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Affiliate Fraud: Detect, Defend & Protect your Brand

Blog, Brand Safety & Suitability / mFilterIt Experts

Affiliate marketing has emerged as one of the most viable sales promotion strategies that brands employ today. However, the rapid rise in affiliate marketing fraud has become a significant concern, as businesses can lose hundreds of billions every year with fraudulent activities. Affiliate marketing campaign are not excluded from these schemes, and mFilterIt reports indicate that affiliate fraud may result in the potential loss of up to 30-35%. For most businesses, affiliate advertising fraud wastes promotional, and marketing spends, which would be recovered with smart monitoring tools. What is Affiliate Fraud? Affiliate ad fraud refers to the fraudulent acts of malicious affiliates that exploit the system to wrongly earn commissions. They go through marketing procedures in the wrong manner and are making false conversions, clicks, or leads, which inflates the marketing metrics and ad spends for a brand without giving a good ROI on the campaign. According to Influencer Marketing Hub, affiliate marketing is expected to grow at a CAGR of 10.1%. Hence, the need for efficient tools to detect and prevent ad fraud is more important to optimize spending, minimize losses, and protect brand integrity. Why is affiliate fraud so serious? It is a very significant reason to distinguish why affiliate fraud should not be taken lightly because it usually leads to severe financial losses and damage to the reputation of the brands. The issues range from ad fraud, both general and sophisticated, to matters such as brand infringements and fake brand communication by affiliates, in which the unethical affiliates misuse the brand resources for personal gain. Ad Fraud Impact Affiliate ad fraud undermines ad effectiveness because it artificially inflates conversion metrics. Ad fraud issues like lead punching, ad stacking where several ads are layered to get impressions, and fake clicks impact brands. Over time, these acts prove counterproductive to marketing efficiency, present misleading data, and tremendous brand loss. In an environment where ad budgets are significantly constricted, failure to focus on these types of fraud will be detrimental to the long-term success of the campaign. Concerns with Brand Infringements with Affiliates Affiliate infringements are far more prevalent than traditional ad fraud. That is a set of practices that are unethical and could damage the reputation of a brand. This includes a practice known as brand bidding, in which the trademark of a brand is used to drive traffic to their sites, or the use of influencer coupon codes to inflate sales metrics artificially. Very often, affiliates would also make IP violations such as typo-squatting, a method that uses small misspell of the brand’s URL to route traffic to harmful websites. Such violations not only compromise the overall effectiveness of the affiliate campaign but also erode the trust created between the brand and its legitimate affiliates. Types of Affiliate Ad Campaigns Brand must Track for Full-Funnel Protection There are different types of affiliate fraud contingent upon the commission structure of the campaign, and they may be classified as follows: Cost-Per-Click (CPC) In this, for every click on an ad, the affiliate would make money. It is prone to click fraud wherein bots and click farms inflate the number of clicks but engage no real customer. Cost-Per-Leads (CPL) The affiliates are paid according to customer actions such as the form submissions and signing up on emails. In this aspect, the most common tactic fraudsters would use is fake leads or invalid customer actions. Cost-Per-Sale (CPS) Affiliates get paid according to commissions on sales that are completed. Manipulation with this model typically happens through the pushing of fake transactions or exploiting return policies for gaining commissions from sales later reversed. Common Issues in Ad Fraud Of course, ad fraud manifests in many ways, and the most common categories included are listed below: Lead Punching- Bad or low-quality leads are generated to earn a commission. These convert into no sales, and hence marketing dollars go to waste. Organic Poaching- They steal the organic traffic and dupe tracking systems to earn commissions for sales they never help make. Ad Stacking- Here, multiple ads are overlapped on top of one another in unnoticeable ways in which impressions are created that seem to be real but do not engage the user. IP & Proxy – There are certain techniques that are used in affiliate marketing fraud and these fraudulent schemes are carried out by creating malicious locations using IP spoofing and proxy servers to generate fake traffic and clicks that cannot be traced. Imperceptible Window – Techniques used include showing advertisements through small, transparent or overlay ads that are not seen by the user hence resulting in fake impressions or fake clicks without the knowledge of the user. Click Fraud – It occurs when a company’s automated bot or dishonest user simply repeats online advertisements or incurs damages to the clicked ad in order to exhaust most of the company’s resources. Brand Infringement Issues for Affiliates Affiliates’ infringement includes unauthorized uses that compromise a brand’s intellectual property or marketing efforts. Affiliate infringements include the following, though not limited to: Brand Bidding- Affiliates use a brand’s keywords or trademarks while competing with that brand. This type of practice has increased costs to a brand and easily confuses consumers. Duplicate Products- Sometimes, affiliates make duplicate products that dilute brand presence, present various prices at these sub-sites while making others unavailable and bring discrepancies in pricing. Misuse of Influencer Coupon Codes- Some affiliates distribute the influencer coupon codes to people beyond the intended reach. Doing this implies that the sales metrics inflated do not represent genuine consumer intent behind it. Violations with IP and Typo-Squatting- Affiliates open up fake websites that resemble the domain of the brand closely and capture the misdirected traffic thus misleading consumers. Brand Information Misrepresentation- Affiliates often give false information about a brand’s products or services that smear the brand’s reputation and result in revenue loss. How mFilterIt protected a Global FinTech Brand from Affiliate Fraud A leading fintech player collaborated with mFilterIt to fight affiliate fraud on a global scale. mFilterIt affiliate

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how-to-save-ad-budget with-bot -detection

How Bot Detection Enhances Marketing Campaign Accuracy and Safeguards Ad Spends

Blog, Ad Traffic Validation / mFilterIt Experts

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

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Misleading Ads and Offers on WhatsApp and Telegram

Blog, Ad Traffic Validation / Jagmeet Singh

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.

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The Menace of Mule Accounts and Fake Gateways offering ‘Money Mule as service’

Blog, Brand Safety & Protection / Dhiraj Gupta

It’s time to peel back the curtain on Mule accounts, drawing off Indian monies to offshore fraudsters. But why are they a big deal? ‘Mule’ better known as ‘Money Mule’ are regular bank accounts, used for collecting payment or to hide the money trail by fraudsters. The BFSI ecosystem is stepping up the security measures, running awareness campaigns but accurately identifying these accounts remains a challenge for banks. Fake gateways offering ‘Mule as service’ have heated up the situation. Prevent the leakage before it breaks down the floodgates drowning the reputation. Let’s explore the threat they pose and how Mule Account monitoring can help. Combatting the Threat of Mule Accounts Mule accounts are bank accounts used to transfer illegal funds, often linked to money laundering schemes, illegal betting gambling sites, Forex trading sites etc. converted to crypto and taken offshore. They pose a significant risk, slipping through the cracks of traditional monitoring systems and enabling cybercriminals to exploit financial operations. Use of Mule accounts is violation of Indian tax laws and GST fraud. For instance, betting sites use mule accounts to take bets associated with Indian payment instruments, bank accounts, UPIs, and mobile wallets. This leads to massive tax evasion as this money gets transferred to offshore accounts from these mule accounts of individuals or companies. As per regulations of the National Payments Corporation of India (NPCI) and Reserve Bank of India (RBI), the Indian payment systems should not be used for blacklisted or banned website categories like gambling, pornographic sites, Forex trading sites, Chinese laundering/loan apps or on other restricted sites. ‘Mule’ as service offered by Fake Payment Gateways Another pressing issue with mule account generating high concerns is Fake Payment Gateways offering mule as service or offshore websites. Yes, Shocking right? These gateways provide such mule, manage them and rotate them frequently to avoid any spike in transaction to come in notice of banks. “Mule as a Service” (MaaS) involves using fake payment gateways in various scams, such as e-commerce, task scams, loan scams, gaming apps, pig butchering scams, and betting sites. For instance, Fake e-commerce sites trick buyers into paying for non-existent products, while task scams lure individuals with promises of high returns for simple jobs. Loan scams often require upfront fees paid through these fake gateways. In gaming and betting scams, in-app purchases and bets are redirected through fraudulent channels. These gateways create and manage a network of mule accounts using link sellers for resetting free invite, sell invites to agents, mule groups that source money mule and rotate them, making the money trail hard to trace. Ways to Identify Mule Accounts Money Mules are a major brand reputation threat to financial institutions as they often use reputed bank accounts, either P2P accounts with a lower threshold or Merchant accounts for high-amount transactions. But there’s a way to turn the tide and protect from these hidden threats. Transaction Monitoring using Bank Data: This method is used by banks to monitor transaction data patterns that highlight any suspected transaction. As fraudsters use hundreds of accounts and keep rotating then it gets to detect patterns in transactions. A prediction score is given to probable mule accounts. This solution requires complex data modelling, transaction analysis and training, which may not always be possible. It also requires a long setup and investment over time. Also with Mule-as-a-Service, since transactions are rotated across hundreds of accounts, this can easily be bypassed. Simply for fraudsters to make small tweaks to their rotation can make them evade detection easily. Detection from Mule Source: This method identifies websites that use mule accounts and detects the mule accounts by DIRECTLY going to those websites and tracking all mules being present there. This method helps identify guaranteed mule accounts without using any bank data and is quick to scale. Since this method goes DIRECTLY to websites where mules are present, it doesnt need complex integrations, doesnt need model training and evading detection is not possible. While both options have their pros and cons, “Detection from Mule Source” is the game-changer in this fight. Detecting mule accounts from source websites such as gambling and betting sites identifies accurate finding with guaranteed mule. At scale. Detection at source also provides proof point to validate findings, help in taking swift action. This data also can be used to train AI-ML based transaction monitoring system to identify mule accounts more accurately at a later stage. A public sector bank consolidated and strengthened its rule engine system to more accurately identify the mule accounts by taking data from “Detection from Mule Source” and feeding to its model training. mFilterIt mule account monitoring tool leveraging advanced algorithms and tech for swift identification of guaranteed mule accounts. No data is required from banks, we detected mules used by the source itself. Identification of suspicious websites using mule accounts with proof points that guarantee mule accounts. This proactive approach ensures banks stay one step ahead of fraudsters, safeguarding financial integrity, but can also take swift action with proof point and strengthening their data model at scale. Currently mFilterIt is monitoring almost 25,000 websites EVERY HOUR at scale by automated browsing and access. This ensures that even payment gateways offering Mule-as-a-Service are getting caught in large volumes, and their ENTIRE load of mule accounts get blocked IMMEDIATELY! Proactive Action on Mule Accounts We detect 18 to 20 thousand mule cases every single day for Banks. These mule accounts are usually owned by regular people, who are either tricked into opening these accounts or knowingly use these accounts at the behest of some monetary incentives. We are also working in collaboration with national banks regulatory bodies, payment apps, and other major platforms in India to spot and safeguard from Mule Accounts frauds. Our role is to track these platforms leveraging OSINT (Open-source Intelligence) and our transaction laundering detection solution as we crawl across a pool of more than 25000 sites for fraudulent activities for various stakeholders. Eg Casino and gambling sites, betting sites, pornographic sites, FOREX

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Content Analyzer – The Game Changer Every E-commerce Brand Needs

Blog, Digital Shelf Analytics / mFilterIt Experts

E-commerce is fueled by customer engagement and purchasing decisions that revolve around content such as product descriptions, images, customer reviews, and even FAQs-composing every piece of it into a buyer’s journey. In this stream of data from a digital platform, how will e-commerce businesses know if their content is doing its job correctly? Here, the Content Analyzer becomes the tool to track, evaluate, and optimize the content across digital platforms. A Content Analyzer ensures that the content is attractive and converts into buyers. It’s a performance measure for knowing which parts of the content work, which part needs improvement, and what is going well with the target audience. For optimizing performance on digital commerce platforms, strong customer interaction and purchase intent, content tracking and analysis are much more significant. Most of them do not realize the importance of e-commerce content marketing until it happens too late: little engagement, lost sales, inefficiency- the list goes on. Metrics of Content Analyzer which brands must track Analyze content based on perfect page scores and unique product code counts. It provides insights into own vs competition with the image analysis which tells various parameters like pixel threshold, DPI threshold, white background, etc. It gives insights about brand-wise organic discoverability shared on a monthly and weekly basis. It provides perfect page analysis with the overall score, content, title, and review score. It provides recommendations for PDP for its brands. There are parameters like traffic keywords used by competition but not own, high traffic keywords used by none, and poorly Q&A content themes. It provides a word cloud based on the title and description. Downsides of Not Monitoring and Analyzing Contents Missing Chances to Optimize Without tracking and content analysis, organizations do not get key insights. Perhaps this means that the content is non-performing, but there is no data-driven feedback to let companies know what needs change. Keywords, metadata, product descriptions, and visuals may not be driving organic traffic or converting site visitors into buyers. Customers have inconsistent experiences E-commerce platforms thrive in seamless, cohesive customer experiences. Without appropriate analysis, your product descriptions are inconsistent, prices are wrong, or images are misaligned, creating confusion. A user who sees many different pieces of information on separate product pages becomes frustrated and leaves the site. No Alignment with Content Strategy Most companies operate based on intuition and are not data-driven when it comes to strategy. Lacking the analysis of content performance, brands cannot align their content with consumer intent or industry trends. Marketing campaigns can prove ineffective when resources are being wasted on the wrong content that does not represent the brand’s objectives or the expectations of the consumer. For e-commerce, trends change rapidly, so not analyzing the content means lagging behind competitors. Unable to Identify Spam or Malicious Contents User-generated content, such as reviews and ratings, influence buyer behavior in e-commerce. The absence of analysis of this kind of data therefore opens the way to spam content or possibly malicious comments. It can therefore damage the reputation of a brand and mislead its customers, which leads to foregone sales. Significance of Content Tracking and Analysis Enhanced Customer Experience Content analyzers help brands grasp how customers interact with their website; which particular kind of content is preferred by customers; and where improvement is needed. Such in-depth analysis helps businesses personalize by channeling the content as per the interest of customers. Increased Conversion Rates Analyzing content helps businesses get a precise idea of what types of content produce conversion. It might be in the style of a product description, an image, or even a tone in customer reviews that influences buying behavior. Content analyzers give the information a business needs to drive improvement in every phase of the buyer journey to ensure they’re serving the right content, at the right time, to maximize conversion. Better Amazon Rankings and Visibility Uninteresting pages are pointed out by content analytics, which act as weaknesses in the ranking perspective. With keyword monitoring, metadata, and content engagement, businesses will be able to optimize their strategy to come out with higher rankings in the Amazon rankings. Decision-Making Using Data The most important advantage of content analysis is helping to make data-driven decisions. It offers in-depth insights about what works and what does not, hence allowing a business to pivot its strategies and put resources in the right places. E-commerce firms are no longer required to make guesses about what can be corrected with content. Improved ROI on Content Investments E-commerce companies are investing heavily in content generation – from professional product photos to comprehensive blogs, videos, and posts on social media. What matters most, however, is not the generation of this content, but rather monitoring and measuring it – so that such investments can produce the maximum return possible. Case Study For instance, one of the largest multinational food, snack, and beverage corporations faced the challenge of optimizing Product Page Content. The brand needed a comprehensive integrated solution to monitor product page cont. Our ecommerce competitive analysis conducted image analysis and keyword analysis for our brand vs competition across platforms and geographies to identify gaps in PDP content. Fig 1: Image Analysis The mFilterIt content analysis checks primary content and secondary content on product pages. The primary content analysis checks the product page on various parameters which include title length as per defined threshold by the brand, brand mention in title product description requirements, analysis of images used, and compliance with platform-specific guidelines. It also checks the presence of the primary image and ASP presence. For secondary parameters, it monitors additional information if applicable on the platform like supporting images, mention of the brand in the description, ingredients, nutrient facts, or how to use as part of the description. The content analyses provide insights and measure product content in terms of score vis-a-vis competition. Quarterly Perfect Page Analysis (Category – Beverage) mFilterIt Impact: The ratio of ‘traffic to page’ to ‘add to cart’ has gone up by 25%. It helped

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Click Tracker: Measurement and Validation of Clicks to prevent ad fraud and click-fraud prevention

Blog, Ad Fraud / mFilterIt Experts

Ensuring the legitimacy of clicks is a key performance measurement metric for marketers and businesses. Verification backed with accurate measures leads to optimized performance. Click Tracking ensures all invalid traffic is identified before it drains the ad budget and skews ad campaign performance data. Let’s delve deeper to understand the need for click tracking and how it can help in campaign optimization. Click Measurement and Validation Consider a scenario where an ad gets a massive number of clicks, generating a good number of impressions but ending up with a low conversion rate. This is a clear waste of spending. Advertisers need to identify gaps across the funnel that start with first and foremost wider end with a flurry of clicks. What does the advertiser need? Advertisers need to optimize their ad campaigns with click fraud detection software, that can bring transparency to the ad campaigns. Weed out invalid traffic starting with clicks Invalid traffic from clicks on Google ads and Meta Ads need to be identified in real-time, ensuring no damage to your ad budget. Identify genuine clicks vs Bots clicks Genuine clicks usually show varied patterns, high engagement, and diverse geolocations. While Bot clicks exhibit rapid, consistent patterns, low engagement, and suspicious IP addresses. Identifying Bot patterns and accurate measurement is the key to protecting wastage of ad spend. Detect Fake clicks Driving genuine traffic led to getting conversions. Detecting fake clicks generated via click spamming in real time improves your quality of traffic and can get you more conversions. Click farms Are large-scale dedicated click fraud operations generating invalid clicks? Are you able to track that? Usually, click farms are hired to inflate the click volume on ads leading to inflated and skewed metrics. Accuracy of Measurement can prevent hefty payout on invalid clicks and bring transparency into the ad campaign across app, web, or programmatic advertising platforms. How can Click Tracker help advertisers? The size of the online click campaign market is growing. This is evidenced by the Google Ad revenue (US) to the tune of $237 billion (2023) and Meta’s ad revenue stands at $131billion (2023). This has grown 6% and 16% respectively in the last year. With the size of the click market growing, it is estimated that digital advertising fraud costs will increase from $88 billion to $172 billion within the next five years. That figure will grow 14% annually and nearly double, as per a Statista Report. This impacts all advertisers’ spending on clicks across platforms as click fraud is a multifaced threat that can take many forms, from sophisticated bots and malicious software to organized human operations like click farms. Advertisers need to understand the need for accuracy and transparency in click measures and validation to protect their investments and ensure that their marketing efforts reach genuine and interested audiences. Ensure Accuracy in Data Collection with real-time tracking and detailed analysis Real-time data on user interactions ensures that marketers have up-to-date information on campaign performance, while analysis and insights into click-through rates (CTR), user behavior, and engagement metrics, help in the precise measurement of campaign success. Fraud Prevention with Click Fraud Detection and Verification Identify and filter out invalid clicks, such as those from bots or repeated clicks from the same user, thereby reducing click fraud. along with verifying source to ensure clicks are coming from targeted geographies, the authenticity of traffic sources is validated ensuring that clicks are coming from legitimate users. Enhanced Campaign Performance with Click to Conversion tracking Monitoring and validation are needed across the funnel. By tracking which clicks lead to conversions, marketers can optimize their strategies for better ROI. Full-funnel protection with ad traffic validation and ad fraud detection can enhance campaign performance. Clear and concise information on campaign performance, making it easier for stakeholders to understand results. Using third-party click trackers such as mFilterIt Valid8, adds an additional layer of trust. Customizable and Tailored Metrics for efficient measurement: Trackers can be customized to measure specific metrics relevant to your campaign goals. With advanced AI, Machine learning tech, and algorithms, campaign performance measurement and optimization. Integration with ad managers also helps automate and provide a holistic view of campaign performance. Final Thought mFilterIt, ad traffic validation, click tracker and ad fraud prevention tools can play a vital role in building trust and transparency in digital marketing by providing accurate data, preventing fraud, ensure click integrity, enhancing campaign performance, ensuring transparency in measurement, and offering customizable solutions. By leveraging mFilterIt Valid8 marketers can gain valuable insights, optimize their strategies, and foster trust with their audience and stakeholders. Get in touch to learn more about click tracker.

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Protect your Brand Reputation with Safety & Brand Infringement Suite

Blog, Brand Safety & Suitability / mFilterIt Experts

Consider your brand being placed alongside unsuitable content or falling victim to a fraudulent website that affects your image and reputation. Brand reputation is a fragile asset related to its online presence through ad placements across digital platforms. According to an advertising standards study, about 35% of Indian brand companies faced reputational damage due to misleading advertisements. Digital brand infringement, such as fake websites and social media handles, led to a loss of up to 25% in the market share of various well-known brands. The irrelevant ad placements and fake websites may quickly harm the reputation of the brand. The damage extends beyond consumer perception. It can lead to diminished sales, loss of customer trust and cost brands long-term financial loss. Concerns related to brand safety Brand safety risks develop when ads appear in circumstances that undermine a brand’s image or don’t align with the real values of the brand. Here is a breakdown of the common concerns related to losing brand reputation: Irrelevant and unsafe ad placement: Placing ads alongside irrelevant content results in the ineffectiveness of the ad campaign. Issues relating to ads being placed on harmful and unsafe content spoil brand image and do not bring any positive campaign metrics. Brand Infringement & Safety Issues: It involves risks such as brands visible alongside inappropriate irrelevant material such as hate speech, or violent abusive content. Issues come due to irrelevant placement of ads Branding campaign wastage: if ads reach the wrong audience diminishing the desired outcomes efforts and disconnect between the brand and the viewers. Maximum Ad skips: if the ads appear in irrelevant & unsuitable settings viewers skip them as it is not important to them, or they are more interested in the content Loss of true views when ads are not delivered to the right audience in appropriate context’ brands lose the chance of gaining genuine connections. Leading to wasted views and ineffective ad spending. Brand Unsafe placements could include the appearance of an ad alongside explicit content that violates the brand safety guidelines such as those of GARM. Various categories of ad placements cause major reputation damage to the brand. Unsafe Categories of ad Placements causing major reputation harm Illegal Drugs Tobacco E-cigarettes Vaping Alcohol Terrorism Online Piracy Adult and explicit content Arms and ammunition Spam or Harmful content Death injury or military conflict Crime & Harmful acts to individuals and society Debated sensitive social issues Hate speech and act of aggression Solution Most above-mentioned unsafe categories require an actionable approach that will ensure brands get protected on all fronts and mFilterIt solution carries a comprehensive way to deal with unsafe media advertisement placements. The key elements of the solution included robust region-specific Content Categories content context analysis, sentiment analysis, video frame recognition, and GARM compliance. It is done through not only keyword searches and meta tags in the description and title but also AI & ML-driven context analysis and sentiment analysis to ascertain relevancy. Furthermore, the frame recognition strategy involves analyzing individual frames of video content where the ads are placed rather than relying on the metadata only. And lastly the, enabling active prevention of brand protection with real-time blocking of brand-unsafe videos was undertaken. Let’s understand this with a case study. Case study A large FMCG brand encountered issues with video placements where heavy ad expenditure resulted due to brands content association with explicit content. To address this issue a brand safety solution was initiated by mFilterIt, and the following results were obtained. Realtime blocking helped in reduction of brand unsafe percentage Impact on business: After identifying the brand unsafe placements the solution started blocking them actively and brand unsafe % declined from 10.73% in Sep 2023 to 1.81% by April 2024, it also observed high ad performance on Brand safe videos as compared to explicit videos. Brand Infringement Brand Infringement occurs due to violation and unauthorized use of name, logo, trademark, patents or copyrights of the company or brand. TRA’s Brand Trust Report says that trust decline to negative brand association and brand infringements in sectors like FMCG and Pharms and top brands trusted dips of up to 20%. A wide range of brand infringement capabilities can be seen that hamper the reputation of the brand. Intellectual property Infringement Fake social media handles APK Copycat Apps Fake brand’s communication like emails ATO (Account Takeover) Logo Misuse Phishing websites Solution mFilterIt proprietary OSINT tech with the use of Open-Source Intelligence, scans the entire digital landscape for identifying possible infringements. AI-Powered automation by which the process of cleaning, tagging, and classification of enormous data gathered through OSINT. The 3-pillar approach solution can be applied to managing and addressing these brand infringement issues if faced by any brand or company. Identification of the list of official brand assets such as the official logo, social media handles, and instant messaging handles. Classification based on potential instances of the above-mentioned infringement capabilities Action included takedowns and blacklisting to clean and refurbish the lost trust in the brand. Let’s understand the whole approach with a successful case study of a construction company. A well-known real estate firm faced various challenges of brand infringement in the form of IPR infringement and fraud occurring on digital and social media platforms. It was challenging to track and monitor violations manually. mFilterIt deployed a solution to scan the multiple digital platforms for solving the potential infringements. The content gathered through OSINT like text videos and images was given a confident score produced by ML. Based on high and low confidence scores the classification is done for further action. The impressive impact can be seen on the real estate firm between Jan- Jun 2024: Cases were found around 1300 with 1001 successful removal of instances of illegal content. Across 7 platforms from where

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Affiliate Fraud Uncovered: Protecting Your Brand and Ad Spends

Blog, Brand Safety & Protection / mFilterIt Experts

The darker side of digital advertising is growing deeper and deeper: more and more attention is drawn to issues connected with unnecessary waste in ad spend, brand safety, as well as infringement concerns. It is estimated that 30% of affiliate marketing losses can be attributed to the phenomenon of affiliate fraud. According to this trend, the losses will amount to more than $1.4 billion in 2024, according to Statista Report. Such fraudulent activities by affiliates take the form of ad fraud and brand infringement which not only wastes marketing spend but also tarnishes the brand image. Affiliate fraud is the type of fraudulent activity that involves an affiliate’s manipulation of the performance of an affiliate marketing program to generate commissions without actually adding value to the advertiser. These losses also highlight the necessity of investment in technology that helps in affiliate monitoring and detecting ad fraud to spare the brands from harm and preserve the sanctity of the different models in affiliate marketing. Major Affiliate Fraud Concerns The two ways in which any brand gets affected by affiliate fraud is when their marketing spend is wasted and their brand image takes a hit. The major threats from which any brand should be protected is: Ad Fraud by affiliates running your Ads: Affiliates run bots on advertising campaigns and end up showing fake performance using bots. This results in achieving lower campaign metrics and greater loss of advertising spend. The ROI’s take a hit and the only one who gains is the publisher. Brand Safety and Infringement Issues: Affiliates and influencers also use brand and advertisers IP like logos, brand keywords and trademarks without their authorization. Moreover, fake brand communication results in the brand suffering major reputational loss. Affiliate Fraud in the Ads Context Ad Fraud refers to the illegal practice of falsehood of advertising metrics to generate illegal revenue. This can happen due to fake clicks, fake impressions, fraudulent apps and more. Fraudsters trick advertisers into paying for activities that never actually happened, draining their advertising budget. Let’s dive into knowing about types of affiliate fraud and understanding the solution to it Types of Ad Fraud Understanding the various forms that affiliate fraud takes is the starting point in building a countermeasure. These include some of the most common types of affiliate fraud: Click Fraud: The act of inflating the clicks on an advertisement artificially is known as Click Fraud. It is a scenario where an affiliate uses automated bots or a click farm to artificially inflate clicks, or even incentivizes real users to click on actual ads with no interest whatsoever in the material advertised. Lead Fraud: Lead fraud generates fake leads through fraudulent activities. The affiliates may use the methods of submitting fake information, using stolen data, or automation of lead generation through bots. Domain Spoofing: Fraudsters will masquerade as valid websites either for deceptions to the advertiser or to get affiliate commissions. They can use similar-looking domain names or replicate the content of an important site. Cookie stuffing: Cookie stuffing is the placement of multiple cookies on a user’s device without them knowing about it. When the sale is made, the affiliate collects his commission even though he had no justified position for the sale to be triggered. Solutions to Prevent Ad Fraud AI-ML Analysis: Such models demonstrate the capabilities of identifying bots and other swindles because of their comprehensive automation pattern and are therefore successfully assisting in easing cases of Affiliate marketing fraud. Device Fingerprinting: Device fingerprinting, or device signature techniques enable identifying the fraudulent devices that give multiple clicks coming from a single device. Specific attributes of the device, such as browser type, and other hardware configurations, aid in identifying suspicious devices that may be part of a device farm or employ VPNs. Deterministic, Behavioral and Heuristic Checks: The traffic validation tool should be enabled with deterministic, behavioral and heuristic checks. The pattern of the actions performed by the users is examined for any discrepancies. Monitoring Affiliates for Brand Infringement & Safety Monitoring affiliates for brand infringement & safety is crucial for maintaining a brand’s integrity and ensuring compliance with marketing guidelines. Affiliates can sometimes engage in practices that may harm a brand’s reputation or violate its policies. Types of Affiliate Infringements & Safety Issues Brand Bidding: Brand bidding is a scenario in which the affiliates are bidding on the branded keywords to attract users to their sites which means brand’s organic may end up being cannibalized. This also leads to increased costs in advertising and misrepresents affiliate performance. IP Violation & Typo-squatting: IP violation and typo-squatting is when affiliates register domain names, which nearly resemble the official web page of a brand. These domains give way for false redirections from users, thus allowing potential data compromise and brand impersonation. Misrepresentation of Brand Information: Affiliates sometimes mislead the branding by making false claims, over-expressing the benefits, or using unauthorized promotional content to attract users. Such misrepresentation might create confusion among customers as well as destroy the brand’s credibility. Misuse of Influencer Coupon Codes: As affiliate programs believe influencer marketing is an integral part, misuse of influencer coupon codes can take the form of an affiliate who publishes them on unauthorized platforms or channels by using those same coupon codes to commit fraudulent transactions. Misuse of Influencer Brand Creatives: Brands have creatives that influencers can promote with, but they may be mis utilized by editing them to make use otherwise than as designated or attaching misleading claims. Solutions to Prevent Affiliate Infringements & Safety Issues Automated driven Brand Asset Recognition: Affiliate monitoring and protection are provided in its full scope in the entire digital landscape with complete brand asset validation. Whenever there is such a necessity, clients incorporate advanced algorithms to search and to monitor physical and virtual properties of brands such as logo, trademarks, product image and the contents. AI driven Multichannel Monitoring: Works as an umbrella on a multitude of online platforms such as websites, social media, paid advertisements, coupon sites, mobile applicators in

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eCommerce Analytics: Strike First and Fast in the Battlefield of eCommerce

Blog, Digital Shelf Analytics / mFilterIt Experts

The rapid rise in eCommerce across geographies especially in countries like Indonesia, Thailand, and Vietnam is experiencing rapid growth, with mobile usage and social media penetration being the highest worldwide. Each country in the SEA region has varying levels of eCommerce penetration and the region’s potential is enormous. Customer behavior is very similar to that of China and Korea where K-pop idols create fashion trends. SEA has approximately 400 million internet users and platforms such as Shopee, Lazada, and TikTok are leading the way providing customers with an immersive, interactive buying experience that combines social interaction with eCommerce capacity. The e-commerce market is expected to grow by $234 billion by 2025, emphasizing the need to make data-driven decisions to navigate this battle swiftly. Every eCommerce participant requires real-time access to actionable business intelligence to meet success metrics. Let’s understand the common types of eCommerce across geographies and the role of eCommerce analytics in striking first and fast in the race. Common types of eCommerce Each of these types serves a particular market need and varies based on the product or service offered to the target audience. There are various common types of eCommerce such as: Quick Commerce – As the name itself suggests rapid delivery of goods often within minutes or a couple of hours. It focuses on near-instant delivery of groceries, and convenience items such as household necessities. Example – Blinkit, Zepto, and GoPuff are q-commerce platforms that offer quick delivery within (10-30 minutes). QSR Commerce– That is Quick Service restaurants serve rapid, efficient, with affordable price cuisines. Customers also can dine in, take out, or have delivery at their place. Example – McDonald’s serves millions of consumers daily as it is a global QSR business. According to Statista, the worldwide QSR industry is expected to reach $1.2 trillion by 2028. Social Commerce- It is an integration of the shopping experience into social media platforms like Facebook, Instagram, TikTok, and others. Social commerce across social media platforms is more effective as it’s not dependent on traffic coming from searches instead consumers are already engaged on the platforms. Marketplace Commerce – The purchase and sale of products via online marketplaces where various suppliers offer goods. The marketplace concept is gaining traction, especially in developing countries. Example– 3rd party merchants can list their products on platforms such as Amazon, eBay, and Flipkart providing buyers with a wide range of options. The FMCG category in marketplace commerce is expected to be the fastest growing with a total market of $375.1 billion by 2025. Are your products the top drawers? on the above-mentioned common commerce types? If not, then there is a great need for e-commerce analytics to be incorporated into your brand. Need for eCommerce Analysis for brands Visibility is an important component of eCommerce business and digital shelf analytics can help with that. Monitoring a brand’s digital shelf performance such as product availability, pricing, and search rankings allows marketers to ensure that their items are easily accessible and competitive consistently across several eCommerce platforms. Sales Growth Improved Visibility Increased conversion rates Enhanced Customer Experience Competitors Insights Metrics for Customer Journey Optimizations It is important to consider the various analytical metrics while forming a strategy to sell on various eCommerce platforms. Customers’ journey is optimized at broader three levels such as: 1.Awareness and interest – It helps brands to track visibility and ensures that the product is accurately featured throughout the marketplace boosting awareness and driving interest from potential customers. It consists Discoverability tool Competitor analysis banner analysis Content recommendation 2.Consideration & Evaluation –The system evaluates critical elements such as bestseller analysis to track the ranking of the products and the factors behind top sellers. Pricing and Discounting analysis Content Analyzer Customer feedback analysis gathers ratings and reviews, and Q&A data to assess customer sentiments. SKU health analysis scores based on content quality, availability discoverability. Delivery TAT 3.Decision / Purchase – Overseas digital shelf performance by sales analysis with scorecards at brand & category level boosts bestselling products and hero products of the brand. How eCommerce analytics helps businesses to grow – Case Analysis Let’s understand this with a case study, a leading FMCG brand was battling to ensure consistent product availability across key geographies. Challenges faced by the brand Their product presence fell behind competitors, resulting in missed sales opportunities and a reduced market position both online and offline. The brand’s principal problem was to ensure product availability in numerous regions. Recognizing the severity of the situation the brand relied on eCommerce analytics intelligence solution mScanIt for actional information. Solution Using the eCommerce analytics tool mScanIt through targeted inventory optimization, data-driven insights, and competitor benchmarking gained detailed product availability data from a variety of online sources. Determined which places their product was underperforming in. Also, identifying the availability against the rivals and areas for improvement in with real-time insights to change online and offline inventory levels ensuring that products are available in the appropriate locations. Outcome The brand was able to close the gap against the competitors and was able to overtake a competitor by increasing its availability by 29%. Fig 1.0 Analyzing the data on availability for the past year Aug 23 – Aug 24 The analytics and solution enabled the brand to not only improve its sales online but also offline too. The case study demonstrates the power of AI-ML data-driven decision–making in enhancing both online and offline retail performance. To sum up Switching to e-commerce analytics can help eliminate friction in the shopping process and make it easier for customers to complete their journey of purchase, and to edge ahead of their competitors across the digital commerce ecosystem. Business intelligence must be granular to the last detail and scalable across geography and platforms. Get in touch to learn more about E-commerce Analytics.

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The Hidden Cost of Digital Advertising: Understanding and Defeating Ad Fraud

Blog, Digital Advertising / mFilterIt Experts

Considering that ad fraud will cost marketers $ 172 billion by 2028, according to Statista Reports, tackling the fraud becomes crucial to safeguarding advertising investments. The digital advertising space is continuously in flux, opening new avenues through which businesses can reach their target audiences. These opportunities also bear the ever-growing threat of ad fraud as one of the biggest challenges facing advertisers today. Ad Fraud is omnipresent across Walled Gardens, direct buying platforms, affiliates, and programmatic channels. It brings a financial drain while weakening the efficiency of advertising campaigns. AI-driven post-bid solutions must be deployed across campaigns to tackle this problem. They should be backed by ML algorithms and big data analytics. According to mFilterIt reports, average fraud is to the tune of 15-25% depending on the channel and tools help bring ROI efficiencies via automated active blacklisting. What is Ad Fraud? Ad fraud is any intentional activity designed to falsify digital advertising impressions, clicks, conversions, or data in general for revenues or metrics manipulation. This might be through bots, fake websites, malicious code, or other devious means. The motive behind such an action most often aims at making money off ad revenue by showing invalid traffic for ads or making the advertiser think the ad is being viewed by real people, which causes ad spend waste and inaccurate performance metrics. To prevent this, it’s helpful to understand how ad fraud works, and the tactics fraudsters rely on. Types of Ad Fraud It can be further divided into two key classes – General Invalid Traffic and Sophisticated Invalid Traffic. Both pose various problems for advertisers. Sophisticated fraud is identified with the funnel approach with a post-bid analysis. General Invalid Traffic (GIVT) This category includes simpler forms of fraud in advertising, often captured by general filtering. Blacklisted IP – Fraudsters make use of IP addresses found as sources of invalid traffic. Ad impressions and clicks served to such blacklisted IP addresses are counted as forged impressions and clicks depleting ad budgets. Geo Fraud – Ads are delivered outside targeted geographical areas, which means money is wasted on irrelevant audiences. This happens mainly in campaigns with tight regional targeting. VPN and Data Center Hosting (DCH) – The traffic usually comes from VPNs or data centers to mask its origin as though it were real. It is a means of creating fake ad traffic that seems to originate from real users but is fraudulent. Sophisticated Invalid Traffic (SIVT) SIVT has some of the more advanced kinds of ad fraud that are trickier to spot and generally require more specialized anti-ad fraud solution. Fake Attribution – This means fraudsters fudge conversion data to take credit for conversions or actions they had no hand in. It might mean the redirecting of real users through fraudulent paths to lay claim to an affiliate or ad revenue. Fake Clicks – This form of click fraud involves creating an impression of organic clicks on advertisements through automated bots or scripts without the involvement of real users with intent. Fake Devices – The fraudster uses device emulators or scripts to fake the user’s behavior of viewing and interacting with an ad, thus creating unreal impressions and interactions. Duplicate Users – Duplicate Users are those ads reporting metrics for the same user on different devices and or platforms at different times in digital advertising and analytics. How Ad Fraud Works To effectively protect against ad fraud, you must understand how it works. Ad fraud isn’t committed against a perfect weak link in the digital advertising ecosystem, but rather fraudsters find ways to game the system to create invalid traffic and deliberately mislead advertisers. Common techniques include: Bot Traffic – A bot is an automated program masquerading as human traffic. These can result in simulated clicks, views, or impressions. Bots can be very sophisticated, emulating the usage patterns of real users in such a way that simple filters cannot catch them. Ad Stacking –This is a tactic in which multiple ads are layered on top of each other inside one ad placement, where all a user sees is the top ad. This means that the advertisers will be charged for all the ads as if they had been viewed completely, creating huge financial waste. Pixel Stuffing – Ads are delivered in a very minute 1×1 pixel, invisible to the web users while the impression is registered in records. This is deceptive and inflates the number of impressions, feigning that ads are viewed. Domain Spoofing – It is essentially ad fraud in which the domain of an app or website is misrepresented as that of a more trustworthy or premium one than that of their own. This is done to deceive advertisers into believing that they are purchasing ads on credible mediums, while the actual ads are being served on unreliable or fake mediums. Low-quality Inventory – They are ad spaces that offer almost nothing beneficial to an advertiser. Such ad inventory includes placements on sites that have very little traffic, little to no genuine activity, and are intended purely to generate ad impressions or clicks deceitfully. These are called Made-for-Ad sites (MFAs). Damaging Effects of Ad Fraud There are various adverse effects caused by ad fraud towards advertisers and, to an extent, the entire digital advertising ecosystem. Investment Loss The business world may lose over $100 billion to ad fraud in 2024 alone. That’s a big chunk of marketing money that could be spent on reaching out to the very real public to drive real results and it affects the ROI. Skewed Metrics Fraudulent activities result in bloated performance metrics, such as impressions, clicks, and conversions. Assessment of real effectiveness in campaigns and implementing changes in strategy thereby becomes hard. Lower Reach Spends on fraudulent ads and fake clicks mean lesser budgets to reach real and quality audiences. The overall reach and impact of ad campaigns are lower this way. Brand Safety Concerns It is important to protect the brand’s reputation by

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