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ad fraud

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

Ad Fraud, Blog /

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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brand safety

What is Brand Safety? The Role of Brand Safety in Digital Advertising

Blog, Brand Safety & Suitability /

Imagine this: your audience associates your brand with inappropriate content that you never had the intention of funding. Recently, advertisers raised concerns when their ads on Spotify were found appearing alongside sexually explicit audio content. The issue wasn’t the platform but the association. Source: Storyboard18 These brands never intended to be associated with such content. The ads were placed through automated digital advertising systems designed to maximize reach and efficiency. Yet, the brands got associated. This is exactly what happens to brands in the digital advertising ecosystem. Ads today travel across thousands of websites, videos, and platforms through automated systems. While brands carefully plan their messaging and targeting, they don’t always have visibility into where their ads finally appear. When an ad shows up next to misleading, controversial, or inappropriate content, the brand gets associated with it, regardless of intent or awareness. Sometimes, one wrong association is enough to damage the brand reputation or trigger backlash. This is where the role of brand safety in digital advertising becomes prominent. It helps brands maintain control over where their ads appear and ensures marketing efforts build trust instead of unknowingly damaging it. That’s why understanding what is brand safety, why it matters, and who needs to care about it is the first step towards ensuring safe advertising. What is Brand Safety? In digital advertising, brand safety refers to ensuring your brand ads do not appear next to irrelevant, inappropriate, illegal, or unsafe content that might harm your brand’s reputation, credibility, and brand values. It includes measures taken to ensure safe ad placements across social media platforms, apps, and websites. For example, when a reputed brand’s ad appears on a gambling or lottery results website, it creates a risky association, even if the ad itself is legitimate. Such placements can mislead users, violate brand safety norms, and damage trust, making brand safety a critical concern for advertisers. However, brand safety is ambiguous as the approach or definition of safety may vary from brand to brand and also from product to product that is being advertised. Thus, the approach taken by different brands, advertisers, or publishers also depends on two other related concepts: Brand suitability Brand suitability focuses on whether content aligns with a brand’s tone, values, and risk tolerance. Content may be safe, but still not appropriate for every brand. Brand relevancy Brand relevancy ensures ads appear in environments that make sense for the audience’s mindset, context, and intent. Therefore, brand safety prevents ads from appearing next to harmful content. Brand relevancy and brand suitability help you ensure your ads appear next to not only safe but also contextually and sentimentally relevant content. Why is Brand Safety Important in 2026? The audience interacts with brands through ads and associates them with the content alongside. However, with digital advertising controls shifting from manual placements to algorithm-driven distribution, content is no longer static. User-generated videos, regional language content, short-form media, live streams, and AI-assisted content now dominate digital platforms. Furthermore, in the era of AI, content is created, amplified, and modified quickly. This also means misinformation and disinformation also spread faster than ever, making it harder for brands to distinguish credible environments from misleading or manipulated ones. Consumers don’t separate ads from the content around them. That is why, when an ad appears next to questionable or misleading content, the negative association sticks, often subconsciously. What are the Risks of Brand Safety Violation? The impact is not always immediate or dramatic. More often, brand safety failures lead to: Loss of trust as users start doubting the brand Wrong brand perception due to controversial or risky surrounding content Lower engagement because users ignore ads in unsafe environment Poor brand recall because the brand is remembered negatively or not at all Weaker campaign results such as lower clicks and conversions Wasted ad spend on impressions or views that bring no real value Higher compliance risk, especially for regulated industries Long-term damage to brand value, which is hard and costly to fix Brand safety issues can cause instant backlash, weaken brand reputation, credibility, and create damage that is harder to reverse easily. Who Should Care About Brand Safety and Related Issues? Brand safety is a shared responsibility. Ensuring safe ad placements is not just a one-person job. It affects everyone involved in the digital advertising ecosystem. Therefore, everyone, including advertisers, publishers, agencies, and ad platforms, plays a distinct role in keeping advertising environments safe, credible, and effective. Advertisers: Protecting Brand Trust and Long-Term Value Advertisers face the most visible and immediate risk when brand safety filters fail. When an ad appears next to inappropriate, misleading, or unsafe content, consumers don’t blame the platform or the algorithm; they associate the experience with the brand. For example, if a baby product ad shows up next to a terrorist attack video will instantly feel out of place. If advertisers don’t actively monitor such placements, the impact goes beyond reputation. Media budgets get wasted on low-quality environments; engagement drops, and performance metrics become misleading. Over time, this erodes brand equity that took years to build. Therefore, for advertisers, brand safety is not just about avoiding embarrassment; it’s about protecting trust, credibility, and ROI. Publishers: Maintaining Credibility and Revenue Potential Publishers depend heavily on advertiser confidence. When a website, app, or channel becomes known for hosting unsafe, misleading, or low-quality content, advertisers start pulling back, even if the publisher has strong reach or traffic. For instance, publishers running sensational or unverified content may still attract impressions, but premium advertisers often avoid such environments. This leads to lower CPMs, reduced demand, and long-term monetization challenges. If publishers don’t prioritize brand safety, they risk being labelled as unsafe or low-quality sites. Once that perception sets in, it becomes difficult to attract high-value advertisers again. Therefore, for publishers, brand safety is directly linked to credibility, sustainability, and long-term revenue growth. Agencies: Preserving Client Trust and Strategic Value Agencies are responsible for planning, buying, and optimizing digital campaigns for various brands. Clients trust agencies not only

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What is Click to Install Time? Why Advertisers Need to Map this to Detect Mobile Ad Fraud?

Blog, Mobile Ad Fraud /

Bots are becoming sophisticated and more human-like every passing day. And with the emergence of AI, it is becoming a dominant force for shaping online traffic.   According to Imperva BadBot Report 2025, 51% of the internet traffic is driven by bots, which is further amplifying with the introduction of AI and LLM. Unlike the basic bot traffic showing abnormal signs like high number of clicks/installs etc., the sophisticated bots can mimic human behaviour, therefore bypassing the validation checks.    As a precautionary measure and to check if your campaigns are impacted by bots/invalid traffic, there are signs that you can look for in your campaign data.   One of them being Click to Install Time to identify invalid installs in your mobile app campaigns.  In this blog we will breakdown how CTIT can be seen as a signal to identify invalid traffic and how marketers can use it to take proactive action against mobile ad fraud.   What is Click to Install Time? How to Identify Invalid Traffic Evaluating CTIT?  Let’s simply breakdown what CTIT means before moving forward to understand the kind of patterns that reveal exploitation of mobile ad fraud and click to install time. Click Time: The moment a user clicks on your ad.  Install Time: When the app actually finishes installing.  Click-to-Install Time (CTIT): The time gap between these two.  It is basically a metric used in mobile advertising to map the time it takes for a normal user to download an app after clicking on an ad.   This gap varies naturally. Real users don’t install apps instantly every time; there can be delays, pauses, network differences, and human behaviour involved. What a genuine user’s install journey looks like  This process takes time, usually a few seconds to a few minutes, depending on network speed and app size.  However, fake installs show different timing patterns.   Here’s are the two types of abnormal CTIT patterns we observed recently that clearly indicate towards install fraud:  Examples of Abnormal CTIT Patterns   Case 1: Extremely short click-to-install time (click injection)  This snapshot compares the click time and install time for multiple installs coming from the same publisher. The gap between click and install is consistently just 1–3 seconds, and in several cases, the values are identical or nearly identical.  Why it is a problem? A real user cannot click an ad, get redirected to the Play Store, download the app, complete the installation, all within a few seconds, repeatedly.   This pattern strongly indicates click injection, where fraudsters:  Detect that an app install is already in progress  Inject a click at the last possible moment  Steal attribution credit for a genuine install  mFilterIt insight: Why this matters? Although these installs appear valid in attribution reports, the timing exposes manipulation. Extremely short and repeated click-to-install times are a strong indicator of high-risk fake attribution, not real user engagement.  Learn more about common techniques of install fraud here. Case 2: Google Play install begins before the user clicked on an ad  In this snapshot, the timestamps reveal something even more concerning. The Google Play install begin time occurs before the recorded ad click time. This results in a negative click-to-install time, meaning the install process started before the user supposedly clicked on the ad.  Why is it a problem? This breaks the basic logic of attribution. A real user cannot start installing an app first and then click an ad for the same app afterward. When install activity precedes the click, it clearly indicates:  Manipulated or falsified timestamps  SDK tampering or fabricated attribution signals  This is not caused by reporting delays or tracking errors; it points to deliberate attribution manipulation.  mFilterIt insight: Why this matters? Any case where the install begins before the ad click should be treated as install fraud by default. These patterns strongly indicate fake attribution attempts, even if the installs are being credited by attribution platforms.  Signs to Identify Abnormal CTIT Patterns CTIT mapping should be approached in two layers: what you can validate manually and what requires advanced detection at scale.  As an advertiser, the following click to install time red flags should immediately raise concern, especially when they appear repeatedly.  Installs within 1–3 seconds of a click  Real users need time to reach the app store, download the app, and complete installation. Consistently instant installs are not normal human behaviour patterns.  Identical CTIT values across multiple installs Human actions vary. When multiple installs show the same or near-identical timing, it often points to automated or scripted activity.  Long delays followed by sudden attribution This pattern is commonly associated with click spamming, where random clicks are generated and later receive credit when an install happens.  Negative CTIT values If an install begins before the recorded ad click, it breaks basic attribution logic and strongly indicates manipulated timestamps or fake signals.  How Advanced Mobile Ad Fraud Detection Solutions Help Mobile ad fraud is often distributed across campaigns, publishers, and devices, making it difficult to detect without advanced analysis.  Attribution platforms answer one primary question: Who gets credit for the install? They do not answer whether the install journey itself was genuine or a fake one. While they work on assigning credits rather than behavioural validation, brands need an advanced mobile ad fraud detection solution to ensure campaign efficiency. Here’s how it helps:  Source-Level CTIT Pattern Analysis – Know who is installing your app Advanced solutions analyze click-to-install time across all campaigns and channels simultaneously. This makes it easier to spot publishers or sources that consistently show unnaturally fast or uniform CTIT patterns. It also helps identify install fraud patterns that may look normal in isolation but become obvious when viewed across the entire account.  Analysis of CTIT with Click Behaviour – Don’t let sophisticated bots slip by Click-to-install time is evaluated alongside click signals such as click frequency, burst patterns, and timing alignment. This helps distinguish genuine user clicks from injected or spammed ones.  Correlation with Device and Environment Signals – Differentiate between bot & human Advanced

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Ad Fraud in 2026

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

Blog, Ad Fraud /

“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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install fraud

What is Install Fraud? How to Solve Install Fraud?

Blog, Mobile Ad Fraud /

Advertising platforms optimize for signals—not intent.   In mobile marketing, the most important signal is the install. More installs usually mean a campaign is working. Platforms see this, assume success, and push more budget in the same direction.   This is where install fraud begins.   Fake installs are easier and cheaper to generate than real users. Fraudsters use bots, device farms, or incentivized tactics to create large volumes of installs that look genuine on the surface. Since the numbers look good, platforms assume the campaign is performing well. Budgets increase. The same sources get more spend.   But the users aren’t real.   At first, nothing feels wrong. Cost per install may even go down. Install numbers keep growing. The problem only becomes visible later, when users don’t open the app, don’t register, and don’treturn. What looked like growth turns into wasted spend.   That’s why fake app installs are so hard to catch early. It doesn’t break campaigns overnight. It quietly trains platforms to invest in fake activity while genuine users get pushed out.   In this blog, we’ll explain what install fraud is, the common ways it happens, and how marketers can spot and prevent it—before it starts impacting real growth.  What is Install Fraud in Mobile Advertising?  Install fraud occurs when fake app installs are generated or manipulated to claim attribution and payouts, without real user intent.  In simple terms, a fake install appears as a genuine app download on your dashboard but doesn’t come from a genuine user who intends to engage with your app. These installs may be created by bots, emulators, manipulated devices, or deceptive techniques designed to game attribution systems.  Install fraud falls under the broader category of mobile ad fraud, and it primarily targets CPI-driven campaigns. Since advertisers pay for installs, fraudsters focus on triggering that one event, regardless of what happens afterwards.  What makes this problem more complex is that modern mobile ad fraud techniques don’t just stop at installs. When install traffic isn’t verified, the same fraudulent activity extends to post-install events as well, such as sign-ups, in-app actions, or other action-driven KPIs. These events may look legitimate in reports, but they’re often designed to reinforce false performance signals.  The result? You end up paying for volume, but you don’t get real value in return, leading to weaker optimization signals and campaign inefficiency.  Common Types of Install Fraud Techniques You Need to Know About  Fraudsters use various techniques to generate fake installs and manipulate last-click attribution. These techniques closely mimic real user activity, making it impossible for basic tools to identifymobile ad fraud. Here are the most common install fraud techniques performance marketers should be familiar with:  Click Injection Click injection happens when a fraudulent source identifies that an install is about to take place. A click is fired right at that moment (by exploiting the narrow attribution window) to steal the last click attribution from the channel that actually drove the install. This is also known as organic poaching or install hijacking.  Click Spamming Click spamming is when a large volume of fake ad clicks are sent and injected into devices in advance. This increases the chance that one of those clicks gets credited whenever an organic install eventually takes place, stealing the attribution as a result.  SDK Spoofing SDK spoofing fakes app installs by imitating devices and app signals through emulators or scripts, making it appear as a real user installed on the app, without any actual download taking place.Fraudsters generate installs only to exhaust advertising budgets and spoof installs.  Fake App Versions Fraudsters use altered or cloned versions of the app that appear legitimate but generate fake installs and in-app events. These versions mimic normal activity and deceive attribution systems into counting non-genuine traffic.  Know what unusual app version patterns look like and how they reveal bot traffic.  What makes all these techniques dangerous is not just how they work but also how normal they appear to human eyes in standard reports.  How Does Install Fraud Impact Mobile Advertising Performance? Install fraud operates silently. It passes basic attribution checks, mimics normal install behaviour, and avoids sudden spikes that might raise alarms. This happens because installs are counted before user quality is proven.   The moment an install is attributed, it’s treated as success, long before anyone knows whether that user will engage, return, or convert. Therefore, the business impact begins to fall. It doesn’tjust affect one metric or one campaign. It spreads across attribution, the entire funnel, optimization, teams, and long-term strategy. Here’s how:  Confusing performance metrics Fake installs inflate metrics like CPI and install volume, masking real weaknesses in retention, engagement, and long-term value.  Misleading attribution signals & optimization decisions Mobile ad fraud techniques steal credit from genuine channels, making fraudulent partners look better than they truly are, leading teams to invest where value isn’t being created.  Lower audience quality Fake installs never engage meaningfully post-install. When these users enter retargeting lists or lookalike pools, overall audience quality drops.  Budget misallocation Because dashboards don’t always show red flags early, money keeps flowing toward channels that appear efficient but deliver little real return.  Cross-team impact Product, growth, and analytics teams end up working with skewed signals, affecting feature prioritization, engagement strategy, and user journey decisions.  Unreliable forecasting and planning Cohort trends, lifetime value projections, and performance forecasts become unreliable when they’re built on compromised data.  Skewed event-level analysis Low-intent users trigger surface-level actions, making it appear as though users are progressing through the funnel. This skews event-level analysis, weakens action-driven KPIs, and makes it harder to identify where genuine drop-offs are actually happening.  But my attribution platforms flag mobile ad fraud? Most attribution platforms are designed to assign credit, not to validate whether an install or event came from real user intent. As a result, sophisticated mobile ad fraud techniques manipulate attribution logic, steal credit from genuine channels, and reinforce false performance signals. This often leads teams to trust reports that look complete, but miss the underlying quality and

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Click fraud

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

Blog, Ad Fraud /

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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CTV advertising in USA

How CTV Advertising Is Reshaping Audience Interest and Need for Smarter Optimization

Blog, Brand Safety & Suitability /

Media and entertainment industry has embarked on a journey towards more personalized and precise shift. People nowadays do not wait for their favourite show to broadcast, they simply pick one and proceed that’s the freedom and flexibility that Connected TV (CTV) brings, and this is the reason people are shifting their focus from TV to CTV.   Brands on the other hand have the perfect chance with CTV to directly target the most relevant audience and this can be possible if ads are optimized rightly, placed besides the right context, and is shown at a normal frequency that the impact sticks by the viewers.  As the audience interest towards CTV advertising grew, it opened the gates of opportunities for brands to target the right audience at the right time while maintaining brand safety standards.   Viewers consuming content on CTV makes it easier for brands to map his/her interest, hence presenting a relevant ad placement which is more likely to be noticed, absorbed, and remembered, unlike background viewing on linear TV.  Now, as a brand, you know whom to target and for branding purposes this means your ad spend is reaching the right audience. This empowers CTV campaigns to deliver more efficient campaign performance and enhanced ROI.  In this blog, you will discover –  Why CTV is central to modern building  Why CTV advertising is a good approach for brand campaigns  How can brands make the most of CTV advertising  Why CTV Is Central to Modern Brand Building Television industry has always been an easier and the most convenient approach for brands to reach to their target audience. Now, when the major talks are about a shift in the way people perceive media and entertainment, CTV has gradually and now much strongly positioned itself as the most invested destination where content is consumed in galore.  This embarked brands on a journey towards more centralized brand building approach to reach wider and most importantly relevant audience. Here’s how it has become a centre for modern brand building –  Rapid shift from linear TV to CTV An eye-blinking shift from tradition TV to CTV emerged from the control on viewability that audience receives. Instead of waiting for a particular time to watch a particular show, viewers can watch anything at any time, make a switch rather rapid and seamless.  Growing base of cord-cutters At the same time, the growing base of cord-cutters has reduced the effectiveness of traditional TV for reaching younger and digitally native audiences. CTV allows brands to stay present where these audiences actually spend time, without sacrificing scale.  High-impact, full-screen brand exposure When brand ads are viewed on CTV especially on the large screens at homes, impact is not restricted to merely seeing, it stays with the audience, driving awareness, recall, and long-term brand perception.  Intent-driven and content-aware audiences From the vast pool of available options, brands can pick any source of entertainment based on their interests, mood, and context. This enables brands to align messages with what audiences are watching, creating relevance without relying on intrusive data signals.  Why CTV Advertising is a Good Approach for Brand Campaigns? While we know the importance of brand ads, merely running an ad is not enough. Brands must also optimize their ads timely to reach the right audience at the right time with the right frequency. Here’s why ad optimization matters more on CTV –  Aligning with the viewer’s mindset Your ads must be optimized in a manner that they are presented to the right audience. For instance, a viewer streaming a live sports match, a crime documentary, or a family movie is in a very different emotional and cognitive state. Ads that align with this mindset feel relevant and natural, while those that can’t feel jarring or out of place. Optimization on CTV therefore requires understanding not just who the audience is, but what they are watching and why.  Contextual ads placements Context directly influences brand perception. The content surrounding your brand’s ads shapes how your ad is perceived by viewers. On CTV, where attention is high and associations are strong, contextual ads can amplify brand reputation.  Eliminating ad fatigue As a viewer who is deeply invested in the CTV content, nothing becomes more frustrating than watching the same ad on repeat. If your ads are being shown repetitively, it impacts your brand’s image negatively, destroying the entire purpose of running an ad. Hence, with right frequency capping, brands can optimize their ads and show to the audience to a point that it remains solely for awareness and doesn’t exploit customer interest.  How Can Brands Make the Most of CTV Advertising? When it comes to CTV ads, brands cannot limit their approach to optimizing ads, they must also ensure their ads are shown beside a contextually safe and relevant content. When ads run on complex CTV systems, here’s how brands can make the most of CTV advertising while simultaneously ensuring brand safety –   Moving Beyond Keyword Blocking: Keyword blocking alone is no longer sufficient. Brands need to assess meaning and intent, not just words, to avoid both overblocking safe content and missing real risks.  Analysing the Full Video Environment: True brand safety requires understanding the entire video experience, visuals, audio, text, and surrounding content, not just titles or metadata.  Maintaining Dynamic Control Over Content & Channels: Brand safety must be flexible. Marketers need real-time control to adapt to changing content trends, channels, and emerging risks.  Accounting for Regional & Language Nuance: Context varies by region and language. Effective strategies consider local culture and sentiment to ensure ads appear in appropriate environments everywhere.  Using AI & ML to Scale Safely: AI and ML enable real-time analysis at scale, helping brands protect campaigns efficiently while maintaining reach and performance. Conclusion  Connected TV has already won attention. The real challenge now is what brands do with it. High completion rates and premium screens mean little if ad placement is not aligned with the right audiences and supported by strong brand safety controls.  A smarter CTV approach moves beyond reach and viewability to focus on relevance, environment, and intent. When brands tailor their CTV strategy—optimizing where ads appear, how they align with content, and how audiences experience them—CTV stops being just a branding channel. It becomes a performance driver.  Want to know how? Contact us now!  FAQs What is Connected TV (CTV) advertising? Connected TV advertising refers to ads shown on internet-enabled TVs through streaming apps and platforms. It combines the impact of television with digital targeting and measurement capabilities.  How does non-contextuality affect brand performance on CTV? The content surrounding an ad directly influences how viewers perceive the brand. Ads placed in mismatched or low-quality environments can dilute brand equity, even if viewability is high.  How can brands avoid ad fatigue on CTV? By applying frequency capping, rotating creatives, and dynamically optimizing placements, brands can maintain awareness without overwhelming viewers or harming brand perception. 

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Brand Bidding for Better Search Performance

Brand Bidding: Dos & Don’ts for Better Search Performance

Blog, Affiliate /

What happens when someone searches for your brand and still doesn’t land on your webpage?  Brand keywords sit at the most fragile point of your funnel: the moment earned intent turns into revenue. When users search with your brand keyword, they are not browsing, they are deciding. For marketers, this is the highest-intent traffic you will ever get, and how you handle it can make or break conversions, attribution, and overall search ROI.  That’s where the dos and don’ts of brand bidding come in.  Do it right, and brand keywords become a powerful lever. Do it wrong, and the damage is subtle but expensive.  What you are going to see isn’t just a tactical checklist. It’s about discipline at the bottom of the funnel. Because when it comes to brand keywords, what you allow and what you don’t, decides whether intent turns into growth or quietly leaks away.  That’s exactly what this blog is going to cover, highlighting:  Who are responsible for brand keyword auction?   What brands should do to improve brand keyword performance?  What brands should not do to while navigating brand bidding checks?  How to identify if your affiliate partners are bidding on brand keywords?  How can you ensure long-term control over your brand keywords?   Key Players in Brand Keyword Auctions Brand keywords are prime targets for anyone trying to steal your organic traffic. To protect your brand, focus on the three main players:  Brand itself – To ensure they are seen when someone searches with their brand name.  Competitors – It is a common norm of competitors bidding on brand keywords to be seen for the similar audience pool.  Partners & affiliates – If affiliate partners bid on brand keywords, it is not ethical as they are paid commissions to bring unique visitors. Knowing these players helps brands take the next step, focusing on the essential actions and best practices (the do’s) needed to optimize and protect brand keyword performance.  Do’s: How to Analyse Brand Keyword Performance Here’s what brands should keep in mind when reviewing brand keyword performance.  Always Own Your Brand Keywords Owning your brand keywords is non-negotiable, even if you rank #1 organically. Brand campaigns give you full control over how your brand appears at the most critical moment of intent, your messaging, sitelinks, extensions, and landing pages. Without this control, competitors or resellers can define the narrative, intercept high-intent traffic, and dilute trust before users ever reach you.  Monitor Who Else Is Bidding on Your Brand Brand keyword auctions are rarely exclusive. Competitors, affiliates, resellers, and even unknown third parties may bid on your brand terms, often appearing alongside or above your ads. Regular monitoring helps you understand who is present in the auction, how aggressive they are, and where brand leakage or policy violations may be occurring.  Align Search and Affiliate Teams Brand keywords sit at the intersection of paid search and affiliate marketing, making alignment critical. Clear rules, shared performance metrics, and consistent communication between teams help prevent internal competition, inflated costs, and attribution conflicts. When teams operate in silos, brand efficiency suffers even when results appear strong on paper.  Measure Incrementality, Not Just Conversions High conversion volumes on brand keywords don’t automatically mean high value. True performance comes from understanding incrementality, how much of that demand is genuinely driven by paid efforts. Evaluating new versus returning users, overlap with organic traffic, and assisted versus last-click conversions reveals whether brand spend is creating growth or simply capturing existing intent.  Don’ts: What Weakens Brand Keyword Performance Here’s what many brands overlook and how it quietly weakens their brand keyword performance.  Don’t Assume Brand Traffic Is Free Brand traffic may look inexpensive, but it’s never free. Every brand click carries a cost, and without active management, CPCs can quietly rise due to competition, inefficiencies, or poor structure. Treating brand campaigns as an afterthought often leads to inflated spend and missed opportunities to protect and optimize high-intent demand.  Don’t Ignore Partner Brand Bidding Brand bidding in affiliate marketing isn’t always wrong if brand has stated the clear guidelines on which keywords are allowed for bidding. However, affiliate partners who bid intentionally or unintentionally on brand terms, and without clear rules or monitoring, this activity can inflate cost per click, distort attribution, and weaken true search efficiency. Controlled participation enables scale; unchecked bidding creates leakage.  Don’t Rely Only on Last-Click Attribution With last-click attribution, credit often goes to partners who didn’t generate demand organically but simply intercepted it by diverting users through their own links. This masks the efforts that actually brought the user in and makes brand keyword performance look stronger than it truly is, while inflating the value of traffic that was never incremental.  What to Do When Your Affiliate Partners Bid on Your Brand Keywords  If partners are bidding on your brand keywords, you need to know and identify it. Here’s a stepwise guide for brands to detect brand bidding violations by dishonest affiliates:  Step 1: Identify Which Affiliates Are Bidding Start by gaining visibility into which partners are bidding on your brand keywords. This includes understanding who they are, how often they appear, and where they show up in the auction. Without clarity on participation, brand control becomes guesswork.  Step 2: Review Their Keywords, Ads, and Landing Pages Look closely at the exact keywords, partners are bidding on, the ad copy they are using, and where that traffic is being sent. Misaligned messaging, misleading offers, or unnecessary redirects can confuse users and weaken trust at the moment of search.  Step3: Ask One Critical Question Evaluate whether the bidding activity is genuinely improving brand keyword performance or simply intercepting demand that would have reached you anyway. This distinction helps separate incremental value from inflated conversions.  Step 4: Act Based on Impact Once performance is clear, decide the right approach and allow affiliate bidding with clear guardrails, restrict specific brand terms, or adjust commissions to reflect true contribution.  How to Maintain Long-term Control Over Brand Keywords Long-term brand protection comes from governance not one-time fixes. Clear rules, visibility, and accountability keep brand keywords efficient and protected. Following guidelines enable brands to own the long-term control over their keywords –  Set Clear Brand Keyword Guidelines Define who can bid on brand terms, which keywords are allowed, and how brand messaging should appear. Clear rules reduce confusion and prevent misuse across teams and partners.  Monitor Brand Activity Regularly Brand auctions change fast, especially during sales and peak periods. Regular monitoring helps catch CPC spikes, new bidders, and compliance issues early.  Share Ownership Across Teams Brand keyword performance spans search, affiliate, and partner teams. Shared accountability keeps costs controlled and goals aligned.  Review

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Brand Safety on Video Platforms

Why Brand Safety on Video Platforms Will Matter More Than Ever in 2026

Blog, Brand Safety & Protection /

In 2026, the biggest risk in video advertising won’t be unsafe content; it will be misplaced trust.  Video has evolved into the most influential layer of the digital ecosystem, shaping brand identity and consumer perception at scale. According to a stat, 95% of the brands see video marketing as a crucial part of the overall strategy. It shapes how audiences discover brands, how narratives are framed, and how trust is built, and ultimately, brand reputation is built or eroded at scale. Unlike static formats, video does not exist in isolation. It creates an immersive environment where advertising, content, and emotion intersect.  As brands increase their investments across short-form UGC content, long-form video, OTT, and connected TV, the line between content and advertising continues to blur. Viewers don’tcompartmentalize their experience. The content they consume and the ads they see are perceived as part of the same moment.  This shift fundamentally raises the stakes for advertisers. Brand safety on video platforms is no longer just about avoiding obvious risks—it’s about ensuring that every placement aligns with how a brand wants to be seen. Heading into 2026, this alignment will directly influence brand trust, recall, and long-term equity.   What we will cover in this blog:   This blog explains why brand safety on video platforms will matter more than ever in 2026, why platform-level controls and keyword blocking are no longer enough, and what a modern, context-driven brand safety approach must account for, including contextual relevancy, placement quality, and audience perception, so advertisers can protect trust, relevance, and long-term brand value in a video-first world.  The Real Brand Safety Problem on Video Platforms Brand safety challenges on video platforms are no longer isolated incidents. They directly impact brand reputation and consumer trust. They are the result of structural gaps in how content is classified, evaluated, and governed at scale.  As video advertising grows more automated and decentralised, brands face a convergence of risks—many of which stem from outdated or incomplete approaches to brand safety.  Standards Exist, but Enforcement Is Inconsistent Industry frameworks such as the Global Alliance for Responsible Media (GARM) provide a shared baseline for defining content risk and suitability. These guidelines help brands, platforms, and agencies align on what constitutes acceptable and unacceptable environments. However, compliance with standards does not guarantee consistent enforcement.  Video platforms interpret and apply these frameworks differently, often optimising for scale and monetisation. As a result, content that meets minimum standards may still be unsuitable for certain brands, categories, or markets.  For advertisers, this creates a gap between policy-level compliance and real-world placement quality. Over-Reliance on Platform-Level Controls Most brands depend heavily on platform-provided brand safety settings and automated content blocking mechanisms. While necessary, these controls are limited.  Platform-native solutions:  Operate on broad, one-size-fits-all thresholds  Lack visibility into placement-level decisioning  Are not tailored to individual brand risk tolerance  This creates a false sense of safety. Brands assume protection because controls are enabled, without truly understanding where ads are appearing or why.  As video ecosystems expand, this reliance becomes a liability rather than a safeguard.  Keyword Blocking Creates Blind Spots Keyword blocking remains one of the most widely used brand safety tactics—but it was never designed for video.  Keywords cannot capture:  Narrative intent  Visual context  Emotional tone or sentiment  As a result, brands either block too aggressively and lose quality reach, or fail to block content that appears acceptable on paper but problematic in practice.  In a video-first environment, over-reliance on content blocking increasingly works against brands rather than for them.  Lack of Content-Level Evaluation Many brand safety decisions are still made based on metadata—titles, tags, and descriptions—rather than the content itself.  This introduces risk in a video ecosystem where:  Titles are optimised for clicks  Descriptions may not reflect actual content  Visual storytelling often contradicts text signals  Without analysing the actual video—visuals, audio, on-screen text—brands lack a complete view of the environment their ads are associated with.  Absence of Contextual and Sentiment Intelligence Most existing brand safety controls treat content as either safe or unsafe. This binary approach ignores context.  Tone, framing, and sentiment play a critical role in how content is perceived. Ads placed next to emotionally charged, alarmist, or sensational content can inherit those cues—even if the content itself is technically compliant.  Without contextual targeting and sentiment analysis, brands are exposed to subtle but significant perception risks.  One-Size-Fits-All Global Frameworks Video platforms operate globally, but brand safety is deeply local.  Cultural norms, language nuances, and regional sensitivities vary widely especially in markets like India, MENA, and South-East Asia. Tools designed for Western markets often fail to capture these nuances.  Brands applying uniform global rules risk misalignment in local markets, where the impact of content can differ dramatically.  Misclassification and Category-Level Risk Accurate content classification underpins brand safety. Yet misclassification remains common on video platforms.  When content is incorrectly categorised, brands lose control over placement suitability—especially in sensitive categories such as children’s content, news, or regulated verticals.  This not only creates reputational risk but also raises compliance and governance concerns.  How do Brand Safety Measures Need to Evolve in 2026? With the digital ecosystem evolving rapidly and the threats increasing, brands need to be proactive with their safety measures. Brands need a modern brand safety solution to ensure they don’t miss any blindspots to non-transparency of ad placements.   Content-Level Evaluation Must Replace Assumptions Brand safety frameworks must evolve from signal-based checks to content-level understanding. This means analyzing what is actually present in the video—visual elements, spoken language, on-screen text, and overall narrative flow.  Relying on metadata alone introduces risk, as titles and descriptions can be optimized for discoverability rather than accuracy. Content-level evaluation provides a more reliable foundation for assessing suitability and risk. Contextual Suitability Will Matter More Than Binary Safety The concept of “safe” versus “unsafe” is no longer sufficient.  A piece of content can meet platform safety standards while still being unsuitable for a particular brand, category, or campaign objective. Contextual suitability focuses on alignment—whether the environment reinforces or undermines brand messaging.  In 2026, advertisers

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Ad Fraud and Brand Safety in Travel Industry

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

Blog, Ad Fraud /

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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