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Is your CTV Ads Budget ROI Optimized with Ad Fraud Detection?

Blog, Ad Traffic Validation / mFilterIt Experts

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

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Ad Fraud in Programmatic Ads: Why Impression-Level Protection Matters

Blog, Programmatic / mFilterIt Experts

Performance Programmatic Platforms follow the same rules as traditional programmatic platforms, but the focus on performance campaigns is to optimize ad placements through real-time bidding and enable data-driven decision-making. These platforms buy and place ads for advertisers with targeting functionalities, dynamic creative optimization, and performance analysis. As they market themselves, the entire process is ML and algorithm-driven. However, there is a prevailing myth that these platforms are fraud-free. Even MMPs are failing to do anything to curb these frauds because they have no protection at the impression level and minimum protection at clicks. Let’s delve deeper to burst the myth and highlight what advertisers need to do to optimize campaigns on performance programmatic platforms. The Myth of Fraud-Free Performance Programmatic Platforms With the sophisticated technology behind performance programmatic advertising, these platforms are propagated to be fraud-free. This misconception arises from the belief that advanced algorithms and data-driven strategies can fully protect against fraudulent activities. However, fraudulent activities such as click fraud, impression fraud, and fake installs continue to plague the programmatic advertising ecosystem. Fraudsters constantly evolve their tactics to exploit system vulnerabilities. Many programmatic platforms claim to have robust ad fraud detection solution mechanisms, but these measures often fall short in practice. That’s where a third-party independent validator is needed. The sheer volume of transactions and the complexity create numerous opportunities for invalid traffic to slip through the cracks. Type of Fraud on Performance Programmatic Platforms Impression Fraud: Impression injection artificially inflates the number of ad impressions, executed by generating fake impressions in the background, later if the user downloads and installs an application organically or inorganically, the attribution gets stolen. This technique manipulates the ad delivery system to make it appear as though ads are being viewed more frequently than they are. Click Fraud: Fraudsters generate fake ad requests that make it appear as though legitimate users are viewing ads. These can be triggered by bots or automated scripts. Imperceptible Window: Ads may be loaded in ways that are invisible to the user, such as in a background process or in a 1×1 pixel iframe, ensuring they are not seen by actual humans. Ad Stacking: Sometimes multiple ads are stacked on top of each other, but only one is visible. Each ad in the stack registers an impression, leading to inflated impression counts. Fraudsters also exploit software development kits (SDKs) within legitimate apps to load ads in the background without the user’s knowledge. Fake Devices: non-genuine or simulated devices to generate fraudulent ad activity or BOT-based impressions that are totally junk. Fraudsters use device emulators or simulators to mimic the behavior of multiple real devices. This allows them to generate fake traffic at a scale. Device Farms: collections of physical devices, often managed by automated systems, that repeatedly engage with ads to create the illusion of genuine user activity. Fraudsters also manipulate device identifiers, such as Android Device IDs, to create fake device profiles. IP Fraud: Fraudsters also use Invalid IP (use of VPNs) and get impressions from regions not targeted which results in high impressions but low ROI. Challenges in Optimizing Ad Campaigns Myth of No Ad Fraud: The first thing is busting the bubble and countering the myth that there is no fraud on impressions, programmatic-based partners, or programmatic performance. Once advertisers accept the fact then proactive measures for full-funnel protection can be put in place to tackle ad fraud at every level and elevate campaign performance. Brand Safety Issues: Next is combating brand safety issues with safe and relevant placement, protecting brand reputation. Frequency Cap Violations: The most common and often neglected issue is FCAP violations along with bots spamming impressions for payouts. Brands need to be vigilant and identify F-cap violations to make sure their ad reaches the broader and relevant audience and is not seen by similar sets multiple times to generate impressions leading to ad fatigue, not conversions. Down-the-funnel KPIs are ignored: Aggregators also mix traffic and sell it by the name of premium inventory. In case of fraud, the clean publisher gets a bad reputation. Since the organic is stolen, down the funnel, is going to be met. Limitations of Mobile Measurement Partners (MMPs) MMPs provide insights into user behavior, campaign effectiveness, and ROI. However, their ability to curb fraud is limited. MMPs typically focus on tracking clicks and conversions but have minimal protection against impression fraud. Impression fraud involves generating fake ad impressions to inflate metrics, which can mislead advertisers about the reach and effectiveness of their campaigns. While MMPs do have mechanisms to identify and filter out invalid clicks, these protections are often not comprehensive enough to detect all fraudulent clicks. Another key issue with MMPs is attribution challenges as fraudsters often use tactics like click injection and click spamming to manipulate attribution models. MMPs, despite their advanced analytics, can struggle to differentiate between legitimate user actions and fraudulent activities, leading to incorrect attribution and wasted ad spending. Let’s get a better understanding of the challenges and how mFilterIt helped resolve them Case 1: How A Popular Gaming Brand Optimizes Performance in USA – App Campaign For instance, Let’s take the case of a trusted and most popular gaming platform in the US. The app offers a variety of games across a range of categories, such as card games, casual games, etc. Fraud at Impression Level: On Android, the fraud at the impression level was as high as 33% among over 56 million impressions generated, while on the iOS platform, 14% fraudulent impressions were detected among 4.8 million impressions. The major chunk of fraudulent impression was due to impression Injection, and the rest came from Invalid IP addresses, or sophisticated invalid traffic (SIVT). Fraud At Click Level: Among million and above clicks 11% were fraudulent on the Android platform. 6.13% of clicks were generated via click Injection and 3.69% came from fake devices while on the iOS platform, among 98, 800 plus clicks, 6% were deemed fraudulent. The breakdown of these fraudulent clicks included 2.13% from Click Injection and

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Validating Leads via Pixel: A Smart Way to Ensure Quality

Blog, Ad Traffic Validation / mFilterIt Experts

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.

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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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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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