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Connected TV (CTV) ads have emerged as a powerful digital advertising medium for brands looking to engage in a more targeted and interactive manner. As CTV viewing time has doubled over the last four years, CTV ad spending is also projected to reach $27.47 billion.  Some of the popular CTV platforms for advertising are Roku, Amazon Fire TV, Apple TV, etc. The radical shift in viewership from traditional TV to streaming TV has opened this great opportunity. However, the opportunity is coupled with threats and challenges to ensure the integrity of ad traffic. Advertisers spending on CTV ads risk wasting precious ad budgets on illegitimate views and impressions, ultimately diminishing their return on investment (ROI).   According to a recent study, in the second quarter of 2024, 19.4% of programmatic ad traffic to CTVs was invalid worldwide. To protect CTV ad campaigns, automation with advanced processes and AI ML-powered solutions is necessary.   Let’s dwell deeper and explore why validating ad traffic is essential for CTV campaigns and how it can significantly enhance your advertising effectiveness and profitability.   What is CTV ad fraud?  Approximately, $1.14 billion in global CTV open programmatic ad spend was lost to invalid traffic in Q2 2024. This highlights the gravity of ad fraud in the CTV ecosystem. CTV ad fraud causes inflation in ad campaign metrics of CTV ads due to invalid traffic, bot-driven engagement, and app spoofing. Some of the most common CTV ad fraud include:   Invalid or Bot Traffic: That does not come from a genuine user, including traffic generated by data centers (DCH), automated clicks, and other non-human sources. Bots generate fake ad views or clicks, making it appear as though there is legitimate engagement from real users.  Device Spoofing via: Creating fake CTV devices or apps to mimic real ones. It often tricks advertisers into buying ads on non-existent or fraudulent platforms.  VPN/DCH: Traffic coming from Virtual Private Networks (VPNs) or data centers, is often used to hide the true location of the user and generate fake engagement from non-targeted regions.  CTV ad fraud also includes device spoofing, running ads through secondary devices while the TV is off, and invalid server-side ad insertion (SSAI).  Campaign Optimization for Frequency Cap Violations: Improve performance while preventing frequency capping violations, which occur when an ad is shown to the same user too often. By monitoring and managing frequency, advertisers can enhance engagement and reduce ad fatigue, ultimately maximizing return on investment (ROI).  Viewability Attention Metrics: The issues occur when advertisements are assessed for their visibility without adequately measuring user engagement. This leads to a mismatch between the visibility of ads and genuine interactions, resulting in distorted performance metrics.  Impact of CTV Traffic Fraud  Addressing CTV ad traffic fraud is essential to protect advertising investments and ensure accurate performance measurement. Implementing advanced fraud detection solution can mitigate these impacts, leading to more effective and efficient CTV advertising campaigns. Wasted Ad Spend: A significant portion of the advertising budget is lost to fraudulent traffic. CTV fraud detection and ad Traffic validation can identify any ineffective allocation of marketing resources.  Distorted Performance Metrics: Inaccurate data on ad impressions and engagement due to skewed metrics leading to misguided strategic decisions.  Reduced ROI: Lower return on investment due to non-human interactions or invalid traffic, which makes it difficult to achieve campaign objectives.  Damage to Brand Reputation: Ads potentially appearing in inappropriate or non-existent placements lead to a loss of consumer trust and brand credibility.  Inaccurate Audience Targeting: Ineffective targeting and personalization efforts due to misleading information about audience demographics and behavior.  Compromised Strategic Planning: Inability to accurately measure campaign success and challenges in optimizing future campaigns based on flawed data.  Competitive Disadvantage: Brands without robust fraud detection may fall behind competitors and reduce the effectiveness of marketing strategies compared to competitors using advanced fraud prevention.  Case Study  Improving CTV Ad Campaign Performance for a Global Energy Player  A leading global player in the energy sector was running CTV audio and display campaigns on Roku TV to boost visibility and audience reach.   Challenge: Despite substantial digital spending, the brand was experiencing low reach and engagement metrics (impression & viewability, completion rate, VTRs, reach and frequency metrics) which did not align with its investment. The primary challenge was the discrepancy between the digital spending and the actual reach and engagement metrics. The brand suspected that a significant portion of their ad traffic might be fraudulent, negatively impacting their campaign performance and ROI.  Solution: mFilterIt was brought in to validate the traffic for the CTV campaigns, ensuring that the brand’s ads were only shown to legitimate viewers. The process involved identifying B.A.V (Brand Safety, Ad Fraud & Viewability) & F-Cap over-exposure prevention to ensure the impression gets served within the frequency defined by the advertiser, brand safe, and non-IVT only.  Results: The mFilterIt analysis for the video campaign showed, that the average fraud was around 16% invalid traffic including 6.11% from invalid geographies, and the rest was from repeated IP bots, device repetition, and low-intent users. Reach and frequency metrics were also analyzed to streamline budget performances.  For the banner campaign, the average fraud was around 17% invalid traffic with a major chunk coming from repeated IP bots 14.49%, and the rest of the invalid traffic was from invalid geographies, DCH, and low intent users.  After implementing mFilterIt CTV ad traffic validation, the brand saw a significant improvement in its campaign performance. This led to a 4% increase in conversion rate and an 11% lift in ROI.  Impact of CTV Traffic Fraud  Addressing CTV ad traffic fraud is essential to protect advertising investments and ensure accurate performance measurement. Implementing advanced fraud detection solutions can mitigate these impacts, leading to more effective and efficient CTV advertising campaigns.  Wasted Ad Spend: A significant portion of the advertising budget is lost to fraudulent traffic. CTV fraud detection and ad Traffic validation can identify any ineffective allocation of marketing resources.  Distorted Performance Metrics: Inaccurate data on ad impressions and engagement due to skewed metrics leading

In the world of digital marketing, getting leads is just one part of the puzzle. Ensuring those leads are high-quality is what truly matters. One of the most effective ways to validate leads is by using a conversion pixel. A conversion pixel is a small piece of code that you add to your website to track and measure specific user actions. When used correctly, it helps you to filter out the irrelevant bots traffic and focus on genuine human filled leads that are more likely to convert. How you can validate leads with a pixel: Set Up Your Pixel: The first step is to install the pixel on your website or landing page. Whether you’re using Facebook Ads, Google Ads, or any other platform, most of them provides an easy guides to integrate their conversion pixel code on your website landing and thank you page. You can also choose any third party pixel to do the same, mFilterIt Visit and Conversion pixels are similar to any of these advertising platforms pixel. Pixels allow you to track user interaction in real-time, from filling out a contact form to clicking on a product page. Track Specific Actions: With the right pixel in place, you can start tracking specific actions that indicate lead quality. For example, you can track form submissions, page visits, or time spent on a particular page. Users who engage more deeply with your content are likely more interested, and therefore, their actions are better indicators of a qualified lead. mFilterIt’s event pixel does the same and provides you tons of data points and KPIs to compare and make insightful decisions from the incoming traffic’s data. Monitor Behavior Patterns: By tracking behavior, you can gain valuable insights on the leads, being which have most engaged with your business and filled by a genuine user. For instance, a user who visits your pricing page and spends time reading your product details is probably more likely to convert than someone who merely clicks through to homepage. Refine Your Lead Generation Efforts: Once you validate leads through pixel tracking, you can optimize your ad campaigns, tailor your content, and refine your targeting to focus on high-quality converting traffic and can weed out the incoming bots traffic whose goal is to simply drain out your campaign budgets. This helps you make better decisions on where to allocate your resources and ultimately boost your conversion rates. Validating leads through mFilterIt’s  Ad Fraud Solution, Visit and Conversion pixels is a smart way to ensure you’re focusing on the right prospects, increasing your chances of engaging genuine user with your ads.

Bots make up about a third of all web traffic, and shockingly, 65% of those bots are classified as “bad bots”. These malicious bots indiscriminately destroy marketing campaigns by inflating impressions and clicks; thus, they financially devastate marketing campaigns. So, ironically, although companies do not intend to, they spend a percentage of their ad budget on fraudulent traffic, making every campaign less effective than it could have been. Ad fraud is rising; therefore, the detection of invalid traffic across all digital campaigns has become necessary for businesses’ success in the digital space.    There is a need for a full-funnel ad fraud detection tool to provide omnichannel protection against general and sophisticated invalid traffic i.e. GIVT & SIVT. An additional brand safety layer of protection helps to boost campaign performance. To guarantee the success of digital marketing efforts, the traffic validation tools should provide coverage across app, web, OTT, and CTV ecosystems.   Let’s understand how bot detection works, why it is so important in marketing campaigns, the technologies involved, and how we help organizations overcome the risks of ad fraud in the bot-driven world and enhance marketing analytics accuracy.   What Is Bot Detection?   Bot detection is the technology and methods used to identify and prevent non-human traffic—specifically bad bots—from serving interaction with a website and other digital destinations.   From a digital marketing perspective, “good bots” (search engine crawlers, etc.) assist in increasing visibility for search engines or in monitoring the performance of a site, bad bots are designed to imitate human behavior, falsify clicks, impressions, and other down-the-funnel engagements. Bots skew digital campaign efficiency for businesses if left unchecked since they adversely affect customer acquisition costs and artificially inflate performance reports. Why Is Bot Detection Important for Successful Marketing Campaigns?  Here’s why tracking and detecting bots in marketing campaigns are important-  Protection of Ad Spend  Bots generate fake clicks, impressions, and visits which means businesses are paying for fake user engagements instead of real ones. Often leads are also filled up by sophisticated bots bypassing OTPs and captchas. A huge portion of the budget spent on advertising goes to waste without proper detection of ad fraud.  Real / Effective Analytics  Bots taint the quality of the data collected from digital campaigns and yield wrong interpretations. In turn, this may lead to wrong business decision-making as well as inefficient use of resources. Businesses cannot rely on their marketing data unless the bots are detected and blacklisted.  Higher ROI  Based on the bot-cleansed, accurate metrics, businesses that deploy full-funnel ad traffic validation tools understand how their marketing campaigns are doing. This leads to a channel-wise analysis and therefore can operationally direct more budgets to the most effective channels, which means a higher ROI.  Brand Safety  Bad bots are often placed by publishers on content that is highly questionable and brand-unsafe. Good fraud detection tools not only end up finding sophisticated invalid traffic but also provide a layer of brand safety from bad inventory and unsafe ad placements.   How Do Bots Get Detected?  There are many advanced techniques and technologies used in bot detection to identify fraud and block such malicious activities-  Device Fingerprinting  Device fingerprinting helps track and identify unique devices across the internet. Each time a particular device accesses a website or clicks on an advertisement, a unique “fingerprint” can be created with various signals and device attributes. This technique is driven by AI & ML algorithms to analyze data and identify anomalies that pinpoint bot traffic in real time. Sophisticated bots are largely identified at down-the-funnel (i.e. clicks, visits, leads, or sales) metrics. The General IVTs are identified at the pre-bid impression stage.  Behavioral Analysis  Human users do not exhibit predictable patterns, but the behaviors are predictable in the case of bots. Behavioral analysis tracks user interactions to detect unusual activities. One of the most common types is mouse movement. Bots tend to have straight-line patterns vis-a-vis a random pattern in the case of real humans. For any traffic validation tool, behavioral checks should feed into the overall ad fraud checks.   Heuristic Checks  Heuristic checks are a technique to detect bad bots by analyzing the known patterns of fraud on the web. This may include checking for indications that correspond with previously established models of fraudulent activities. Heuristic checks often include monitoring for unusual user-agent strings, changing IP addresses, or clicking patterns. Bot detection tools need to continually upgrade their techniques for detecting bots by keeping heuristics models current and updated to adequately monitor new tactics of fraud.  Deterministic Checks  Deterministic-based bot detection manifests itself by applying an actual, predefined set of rules in detecting bots. These include data on hard data such as the IP address, session cookies, and other metadata about a user. For example, if the known bad bot’s IP address is giving traffic to a website, the system can deny it on plain identification. Deterministic checks can filter out low-level bot activity quickly and are often used in conjunction with more advanced techniques, such as behavioral analysis.  How mFilterIt helped a Global Conglomerate in Bot Detection and Ad Traffic Validation  mFilterIt started working with the advertiser to deliver ad fraud detection solutions, which involve a multi-layered approach that can protect digital marketing campaigns from fraudulent traffic. Both branding and performance campaigns across walled gardens (like Google & Facebook), programmatic channels, and affiliates were validated by mFilterIt.   The bounce rates on their website were sky-high and YouTube VTRs & CTRs were under suspicion. The mFilterIt solution provided the following to the advertiser:    Full-Funnel Protection – Our technology protects your business on a Full-funnel level, ensuring bots can be detected regardless of whether they interact with display ads, video ads, or mobile app ads.   Real-Time Monitoring – This solution continually monitors traffic in real-time and detects bad bots before the event can affect performance metrics. It effectively filters out non-human traffic with a combination of behavioral analysis, device fingerprinting, and deterministic checks.  Comprehensive Reporting – We provide complete reports on bot traffic

Misleading ads on popular messaging platforms like WhatsApp and Telegram have opened the door for fraud to swiftly take place. The infringement and impersonation of trusted brands lure people into believing in the misinformation.  These misleading ads often generate high traffic, but the conversion is not a long-term prospect as all clicks and app installation are there in the lure of getting incent rewards. These types of frauds are prevalent on messaging platforms like Telegram and WhatsApp due to their broad reach and ease of communication.  Misleading ads or offers generate incentivized traffic  Affiliates promote offers in Telegram groups or WhatsApp chats and offer users small rewards such as cashback, points, gift cards, etc. for completing tasks such as clicks, app installs, or sign-ups. Users are incentivized by the affiliate to complete the tasks not out of genuine interest but to receive the reward. This leads to a high volume of low-quality traffic, installs, or sign-ups that do not translate into actual engagement or revenue for the advertiser.  Such ads or offers directly impact the advertisers, leading to financial losses and reputation damage. Advertisers pay for traffic that does not convert into meaningful user engagement or sales. It also results in Market Distortion as low-quality traffic distorts performance metrics and analytics. It becomes challenging for advertisers to assess the true effectiveness of their campaigns and make informed decisions. Brands associated with fraudulent activities can suffer reputational damage. When users do not get incentives as promised in misleading or fake ads, they lose trust in the brand whose name is being used in such ads.   Enhance tracking and monitoring with advanced AI-ML powered tools to identify too good to be true click to install or install to a registration issue. Down the funnel event quality degrades with low-quality users coming from misleading ads. Case of Misleading Offers or Ads   The misleading ads include offers, incentives, investments lure, or links to install apps. For instance, let’s consider a scenario where a misleading ad offers to win a jackpot-sharing app with the circle to win ₹1000. But when you or the people install the ad it does not get a reward.   Sample Misleading Ad  Such scenarios can erode trust and damage a brand’s credibility, even if the brand isn’t directly involved. Users, lured by ads promising rewards or discounts, often generate high traffic and numerous installs. However, when these promises aren’t fulfilled, users feel betrayed and uninstall or stop using the app. This leaves the brand with no benefits and a pool of low-quality users  In hard KPI events, the quality of users varies significantly when comparing organic or walled garden sources to affiliates promoting misleading ads. Affiliates tend to drive more low-ticket value events, often compromising user quality.   For instance, in arbitrage cases, publishers pay users an amount less than what they earn from the brand. 40% of events from affiliates had ticket values between ₹50 – ₹100, while in the case of Organic and Walled Gardens, only 2% of events with such low-ticket values occurred. However, for ticket values above ₹300, the percentage of contribution from affiliates drops drastically to 1%, with organic sources contributing 23% and walled gardens 29%. This clearly shows that higher-quality users are more prevalent in organic and walled garden sources compared to affiliates.  The average order value for these cases is lesser as compared to other sources.  How to Combat Misleading Ads  Combating misleading ads by affiliates on Telegram and WhatsApp requires a multi-faceted approach involving monitoring, reporting, and educating both affiliates and consumers.    Monitoring and Detection with Automated Tools – Deploy AI, ML, and OSINT-powered tools to automatically scan and detect misleading ads based on keywords, phrases, and image recognition. Utilize data analytics to gain insights into affiliate performance and identify patterns of misleading behavior. Combine technological solutions with strict monitoring.  Regular Audits are Must – Conduct periodic audits of affiliates to ensure compliance with your advertising policies. Implement a rigorous vetting process for new affiliates to ensure they adhere to your advertising standards.   Reporting and Enforcement – Establish clear Reporting Channels to report misleading ads on social media channels, messaging platforms, etc. Develop a protocol for the immediate takedown of misleading ads once detected or reported.   Penalties for Affiliates – Enforce strict penalties for affiliates who repeatedly share misleading ads, including suspension or termination of their affiliate status. Ensure traceability and accountability in affiliate marketing. This can help in tracking the source of misleading ads and enforce compliance with advertising standards.   Set Clear Guidelines – Provide clear and comprehensive advertising guidelines to affiliates, emphasizing the importance of honesty and transparency. Stay informed about the latest regulations regarding online advertising and ensure your policies are compliant. Be prepared to take legal action against affiliates who engage in deceptive practices.  Conclusion  Misleading Ads or Offers on various platforms have major repercussions on brand reputation along with the final losses. Brands can even end up being penalized for such activities that tarnish brand image. Monitoring across platforms including messaging apps such as Telegram and WhatsApp can help proactively identify such activities before, leading to trust erosion and customer end up losing faith. mFilterIt Affiliate Monitoring Solution leverages advanced AI, ML, and OSINT Tech to detect misleading ads more effectively with prompt reporting to safeguard brand repute. This ensures you get sustainable engagement.   Get in touch to learn more about the Misleading Offers on WhatsApp and Telegram.