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influencer monitoring

Why Is Influencer Monitoring Critical for Brand Trust?

Blog, Affiliate /

People don’t buy from brands; people buy from people. And this shift in consumer mindset has led to the increase of brands inclining towards influencer marketing. Brands work with influencers to reach their audiences who already trust them and their content. But as influencer marketing grows, so do the challenges. Not every influencer uses that trust responsibly. Some take advantage of brand campaigns through misleading or fraudulent practices, which can impact performance and damage brand identity. For example, imagine a skincare brand launching a new serum through a group of influencers. Within days of the campaign going live, a few creators began promoting a “40% off” offer instead of the approved 20%, others used outdated product images they found online, and a couple even made claims the brand never endorsed. Soon, customers were confused, comments turned negative, and the brand’s messaging looked inconsistent across platforms. Situations like these are more common than brands expect—and they reinforce why active, real-time influencer monitoring is no longer optional. In this blog you will discover –  Why influencer marketing is important for brands today When influencer marketing turns into exploitation Why influencer fraud is difficult to detect How mFilterIt helps brands monitor influencers effectively Why Influencer Marketing Holds So Much Power Today  The rise of influencer marketing has made it a highly effective strategy for brands. Here’s why: Influencers have built strong trust with their audiences Influencers grow their communities through credibility and consistent engagement. This trust makes influencer marketing an effective way for brands to reach large, highly engaged audiences through a single, trusted voice. Creator recommendations feel more authentic than ads People follow creators because they value their opinions and recommendations. When influencers promote a product or service, it feels more like a personal suggestion than an advertisement, making brand messaging more believable and impactful. Influencer content blends naturally into social feeds Unlike traditional ads, influencer content fits seamlessly into everyday social media feeds. It also extends beyond an influencer’s followers, helping brands reach new audiences who may discover the content organically. Platforms algorithmically boost creator-led content Social platforms prioritize content that drives engagement and delivers value. Influencer-led content often benefits from this algorithmic boost, helping brands reach the right audience more effectively. When Influence Becomes Exploitation Influencer marketing is beneficial but only when influencers bring value in action. However, many influencers exploit the brand awareness campaigns in the following ways – IP violations Influencers may use brand assets such as logos, creatives, or messaging on unauthorized platforms or formats. These unauthorized uses can lead to intellectual property violations and raise questions around brand credibility and control. Typo-squatting Instead of driving new organic demand, some influencers create lookalike URLs that closely resemble official brand domains. This redirects traffic that would have reached the brand organically, misrepresenting true performance and inflating attribution. Brand bidding Influencers may bid on branded keywords and run paid ads to capture organic brand demand, causing brand bidding violations. As traffic flows through influencer tracking links, acquisition costs rise and bid prices increase, despite no incremental value being created.  Brand misrepresentation Unapproved or inaccurate brand messaging such as false discount claims or misleading product information, can surface across influencer content. This leads to compliance risks and erodes consumer trust. Coupon code misuse Some influencers misuse promo codes through invalid offers, self-use, or counterfeit coupons. While these actions trigger commissions or discounts, they fail to attract new audiences or deliver genuine campaign value. Why Influencer Fraud Is Hard to Detect  Influencer fraud is not an easy catch. With the evolving tactics, it becomes more critical to catch because basic monitoring cannot address the following questions – Are influencers reaching the intended audience? Reach is frequently inflated through fake followers or audience manipulation. While surface-level metrics may look strong, they fail to show whether the audience is real, relevant, or capable of driving genuine value. Is content aligned with brand guidelines? Influencer content exists outside controlled ad environments. Non-compliant or misleading posts can blend seamlessly into organic feeds, allowing brand guideline violations to go unnoticed at scale. Are commissions tied to genuine performance? Performance-based payouts are a prime target for abuse. Promo code exploitation and self-referrals can trigger commissions without real customer intent activities that traditional monitoring often misses. Know more about referral and coupon fraud How mFilterIt Enables Smarter Influencer Monitoring Influencer monitoring must be done on various parameters that holistically analyse the authenticity of influencers. A renowned electronics company implemented mFilterIt’s influencer monitoring solution, Effcent to evaluate if their influencer partnerships are driving true engagement to their campaigns or not. Here’s what our influencer monitoring includes Influencer Profile Analysis – Evaluates an influencer’s credibility using key indicators such as engagement rate, follower quality, and audience authenticity to determine whether a campaign will truly reach the intended audience. Influencer Posts Analysis – Assesses the quality and impact of influencer-created content by analysing likes, sentiment, and content relevance to gauge overall effectiveness. Influencer Followers Analysis – Uses a comprehensive 13-point checklist to assess audience quality, filter out fake or low-value followers, and deliver a composite score out of 30 that helps brands decide whether the influencer is worth investing in. Conclusion: Turning Influencer Marketing into a Controlled Growth Channel Influencer marketing continues to be one of the most powerful ways for brands to connect with audiences but only when it is built on transparency, accountability, and trust. This is where structured influencer monitoring becomes critical. By combining performance analysis, audience intelligence, compliance checks, and commission validation, brands can move from reactive detection to proactive control. For right influencer monitoring solution like mFilterIt’s Effcent becomes essential to monitor marketing more intelligently. Want to know how? Contact us now. FAQs What is influencer marketing? Influencer marketing is a form of marketing where brands partner with social media creators or influencers to promote products or services to their audience. What is influencer fraud? Influencer fraud refers to deceptive practices such as fake followers, inflated engagement, brand bidding, coupon misuse, or misrepresentation that distort campaign performance. How can influencer fraud

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Attention Metrics

What Real Campaign Data Analysis by mFilterIt Reveals About Viewability and Attention Metrics

Blog, Viewability /

Many advertisers still believe ‘if an ad is viewable, it means the user actually saw it.’  But that’s not the case anymore. Impressions and viewability still dominate reporting, but they don’t reflect whether audiences actually watched or engaged with an ad. That is why the digital marketing industry and advertisers have now started shifted their focus towards a more reliable metric – attention measurement.  The recent campaign analysis conducted by our experts shows this major shift: attention metrics reveal intent and creative effectiveness in a way traditional KPIs simply can’t.  How Are Attention Metrics Better than Viewability?  Unlike viewability, attention is measured based on multiple signals and human behaviour patterns, not one parameter. The parameters include time-in-view analysis, completion rate, drop-offs, mute rate, picture-in-picture behaviour, etc. These signals are processed using an ML-driven model to estimate likelihood of genuine attention given by a user to an ad.  This multi-signal view creates a far more accurate representation of user intent. It uncovers whether the audience accepted the experience, tolerated it, or tried to avoid it. And as seen in our recent campaign, these behavioral indicators can completely change how creative formats are evaluated.  Two formats. Same brand. Yet drastically different attention outcomes. Here’s what the data really revealed.  What We Observed in Format-Level Analysis: Understanding Audience Engagement Behaviour with Attention Metrics  We analyzed audience interactions across two non-skippable video formats – 15 seconds and 25 seconds, to understand how runtime influences attention quality. Both formats were served to similar targeting sets across comparable inventory, ensuring the behavioural differences were meaningful.  Completion Rate Revealed Early Fatigue 15s format: 95.05% completion  25s format: 84.31% completion  The longer format triggered noticeably higher drop-offs, suggesting that even a small increase in duration can introduce friction.  Mute & PIP Exposed Active Disengagement Mute rates:  15s format → 3.38%  25s format → 4.52%  PIP rates:  15s format → 4.14%  25s format → 5.46%  Both indicators rose significantly for the 25-second version. These behaviours aren’t accidental; they are user choices to reduce exposure, showing clear discomfort with the longer ad runtime.  Final Analysis 15s format attention score: 90.15%   25s format attention score: 79.65%  When all signals were combined, the 15-second creative clearly showed stronger intent, lower disruption, and higher-quality engagement. Know more about how mFilterIt attention metrics tool differs from competitors.  What This Means: How Advertisers Can Improve Ad Engagement Using Attention Metrics The behavioural signals from this campaign clearly show what audiences accept and avoid. By focusing on attention metrics, brands can shape smarter creative decisions and optimize media planning for real engagement, ensuring every rupee spent earns genuine consumer attention. Here’s how:   Creative Video Strategy: Build Ad Campaigns People Stay With  The campaign highlights clearly demonstrate that audience attention is not guaranteed; it must be earned through thoughtful creative choices.  Key implications:  Shorter formats respect user choices and reduce cognitive ad fatigue.  Creative storytelling should prioritise clarity and impact within tighter runtime limits.  Low mute/PIP levels indicate the ad was accepted rather than avoided.  Attention metrics data replaces guesswork with evidence, helping brands choose formats that not only convey their message but keep viewers engaged.  Media Efficiency: Focus on Attention Metrics, Not Just Viewability Ad engagement is no longer determined by cost per impression but by cost per high-attention impression. The campaign findings suggest:  High viewability doesn’t guarantee valuable ad exposure.  Formats with higher attention scores should receive higher budget allocation.  Advertisers should prioritise placements that reduce disruptive behaviours (mute, PIP).  Optimizing campaigns based on attention metrics allows brands to maximize real engagement and improve overall campaign performance, not just reported visibility.  How Attention Ad Measurement Enhances Fraud Detection Signals Attention metrics offer value beyond creative and planning insights; they strengthen ad fraud detection. Fraudulent traffic can mimic traditional metrics like impressions and viewability, but it cannot replicate the natural variability of human attention. Bots do not:  Display inconsistent completion patterns  Trigger realistic mute or PIP behaviour  Show behavioural fluctuations across formats  When attention signals appear unnaturally uniform or abnormally perfect, they help identify suspicious activity that standard fraud filters may not catch.  This creates a powerful layer of quality assurance by merging traffic validation with engagement behaviour, ensuring advertisers pay only for impressions that reach real, attentive users.  Also know why attention metrics matter for marketers to eliminate MFA sites.  Conclusion: Drive Ad Effectiveness with Attention Metrics We go beyond viewability to offer a detailed analysis of behavioural signals that reveal how audiences truly interact with your ads. Attention metrics uncover the difference between an impression that’s merely delivered and one that’s genuinely absorbed. By understanding real user behaviour using ad fraud detection tool, brands can optimize creative choices, refine media planning, and eliminate wasted spend with far greater precision.  When attention becomes the foundation of measurement, campaigns become sharper, more efficient, and more aligned with what today’s audiences actually respond to.  Want to ensure that your ads are only seen by humans and not bots? Connect with our mFilterIt experts to create a high-performing campaign by evaluating your impressions quality and ensure that your ads are only seen by humans.  

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Ad Fraud in USA

How Ad Fraud Quietly Damages Your Bottom-Funnel Performance

Ad Fraud, Blog /

Think of your funnel like a tower: the bottom is what holds everything together. If the base is weak, the entire structure becomes unstable, no matter how strong or beautifully designed the top floors are.  Your bottom funnel works the same way. It’s the foundation of your growth, where real outcomes finally happen, purchases, sign-ups, subscriptions, and revenue.   But when fraud creeps into this stage, the damage is far greater than just a few bad metrics. It shakes the entire system.  A compromised BOFU means you are building success on numbers that don’t exist. And when the foundation is fake, everything that depends on it eventually falls apart.  In this blog, you will discover –  The two major BOFU fraud traps  How click fraud distorts final conversions  How incent fraud fakes success  Warning signs your BOFU metrics are corrupted  Steps to rebuild BOFU integrity  Click Fraud: Fake Click Journeys That Mislead Optimization   Click fraud is no longer just a top-funnel nuisance. Modern fraud networks simulate entire user journeys where the impact extends to bottom-funnel as well. The sophisticated kind of click fraud impacts bottom of the funnel metrics with the following fake clicks methods –  Click Spamming When fraudsters fire multiple fake clicks often through device simulators or bulk click scripts, they clutter the system until a real user eventually installs the app. Because this happens within the attribution window, the system mistakenly credits the install in fraudster’s name. This click spamming skews your bottom-funnel metrics by turning genuine installs into “paid” conversions, inflating acquisition costs and hiding true organic performance.  Click Injection Click injection is an advanced form of click fraud where a malicious app tracks when a real user is about to install another app and fires a perfectly timed fake click just moments before the install completes. Because the timing appears legitimate, the attribution system credits the fraudster for the install, corrupting bottom-funnel metrics and misleading optimization algorithms toward fraudulent sources—ultimately polluting the stage where real revenue and true performance should be measured.  Impacts of Fake Clicks on Bottom of the Funnel Metrics  Fake clicks severely damage the conversion stage due to the following impacts –  Inflated CTR & Depressed Conversion Rates Fake clicks spike Click Through Rate (CTR) but never convert, making genuine add-to-cart, sign-up, app install, and purchase rates look significantly weaker.  Budget Drain & Higher Cost-per-Activity Fraud wastes spend on non-human traffic, pushing up CPA and reducing the volume of real users who reach the bottom funnel.  Polluted Retargeting Signals & Skewed Optimization Bots enter remarketing pools and send false engagement signals, causing ad platforms to optimize toward low-quality audiences.  Distorted Attribution & Misleading Performance Metrics Click fraud manipulates what looks “effective,” misguiding decisions across channels, creatives, affiliates, and campaign strategy.  Inaccurate ROAS Projections & Direct Revenue Loss With fake interactions replacing real intent, projected ROAS becomes unreliable, actual conversions drop, and long-term revenue suffers.  Incent Fraud: Incent Traffic That Looks Real but Acts Fake  Incentivized traffic, or incent traffic, 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. This creates incent fraud, where actions look real on paper but come with no genuine interest or long-term engagement. Incent walls, often seen in reward apps, fuel this by offering perks in exchange for installs or tasks. While incent traffic may seem like a quick way to boost numbers, it becomes a problem for advertisers who want quality users, because these “reward-driven” installs rarely convert, engage, or deliver true value.  Common Methods of Incent Fraud  Fraudulent affiliate cause incent fraud through following common methods –  Sub-affiliate Routing Dishonest affiliates cause affiliate marketing fraud where they pass incent traffic through multiple sub-affiliates, so it looks “organic,” hiding the fact that users were rewarded to perform the action.  Device Farms Workers install farms that repeatedly install/uninstall apps on many devices to mimic real users and generate fake conversions.  Know more about device fraud damaging your ROAS  Device Fingerprinting Manipulation Changing device IDs, IPs, or system parameters to make one device appear like multiple unique users, inflating installs or events.  Proxy/VPN-Based Identity Masking Using proxies or VPNs to switch IP addresses so fraudsters can imitate traffic from different locations and avoid detection.  Impact of Incent Fraud on Bottom of the Funnel  Biased Engagement Metrics: Fake or uninterested users distort event-level KPIs (add-to-cart, sign-ups, purchases), making optimization harder.  Wasted Retargeting Spend: You end up retargeting wrong users who never intended to convert, burning remarketing budgets.  Misleading Attribution Signals: Incent traffic inflates lower-funnel events, causing attribution platforms to credit the wrong partners or campaigns.  5 Signs Your Bottom-Funnel Metrics Are Getting Polluted    Top warning signs that brands can watch out to identify if their bottom of the funnel is getting polluted –  CPA/CPI rising with no improvement in quality  LTV tanking even though installs look strong  Retention dropping sharply after Day 1  Events coming in “too perfectly” or unusually fast  Top-performing sources delivering zero real revenue  Algorithms optimizing toward partners that don’t scale  How to Reclaim Your Bottom-Funnel Integrity   To protect the lower funnel, brands need more than top-funnel detection. They need to shift from “Is this click valid” to “does this behavior make sense end to end?” Here comes mFilterIt’s Valid8, an ad fraud detection software that safeguard brands through –  Click-to-install pattern analysis: Identifies abnormal click and install behaviors so you can spot fraud early and ensure only genuine installs are counted.  Device identity integrity checks: Validates real devices and filters out spoofed, cloned, or manipulated ones, keeping your bottom-funnel data clean.  Incent traffic classification: Separates organic users from incentive-driven ones, helping you protect quality and prevent inflated performance metrics.  Uninstall velocity monitoring: Tracks how quickly users uninstall after installing, revealing fake, forced, or low-intent traffic instantly.  End-to-end source clarity: Gives you full visibility into where each install, click, and event is coming from, removing blind spots in attribution.  User intent scoring: Measures the true intent behind user actions, helping you prioritize high-quality users and reduce wasted spend.  With the right ad traffic validation platform, marketers can finally distinguish between: Real users vs. scripted users   Genuine conversions vs. incentivized behavior   Authentic engagement vs. manipulated KPIs   Legit installs vs. farmed installs  Conclusion  Your bottom of the funnel shapes everything that happens above it. If what flows into your top funnel is already polluted with fake users, invalid clicks, or low-intent traffic, your entire marketing engine starts making the wrong decisions. That’s why maintaining bottom-funnel hygiene isn’t optional—it’s the foundation of accurate attribution, reliable CAC, and meaningful conversions.  Don’t let fraudulent activity dilute the results you’ve worked so hard to achieve. With the right ad fraud

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Bot Traffic Spotted

Bot Technique Spotted: How Unusual App Version Patterns Signal Bot Traffic & Fake Installs

Blog, Mobile Ad Fraud /

One of the common pain points our clients have expressed is that their number of installs doesn’t match the number of conversions. They see a high volume of installs happening, but this doesn’t convert into outcomes.   There is not just one reason behind an unusual spike in installs. It can be bots, or something more advanced which a human eye might miss easily. In one of a recent campaign data we evaluated, we observed that a new and unusual technique is used to hide invalid traffic driving fake app installs from Android mobile versions.   In the sections ahead, we’ll break down how fraudsters are hiding their trails by spoofing device related information – like mobile app versions and why it matters more than you think.   What We Found: Unusual App Version Patterns at Device-Level Validation Real Android applications follow a consistent version structure that looks like 3.1.1, 3.1.2, 3.1.3 (dots/separators placed at the bottom).  However, malicious application versions or bot-generated installs fail to replicate this accurately. These unusual app version patterns appear as 3·1·1, 3·1·2, 3·1·3, where dots/separators are placed in the middle instead of the bottom.   In legitimate mobile app versions:  The separators follow a standard baseline.  Formatting is uniform across devices.  This structure cannot vary from user to user.  But fraudulent installs often contain mobile app versions where:  The separators appear significantly higher than the baseline.  The structure does not match any valid release patterns.  These occur when bots simulate installs without replicating the technical precision of real app metadata.  Moreover, this pattern has been noticed across multiple publishers working with advertisers. Despite high install counts, purchase rates remain very low, confirming a clear metadata-level signature of bot traffic and app fraud.  Learn about the signs you might be experiencing device fraud. How These Unusual App Version Patterns Impact Campaign Performance While this technique might look very low impact, the consequences are not limited to just monetary.   Inflated Install Numbers Bot-generated installs boost the number of total installs, making campaigns appear to be high-performing. This masks real performance and prevents marketers from spotting underperforming channels early.  Misleading Optimization Decisions Fraudulent activity creates false engagement patterns. Due to this, marketers end up shifting budgets toward traffic sources that appear effective but are actually driven by bots, wasting spend and hurting long-term growth.  Unreliable Funnel Metrics Fake installs never convert, engage, or retain. This skews entire funnel data, making it harder to understand real user behavior and accurately measure the quality of your audience.  Misleading Attribution and Affiliate Payouts When bots generate fake installs, publishers or affiliates receive credit for traffic they didn’t actually deliver. This results in unfair payouts and inaccurate performance evaluation.  Lower ROI and LTV Ratio Fake users add no revenue or long-term value, which pulls down overall ROI and LTV benchmarks. This leads marketers to overestimate channel performance while significantly underestimating the actual cost of acquiring genuine, high-quality users.  Therefore, one technical discrepancy like a misplaced dot can impact your entire growth strategy.  How Does mFilterIt Identify this Mobile Ad Fraud Technique? Our ad fraud detection solution conducts a deeper analysis on every traffic source, to differentiate between human and bot-driven data. Some of the parameters used to identify these anomalies:   Analyses metadata that your dashboards can’t see Studies deep metadata—mobile app versions, OS details, device integrity, APK sources, and user-agent patterns thoroughly, revealing subtle bot traffic signals that dashboards and attribution platforms cannot identify independently. It also helps confirm whether the installs came from trusted channels/sources or not.  Identifies abnormal OS-version distributions and mobile ad fraud clusters It compares device versions against normal population patterns, flagging spikes in outdated or scripted versions, common in bot-driven installs operating on obsolete or emulated environments. It also maps recurring inconsistencies, like identical malformed app versions, repeating device signatures, or tight timestamp groupings, to uncover coordinated bot activity rather than isolated technical errors.  Assesses IP reputation and network behaviour Our app fraud detection tool also checks whether installs originate from proxies, VPNs, or data-centre networks, revealing non-consumer routes often used by bots to mask location, identity, and device validity.  Analyses timing behaviour to detect injection patterns By examining click-to-install and event timings, the tool identifies unrealistic timelines, signaling forced installs or automated triggers that do not follow natural user behaviour, generated by affiliates solely to achieve targets and earn payouts.  Blocks invalid traffic before attribution Through pre-MMP and post-MMP checks, the app fraud detection tool stops fraudulent installs in real time, preventing bot traffic from entering analytics, inflating KPIs, or misleading optimization decisions.  Know why attribution platforms miss mobile ad fraud.  Way Forward Unusual app version patterns are just one of many techniques fraudsters use to manipulate installs, which can be used as a red flag to identify an anomaly in your campaign data. However, mobile ad fraud involves multiple evolving techniques that often go undetected in surface-level checks. Identifying these signals is only the beginning. Advertisers need deeper, continuous ad fraud validation to detect anomalies across devices, metadata, networks, and user behaviour. Using an advanced app fraud detection tool enables advertisers to optimize confidently, prevent budget leakages, and build campaigns on clean, trustworthy data.  Strengthen your campaigns. Connect with mFilterIt experts and secure your traffic quality today.

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Referral Wins as Genuine

Are Your Referral Wins as Genuine as They Seem?

Leave a Comment / Blog, Mobile Ad Fraud, USA /

You launch a referral program with the intention to acquire new users to your app. When analysing your campaign progress, you see that your referral champion is a single user who somehow ‘invited 200 people overnight’.    You feel confident to spend more on these campaigns. On your dashboard there are 200 people, but upon deeper analysis you see that only a few retain as users. That’s a sign that something is not right. It’s not the usual invalid traffic, but a more advanced and difficult version of ad fraud. Referral and coupon fraud are advanced types of mobile ad fraud which impacts the action driven KPI’s and can skew your data. In this blog, you will discover –  In this blog, you will discover –  What is referral and coupon ad fraud?  How does it happen?   Impact of referral and coupon fraud on campaigns?   How can advertisers protect their referral campaigns?  Real case of a Referral coupon fraud   What is Referral and Coupon Fraud? Referral and coupon fraud are among the most common affiliate marketing fraud that can significantly drain a brand’s paid marketing efforts. Referral Fraud Referral fraud happens when people exploit referral programs to earn rewards without bringing in genuine new users.  What leads to referral fraud?  Creating fake accounts to claim referral bonuses  Using bots or emulators to trigger installs or sign-ups  Sharing referral codes on unauthorized platforms to mass-farm rewards  Coupon Fraud Coupon fraud occurs when fraudsters including dishonest affiliates use invalid codes, promo offers, or counterfeit coupon to claim false monetary benefits without enabling campaign to reach to the new audience.  What leads to coupon fraud?  Using expired or unauthorized coupon codes  Creating multiple accounts to repeatedly use new user offers  Manipulating apps/websites to redeem the same coupon multiple times  Circulating leaked internal or partner-only promo codes  How does Referral and Coupon Fraud happen? In affiliate marketing, referral and coupon ad fraud can happen at large scale. Here’s how each of these tactics contributes to fraud –  First-Time User Fraud This is the most common form of referral fraud. Referral codes are usually meant for new users only, but fraudsters reuse these codes by creating multiple fake accounts. They rely on bots, simulators, device farms, fake emails, and virtual phone numbers to look like new users each time.  Example: In ride apps like Ola or Uber, people often create fake accounts just to claim first-ride promo codes, which drains the advertiser’s budget and inflates fake new-user number.  Self-Referral Fraud In this type of fraud, the same person acts as both the referrer and the referee. Instead of genuinely referring a new user, they use their own information in different combinations such as multiple email IDs or different phone numbers to claim both rewards. Sometimes, they even reuse the same number with minor variations just to game the system.  Fraudulent Coupon Codes Fraudsters misuse promo or referral codes by circulating fake, unauthorized or expired coupon. Users who try to redeem these, end up disappointed when the brand cannot honor them, leading to mistrust and damaging the brand’s credibility. This type of fraud turns simple reward programs into a loophole for illegitimate incentives.  App Cloning (Parallel Space / Dual Apps) App cloning or parallel space gives an advantage to the user to log into two different user accounts simultaneously by creating a separate parallel space on Android devices. This enables them to create multiple accounts, refer themselves, and repeatedly redeem referral rewards. Ride-hailing apps, gaming apps, and social apps often become the targets of this trick.  Gift Card & Return Fraud Losses Fraudsters misuse gift cards, exploit refund loopholes, and combine them with referral rewards to extract illegitimate value leading to revenue loss and compromised program integrity.  Bots, Emulators & VPN/Proxy Abuse Fraudsters rely on automated tools and device manipulation to fake new users. Using bots and emulators, they reinstall apps, regenerate device details, and pretend to be different users. VPNs and proxies hide their real IP addresses or fake their location, making detection difficult. Advanced bots can even use real user information to redeem referral benefits without being caught.  Impact of Referral and Coupon Fraud on Brand The impact of such deceiving tactics falls on brand in the form of –  Wasted Ad Budget: Fraudsters generate fake users, false installs, and invalid coupon redemptions, causing the advertiser’s referral budget to drain quickly.  Lower ROI: Campaign performance looks inflated, but the actual return drops sharply because none of the fraudulent activity contributes to real growth.  Damaged User Trust: Genuine users lose confidence when fraudulent coupon misuse prevents them from accessing the benefits they were promised.  Poor Customer Experience: When rewards fail or referral codes don’t work, both new and existing users feel frustrated and disengaged.  Brand Reputation Hit: Ongoing fraud creates the impression that the brand’s referral or reward system is unreliable, negatively affecting long-term loyalty.  How Advertisers Can Protect Their Referral and Coupon Campaigns When tactics of fraudsters evolve at an alarming pace, protecting referral and coupon campaigns for brands become more important –  Start with Basic Manual Checks Advertisers can begin by reviewing phone numbers, email IDs, and domains used in referral or coupon redemptions. This helps identify obvious patterns like repeated domains, suspicious email formats, or invalid phone numbers. While this offers quick surface-level detection, manual checks cannot catch sophisticated bot activity, device manipulation, or high-volume fraud operations.  Strengthen Protection with a Fraud Detection Solution To truly safeguard referral and coupon campaigns, advertisers should work with a dedicated ad fraud detection provider. Advanced platforms look beyond simple IP repetition and analyze the entire device environment. This includes detecting bots, emulators, disposable emails, virtual phone numbers, app cloning, and suspicious domains, ensuring clean and legitimate referral traffic.  Use Deep Validation & Real-Time Blocking Integrating advanced solutions like an SDK for apps and DSS for websites allows advertisers to validate traffic at every touchpoint. Key checks such as device IDs, email authenticity, domain hygiene, digital reputation, phone number legitimacy, cloning flags, and IP behavior, help identify fraud instantly. Advertisers receive real-time fraud scores and can automatically block fraudulent device IDs before they drain budgets or degrade user experience.  How mFilterIt Safeguard Against Sophisticated Referral and Coupon Fraud mFilterit’s ad fraud protection tool Valid8 enables advertisers and brands to have a keen look on whether the traffic is coming from a legitimate source or not. Here’s how the right solution tackles referral and coupon fraud –  Validates Every User at a Device-Environment Level Valid8 goes beyond basic IP checks and analyzes the complete device environment. It detects

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affiliate fraud

Affiliate Fraud in MENA: How to Protect Your Brand from Lead Generation Fraud

Ad Fraud, Blog, MENA /

4,000 Leads. 18 Real Buyers. That’s the Problem.  “More leads = more business” is one of the most expensive myths in affiliate marketing, especially in the MENA region.  Affiliate campaigns promise scale, speed, and volume. Dashboards light up. Weekly reports look impressive. But when those leads hit your CRM, the reality is far less glamorous: duplicates, unreachable contacts, irrelevant users, and leads generated purely to meet affiliate targets, not to convert.  Karim Bekka, Business Director at Assembly Global, shared a perfect example of how this gap plays out in the real world. A real estate brand demanded 4,000 leads every week. What they actually got? Just 18 genuinely qualified prospects. The rest clogged CRMs, wasted sales hours, and quietly eroded trust between marketing and revenue teams.  This isn’t an isolated incident. As affiliate ecosystems in MENA scale, fraud and low-intent traffic scale with them. Incentivized sign-ups, lead recycling, form-filling bots, and publisher shortcuts are becoming more sophisticated—while many marketers are still optimizing for volume alone.  The cost? Budgets spent on numbers that look good on reports but contribute nothing to pipeline or revenue.  In this blog, we unpack the real risks behind affiliate lead generation fraud in the MENA region—and the must-have safeguards brands need to move from a volume-first mindset to a quality-led affiliate strategy that delivers leads your sales team actually wants to call.  How Fake Leads and Cheap Lead Offers Increase the Risk of Affiliate Fraud in MENA Cheap leads are one of the biggest traps in affiliate marketing. Especially in competitive industries like real estate, fintech, education, insurance, etc., the promise of getting ‘leads in 3 dollars’ is the most common red flagof lead generation fraud by affiliates. Such claims always indicate low-intent traffic. Here’s how they do it:  Fake lead submissions Affiliates recycle the same data, use automated bots, scripts, or employ click farms to produce volume.  Duplicate leads Fraudsters submit the same lead multiple times using slight variations like different email formats, altered spelling, or the same user across multiple affiliate IDs.  Click hijacking Affiliates steal last click attribution right before a real user completes an action, hijacking users that were actually driven by your paid, organic, or social campaigns.  Event spoofing Fraudsters fake user actions like pageviews or form submissions, tricking marketers into believing signals that distort conversions and make campaigns look falsely successful.  Bulk low-intent traffic Affiliates buy cheap, irrelevant traffic just to hit lead targets. These users have no interest in your brand, submit low-quality forms, and never convert into real customers.  Coupon fraud Fraudsters may offer small rewards, cashbacks, coupons, or points to users for filling out forms to earn undeserved payouts.  Moreover, brands unintentionally worsen this problem by using weak internal setups like:  Using outdated SDKs unknowingly  Leaving MMP fraud controls under configured  Missing integrations between CRM, MMP, and affiliate tracking data  The result? Unqualified, fake leads enter the dashboards, leading to wasted spend, efforts, misleading optimizations, and ineffective campaign efficiency.   Why “Guaranteed ROI” Claims by Affiliates are Misleading in Lead Generation Campaigns The idea of ‘guaranteed ROI’ also seems promising, but in affiliate marketing, it’s completely misleading. As Karim Bekka mentions, affiliates can guarantee actions (clicks, impressions, form submissions, leads, installs), but they cannot guarantee outcomes (qualified appointments, conversions, or revenue). No affiliate partner controls user intent, brand trust, market maturity, or competitive context.   Yet marketers in MENA frequently fall for these fake promises of guaranteed sales or predictable acquisition costs across every campaign. These are especially problematic for newer or low-traffic brands. Without adequate awareness and consideration built through mid-funnel channels, affiliates have nothing to work with. They may resort to aggressive discounting, incentivized traffic, or low-quality sources to meet guaranteed numbers of leads, further diluting brand value, and corrupting attribution data.  Therefore, Bekka recommends brands to follow a staged funnel approach – invest first in awareness and consideration, then bring affiliates in at the lower funnel once there is brand demand and baseline volume.  This approach ensures affiliates operate on top of real intent signals rather than generating irrelevant volume. By setting realistic expectations and aligning affiliate activity with brand maturity, marketers avoid costly inefficiencies and inflated performance metrics.  Check out the full episode here How Brands Can Build a Fraud-Resilient Affiliate Lead Generation Ecosystem A sustainable affiliate strategy requires a balance of rigorous validation, selective partnerships, and a strong technology backbone. This approach blends operational discipline with the right layers of verification to create an affiliate marketing ecosystem built for quality, not lead generation fraud or inflated metrics. Here’s how it works:  Lead scoring as a quality filter Lead scoring ensures that every submission is evaluated for completeness, behavioral relevance, intent, and device integrity. A simple (high, moderate, low) scoring system helps teams instantly separate high-value leads from noise. It allows marketing teams to optimize budgets, prioritize leads for sales, and maintain consistent quality benchmarks.   Advanced ad fraud detection solution: Tracking, detection & human review Brands must leverage a combination of real-time tracking, multi-layer fraud detection, and manual analysis to run high-performing affiliate marketing campaigns. Key layers include:  Visit-intent scoring to evaluate the quality of each visitor before they become a lead  AI-based detection to identify unusual behavioral patterns  Human-led investigations for nuanced or emerging fraud behavior  Lead validation integrated directly into CRM to automate prioritization  The ad fraud detection tool should track users across every step – from clicks to visits to leads and finally to sales, ensuring that only legitimate leads are forwarded to sales, lowering churn in CRM and protecting revenue.  Build selective, vertical-specific affiliate networks Instead of onboarding dozens of broad-reach affiliates, brands should curate partners based on vertical expertise and verification ability.  For real estate, this means affiliates with call centers or pre-qualification teams who verify user details before submission. For B2B, it means niche content partners or appointment-setting specialists that influence mid-to-bottom funnel outcomes, not just lead volume.  Real-time blocking Fraud prevention isn’t only retrospective; it must be real-time. Brands should implement:  IP & placement blacklisting to stop

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Ad Fraud in USA

Ad Fraud Signals Your Attribution Platform Misses and How to Fix Them

USA, Blog, Mobile Ad Fraud /

If you’re running app campaigns at scale, you’ve probably seen this before. Your attribution reports look clean, installs are coming in, and your ad fraud detection tool shows no major issues—yet the overall quality of users doesn’t feel right.  For app marketers, with fraud checks now bundled into most attribution platforms, it’s easy to assume traffic quality is covered. But these validations are mainly built to ensure installs are attributed correctly, not to deeply assess how users behave after they enter the app. And that’s where things start to drift.  The challenge for marketers isn’t spotting obvious fraud anymore; it’s making sense of why validated traffic still underperforms. Cohorts don’t retain as expected. Conversions don’t scale the way spend does. Business impact feels weaker than what the top-line numbers suggest.  In this blog, we cover:   The key signs attribution platforms miss   Impact of missed ad fraud signals on app campaigns   How mFilterIt helps marketers to solve this  Key Signs Your Current Tools Might Be Missing Manual and traditional monitoring tools overlook some serious ad fraud signs that lead to long-term impacts. Let’s understand each of them –  Abnormal Click-to-Install Ratios Abnormal click-to-install ratios are one of the clearest signs that something is off. In our 8-day analysis, we saw an extremely high number of clicks but almost no installs, resulting in a CTIT of just 0.01% on 03-08-2025. Such unusual click patterns cannot happen with real users. It’s a strong indicator of bot activity, where automated systems continuously click on ads without ever converting, making it harder to detect.  Spam + Bot Traffic Masquerading as Average Let’s take it a step further. We already saw high clicks with very few installs, but the conversion rate makes it even more suspicious. Out of all the installs, only a tiny fraction went on to make a purchase. For example, in one case with 170 million clicks and 249K installs, only 384 real orders were placed, resulting in a conversion rate of just 0.154%. This gap strongly suggests spam or bot traffic rather than genuine users that cannot be tracked with manual monitoring or traditional monitoring tools. Sudden Increase in Low-Value Orders There was a sudden and noticeable surge in low-revenue orders, which is a clear sign of arbitrage. This usually happens when dishonest affiliates pay users a small amount to place very cheap orders, just to make it look like their channel is driving sales. In reality, these orders are fake signals meant to earn them higher commissions.  Bot Impressions at Odd Hours The graph shows impression rate on y-axis and hours on x-axis. As it indicates, impression rate surges exorbitantly at 3 am in night which cannot be a possible human activity. After observing the pattern of 10 consecutive days, it defines clearly that impression rate rises at night everyday hence indicating a huge bot or emulator involvement.  What Happens When These Threats Go Unnoticed Attribution tools miss these sophisticated fraud patterns, allowing hidden ad fraud threats to slip through, ultimately causing the following impacts:  Wasted ad spend on non-human or low-quality traffic The impact of the above threats is severe especially impacting your budget spend. Imagine you putting every stretch of budget in optimizing your resources to attract organic users. However, bots, emulators, or low-quality sources flood your campaigns. Over time, this wasted spend snowballs, pulling budget away from high-value channels and slowing down growth when it matters most.   Inflated KPIs that distort optimization and scaling decisions Fraud-driven traffic artificially boosts campaign metrics like clicks, installs, CTRs, etc., creating an illusion of performance that leads to no conversions. When teams optimize or scale based on these inflated KPIs, campaigns drive in the wrong direction. This leads to misallocated budgets, misguided testing, and strategies built on numbers that don’t reflect real user behavior.  Misattribution of conversions, hurting partner relationships When campaign metrics are inflated due to fake engagement by fraudulent sources, wrong partners get the credit. Authentic publishers or affiliates lose credit for the users they genuinely bring in, damaging trust and straining relationships. Over time, this misalignment makes teams second-guess which partners to scale or pause.  Lower ROI and disrupted campaign performance When fake or low-quality traffic pollutes your funnel, your cost per outcome increases while real conversions stagnate. This directly erodes ROI and disrupts campaign efficiency. Fraud pushes teams to spend more to chase the same results, ultimately dragging down overall marketing profitability.  Compromised long-term growth due to unreliable data Fraud doesn’t just distort today’s numbers, it corrupts the historical data you rely on for forecasting, budgeting, audience insights, and long-term strategy. When data integrity slips, so does decision quality. This creates a ripple effect: inaccurate models, misinformed planning, and slower growth across channels and quarters.  How mFilterIt helps App Marketers Optimize their Campaigns? Advanced traffic validation solution like mFilterIt’s Valid8 fill the critical gaps left by manual and legacy monitoring, offering deeper protection and smarter insights. Here’s what right ad traffic solutions brings –   Know Exactly Where Your Traffic Comes From With source-level transparency, gain a clear visibility into every source, sub-source, and placement. This helps you quickly spot unusual patterns, identify underperforming partners, and understand which channels actually drive real value.  Catch Fraud the Moment It Happens Real-time alerts enable you stop suspicious clicks, installs, or spikes instantly before they drain budgets or skew your results. No waiting, no guessing.  Verify If a Device Is Genuine With enormous bots and emulators hampering the performance metrics, advanced solutions check whether each device interacting with your ads is real, active, and human-driven. These checks filter out bots, emulators, cloned devices, and anything pretending to be a real user.  Uncover Advanced Fraud Tactics With advanced ad fraud detection tools, go beyond basic red flags. Detect all the sophisticated fraud tactics like click flooding, install hijacking, etc. To outsmart tricks that are built to look “clean” but quietly damage performance.  Way Ahead Indeed, digital advertising opens windows of opportunities for you, but it also opens the doors   for fraudsters as well. While attribution tools are still helpful in surface-level analysis, they cannot simply outsmart the sophisticated fraud types. By implementing the right ad fraud detection tool, there will be visible impacts in the form of –  Cleaner, High-Quality Traffic: Blocks bots, farms, and spoofed devices so only real users enter your funnel from the start.  Better Campaign Performance: Removes fake activity to make accurate, decisioning sharper, and optimizations better.  Higher ROAS, Lower Waste: Brings your budget to real users, reducing acquisition costs and improving returns across every channel.  Improved Partner Transparency: Identifies quickly the underperforming or suspicious affiliates, networks, and publishers. Hence, the right ad fraud detection software is must for you to win the digital advertising game and continuing to win in the future.  To know how

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Device Spoofing

Device Spoofing at the Impression Level: How User-Agent Anomalies Impact Campaign Data

Blog, Mobile Ad Fraud /

Detecting fake devices isn’t as easy as it used to be.  There was a time when the red flags used to be clearly visible – random clicks at odd hours or sudden traffic spikes from unfamiliar regions. Those patterns made it simple to tell when bots were at play.  But fraudsters have now become smarter. Instead of relying on easily traceable bot behavior, they now use sophisticated techniques to spoof impressions and pretend to be real users on real devices. This next-level masking of fake devices as real is known as device spoofing, and it’s quietly reshaping how ad fraud hides in today’s campaigns.  Unlike traditional bot traffic, spoofed devices mimic genuine user behavior so well that standard validation platforms struggle to tell them apart. The result? Campaign dashboards that look real but are actually filled with fake interactions. What is Device Spoofing? Techniques Used for Device Spoofing Device spoofing is a sophisticated ad fraud technique where fraudsters disguise fake, old, or low-quality devices to appear as genuine, tricking marketers into counting them as real users. Common device spoofing techniques include:  Geo-Location Manipulation Fraudsters spoof GPS coordinates or use proxies/VPNs to mimic users from premium regions. This helps them access high-value audiences and inflate campaign reach in targeted geographies without using real devices.  Simulated or Fake Devices Fraudsters alter device IDs, OS versions, screen resolutions, or hardware profiles to make outdated or non-existent devices appear new, authentic, and high performing.  Browser Spoofing They alter browser fingerprints, including headers, plugins, versions, and configurations, enabling fake environments to perfectly mimic real browsers and bypass platform-level verification or anomaly detection systems.  User Agent String Manipulation Fraudsters manipulate technical details like device IDs, operating system details, user-agent strings, browser information, etc., making it difficult for ad platforms to detect them as fake devices.   This means, while you believe your ads are reaching real people, a portion of that ‘audience’ might actually be nonexistent, generating fake impressions.   In a recent case, our tool detected a surge of ad impressions coming from devices supposedly running on Android OS 6. On the surface, that seemed fine until our system cross-checked the actual device model metadata.  It turned out that these devices were originally released with Android 9, supporting upgrades to Android 10 or 11.  This technical mismatch, an outdated OS appearing on a newer model, was a clear red flag. It revealed that fraudsters had manipulated the device identity to make fake traffic appear genuine, hoping to slip past standard detection systems.  Why Marketers Need to Validate Device IDs in Their Campaigns? Device IDs and user-agent strings are two of the earliest signals that define “who” your ads are actually reaching. Device spoofing doesn’t just manipulate campaign data because when these signals are manipulated, everything built on top of them becomes unreliable. Here’s how:  Device IDs are the backbone of attribution, frequency & measurement Device IDs determine how platforms track reach, frequency, and conversions. When fraudsters spoof or rotate them, one device appears as hundreds of unique users. As a result, frequency caps stop working, and attribution becomes inaccurate. Marketers unknowingly pay more to reach fewer real users because the foundational identity signal is corrupted from the start.  User-agent strings validate whether a device ID is plausible A device ID is easy to fabricate, but a user-agent string provides context about OS, browser, and device type. When these don’t match, like a new device running an outdated OS or many IDs sharing the same UA, it signals synthetic traffic. UA validation helps separate real devices from scripted environments.  Unchecked device IDs pollute media optimization & audience models Fake device IDs contaminate remarketing pools, distort lookalike audiences, and push automated bidding systems toward non-human traffic. Over time, targeting becomes less accurate and more expensive, even if campaign metrics appear stable. The performance engine quietly drifts away from real customers because its learning data comes from manipulated or spoofed identities.  Device & user-agent inconsistencies reveal impression-level fraud early Device and UA metadata are available from the first impression, making them valuable for early fraud detection. Mismatches in OS versions, device lifecycle, browser type, or session stability quickly expose spoofed devices. Identifying these inconsistencies upfront prevents downstream wastage and protects campaign performance before budgets begin to leak.  This helps marketers ask better questions Understanding device IDs and UA signals helps marketers challenge suspicious reach spikes, inconsistent delivery, or unexplained traffic patterns. It encourages smarter conversations with publishers and reduces reliance on surface-level KPIs like CTR. In an ecosystem full of manipulated traffic, signal literacy becomes a strategic advantage for protecting ROI and improving transparency.  Therefore, advertisers need advanced ad fraud detection solutions like Valid8 by mFilterIt to protect their ads from device spoofing. Our tool helps validate device IDs, user-agent strings, and impression-level signals in real time.   This enables the marketer to take investment decisions with confidence and ensure their ads reach the right audience.   For more information, contact our experts.

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Brand Bidding Violations in USA

Brand Bidding Violations in PPC Campaigns: What It Is, How It Hurts, and How to Stop It

USA, Affiliate, Blog /

Your brand name is the busiest doorway in town, so who’s greeting your customers before you even get there?  Every day, thousands of shoppers search for brands directly on Google and more than half of them are discovering or choosing a brand at that very moment. This makes your brand keywords the heart of your PPC strategy, driving the most ready-to-convert users straight to you.  Brand bidding in USA is a common practice, but brand bidding violation is a hoax caused by foul market players who target your brand keywords by quietly bidding on them, hijacking your organic conversions, and damaging your brand reputation.  While teams try to manually monitor these violations, their checks often remain limited to what can be seen on the surface. With so many variables and constant shifts in search environments, even strong teams may miss deeper violations.  In this blog, you will discover –  How brand bidding violations ruin your PPC campaigns  Who is responsible for brand bidding violations  What makes brand bidding violation tough to tackle  Signs to identify brand bidding violations  Why manual monitoring isn’t enough to catch PPC fraud  How mFilterIt puts a defined halt to brand bidding violations  How Brand Bidding Violation Impacts Your PPC Campaigns  PPC is all about showing your brand to the most relevant audience at the right time. You bid on keywords your audience is searching for, your ad appears instantly, and you capture demand from users already interested in what you offer.  But foul market players redirect your traffic to their landing pages or make your organic traffic reach your website through their tracking link. This advertising abuse damages your PPC campaigns and cause –  Higher CPC With No Real Gain When dishonest players bid on your brand terms without permission, they drive up the auction price, making you pay higher for your own organic traffic.  Stolen High-Intent Traffic Brand bidding violations hijack the most expected conversions to someone else’s landing page.  Skewed Campaign Performance & Reporting Lower CTR, inflated spend, and misleading attribution make it harder to evaluate your campaign metrics.  Reduced ROAS & Wasted Budget You lose budget fighting against unnecessary competition, reducing ROAS and hurting efficiency across the entire funnel.  Brand Dilution and Confusion Unapproved ads can create misleading ad copies, and wrong offers on brand’s name, diluting brand credibility.  Leakage in the Conversion Funnel Traffic that should have organically reached your website gets rerouted to fake coupon sites, unapproved resellers, creating huge drops in conversion rate.  Who Are Bidding on Your Brand Keywords?   Multiple fraudulent players are responsible for advertising abuse like brand bidding violations as they gain traction for the traffic that was already yours. They include –  Affiliates Some affiliates bid on your branded keywords, causing affiliate marketing fraud to hijack your organic traffic and then sell it back to you for a commission. They run ads above your website on search engines like Google and capture audience’s attention.  For instance – A user aimed at purchasing cosmetics from your brand and while he was willingly going on the website, an eye-catchy phrase like “Grab Best Offers – Avail Now,” caught his attention and he clicked on it. That is, your organic traffic getting redirected through an affiliate’s link, making affiliate earning commission on it.  Competitors/Rivals Many competitors directly bid on your branded keywords, openly stealing the attention followed by stolen conversions.  For instance – A user searches for “YourBrand shoes” on Google, intending to buy directly from you. But instead of your ad appearing first, a competitor like “StrideMax Shoes – Better Than YourBrand” shows up at the top with a paid ad. This way user gets redirected to the competitor’s product page, and your high-intent customer is captured by a rival brand.  Resellers/Distributors Resellers use your brand keywords to push their own listings, often outranking your official ads.  For instance – A user searches for “YourBrand smartwatch” on Google. Instead of your official product page showing at the top, a reseller runs an ad like “Buy YourBrand Smartwatch – In Stock at Reseller’s Name.” Their paid listing appears above your own ad, so the shopper clicks the reseller’s result and buys from them instead of your official store.  Coupon/Deal Sites Coupon sites run Google Ads on searches like “YourBrand discount,” “YourBrand offers,” “YourBrand coupon,” or even just your brand name.  For Instance – A user searches for “YourBrand promo code” or even just “YourBrand” on Google. At the top, they see a paid ad from a coupon site like “SaveBigDeals – YourBrand 50% OFF Today!” The user clicks the ad expecting a real discount, lands on a coupon page with generic or expired codes and then gets redirected to your website.  Why it is difficult to detect Brand Bidding Violation?   There are many sophisticated techniques used by fraudsters to perform the violations of brand bidding practices. These tactics often remain undetected due to limited capabilities of manual monitoring. Here’s what they include –  Geo-targeting tricks – They show violating ads only in countries you don’t check.  Dayparting – Ads run late at night or early morning when no one on your team is watching.  Cloaking – They hide their violating ads from your team by blocking your IPs, devices, or browsers, so the ads appear only to real customers and stay invisible during your checks.  Dynamic ad copy switching – Some affiliates change their ad text automatically depending on who is searching. To real customers, the ad shows trademarked terms like “YourBrand Deals”, but when your team or monitoring tools check, the ad instantly switches to safe, generic text, making the violation hard to detect.  Know why brand bidding in affiliate marketing is riskier in 2025 Top 5 Red Flags to Identify Common Brand Bidding Violations  Brand bidding violations are critical to identify. Watch out for these red flags to spot them before the damage is done.   Top Signs to Identify Brand Bidding Violations Branded CPC Spike – Your cost per click suddenly rises on your own brand name.  Organic Traffic Drop – Fewer users reach you through branded organic searches.  Paid Clicks Surge – You start paying for traffic you normally get for free.  Misleading Ad Copy – Ads use phrases like “YourBrand deals” or “official offers.”  Odd Conversion Patterns – Conversions spike at unusual hours or unfamiliar locations.  How Your Team Misses Brand Bidding Violations  Brand bidding looks easy to monitor until you realize how many combinations you actually need to check. Affiliates don’t violate rules everywhere, they do it selectively, in places your team isn’t watching.  The Real Monitoring Load (Simplified Example) Say you have:  40 branded keyword variations  8 regions to monitor  3 browsers (Chrome, Safari, Firefox)  2 device types (mobile + desktop)  That’s:   40 × 8 × 3 × 2 = 1,920 checks per audit  Now add time:  1 search ≈

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Advertisers learning how to avoid underblocking and overblocking to ensure brand safety in ad placements

Brand Safety: How Advertisers Can Avoid Underblocking and Overblocking Ad Placements

Blog, Brand Safety & Suitability /

What if your ads aren’t reaching the people you want, but are still showing up in places you never approved? That’s the hidden problem with keyword overblocking and underblocking. When you overblock, your brand loses visibility in safe and relevant environments, allowing competitors to dominate the spaces you should have owned. When you underblock, your ads risk appearing next to unsafe or irrelevant content, hurting your brand reputation and audience trust. This creates a clear domino effect: lost reach, weaker impact, wasted budgets, and declining brand credibility. And most brands don’t even realize that these issues start with one flawed assumption; that keyword blocking alone can keep their ads safe. In this blog, we break down why keyword blocking as a brand safety strategy is fundamentally limited, what brands really need to understand about ad placement quality, and how they can make better, context-aware decisions. We’ll also explore how mFilterIt’s brand safety solution gives you accurate, real-time visibility and ensures your ads show up exactly where they should, without compromising reach or brand reputation. What is Underblocking and Overblocking of Ad Placements and What Causes Them? Underblocking happens when ads appear next to content that is unsafe, unsuitable, or misaligned with the brand’s image. This includes news about violence, misinformation, hate speech, extremist views, or overly negative content. What causes underblocking of ad placements? Relying only on basic keyword blocklists methods No understanding of page-level meaning, intent, or sentiment Filters that cannot interpret regional languages or cultural context Lack of monitoring of publishers, apps, or channels Limited detection of low-quality or fraudulent environments Overblocking occurs when ads are restricted from being placed next to content that is actually safe, relevant, and appropriate. This reduces delivery scale and prevents ads from reaching potential audiences. What causes overblocking of ad placements? Relying on broad keyword filters One-size-fits-all global safety settings Filters unable to differentiate between informational vs harmful content Blocking entire topics or domains due to a single keyword Misinterpretation of regional or cultural content Consequences of Keyword Overblocking and Underblocking: What Advertisers Miss Many advertisers feel that keyword-based filters are enough to prevent their ads from unsafe placements. This has been their go-to strategy for brand safety. However, this approach leaves huge gaps in the process of brand safety and protection. Keyword systems only catch what they are explicitly asked for, leading to overblocking or underblocking of ad placements in many scenarios. For example, a family-focused FMCG brand’s ad may appear beside a YouTube video with explicit visuals, even though the video title contains harmless keywords. Without visual and sentiment analysis, the placement slips through keyword filters, allowing the ad to run beside inappropriate content that damages brand perception. This is an example of underblocking and here are the consequences underblocking of ad placements leads to: Brand Safety Concerns When ads appear next to harmful or controversial content, consumers may directly associate the brand with negativity. A single unsafe ad placement can trigger brand reputation risks and backlash across social platforms. Exposure to Fraud or Low-Quality Traffic Underblocking often means ads might show up on low-quality MFA websites or spam pages, leading to inflated impressions and wasted spending. Poor Conversion Efficiency Ads may appear in irrelevant or unsafe environments, where users are less likely to engage or convert—driving up CPA and reducing return on ad spend. Compliance & Suitability Failures Regulated industries like BFSI, healthcare, or kids’ categories face stricter content rules. Underblocking increases the risk of ads appearing in restricted categories. Let’s take an example of overblocking. This approach cuts advertisers off from massive amounts of safe, high-quality content. An article with a headline like “Virat Kohli’s cover drive continues to kill it this season” triggers the word ‘kill’ (in the keyword blocklist) even though the tone is celebratory and the content is 100% safe. Yet sports advertisers lose access to one of the most engaged audiences in India. Here are the consequences overblocking of ad placements leads to: Loss of High-Quality Inventory High-quality audiences and impressions on safe and suitable pages are blocked due to overly broad filters, shrinking available inventory. Limited Reach & High Media Costs Campaigns struggle to meet delivery goals because a large number of potential placements are unnecessarily excluded. When inventory is restricted, competition increases. CPMs, CPCs, and CPA all rise, impacting efficiency. Missed Opportunities During festivals, sports events, elections, or national news moments, content volumes surge. Overblocking prevents advertisers from leveraging these high-engagement opportunities. How Advertisers Can Avoid Overblocking and Underblocking of Ad Placements To avoid underblocking and overblocking, advertisers need to leverage advanced brand safety solutions to evaluate each ad placement not just by keywords but by what the content means, what message it communicates, its sentiment, and how it is interpreted in a regional context. Analyse Placements Based on Content Understanding the actual subject of a page (and just matching keywords) helps ensure ads appear in relevant, safe environments. Content-level analysis prevents brands from unintentionally blocking large volumes of safe inventory simply because they contain a certain word, phrase, or product reference. This safeguards scale without compromising suitability. Analyse Placements Based on Context Context determines whether a keyword appears in an informative, neutral, or harmful environment. Contextual targeting helps advertisers distinguish between high-quality content and genuinely unsafe pages. This prevents underblocking on risky pages and overblocking on harmless ones, improving both brand safety and delivery efficiency. Analyse Sentiment to Understand Tone of the Content Sentiment analysis evaluates whether the content is positive, neutral, or negative. Even if the topic is safe, negative sentiment can misalign with brand values. Sentiment filters help advertisers avoid negative associations while still accessing neutral or positive content that offers high engagement potential. Analyse Regional Nuances of the Ad Placements In diverse countries like India, regional content often includes multilingual terms, cultural references, slang, and context-specific meanings. Generic filters miss these nuances, leading to misclassification and misalignment of content. Brand safety solutions with regional intelligence filters help advertisers stay safe across diverse markets while maintaining access to

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