Ad Fraud - mfilterit

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

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

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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.

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How to Know If Your Campaign is Affected by Ad Fraud: 5 Signs Marketers Often Miss

Blog, Ad Fraud / mFilterIt Experts

Bot traffic is taking up more than half of the internet traffic. Out of which, 37% of the traffic is driven by bad bots. (Source: Imperva) And this bot traffic is beyond just inflated activity. Sophisticated ad fraud techniques penetrate the funnel, impacting not just analytics but end goals like sign-up, purchase, etc. They can bypass basic ad fraud detection methods easily, mimicking human-like behaviour. It skews the data, further impacting decision-making, conversion rates, and retention across mobile and web campaigns. That is why it is important for advertisers to know about not just surface-level signs of ad fraud but also the sophisticated indicators. In this article, we’ll break down some of the signs of ad fraud that we have observed in the campaign analyzed. Let’s dig in. What Differentiates Sophisticated Ad Fraud Techniques from Basic Bot Traffic? Basic bot traffic is easier to detect. It often creates visible spikes like traffic coming from locations outside the targeted region, same devices, unrealistic click volumes, or abnormal engagement patterns. On the other hand, sophisticated ad fraud is different. Instead of obvious anomalies, it mimics human-like behaviour. The manipulation happens inside patterns that are harder to identify and detect: OS distributions, CTIT inconsistencies, imperceptible ad placements, IP clustering, or traffic coming from incent fraud. Basic bots inflate numbers. Sophisticated fraud impacts performance intelligence. That is what makes it more dangerous. It does not just waste budget. It influences optimization decisions, attribution models, and scaling strategies, without triggering immediate suspicion. Therefore, understanding this difference is the first step toward detecting it. Now, let’s have a look at some of the sophisticated ad fraud signals. Sign 1: Heavy install coming from older Android OS versions Fraudulent affiliates using bots and emulators running on older Android OS versions to generate fake app installs. After comparing OS version install distribution across different traffic sources: Google installs were spread across multiple OS versions (10-16), reflecting a healthy and natural user base. However, two affiliate partner sources revealed a very different pattern. Partner A and Partner B showed a heavy concentration of installs on OS 12, 13, and 14 While the benchmark (Google) traffic was distributed more broadly across OS 10–16 The mismatch clearly indicated emulator-based or bot-driven installs. Sign 2: Google Play installs happened before the user clicked on an ad Click-to-install time (CTIT) measures how long it took a user to install an app after clicking on an ad. Naturally, an app install takes up to minimum 20-30 seconds. However, in one of the campaigns we noticed app installs taking place even before the users clicked on an ad, resulting in negative CTIT. This is a clear indicator of mobile ad fraud. Therefore, extremely short or negative click-to-install time indicates click injection. If your CTIT distribution doesn’t resemble a natural curve, it’s worth investigating further. Know how. Sign 3: Inflated Installs Coming From Incent App In one of the campaigns, we observed that a telecom provider was unknowingly running ads on an incent app. Users were redirected through a shared link, asked to install the app, and complete specific steps to earn rewards. This resulted in a high number of installs, but the actual engagement remained low. The majority of users completed the required action only to earn coins and did not return. This clearly indicated incentive-driven traffic rather than genuine user acquisition. Read this to know about incent apps and low-quality traffic in detail and how advertisers can protect their mobile app campaigns. Sign 4: Invalid Traffic Coming From Imperceptible Window In one of the web campaigns, 99% of traffic was coming from an imperceptible window (also known as pixel stuffing ) through a specific publisher source. This means the ad was technically loaded in 0x0 iFrames, but not actually visible to users. Although impressions and traffic volumes appeared normal, user engagement metrics clearly indicated non-human behavior. Analysis revealed: Repetitive browser agent across sessions Over 70% of data originating from a single IP cluster Zero scroll activity and no sales generated This means advertisers must check not just if the ad was delivered but also if the ad was actually viewable. We have broken down how to move beyond the viewability myth. Check it out here. Sign 5: Repeated Ip Traffic From The Same Subnet (Invalid Traffic Pattern) In genuine campaigns, IP addresses are typically distributed across diverse networks. But what we observed was different. At first, the traffic appeared strong. But on deeper evaluation of IP-level data, we found that a large portion of clicks and visits were traced back to a single IP subnet. Each IP was generating more than 70+ clicks, consistently inflating traffic. The concentration of activity within a contiguous subnet suggested coordinated or automated behavior rather than random user traffic. If a significant share of your traffic is coming from closely grouped IP ranges. especially those flagged under VPN or proxy networks, it requires immediate audit. Volume alone does not indicate performance. Source diversity does. How Can Advertisers Identify Sophisticated Bot Traffic? Detecting sophisticated ad fraud requires moving beyond surface-level indicators. Here are key actions advertisers should take: Analyze Deeper Behavioral Patterns Validating only surface-level signals like clicks and installs is not enough. You need to monitor click-to-install timing distributions, engagement depth beyond first interaction, repeat device and IP behaviour, etc. These patterns uncover anomalies that standard filters miss. Benchmark Across Trusted Sources Compare partner traffic against known clean channels, ecosystem adoption trends, and natural engagement ranges. Discrepancies from benchmarks often reveal non-genuine or invalid traffic behaviour. Validate Before Scaling Budgets Campaign scaling should never happen without ad traffic validation. High volume doesn’t mean high value. Invest in tools that provide real-time ad fraud detection, cross-source transparency and analysis, alerts for sophisticated patterns with proofs and in-depth understanding of new emerging patterns as well. At mFilterIt, our ad fraud detection tool – Valid8, helps detect ad fraud signals that ad platforms and MMPs often overlook. They also allow you to: Understand true user intent Exclude invalid traffic before optimization

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15% of Ad Impressions were Exceeding Frequency Capping: Here’s How We Fixed It in a CTV Campaign

Blog, Ad Fraud / Shiraz Noor

Your ads are getting delivered, but to a limited audience pool. This is what we recently saw in one of the campaigns running on CTV platforms. While impression delivery appeared strong for this Indonesian brand, engagement metrics did not align proportionately with campaign spend. On deeper analysis, it was observed that ad impressions were being served repeatedly to a limited set of devices instead of expanding to new viewers. This indicated a potential frequency capping breach, where ads were being delivered beyond the defined exposure threshold. Impact? Poor campaign efficiency and ad fatigue. So, if you are an advertiser running OTT & CTV advertising campaigns, or your audience is experiencing something similar, this is must-read. Deep-Down to Identify the Problem Throughout the campaign, 6.02 million ad requests were evaluated through VAST-level validation signals. What the Data Revealed About Frequency Capping Breach The evaluation uncovered that: 15.86% of impressions were exceeding the defined frequency cap A total of 16.47% delivery was prevented, combining frequency capping breach and invalid traffic filtration Over 950,000 impressions were blocked due to frequency violations alone. Certain Smart TV device IDs generated thousands of repeated ad requests within short time windows In one instance, a single device triggered over 7,600 ad requests in a single day, clearly indicating abnormal repetition behavior. Additionally, a small portion of traffic (0.61%) was linked to data center and VPN-based IP activity, pointing toward advanced traffic manipulation patterns. The Action Taken To address this, real-time frequency validation was implemented at the VAST integration level. Every ad request was evaluated against the predefined frequency threshold before delivery. If a device had already crossed the limit, a no-ad response was triggered, preventing further exposure. Repeated device patterns and abnormal request spikes were filtered out without impacting legitimate delivery. This ensured that exposure remained controlled and aligned with the campaign’s intended frequency settings. The Measurable Impact After filtration and enforcement: 691,691 impressions were validated and served cleanly. Video engagement remained strong. These results demonstrated that once frequency capping was enforced and invalid traffic was removed, genuine viewer engagement remained stable and healthy. More importantly, reach distribution improved, budget wastage was reduced, and exposure became more balanced across devices. What This Means for OTT & CTV Advertisers To prevent frequency capping breaches, simply setting up a frequency cap is not enough. What matters is whether that cap is actively enforced at the moment of ad delivery or not. mFilterIt ensures that ad exposure remains controlled, balanced, and performance-driven through real-time frequency governance. Here’s how advertisers can benefit: Ensures Proactive Enforcement Of Frequency Caps By validating ad requests before they are served, exposure thresholds are actively monitored and enforced. This prevents ad impressions from exceeding defined limits and ensures campaigns remain compliant throughout their lifecycle. Prevents Impression Wastage On Limited Devices Device identities are analyzed to monitor how frequently a specific device has been exposed to an ad. By tracking repetitions at the device level, advertisers can clearly identify when impressions are being served within a limited audience pool and take corrective actions accordingly. Maintains Clean Reach By Combining Frequency & Traffic Quality Ad frequency overshoot can sometimes overlap with invalid traffic signals. By evaluating both exposure limits and traffic quality together, mFilterIt ensures campaigns maintain clean reach without inflating ad impressions through excessive or abnormal delivery. Protects Viewer Experience With Balanced Exposure By maintaining balanced exposure levels, advertisers can ensure that audiences are not overwhelmed by repeated messaging. This helps create a more relevant and engaging viewing experience while preserving brand perception across OTT and CTV environments. Enables Smarter Campaign Optimization Insights from frequency analysis allow advertisers to refine targeting strategies, adjust exposure thresholds where necessary, and improve distribution efficiency. This ultimately supports stronger reach expansion and better use of media investment. Conclusion OTT and CTV advertising is built to deliver premium, high-impact brand moments. But without proactive validation, campaigns generate ad impressions within a limited device pool, restricting reach and draining budget on repetitive exposure. Advanced frequency capping using ad traffic validation solution is a performance safeguard. With this approach, brands can protect reach, maintain engagement quality, and ensure that budget is directed toward incremental audience expansion, not overserving the same devices. If you want your branding campaigns to deliver genuine impressions, balanced reach, and measurable ad viewability across OTT and CTV advertising, it’s time to move beyond settings and into enforcement. Connect with our experts to know more! FAQs What Is Frequency Capping? Frequency capping refers to the maximum number of times an advertisement should be shown to a user or device within a defined time frame. For example, a brand may decide that a viewer should not see the same ad more than three times per day to maintain optimal exposure without causing ad fatigue. What Is Frequency Capping Breach? A frequency capping breach occurs when an ad is shown to the same user beyond the predefined limit. Moreover, this can happen even when you’ve set a frequency cap in your ad manager, due to platform-level inconsistencies, device-level repetition, or synchronization gaps across systems. How Are Frequency Caps Configured? Frequency caps are typically configured based on: Campaign Publisher Geography Time duration (daily, weekly, monthly) Why Does Frequency Capping Matter? Effective frequency capping ensures that users aren’t served the same ad too many times within a short period. This enhances ad campaign performance, effectiveness, prevents irritation, and maintains a positive viewing experience. However, finding the right balance requires data-driven decision-making, continuous testing, and collaboration with advertising platforms.

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How to Identify Affiliate Fraud: Key Signs, Impact & Prevention Strategies

Blog, Ad Fraud / mFilterIt Experts

Consider a fast-growing ecommerce brand with strong organic traffic and a well-run affiliate program. Revenue looks solid every month, but one odd trend appears: a mid-tier affiliate suddenly becomes the highest contributor, while trusted, high-quality partners stay flat. At first, it feels like a performance win. However, a closer look reveals the truth. Most of those “affiliate-driven” traffic was from users who were already interested to buy from the brand. At the last moment, the credit shifts to the affiliate — even though they didn’t bring in a new customer. To burst this bubble, focus on what really adds value. In this blog, you will discover – The real-world signs of affiliate fraud How to detect it using actionable data signals And how to prevent it without hurting scale or genuine partners Signs to Identify Affiliate Fraud in Programs Brands running affiliate marketing programs can spot key warning signs triggered by fraudulent activity, understand the mechanisms behind them, and uncover what these indicators truly reveal – Unusually high clicks with low engagement or conversions What it is: Campaigns receive a high number of clicks but very few real actions like sign-ups, purchases, or engagement. How it happens: This is usually caused by click spamming, bot traffic, or forced redirects that create fake or unintentional clicks. What it indicates: Artificial traffic inflation aimed at organic hijacking, manipulating attribution and making performance appear better than it actually is. Inflated installs with distorted click-to-install ratios What it is: High install volumes paired with unusually short click-to-install times or irregular conversion paths. How it happens: Driven by click injection techniques that hijack organic or paid traffic at the last moment. What it indicates: Attribution manipulation and conversion theft from legitimate marketing channels. Abnormal growth from a small group of affiliates What it is: A few affiliates show sudden, disproportionate growth while overall program performance remains flat. How it happens: Often due to last-click hijacking of organic and paid installs What it indicates: Skewed performance reporting and possible conversion stealing rather than incremental growth. Sudden spikes in installs from limited device models, OS versions, or IP ranges What it is: High volumes of activity originating from a narrow set of technical identifiers. How it happens: Generated using device farms, emulators, or automated traffic systems. What it indicates: Non-human traffic rather than genuine user acquisition. Installs originating from unauthorized or unverified sources What it is: App installs coming from unofficial app stores, third-party APKs, or unknown publishers. Why it happens: APK tampering or manipulated distribution channels. What it indicates: High risk of fraud, poor user quality, security vulnerabilities, and low lifetime value. Sharp spikes followed by rapid drops in activity and retention What it is: Sudden bursts in installs or sign-ups that collapse shortly after. Why it happens: Incent-based campaigns that attract reward-seeking, low-intent users. What it indicates: Artificial scale that fails to generate long-term engagement, retention, or revenue. High volume of users completing only minimal actions What it is: Users perform just enough actions to trigger payouts and then disengage. Why it happens: Incent fraud, forced actions, or scripted behavioral flows. What it indicates: Low-quality acquisition that inflates metrics but delivers no sustainable business impact. Traffic spikes during odd hours or irrelevant geographies What it is: Large traffic volumes (including clicks and impressions) coming in at unnatural times or from low-relevance regions. Why it happens: Bot networks, proxy servers, or geo-masking fraud operations. What it indicates: Automated or manipulated traffic designed to bypass detection. High uninstalls or drop-off rates within the first 24–48 hours What it is: Users churn almost immediately after installation or signup. Why it happens: Forced installs, incentive-driven behavior, or misleading creatives. What it indicates: Poor user intent, weak onboarding quality, and wasted acquisition spend. Unusually High Retargeting Conversions What it is: A sudden or consistent surge in conversions attributed to retargeting campaigns. Why it happens: Fraudulent sources manipulate attribution using techniques like click spamming, cookie stuffing, or last-click hijacking. What it indicates: Conversion hijacking rather than genuine retargeting impact. How to detect and prevent Affiliate Fraud? Your legacy tools might be validating traffic at initial stages but is it going deeper to analyse compliance as well? Once the signs are identified, the next approach for brands must be to opt for a comprehensive AI-driven solution that keeps their affiliate programs intact by also extracting the metrics that is not inflated by wrongful conversions. One such solution is Valid8 by mFilterIt that strengthens brands against affiliate fraud while maintaining affiliate integrity – Build Source-Level Transparency Monitor every click and conversion comes from. When you see the true source of performance, you can reward real partners, eliminate hidden leakages, and invest with confidence not assumptions. Enable Holistic Coverage Detect and block traffic from incent walls, curb unauthorized coupon usage, and ensure your program rewards only genuine, high-intent users — not incentive-driven or commission-leaking conversions. Protect Retargeting from Fake Audiences Retargeting only works when the original data is clean. Filter invalid traffic early so your budget reaches high-intent users not bots or recycled audiences. Turn Insights into Smarter Investments Real-time, advanced analytics show what’s truly driving ROI. Double down on winners, cut risky sources fast, and optimize with speed. Combine Machine Speed with Human Intelligence Automation detects anomalies instantly; expert analysis adds context and action. Together, they resolve threats faster and keep performance on track. Conclusion Brands running affiliate campaigns must first ensure the quality and authenticity of the traffic generated by their partners. This not only protects brand investments but also safeguards genuine affiliates from being impacted by fraudulent practices. To effectively break these patterns, a robust ad fraud detection solution is essential and mFilterIt’s Valid8 validates full-funnel ad activity in the most comprehensive way. Want to know how? Schedule a call now! FAQs What is affiliate fraud in digital marketing? Affiliate fraud refers to deceptive practices used by fraudulent partners to generate fake clicks, installs, leads, or conversions in order to earn illegitimate commissions, causing financial loss and inaccurate performance data for brands. How can brands detect affiliate fraud early? Brands can detect affiliate fraud early by monitoring traffic quality, analyzing engagement metrics, tracking source-level data, validating full-funnel performance, and using AI-driven fraud detection solutions for real-time monitoring. What is organic hijacking in affiliate fraud? Organic hijacking occurs when fraudsters intercept organic user journeys and falsely attribute conversions to affiliate channels using last-click manipulation or forced redirects.

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What is Ad Monitoring? Why Does it Matter During IPL Advertising?

Blog, Ad Fraud / Durgesh Rathore

The Indian Premier League (IPL) is not just one of the biggest sporting events in the country; it’s also one of the most powerful advertising platforms for brands. Every season, millions of viewers tune in across TV and digital platforms, making IPL a high-impact opportunity to drive brand visibility at scale. Naturally, brands go all in. From live broadcast placements and digital ads on OTT platforms to in-play overlays during matches, IPL advertising budgets are spread across formats with one common expectation: maximum visibility. But in a multi-feed broadcast environment like IPL and T20, heavy spending does not automatically guarantee that audiences actually saw what you aimed for until you see the final reports from broadcasters post matches. This brings us to a critical challenge for advertisers – ad monitoring. During IPL advertising, it’s no longer enough to buy ad slots and assume the ads will be delivered diligently. Brands need to understand when ads appeared, in what context, on which screens, at what time, and for how long they were actually visible. How Ads Are Actually Delivered During IPL Matches? IPL advertising is not a single media buy. Ads are delivered on multiple placements, platforms, and locations. Live match branding on jerseys, boundary boards, pitch mats, and other on-ground assets Ads during commercial breaks on TV and OTT platforms Digital display and video ads on streaming apps In-stream overlays like Aston ads and L-band ads that appear during the live play Such fragmented broadcast ecosystems make tracking difficult and inconsistent, creating a visibility gap. The Visibility Gap: What Brands Don’t See During T20 and IPL Broadcasts Many advertisers investing in IPL advertising focus on placements purchased — number of ad slots, jersey branding, etc. However, here’s what brands might not see. Brand Exposure During Live Match Coverage On-ground brand assets such as jerseys, boundary LED boards, pitch mats, stumps, and skirting depend entirely on broadcast framing for visibility. Whether a logo appears on screen, and for how long, it is decided by the flow of the match. Close-ups, replays, or action-focused shots can easily sideline on-ground branding. As a result, a brand may be present throughout the stadium but appear on screen only for a shorter span of time. FCT Ads During Breaks and Digital Insertions Commercial break advertising is often assumed to be the most predictable part of IPL campaigns. However, ad delivery and performance during breaks can differ significantly. Their effectiveness depends on cut percentages, placement, and visual clutter at the moment they appear. Short exposure windows can significantly reduce impact, yet these formats are rarely measured beyond confirmation of placement. However, without regular monitoring, advertisers are left relying on aggregated post-campaign reports rather than verified ad delivery. Non-FCT Ads: In-Stream Overlays During Live Play In-stream formats like L-band ads (horizontal strips across the bottom of the screen) and Aston ads (lower-third graphics that appear without disrupting the match feed) appear during live gameplay, positioning them as high-impact placements. However, their actual visibility depends on how long they remain on display during active play and whether it was delivered or not. Without in-stream ad monitoring, brands have limited clarity on whether these overlays were prominently visible or quickly overshadowed by live action and graphics. What Most Advertisers Investing in IPL Advertising Miss Without Advanced Ad Monitoring? Broadcaster reports focus on delivery — spots aired or planned reach. What they often miss is: Frame-level confirmation of brand visibility Feed-by-feed ad delivery and visibility differences Differences across regional and OTT feeds Duration, cut percentages, and prominence of on-screen ad exposure Competition mapping while the matches are happening Continuous ad tracking and verification Incomplete tracking leaves advertisers with incomplete verification. More importantly, these reports rarely provide evidence to question under-delivery. Without visibility data such as early cuts, missed peak moments, or feed-level gaps, advertisers have limited grounds to seek clarifications on ad delivery. Hence, the need for ad monitoring. So, What is Ad Monitoring in IPL Advertising? In simple terms, ad monitoring is the process of independently verifying what actually appears on screen during IPL broadcasts across live play, commercial breaks, OTT platforms, and in-stream overlays. Without active ad monitoring, advertisers risk losing both visibility and impact. Performance dips, under-delivery, missed geographies, or incorrect creatives damage campaign effectiveness, leading to lost opportunities in a media moment that’s all about timing and precision. An ad monitoring solution verifies what is actually shown on screen across all formats, platforms, and feeds. It tracks: If live match branding was visible or not How long a brand stayed on screen Whether each ad in commercial breaks played as planned Which feeds and regions saw the ad Presence of overlay ads and their duration Any discrepancies between planned and delivered visibility Did the ad run at the scheduled time How many times did each creative appear Was the brand visible during peak match moments Instead of relying just on broadcaster reports alone, it uses frame-by-frame detection and automated AI parsing to independently verify visibility and brand presence throughout the entire broadcast – live or recorded. Benefits of Ad Monitoring During IPL Advertising Ad monitoring helps advertisers move from assumptions to evidence, offering clear, actionable insights across every advertising format. Here’s how: Any-Asset, Frame-by-Frame Detection Tracks every brand element, logos, visuals, taglines, and products, frame by frame, ensuring no on-screen exposure is missed during live matches, replays, or commercial breaks. Platform & Format Agnostic Visibility Monitors ad visibility across TV, OTT, mobile, and CTV platforms, giving advertisers a consistent view of exposure regardless of where audiences are watching. Multilingual & Regional Precision Breaks down visibility by language feeds and regions, helping brands understand how exposure varies across geographies and ensuring regional campaigns deliver as planned. Contextual Advertising & Performance Benchmarking Supports stronger contextual advertising decisions by comparing brand visibility, measuring placement quality, engagement potential, and screen presence to assess true performance. Competitor Visibility & Insights Ad monitoring enables brands to see how competitors are advertising during the same

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What is Ad Fraud? Answering The Most Asked Questions About Ad Fraud

Ad Fraud, Blog / mFilterIt Experts

Ad fraud is an evolving threat and no longer linear. It is becoming more advanced everyday with AI and automation also contributing towards speed and scale. What once looked like normal bot activity has now become far more sophisticated, subtle, and harder to distinguish from genuine user behavior. This sophistication of ad fraud raises a lot of questions in the minds of advertisers, marketers, publishers, brand owners, or anyone involved in the digital advertising ecosystem for that matter. Hence, the purpose of this blog. To ensure you get answers to the most asked questions about ad fraud in one place. We will talk about everything from what is ad fraud to knowing how to respond to it with clarity and confidence. Let’s get started. What is ad fraud? Ad fraud is an attempt to generate fake, invalid traffic, or low-quality interactions on digital ads to manipulate campaign results. These interactions often appear real on the surface, such as impressions, clicks, leads, and installs, but actually come from bots, emulators, or click farms. By using various ad fraud techniques, fraudsters exploit payment models like CPM, CPI, or affiliate commissions. As a result, advertisers lose their ad budget on fake trafficand end up optimizing campaigns based on misleading metrics, leading to campaign inefficiency. What are the different types of ad fraud? Ad fraud shows up in different forms depending on the campaign objective, platform, pricing model, and even targeting. In case of web campaigns, it commonly appears as fake impressions, invalid clicks, or invalid traffic to exhaust budgets and inflate engagement metrics. In case of mobile app campaigns, ad fraud is more deeply tied to attribution and installs. Fraudsters exploit CPI and CPA models by generating fake installs, click injections, or install hijacking tactics that claim credit for users who would have installed organically. In case of affiliate campaigns, it takes the form of fake leads, fake installs, incentivized traffic, cookie stuffing, or unauthorized brand bidding, etc. The intent is to claim payouts without delivering genuine results. This results in poor partner performance, reduced ROI, and loss of trust in affiliate ecosystems. Get your hands on our ad fraud guide to learn more about different types of ad fraud techniques in detail. Who is affected by ad fraud? Everyone in the digital ecosystem is affected by ad fraud. Marketers and advertisers suffer direct budget losses and are left explaining poor performance and low-quality leads. Legitimate publishers face unfair competition from fraudulent inventory, revenue loss, reputational risk, and even potential network penalties. Agencies struggle with compromised data that weakens optimization and client trust. Ad networks and platforms risk credibility, higher operational costs, and compliance challenges. Affiliate managers deal with incentive-driven, low-intent users that inflate numbers while damaging long-term brand perception. How do I know if my campaigns are being affected by ad fraud? Ad fraud has moved beyond obvious bot techniques that were easier to identify. It has now evolved to mimic real user behaviour. However, to identify if your campaigns are being affected by ad fraud, you must notice the following signals: Sudden spikes in traffic or clicks without a proportional increase in conversions or meaningful engagement High engagement metrics but low downstream actions such as purchases, sign-ups, or app usage Repeated interactions from similar device types, locations, or behavioral patterns that appear “too consistent” Abnormally short or uniform session durations that don’t reflect natural browsing behavior Leads or installs that fail validation checks, show no post-conversion activity, or quickly drop off Campaign performance improving on dashboards while business outcomes continue to decline Individually, these signals may seem harmless, but they clearly indicate fraudulent or low-quality traffic is manipulating campaign performance. What is click fraud? Click fraud is a type of ad fraud technique where bots are used to generate fake or automated traffic clicks on ads without any real interest in the product or service being promoted. These clicks are created to look like genuine user interactions, making them difficult to identify at first glance. These fraudulent clicks also trigger actions like app installs, conversions, or website visits, further masking their true nature. In pay-per-click (PPC) advertising, publishers earn revenue every time an ad is clicked. Fraudsters exploit this model by creating fake websites or placements and artificially inflating click volumes using bots. As a result, advertisers end up paying for invalid clicks that deliver no real value, while fraudulent publishers profit from traffic that was never genuine in the first place. I often see high clicks but low conversions on my campaigns. Is this ad fraud or just poor performance? High clicks with low conversions do not always mean ad fraud. In many cases, poor performance can be caused by factors such as ineffective creatives, incorrect targeting, slow or confusing landing pages, or a mismatch between the ad message and the offer. However, ad fraud becomes a strong possibility when certain patterns start to appear. Sudden increase in clicks without any changes in targeting, creatives, or budgets. Clicks with little to no intent-driven actions such as form fills, purchases, or meaningful engagement. Clicks coming from repeated IP addresses or devices. The key is to look at behavioural signals to identify click fraud. Single metrics can be misleading, but consistent patterns of activity without business outcomes often signal something deeper than normal performance issues. Do ad platforms like Google and Meta already block ad fraud? How to prevent invalid traffic from Google? Yes, ad platforms like Google and Meta do have built-in systems to detect and block ad fraud. They do filter out a significant amount of invalid activity. However, these platforms operate in a closed ecosystem as walled gardens, hence posing limitations. This means advertisers have limited visibility into how traffic is generated, how users behave beyond surface metrics, and how fraud decisions are made. This lack of transparency creates blind spots. Fraudsters exploit these gaps using bots, click farms, and automated scripts that mimic real user behavior closely enough to bypass platform-level checks. As a result, some fraudulent

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Ad Fraud Explained: Types, Impact, and How Advertisers Can Fight Back

Blog, Ad Fraud / mFilterIt Experts

“Half the money I spend on advertising is wasted; the trouble is, I don’t know which half.” This line has followed marketers for decades, and in today’s digital-first world, it feels more relevant than ever. Ad fraud in 2026 is emerging as a global problem. With global digital marketing spends hitting USD 21.2 billion in 2024 and projected to grow to USD 51.1 billion by 2034, expanding at a 9.2% CAGR, brands are investing heavily across platforms, formats, and audiences, betting on data-driven precision to deliver results. But as digital advertising scales, so does its biggest hidden challenge: digital ad fraud. Brands still see ad fraud as a linear problem, neglecting the roots till which it has extended its feet. This blog is going to highlight – The common types of ad fraud impacting each funnel Impacts of ad fraud on brand campaigns Measures brands can take against ad fraud What are the Common Types of Ad Fraud? Digital ad fraud is no longer a linear problem, it has extended its reach across the marketing funnel, impacting performance at each level. Let us understand the types of ad fraud based on each funnel. Stage 1 – Impression Level Fraud Viewability of your ads does not define whether your ads are viewed by the right audience. Fraud is happening at that level as well including, Ad Stacking In ad stacking, multiple ads are placed on top of each other in the same ad slot. Only the top ad is seen, but advertisers are charged for all of them. Pixel Stuffing In pixel stuffing, ads are squeezed into extremely small spaces that users cannot notice, yet impressions are still counted and billed. Frequency Cap Violation Fraudsters show ads to the same user far more times than the set frequency limit. It often happens due to bot activity, cookie manipulation, or device spoofing, causing ads to be repeatedly served to non-genuine users. As a result, budgets are drained, reach is distorted, and real users may see fewer ads than intended. Domain spoofing Fraudsters disguise low-quality websites as premium publishers to sell cheap inventory at higher prices. Made-for-Advertising (MFA) sites These websites are built to only generate ad revenue, with thin content and little to no real user engagement. Stage 2 – Click Fraud Once your ad is viewed, it is important to know who have clicked on your ad. When you believe your campaign is getting all the right clicks, here’s a trap that fraudsters have laid, baiting you to believe that your campaigns are performing well in the metrics, whereas conversions fail. Types of click fraud include – Click Farms Fraudsters hire low-paid workers or coordinated setups that manually generate fake clicks, installs, or engagements on ads to make campaigns appear more successful, even though there is no real user interest or intent. Organic Hijacking Fraudsters take credit for genuine user actions like app installs or conversions that would have happened naturally, making it look like their traffic drove the result and stealing attribution from the real source. Click Spamming Fraudsters generate a large number of fake or low-quality clicks across multiple ads in the hope that one of those clicks gets credit for a conversion. These clicks usually come from bots or automated scripts and inflate click metrics without showing real user intent. Click Injection Fraudster sends a fake click at the exact moment a user is about to convert (such as installing an app). This tricks attribution systems into crediting the fraudster for a conversion that would have happened anyway. Stage 3 – Event Fraud While you may not notice, fraud is happening even at the stage of soft KPIs (installs, signups, etc.) where low-quality users are draining your ad budgets. The kinds of fraud happening at the event level include – Incent Fraud Incent fraud happens when fraudsters run incentive campaigns to attract users by offering points, cash, discounts, or in-app currency for completing actions like clicking an ad, installing an app, or signing up. Coupon/Referral Fraud Here, fraudsters misuse discount codes or referral programs to gain benefits they are not entitled to. They may create multiple fake accounts, use bots, or exploit loopholes to repeatedly apply coupons or generate false referrals, leading to revenue loss and skewed performance metrics. Lead Punching Lead punching happens when fraudsters submit fake or low-quality leads into a system—often using bots or fake forms—to claim credit or commissions, even though these leads have no real potential to convert. Retargeting Fraud Retargeting fraud occurs when fake users or bots are made to appear as interested visitors so ads can be repeatedly shown to them. Since these “users” are not real potential customers, retargeting budgets get wasted on impressions and clicks that have no chance of converting. What is the Impact of Ad Fraud on the Campaign Budget of Advertisers? Ad fraud affects not just spend, but also how campaigns are measured, optimised, and scaled. The following are the impacts of ad fraud – Loss of Media Spend to Invalid Activity: Budgets are spent on clicks and impressions generated by bots, click farms, or MFA sites that never lead to real users or conversions. Reduced Efficiency of Campaigns: When invalid traffic consumes impressions and clicks, genuine users see fewer ads, lowering reach, conversion rates, and overall return on ad spend. Misleading Performance Signals: Inflated metrics such as CTR, installs, or engagement make low-quality inventory look effective, leading to repeated investment in the wrong channels. Brand Safety and Trust Impact: Fraudulent traffic often originates from deceptive or low-quality environments, increasing the risk of ads appearing alongside misleading or unsafe content. Rising Acquisition Costs: Artificial demand created by fraud drives up CPMs and CPCs, forcing advertisers to pay more to reach legitimate audiences. How can advertisers solve ad fraud? To fight ad fraud, advertisers must see Ad Fraud Beyond the Linear Lens. For this, the right ad fraud detection tool like mFilterIt’s Valid8 is required which will not only track your ad performance funnel-wise but also ensure all your channels (app and web) are covered to make it your one-point destination for all the traffic validation activities. Below are the key areas this approach covers: Validate Impression Quality at the Source: Continuously monitor placements, domains, and apps to detect impression-level fraud such as ad stacking, pixel stuffing, MFA sites, and domain spoofing, ensuring ads are served in viewable, brand-safe, and genuine environments. Stop Invalid and Manipulated Click Activity: Identify and block click fraud tactics like click spamming and click injection by analyzing click frequency, timing and source anomalies before

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AI vs AI: How AI powered tech can help to detect advanced click fraud?

Blog, Ad Fraud / mFilterIt Experts

AI is no longer just accelerating digital advertising; it’s powering a new generation of bot-driven fraud. As AI adoption surges, growing from USD 8.6 billion market in 2023 to a projected USD 81.6 billion by 2033, it has unlocked unprecedented speed and scale. But this same power is now being exploited to generate massive volumes of intelligent bot traffic that looks, behaves, and performs like real users. These AI-driven bots don’t raise obvious red flags. They blend into campaigns. AI has made click fraud faster, smarter, and harder to detect. And the only way to fight it is with AI itself. This blog will uncover – How AI becomes a fraud enabler? What are the impacts of AI-driven fraud on ad performance Signs to Identify Advanced AI-Driven Click Fraud How AI shields brands against advanced fraud tactics How AI Becomes a Fraud Enabler Earlier, fraudulent activity was easier to spot repetitive patterns, obvious spikes, or low-quality traffic that clearly looked non-human. Today, AI has changed the game click fraud has evolved with sophisticated tactics like click spamming and click injection. Fraudsters now use intelligent bots that analyse campaign behaviour, mimic real user journeys, and continuously adapt to evade detection. The result is an illusion of performance. The most damaging outcomes of AI-driven click fraud include: Bot-Driven Automated Clicks AI-powered bots now simulate real human browsing behavior, mimicking scrolling, dwell time, and natural click patterns to quietly manipulate engagement metrics and drain ad budgets without raising suspicion. Emulator and Device Farm Traffic Fraudsters deploy emulators and device farms, using AI to manage thousands of virtual devices that generate fake clicks, installs, and events. To ad platforms, this traffic looks legitimate, diverse devices, consistent behavior, and clean signals. Ad Stacking and Hidden Ads AI also enables ad stacking and hidden ad techniques, where multiple ads are layered or concealed behind visible elements. Impressions and clicks are generated without any real user intent Geo and IP Rotation To further evade detection, AI-driven systems continuously rotate IP addresses, geographies, and device identities, making fraudulent traffic appear like it’scoming from genuine users across multiple regions. Know how click fraud impacts performance campaigns in walled gardens What is the Impact of AI–Driven Bots on Ad Performance? As the evolution of AI is expanding its feet across the digital ecosystem, its real-world impacts on ad performance are clearly visible. Here’s how they function – Because these bots adapt to platform rules, they often bypass basic fraud checks and continue running undetected. By copying real user behavior, bot clicks look genuine, making fraud hard to spot. Fake clicks and engagement corrupt performance data, so reports no longer reflect reality. This misleads bidding, targeting, and optimization algorithms, pushing spend toward fraudulent traffic. Over time, ROI, attribution, and conversion metrics get distorted, hiding real performance issues. Worst of all, this activity can look clean in dashboards, while quietly eroding returns across paid media campaigns. AI as the Defense Layer: Role of AI Against Click Fraud AI-driven fraud prevention systems track unusual user behavior and uses past data to predict fraud, helping advertisers stay ahead of scammers causing click injection. Here’s how AI-powered ad fraud detection solution like Valid8 empower brands against click fraud – Detecting Click Repetition and Abnormal Behaviour Patterns AI keeps an eye on clicks across devices, IP addresses, and sessions to spot unusual patterns—like repeated clicks, sudden spikes, or traffic coming from suspicious IPs, proxies, or VPNs. By identifying these signs of bot activity in real time, AI can block fraudulent clicks before they waste your budget or give you misleading performance data. Filtering Invalid Devices Through User-Agent Analysis Fraudulent traffic often reveals itself through abnormal or manipulated user-agent strings. AI examines device, OS, and browser combinations to detect inconsistencies that don’t align with real-world usage patterns. Invalid or spoofed devices are flagged before their clicks are counted as genuine engagement. Know what to look for in a click fraud detection tool Validating Geographic Authenticity Through IP Intelligence AI verifies whether traffic is coming from applicable and relevant geographies. Mismatches between campaign targeting, and user behaviour often indicate fraud. By performing geo-validation in real time, AI ensures only legitimate, location-relevant clicks influence campaign performance metrics. Detecting MFA Sites Using Impression/Click-Level Intelligence Made-for-Advertising (MFA) sites are designed to generate ad revenue rather than real engagement. AI analyses impression and click level data coming from low-quality users of these sites, captured via tracking pixels and runs it through fraud detection algorithms and blacklists. Once identified, these MFA sources are automatically blocked within ad managers, preventing further spend leakage in real time. Enabling True Source-Level Transparency AI-driven defence systems provide granular visibility into traffic sources, revealing exactly where clicks originate. This source-level transparency helps marketers distinguish high-quality inventory from fraudulent or low-value placements, allowing smarter optimisation decisions and greater control over media spend. Conclusion The real challenge for marketers isn’t whether to adopt AI, it’s how to use it responsibly and defensively. As AI-driven bots become increasingly human-like and adaptive, traditional fraud controls are no longer enough. Invalid traffic blends seamlessly into campaigns, and performance metrics alone can no longer be taken at face value. The solution is clear: AI must fight AI. With mFilterIt’s AI/ML powered click fraud prevention tool, Valid8, brands can protect their campaigns, safeguard budgets, and boost ROI without compromising trust or data quality.

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Why Do Travel Industry Campaigns Underperform Even When Metrics Look Strong?

Blog, Ad Fraud / mFilterIt Experts

As the global travel industry moves toward a projected US$1063.00bn market by 2028, competition for traveller demand has never been more intense. Airlines, OTAs, hotels, and travel apps are investing aggressively across digital channels to capture attention early, shape intent, and convert inspiration into bookings. To achieve this, travel industry typically run a mix of awareness campaigns to spark destination interest and performance campaigns to drive booking intent and improve the look-to-book ratio. Today, more than 78% of travel advertising budgets are allocated to digital ads, representing $7.73 billion in spend this year alone. However, there is a catch. Campaigns may appear healthy on the surface, a significant portion of this spend never reaches a real traveller or influences a genuine booking decision. What often goes unnoticed is what happens after a campaign goes live. Performance seems to perform well, but beneath the dashboards, early warning signals begin to emerge, signals that quietly erode efficiency, inflate results, and dilute real demand. By the time the impact is visible in bookings and revenue, the damage is already done. This raises critical questions for travel marketers: What hidden signals are affecting both awareness and performance campaigns? How do these issues distort demand and ROI? And most importantly, how can travel industry brands safeguard their campaigns before budget leakage turns into lost revenue? Signs Travel Industry Brands Must Not Overlook in Their Campaigns Travel brands run multiple campaigns without paying much heed to the signs that cause devastating impacts, directly hampering brand’s ROI. Sneak into these signs before they sneak in your campaigns – Sudden spikes in clicks Exorbitantly high clicks in your campaigns causing click fraud without any significant conversions. Impact – Your budget drains faster, performance looks better than it actually is, and attribution hijacking shifts credit to the wrong channels, leading to decisions based on false data, ads being pulled away from real travellers, and a direct drop in your search-to-book ratio. Read in detail about click fraud Artificially increased engagement High engagement from low-quality users who later uninstall the app. Impact – Campaigns show high clicks, installs, or interactions driven by low-quality or non-genuine users who uninstall the app shortly after, delivering no real retention, revenue, or long-term value, contributing to invalid traffic and hampering the lifetime value of travellers Read in detail about incent fraud Impressions generating from unexpected geographies Ads getting viewed from locations that were never your target on the first place. Impact – Impressions from unintended geographies lead to geotargeting fraud, causing wasted spend, diluted audience relevance, and misleading performance metrics that don’t translate into real demand or conversions. Abnormal promo code or loyalty point redemptions Unusual spikes or repeated redemptions indicate misuse of discounts or rewards, often through unauthorized sharing, automation, or expired codes. Impact – It leads to unearned discounts, direct revenue loss, distorted campaign results, and reduced value for genuine customers. Know more about how referral and coupon fraud exploit campaign performance Keyword bid price rising alarmingly Constant bidding on branded keywords by competitors or affiliates. Impact – When competitors repeatedly bid on your brand keywords, bid prices rise and their ads appear above your official site, diverting high-intent traffic, inflating acquisition costs, and quietly eroding the effectiveness of your campaigns. Read more about how brand bidding violations impact PPC campaigns Ads appearing on irrelevant or unsafe content Ads getting misplaced by fraudsters who manipulate systems using bots, spoofed domains, hidden ads, or fake apps. Impact – It causes wasted ad spend on non-human or low-intent traffic, inflated reach and engagement metrics, misleading attribution and ROAS. How Travel Industry Brands Can Safeguard Their Ad Campaigns For travel brands, protecting both brand reputation and campaign performance is critical. Awareness and performance campaigns rely on accurate signals, safe placements, and genuine user actions. While in-house monitoring can address some risks, it often falls short against sophisticated fraud tactics and scale-related challenges. This is why travel brands need a trusted and holistic ad fraud solution that validates traffic, ensure safe brand asset placements, and secures brands at all levels. mFilterIt brings a unified solution to safeguard travel campaigns end to end. Here’s what the comprehensive solutions includes – Fraud prevention across all stages (From viewing to purchasing) Maintains source-level transparency and validates traffic at every stage of the funnel, not just at the impression level, ensuring genuine engagement and conversions. Identifying safer, high-quality inventory Detects suspicious, inappropriate, and Made-for-Advertising (MFA) sites and delivers placement-level visibility. This enables brands to proactively block unsafe environments and focus spend on premium, brand-safe, and contextually relevant inventory, ensuring ads appear only in suitable settings that protect brand reputation and drive meaningful engagement. Clean and accurate attribution Clearly identifies which channels and partners are driving real outcomes, enabling fair attribution and informed optimization decisions. Detection of brand bidding violations Actively identifies competitors or affiliates misusing brand keywords and bidding on branded terms, helping protect paid search performance. Real-time, customizable infringement alerts Provides instant alerts for potential violations or unauthorized brand usage, allowing teams to act quickly before issues escalate. Conclusion While your focus should be on scaling future campaigns and capturing the next wave of traveller demand, many brands are quietly losing efficiency in their current campaigns where they shouldn’t be. These leaks are rarely dramatic at first, but left unchecked, they compound over time. Hence, protecting your campaigns demands more than basic checks or surface-level metrics. With mFilterIt ad fraud solution – Valid8, travel industry brands gain a unified solution that goes deeper validating traffic quality, uncovering hidden risks, and ensuring media investments drive real traveller engagement and measurable business outcomes. So, your campaigns don’t just scale, they scale cleanly, safely, and sustainably. Want to know how? Contact us now FAQs What is click fraud in travel industry campaigns? Click fraud occurs when automated bots or low-intent users generate clicks that appear genuine but don’t lead to real conversions, inflating metrics and wasting ad spend. What is invalid traffic (IVT) and why does it matter? Invalid traffic refers to non-human or low-quality interactions that distort campaign performance, mislead optimization, and reduce ROI. What is programmatic fraud? Programmatic fraud manipulates automated ad buying to place ads in low-quality or non-human traffic sources, inflating costs without delivering real audience engagement.

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