partners and unknown bad actors Make decisions with confidence – Accurate, low-noise insights you can actually act on Hold the right partners accountable – Clear attribution helps you take precise, effective action Understand your true customer journey – See exactly how users reach and convert on your platform Protect revenue in real time – Identify and stop fraud before it impacts your bottom line Conclusion The key to a smooth affiliate program is visibility to understand real user journeys and know where attribution is coming from. Brands that focus on transparency and proactive monitoring through holistic ad fraud solution can prevent revenue leakage and build stronger, more reliable affiliate programs. FAQs What is cookie theft? Cookie theft is when someone steals a user’s cookie or places their own cookie in the user’s browser to wrongly take credit for a purchase they didn’t influence at the first place. How to prevent cookie hijacking? Monitor affiliate traffic and user journeys closely Block suspicious extensions, redirects, and unknown sources Validate partners and enforce strict program rules Use advanced tracking/monitoring tools for better visibility Why is cookie hijacking difficult to identify? Cookie hijacking is difficult to identify because it often happens silently in the background. Since the user still completes a genuine purchase, the fraud appears legitimate in standard attribution systems, making it harder for legacy tools to flag. What are the common signs of cookie hijacking in affiliate programs? Common signs include sudden spikes in conversions, abnormal click patterns, high traffic from unknown sources, and mismatched user journeys that indicate unauthorized tracking activity What impact does cookie hijacking have on attribution and commissions? Cookie hijacking manipulates attribution by assigning credit to fraudulent affiliates, leading to incorrect commission payouts and reduced returns for genuine marketing efforts. What compliance measures help prevent affiliate fraud in the US? US advertisers can enforce strict affiliate policies, conduct regular audits, use fraud detection tools, and follow FTC guidelines to ensure transparency and prevent fraudulent activities.

$14.8B at Stake: Will You Let Cookie Hijacking Slip Through? Read More »

Data as a Service for OTAs: How Pricing Intelligence Helps Identify Competitor Pricing Gaps

Blog, Digital Shelf Analytics / Noopur Mathur

Pricing in the travel industry moves faster than most teams can keep up with. A difference of even ₹200 on a flight ticket is enough to lose a booking and send a potential customer straight to a competitor. Pricing is a decision metric for customers, and pricing intelligence is what makes your platform compete with a strategy. If you don’t have a clear view of what your competitors are charging, you are losing potential bookings, burning discount budgets, and creating pricing gaps that directly impact margins. This is what a major OTA platform was dealing with – limited visibility into competitor pricing across key routes. Continue reading to find out how we helped them identify the gaps using data as a service. The Challenge: Limited Visibility into Competitor Pricing This major OTA platform had limited visibility into what their competitor platforms were pricing flights at, in real time. Their pricing optimization, discount updates were delayed and inconsistent. Sometimes they were underpriced, giving away margins unnecessarily. Other times, they were overpriced due to demand surge or other factors (which hikes prices – research a bit), sending customers to a competitor with a better deal. However, without consistent pricing analysis, their aim was to stay competitive and to be considered by customers at all times. The Fix: Proactive Data as a Service Using Pricing Intelligence to Map Exactly Where the Gaps Were To address this, they partnered with mFilterIt to gain better visibility into what their competitors were doing as part of their pricing strategy. Using DaaS (Data as a Service) feed, the pricing intelligence tool provided a structure data pipeline built on ecommerce analytics principles that gave the OTA platform a clear, side-by-side view of its own pricing vs a key competitor’s pricing across 100+ mapped flight sectors. It also enabled an automated price gap detection system for quick discount recalibration. Whenever a pricing difference was mapped between the two platforms, the system flagged it automatically. Here’s What the Data Actually Showed & Why It Matters for OTAs Once the data started flowing, the insights were immediate, and the findings were structured across three clear layers of pricing. Finding 1: Clear MRP gaps, the competitor was already pricing cheaper, even before the discounts were applied The first data set compared base ticket prices (the price before any coupon or offer is applied) across 8 airlines on both platforms. For several airlines, the competitor’s base price was consistently lower than the OTA’s. Why does this data & insights matter? A higher base price puts you at a disadvantage before discounts are applied or even seen. Without competitor mapping, a systematic undercut on key routes can go undetected for months. Finding 2: The competitors were giving bigger discounts on the same route flights The second data set examined the coupon values (the discount amount each OTA was applying on top of the base price). This is where OTAs most actively compete, and the data showed the competitor was being significantly more aggressive with discounts on several airlines. Why does this data & insights matter? If you don’t know how aggressively the competitor is discounting, you either lose bookings by under-spending or lose margin by over-spending. Platforms end up providing blanket discounts instead of targeted ones, which directly eats into margins. Finding 3: The final price that customers saw were lower on the competitor’s platform The third data set showed the price a customer sees when they’re ready to book, after all discounts have been applied. This is the number that makes or breaks a booking decision. Why does this data & insights matter? The final price is the only number the customer acts on; if it’s higher than the competitor’s, nothing else on your platform matters for that booking. Route-by-route visibility into the final price gap gives the pricing team a clear, prioritized action list instead of broad, reactive fixes. The same gaps exist across ecommerce too. Here’s what brands are missing. The Outcome: How a Pricing Intelligence Tool Helps Identify Pricing Gaps With the right pricing intelligence tool in place, the OTA platform was able to get a clear view of where gaps existed to make smarter decisions in both directions – when to discount and equally important, when to not. Here’s how a pricing intelligence tool helps: Discount updates became faster and more targeted instead of blanket coupon changes. The team could identify exactly which airlines and routes had a gap worth closing and act on them immediately. Margin leakage reduced on routes where the OTA’s final price was already lower than the competitors. The team pulled back on unnecessary discounts entirely. Every rupee saved on an over-discounted route goes straight back to margin. Strategic price increases became possible when demand spiked on a specific route and competitor data showed rivals were also priced high; the OTA could price upward with confidence rather than holding prices down out of habit. Knowing when the market will bear a higher price is just as valuable as knowing when to discount. Price rank awareness improved. Not every route needs to be the cheapest option. With real-time data, the team could make a deliberate call to hold at rank #2 with a healthier margin rather than defaulting to the lowest price out of uncertainty. Competitive positioning strengthened across 100+ mapped sectors. The OTA moved from reacting to pricing problems to anticipating them, closing gaps before bookings were lost and capturing margin where the market allowed it. The OTA platform was now able to anticipate pricing gaps and address them before customers had a reason to leave. This is what separates a reactive pricing team from a strategic one, and it starts with the right ecommerce analytics solution powering every decision. Conclusion: Is Your Pricing Team Working with Complete Data? If you’re in travel, e-commerce, or any industry where competitor pricing directly drives customer decisions, the question isn’t whether you need pricing intelligence. It’s whether you can afford to operate without it. Talk to us about how pricing intelligence can work for your business. FAQ,s What is data-as-a-service (DaaS) in OTA pricing intelligence? DaaS provides real-time OTA pricing data through cloud platforms, helping businesses access and analyze market insights without managing complex data infrastructure. How can businesses detect pricing discrepancies across OTAs? Businesses can use automated tools to track and compare prices across OTAs, quickly identifying inconsistencies, and maintaining pricing accuracy across all channels. Can pricing intelligence tools improve revenue management strategies? Yes, they offer insights into competitor pricing and demand trends, helping businesses optimize rates, increase bookings, and maximize overall revenue. How does OTA pricing intelligence help travel businesses stay competitive?

Data as a Service for OTAs: How Pricing Intelligence Helps Identify Competitor Pricing Gaps Read More »

What 756+ Million OTT Ad Requests Revealed About Where Media Budget Really Goes

Blog, Ad Fraud / Shiraz Noor

OTT advertising seems to be a safe bet right now. Brands are moving serious budgets here to reach a wider segment of audience at once. In 2025, 28% of total digital ad spend in India was heavily driven towards OTT platforms and video content. (Source: Exchange4Media) But what if we told you that the audience pool that you are reaching right now is limited? The numbers that you see on your dashboard are not always true. This is exactly what came to light during a recent campaign analysis we conducted for a large automotive brand running video ads across two of India’s leading OTT platforms. We went beyond what the platform reported and validated what was actually happening at the delivery level. Over 756 million ad requests were reviewed across three months. Here’s what the data revealed, and what it means for every marketer running branding campaigns on OTT today. The Scale of the OTT Advertising Campaign & Why It Matters The campaign ran across two major OTT platforms simultaneously, covering both CTV and mobile inventory. It covered multiple brand lines, from regional language campaigns tied to popular content properties, to national-market brand pushes. In total, over 756 million ad requests were reviewed during the assessment period. Across Platform A, 1.18% of ad requests were blocked before delivery. However, the figure was significantly higher at 7.41% for Platform B. This gap between the two platforms is not incidental. It reflects differences in inventory quality, frequency capping behavior, and bot traffic patterns. Finding 1: Frequency Capping Violations – The Reach Problem Hiding in Plain Sight A frequency cap exists for two reasons: To protect the viewer from ad fatigue To protect the advertiser from burning budget on an audience that has already been saturated. When it is not enforced at the delivery level, both goals fail simultaneously. In this campaign, frequency overshoot was the single largest driver of blocked impressions, particularly on one of the two platforms, where it ran as high as 8.32% in a single month. At the device level, the problem was even more stark. A single CTV device was found to have accumulated 711 ad requests over a span of just 10 days, against a defined frequency cap threshold of 3 impressions per device. Multiple other devices on the same campaign showed repetition counts ranging from 245 to 510 requests across the same period. Action Taken to Prevent Frequency Capping Breaches Every ad request was evaluated in real time against the predefined frequency capping before the impression was served. When a device had already crossed its exposure limit, the ad request was blocked automatically. Impact Impression delivery shifted from repeatedly exposed devices to a new audience base. Budget was redirected toward incremental reach. Reach distribution became more balanced across devices. Every counted impression met the defined frequency and traffic quality thresholds Finding 2: Brand Safety – What Content Were the Ads Actually Running Against? Brand safety on OTT is not a binary condition; it depends on what specific content a particular ad placement is running against, and whether anyone is actually checking. During this campaign, content-level placement analysis was conducted using Video ID signals available from the platforms. It revealed that a portion of ad impressions were being served alongside content that no automotive brand would knowingly approve. Specific placements were identified and blocked that fell into the brand unsafe content categories: 1. Obscenity & Profanity: Adult content classified under the GARM video safety framework 2. Crime & Harmful Acts: Films with depictions of violence and criminal activity 3. Arms & Ammunition: Content featuring weapons as a central theme 4. Illegal Drugs: Content involving drug-related imagery These were not obscure placements on low-quality inventory. They were identifiable content URLs on mainstream OTT platforms, surfaced through systematic placement-level analysis. Action Taken to Prevent Ad Placements Besides Unsafe Content Each placement was analyzed based on text, frame-by-frame classification, and GARM-aligned video-level analysis. Once categorized as brand-unsafe, impressions associated with those placements were blocked from delivery. This ensured that the Impact Brand ads appeared only against content that met its defined safety standards. No brand-unsafe impressions were counted as delivered. Brand’s media team received verifiable assurance, not just a platform-level declaration. Brand integrity was protected at the most granular level possible Finding 3: Invalid Traffic – The Bots That Looked Like Genuine Viewers Invalid traffic on OTT does not look like a flood of suspicious clicks. In this campaign, it showed up in three distinct forms. 1. Outdated OS signals: Devices running Android versions 5.0, 5.1, and 6.0 were generating ad requests in December 2025. These are operating system versions that are years past their support lifecycle. 2. Outdated browser signals: Smart TV devices were detected running browser versions from nearly a decade ago, like Chrome 53 and Chrome 68. In-use CTV devices do not carry browser fingerprints this outdated. These signals point clearly to spoofed or manipulated device identities. 3. Data Center IP activity: A subset of traffic was traced to IP addresses belonging to data centers and VPN infrastructure providers. These IPs were routing traffic to mimic genuine viewer behavior, appearing to originate from real residential locations while actually passing through commercial data center networks. Action Taken to Reduce Bot Traffic Each signal was evaluated in real time as part of the VAST-level ad traffic validation process. Requests carrying bot traffic indicators were flagged and blocked before an impression was served. Impact On Platform A, invalid traffic stayed between 0.55% and 0.67% across the quarter. On Platform B, despite higher inventory variability, IVT was actively contained through continuous real-time filtering. Zero IVT-affected impressions were passed through as billable delivery across either platform, every impression that was counted was a genuine one. As a result, once all three layers of validation were in place, the campaign delivered exactly what it was planned to. Viewability held above 92% throughout the quarter. Geographic delivery aligned closely with targeting intent; regional campaigns delivered impressions in their intended language markets. Moreover, CTV advertising accounted for nearly 99.9% of delivery across both platforms, confirming the campaign was genuinely reaching the living room screen it was built for. Why is Ad Traffic Validation Non-Negotiable for OTT & CTV Campaigns? Frequency violations, unsafe placements, sophisticated invalid traffic – these patterns exist across OTT campaigns broadly. They go undetected simply because advertisers often don’t look at the right layer of data. Here’s what mFilterIt’s proactive ad traffic validation solution – Valid8 makes possible for brands: Ensures your frequency cap is actually working, not just set Enforces frequency cap thresholds at the device level, so before the impression is served, overexposure is stopped before it costs you, not

What 756+ Million OTT Ad Requests Revealed About Where Media Budget Really Goes Read More »

Are You Competing Against the Market or Against Your Own Affiliates?

Blog, Ad Fraud / mFilterIt Experts

Affiliate programs are a powerful revenue driver and bring undeniable scale and performance to the table. It’s no surprise that brands continue to increase their investment in the channel. Global affiliate marketing spend is expected to reach $17B in 2025 (up from $15.7B in 2024) and is projected to surge to $38.35B by 2030. (Source) But as investments rise, one question remains: how deeply is this performance really being evaluated? Nearly 22–30% of digital ad spend (Source) is lost to invalid traffic or fraudulent activity and affiliate campaigns are one of the easiest places for it to hide. The affiliate ecosystem is revenue-driven but complex with multiple partners involved and that makes it more vulnerable to performance leakages. When some partners take credit for users you already acquired organically, you unknowingly start competing with your own growth. You know your external competitors. What you don’t see is the partner within your own ecosystem quietly draining your ad budget. These bad partners not only impact you but also steal the credit of genuine partners, impeding their growth. Sounds like a big claim? Let’s uncover it. Steady Growth or Midnight Spikes? What Affiliate Data Is Telling You Your genuine affiliate partners will show a steady and explainable growth pattern. The installs and traffic driven by them will not be restricted to a specific time window or sudden spikes. Instead, you will see natural variations; some days higher, some lower based on seasonality, campaign activity, and normal user behaviour, making the performance look realistic and trustworthy. Whereas, in case of fraudulent affiliates, you will notice a sudden spike in the number of installs. The user journey will not be mapped, and apps can get installed on always-on basis especially during the times when no normal person will install your app (3-4 am). From a marketer’s perspective, sudden out performance without clear explanation often signals inflated or manipulated metrics, not real user acquisition. The graph below shows the exact odd-hours spike happening at peak night where y-axis highlights the install rate and x-axis, the time in hours. How is Wrong Affiliate Intervention Rewriting your Growth Story? You built a strong affiliate network but what if it is rewriting your growth story? Affiliates that do not bring valid traffic and yet win the attribution race are actually not contributing to your ROI. Here’s what the wrong affiliate intervention looks like – This data of 7 days indicates campaign performance of various affiliates. In just seven days of campaign data, the gap between clicks and installs shows major discrepancies. One partner alone generated 29.03 million clicks but delivered only 45,501 installs, an extremely low 0.16% click-to-install rate while others also failed to cross even the 1% install rate mark. On the surface, the program appears to be scaling through massive traffic, but in reality, the growth narrative is being shaped by inflated clicks rather than real users, distorting performance, budgets, and optimization decisions. From Attributed Performance to Real Incrementality: The Shift You Need This time, you are not required to increase the budget of affiliate programs, instead what you require is a comprehensive approach that provides right attribution to deserving partners, cutting noise of fraudulent affiliates. Here’s how mFilterit’s holistic ad fraud solution Valid8, empowers your brands with an added layer of attribution integrity – Eliminate odd-hour install spikes by closely monitoring the full user journey and identifying suspicious patterns at the source level before they drain your budget. Demand true source-level transparency to shift budgets toward partners delivering genuine installs and cut spend on hidden, low-quality traffic sources. Detect traffic quality issues and behavioural anomalies early to optimise campaigns toward high-intent users instead of inflated performance numbers. Automate blocking, protect payouts, and optimise partner performance to reduce wasted spend, safeguard ROI, and scale confidently with partners that truly drive results. How We Tracked Down IVT: Saved $1.3 Million in Just 3 Months ? For a major travel portal running performance campaigns to acquire new customers, the problem wasn’t the budget, it was the lack of visibility into where the traffic was actually coming from. Despite healthy spending, the brand could not clearly distinguish between genuine and low-quality affiliate sources. We stepped in and closely monitored affiliate performance across the program. By identifying the partners driving fraudulent and non-incremental activity and stopping payouts to them, the brand ensured that only genuine contributions were rewarded. As a result, it was able to save up to $1.3 million in just three months while bringing back control over its performance spend. Conclusion The last thing you must worry about while running an affiliate program is to fight against your own affiliates. Affiliate marketing program are not the problem; the real opportunity lies in making them work the way they are meant to. To unlock their true incremental value and eliminate dishonest contributions, brands need to evaluate the entire affiliate journey, not just the final attribution. Only then they can fight affiliate marketing fraud and reward genuine partners, stop performance leakages, and turn the channel into a reliable, growth-driving engine. Want to know how? Schedule a call! FAQs How Can You Tell If An Affiliate Is Driving Real Growth? Real affiliates show consistent, natural performance trends. Sudden install spikes, odd-hour conversions, or a big gap between clicks and installs are signs of non-incremental or low-quality traffic. Why Do Affiliate Programs Sometimes Waste Ad Budget? Because last-click attribution can reward partners who didn’t create real user intent, brands end up paying for users they would have acquired organically, leading to inflated metrics and lower ROI. How Can Brands Stop Affiliate Fraud And Protect Roi? By analysing the full user journey, identifying traffic sources, and rewarding only genuine incremental conversions while blocking invalid partners and payouts. What are the signs of affiliate fraud in campaign data? Common signs include sudden traffic spikes, odd-hour conversions, unusually high clicks with very low installs, poor click-to-install rates, and conversions from suspicious referral sources. These patterns often indicate invalid traffic or attribution of manipulation. How can brands audit their affiliate partners effectively? Brands can evaluate affiliate partners by reviewing traffic quality, conversion behaviour, referral sources, and adherence to program guidelines. Ongoing

Are You Competing Against the Market or Against Your Own Affiliates? Read More »