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Traffic Quality vs Invalid Traffic Volume: What Really Drives Campaign Performance?

Every brand’s marketing program runs on one principle – more visits amount to more leads. While Google and META highly influence users’ journey but not all that stands true. The journey is simple but equally prone to the complexities of digital advertising ecosystem. This shifts the real question from how many visits your campaigns generate to where those visits are coming from or if they are leading to any conversions? We saw this firsthand while working with one of the USA’s leading aggregator players. For them, deeper validation of their campaign traffic was the turning point. When they looked closer at where their visits were actually coming from, the picture changed entirely. Irrelevant, low-quality sources were quietly eating into their budget and polluting their campaign data, making it nearly impossible to measure what was truly working. So, they made a call: blacklist the bad sources. Clean the data. And rebuild on a foundation they could actually trust. In this blog, we break down exactly how that played out: Sources that pollute Google and Meta campaigns and their impact How blacklisting changed the game The measurable impact of defending against fraudulent traffic Key takeaways for marketers Conclusion Source-Level Fraud in Google and META Campaigns We did a thorough analysis of the brand’s campaigns running on Google and META and here’s what we found – From META, brand received the highest Invalid Traffic (IVT) of 28.51%. From Google, brand got 8.15% IVT from various fraudulent sources. The difference in invalid traffic across platforms clearly shows that not every traffic source delivers the same quality of users. A campaign may generate high traffic numbers, but that does not always mean the visits are genuine or valuable. In some cases, a large portion of traffic can come from fraudulent or low-quality sources that never convert into real customers. For Google campaigns, the IVT percentage may appear lower compared to other platforms, but the advertising costs on these walled gardens are significantly higher. This means that even a small percentage of invalid traffic can result in substantial budget wastage and reduced campaign efficiency. To understand this better, let’s look at the major sources contributing to IVT and the direct impact they have on marketing campaigns – VPN/ Proxy Fraud: Traffic routed through VPNs or proxy networks to disguise real user identity and location. Impact: Bypasses geo-targeting and fraud filters, making fake traffic appear legitimate. Geo Fraud: Traffic coming from the geographies that were never a target at the first place. Impact: Creates a false sense of campaign success in priority markets. Behavior Fraud: Bots or automated scripts designed to mimic real user actions like fake clicks, scrolling, session duration. Impact: Inflates engagement metrics while delivering zero real intent. Device Repetition: Repeated interactions from the same device or a controlled pool of devices also called device farms. Impact: Indicates click farms or emulator-driven traffic, skewing user-level data. Pop-Under Traffic: Ads triggered in hidden or background windows without active user intent. Impact: Generates low-quality visits that look like traffic but don’t convert meaningfully. mFilterIt’s Solution: How Blacklisting Changed the Game for Leading Aggregator mFilterIt transformed campaign performance by shifting the focus from traffic volume to traffic authenticity. Through our ad fraud detection solution, brands attained real-time traffic validation and source-level analysis, identifying and blocking fraudulent or low-quality sources before they impact campaign outcomes. This enables brands to take precise actions like blacklisting, ensuring that only genuine users move through the funnel. Here’s how it changed the game – IVT Dropped by 42% in META Campaigns What began at 38% dropped down to 22% in just three months, a major 42% reduction in IVT. This was not a one-time correction; it indicates a consistent, ongoing improvement driven by focused campaign optimization. As deeper traffic validation was done and low-quality sources were identified, the system was able to filter out fraudulent sources such as geo-masking and repeated device activity. Over time, this led to cleaner inputs, better targeting decisions, and more reliable performance signals. The continuous decline also indicates that optimization efforts didn’t just remove existing fraud but actively prevented its recurrence. IVT Dropped by 8.4% in Google Performance Max Campaign Below graph highlights reduction of IVT in Google performance max campaigns by 8.4%. Invalid traffic dropped from 15.84% in September to 14.51% in November—a noticeable improvement over a short period. In Performance Max campaigns, even a single percentage point reduction matters because these campaigns operate at scale and involve higher media spends. So, while the IVT reduction is 8.4%, the real impact goes beyond that number. Less wasted spend on invalid traffic means more budget is directed toward. Campaign Performance Over Time: The Impact of Traffic Quality Optimization Once the blacklisting began, the campaign showed progress in terms of traffic quality in both Google and META campaigns. Let’s see what each denotes – Performance Improvement in META Campaigns This table highlights how campaign performance evolved over a five-month period, Initially, when all traffic sources were allowed to run freely during August, the campaign delivered 1,753 clicks and 101 conversions, resulting in a conversion rate of 5.76%. While costs were moderate, performance was held back by poor traffic quality. As shown in the image below, the conversion rate significantly improves post blacklisting. Moving into September 2025, there’s an interesting shift. Although costs increased by 23.5%, the conversion rate improved to 6.31%. This suggests that cleaning up low-quality traffic sources (likely via blacklisting or filtering) began to pay off. Even with higher spend, the campaign became more efficient because the traffic quality improved. By October 2025, performance stabilizes. Costs remain nearly flat (+0.5%), but the conversion rate climbs further to 6.64%. This indicates that earlier optimizations are holding strong, and the campaign is now reaching a more relevant audience consistently. In November, the conversion rate jumped to 7.36%. The upward trend in conversion rate from 5.76% to 7.36%; is significant. It reflects a clear improvement in traffic quality and campaign efficiency, not just increased spend or scale. Performance Improvement in Google Campaigns The data highlights a clear turning point in campaign performance before and after blacklisting was implemented. In August, the campaign struggled with high cost per conversion (519) and a low conversion rate (0.48%), indicating inefficient spend driven by poor traffic quality. The campaign improved once blacklisting was brought in action as reflected in conversion rates. Post-implementation, starting September, performance improved significantly. Cost per conversion dropped sharply from 137 in September to as low as 67 by early November while conversion rates increased from 0.48% to 2.07%. This reflects the direct impact of filtering out low-quality and fraudulent traffic, allowing the campaign to focus on more relevant users. Overall, the trend demonstrates how

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Merchant Risk Monitoring in the Evolving Digital Payments Scenario

Blog, Brand Protection / mFilterIt Experts

Digital payments in India, led by Unified Payments Interface, are growing at an exceptional pace. Transaction values continue to rise year after year, reflecting how deeply digital payments are now part of everyday life. The graph below highlights this momentum, with transaction volumes growing YoY and an expected 300% increase in both transaction volume and rupee value by FY30 vs FY25. Emerging Trends: UPI payments are the key driver of this growth – with its ecosystem of participating Banks (~700), merchant ecosystem (~65 million) and serving nearly half a billion customers. UPI in terms of Volumes is expected to grow contribute ~85% in terms of rupee value of all digital payments by FY30. Add to this the 40 authorised TPAPs, the ecosystem is bound to flourish and is also insulated from external threats (eg. GPay, visa, mastercard shutting down in Russia); given that this is completely Made in India tech stack. A key component of UPI Payments is the Merchant Eco System – currently contributing ~30% of current value to ~63% in terms of Rupee value. Debit and Credit Cards comprising POS and Digital spends, expected to grow at a CAGR of 25%, with spends primarily coming from credit cards and increase penetration of UPI linked credit cards. The spends growth will be primarily driven by digital spends which is expected to be around 72-75% (FY30) from the current 63%. – this represents a a perceptible shift away from the traditional Brick and Mortar Merchant. Prepaid / Fastag & SI transactions is expected to see a steady growth with primary spends being digital in nature. Emergence of Fintechs, TPAPs, Payment Banks, Telcos once dominated primarily by banks and technology firms, the sector now attracts players from diverse fields such as retail, telecommunications, FinTech, and e-commerce, enabling cross-sector innovation and new market entrants. The above trends is a paradigm shift vis a vis what we have seen till a few years ago and brings with it, its own perils such as cybercrime and fraud risks. Cybercrime has evolved to such and extent that FY25 has seen a 24% spike in cybercrimes reported on the NCRP portal, with the rupee value of reported fraud loss being INR 22,495 crores. This is the reported numbers and the overall non reported instances could be approximated to be 1x in terms of rupee value. Given that this is majorly digital, the merchant eco-system plays a key role in the same; payment instruments like VPAs, underlying merchant accounts etc are used extensively as collections accounts to receive the proceeds of such crime or act as an intermediary in the laundering of such proceeds. Hence it is that much more imperative that there is a lot more focus on the kind of merchant who are being on boarded; given the digital nature of merchant spends and the ease at which the merchant can evolve post onboarding, a Life Cycle based Risk Management Approach is the need of the hour. Every merchant you onboard is a vote of confidence in your platform. But every transaction they process is also a new surface for risk. Hence, what starts at onboarding shouldn’t stop there; it needs continuous attention. In this blog, we will discover- How one-time checks are not enough in merchant onboarding How rapid growth in digital payments is increasing risk And how end-to-end merchant evaluation can make a real difference How One Time Risk Checks are not Enough in Merchant Onboarding? There is a traditional belief that all merchant risks can be tackled before onboarding and once the onboarding is completed, no merchant risks can travel, making the following, the key checkpoints of merchant onboarding – Verify business details Review website Approve and go live But as the digital payment ecosystem is growing more complex, merchant risk monitoring does not stop at onboarding. Assuming that merchants will remain compliant after initial checks is often unreliable. In reality, risks can emerge at any stage post-onboarding, such as – Change in website content Sale of restricted / banned / high-risk products Manipulate redirects or hidden flows Drift away from declared business categories All of this happens after onboarding, when visibility is often limited. How Scaling Digital Payments Elevates Risk in Merchant Onboarding While digital adoption is speeding up, it also brings in new risks that don’t just appear at the start, they continue throughout the merchant journey. More merchants mean: More variation in quality and intent More edge cases that manual checks miss Higher probability of fraudulent or non-compliant entities slipping through However, stopping the merchant ecosystem from evolving is not the solution- “Continuous Monitoring”, is. Why Onboarding Checks Alone aren’t Enough & How Continuous Monitoring Fills the Gap? Here’s how comprehensive and continuous merchant risk monitoring strengthens payment getaways, enabling them to fill the gaps created by just focusing on merchant onboarding – (also include business outcome) Onboarding-only monitoring limitations How holistic monitoring helps One-time snapshot of the merchant Continuous tracking of merchant behavior Misses post-onboarding risks Detects emerging risks in real time Relies on static documents Validates ongoing digital presence and activity No visibility into changes in offerings Flags deviations in products/services Cannot track compliance drift Monitors policy compliance risk continuously Misses malware or suspicious activity later Scans regularly for threats and anomalies Reactive issue resolution Enables proactive risk prevention Higher risk of regulatory/reputation impact Strengthens against compliance risk and brand protection End-to-End Merchant Evaluation: A New Outlook to The Merchant Life Cycle. Merchant onboarding now demands confidence that cannot be achieved without holistic evaluation of merchant lifecycle, one that goes beyond onboarding and keeps validating risk at every stage. That’s where mFilterIt’s approach and product offerings steps I; not only focussing on onboarding but also monitors and evaluates risk which may evolve at a later stage. Business & Operational Analysis We help you truly understand who you’re onboarding by looking beyond basic details-analyzing the business, verifying promoters, and checking location authenticity. Outcome: You onboard genuine, trustworthy merchants from the start. Pre-screening & Identity Verification We validate merchants using multiple signals like contact details, device, IP, and KYC documents to catch inconsistencies early. Outcome: Lower chances of fraud and stronger defence against compliance risk from day one. Website Risk Analysis (and beyond) We continuously monitor the merchant’s digital presence-checking website content, offerings, compliance, and any suspicious activity. Outcome: Early detection of risks and better control even after onboarding. Overall impact: You move from one-time checks to continuous visibility—helping you build a safer

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How a D2C Beverage Brand Drove 82% Orders Growth on BlinkIt in 30 Days Using Unified Ad Manager

Blog, Digital Shelf Analytics / mFilterIt Experts

This blog is a look at what changed when structured campaign management replaced manual processes, and what the data showed one month later. Here’s some background for you. A fast-growing beverage brand was running keyword and product listing campaigns on Blinkit across multiple cities in India. While the brand had an established product catalogue and a reasonable ad budget, their campaign performance was inconsistent. To overcome this, the brand adopted a performance marketing management platform to bring structure and intelligence to their campaign and optimization processes. The result was a 60% increase in revenue within a month. Continue reading further to find out how Unified Ad Manager (UAM) worked. Three Gaps That Were Holding The Performance Campaign Results Back Most brands running campaigns manually hit at least one of these problems. This D2C beverage brand was dealing with all three simultaneously. Campaign inconsistency Between 6th to 21st November 2025, campaigns were near-inactive for 15+ days, which means effectively zero visibility on the platform during that period. A day without active spend is a day where competitors fill the shelf space. Limited keyword coverage Keyword gaps mean entire search queries go uncontested. Only 77 keywords were active, leaving large portions of search demand for tonic water, mixers, and sodas uncaptured. High cost per order An ACoS of 69.2% and ₹189 per order made scaling the campaigns economically difficult to justify. Without time-aware budget pacing, money gets spent during off-peak hours when purchase intent is low. What Automated Campaign Management Using Unified Ad Manager Changed The brand adopted mFilterIt’s Unified Ad Manager (UAM), a performance marketing management platform that provides ecommerce analytics in a single view to address the structural gaps across three areas: campaign scheduling, keyword discovery, and bid optimization, with one additional feature that turned out to be the most impactful of all. Here’s how: Continuous campaign activation Automated scheduling, dayparting, and budget pacing ensured campaigns ran without gaps every day. The extended blackout periods that had cost the brand significant impression share in November were eliminated entirely. Daily spend remained active and performance compounded week over week as optimizations had time to take effect. Broader keyword coverage The keyword discovery capabilities of Unified Ad Manager (UAM) expanded active targeting from 77 to 130 keywords (a 60% increase). The expanded keywords were kept tightly aligned to the brand’s product categories (tonic water, soda water, ginger ale, cocktail mixers), so reach grew without ignoring relevance. Dynamic bid and budget optimization The Unified Ad Manager (UAM) also helped the brand adjust bids continuously based on real-time performance signals, rather than keeping all bids static across all keywords. Budget was also systematically reallocated toward product listing campaigns, which delivered stronger ROAS, received a larger share, while product recommendation spend was scaled to a more appropriate level. The Day-Parting Insight That Showed Major Impact Of all the changes made between November and December, automated day parting showed significant and measurable results. In November, spend was distributed without any time-based structure. This meant ad budget was regularly consumed during periods of low traffic, like early mornings when conversion rates were lowest. The ROAS across those campaigns was 0.92x: spend was effectively generating less than ₹1 of revenue per ₹1 spent. In December, with unified ad management platform, the brand identified evening hours as the peak demand window for quick commerce beverage purchases and shifted 99% of active spend there. The outcome was a ROAS of 1.58x on those campaigns (a 72% improvement), driven not by increasing the budget but by changing when it was used. This means the ads were showing at exactly the moment purchase intent was highest, improving both conversion rates and the return on every rupee spent. The Impact D2C Brand Saw After Leveraging Campaign Management & Insights from Unified Ad Manager Metric Nov 2025 Dec 2025 Change Orders 1,211 2,204 +82% Revenue ₹3.3L ₹5.3L +60% ROAS 0.92x 1.58x +72% ACoS 69.2% 63.6% −8.1% Cost per Order ₹189 ₹153 −19% Active Keywords 77 130 +60% Ad spend grew by 47% month-on-month. Revenue grew by 60%. This gap where revenue outpaces spend is the signature of genuine efficiency improvement, not simply higher investment producing higher output. In simple terms, each additional rupee invested in December generated more revenue than it did in November, even as order volumes nearly doubled. This wasn’t just seasonal demand; it points to smarter, more optimized campaign performance. The same trend is visible in cost efficiency. Cost per order dropped from ₹189 to ₹153 (a 19% reduction) despite operating at twice the scale. The result: higher volumes, lower acquisition costs, and stronger returns, all at the same time. Key Takeaways: What Ecommerce & Quick Commerce Brands Actually Get When They Move to Unified Ad Management The results above are specific to one brand and one platform, but the underlying problems they solved are not. Fragmented campaign visibility, reactive bid management, unstructured budget spend, and keyword gaps are patterns that show up across categories and platforms whenever campaigns are managed manually. Here’s what changes when marketers start leveraging Unified Ad Manager by mFilterIt. You stop managing campaigns across platforms in silos A unified interface brings everything together in one place, making it easier to create, update, and monitor campaigns. This reduces operational effort and improves consistency in execution. Bids adjust in real time, not once a week when someone reviews the dashboard With dynamic bid management and optimizations, bids are continuously updated based on performance signals like ROAS and traffic trends. This ensures campaigns stay aligned with performance at all times, even beyond working hours. Budgets start moving towards what’s actually working A rule-based budget reallocation engine shuffles spend across campaigns in real time, shifting budgets towards high-performing sources and pulling back from lower-ROAS ad types. AI and ML-based campaign rules replace guesswork or manual decision-making A custom rule engine lets brands define the conditions under which bids should increase, budgets should be reallocated, or campaigns should pause. It executes those decisions automatically when the conditions are met. This removes the human bottleneck from time-sensitive optimisation and ensures the campaign strategy is actually reflected in live spend, not just in a planning document. Performance is tracked at the granular level Keyword-level, campaign-level, and platform-level logs tell you why and where to fix it. Detailed activity tracking across campaign overview, ad group, keyword, and rule engine logs helps in accuratedecision-making to improve campaign efficiency at scale. Therefore, by bringing structure, automation, and real-time decision-making into campaign management, the brands can grow faster while becoming more efficient at the same time. Ready

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

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

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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

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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

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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

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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”

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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.

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$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.

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