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What Should Marketers Look for in a Bot Protection Tool?

Blog, Ad Traffic Validation / mFilterIt Experts

In today’s digital marketing landscape, bots impacting your campaigns, website analytics, and overall performance is becoming an enormous issue with all the organizations running any kind of advertising campaign. To put a stop on the ad fraud that is caused by bots, having bot detection tools is vital. After all, how can you put a stop to bots if you can’t detect. Unfortunately, many firms struggle to detect bot traffic, and the methods they employ are not all created equal. Choosing the right bot detection tool is essential for safeguarding your marketing efforts. Here’s a comprehensive guide to help you select the best tool for your needs. What Is Invalid Traffic and How Does It Relate to Bots? Bot fraud in digital advertising generally falls in the category of invalid traffic by the marketers no matter good or bad since the bots are not the target audience and cannot be converted into a protentional lead. Types of Invalid Traffic: General Invalid Traffic (GIVT) : It is one of the simplest bots that can be detected easily, and a lot of good bots traffic comes under GIVT as they are not meant to fool the bot detection tool. But some fraudsters may also deploy GIVT as they are easy to make and work against some of their targets. Sophisticated Invalid Traffic (SIVT) : SIVT detection is the bots that one should look out for as these are more capable and are often designed to target to bypass cybersecurity and ad fraud prevention tool. For Example – sophisticated bots might imitate how a human would use a website so it would be difficult to identify between a human and bot. SIVT is common in ad fraud schemes. How Bots Impact Your Marketing Campaigns Bots can have a negative impact on your digital marketing campaigns in several ways: Wasted ad spend: Bots can boost your impressions and clicks, resulting in ad spend burn. For example, if you bid on a brand term that costs $1 per click and a click bots on your ad 1000 times, you would have wasted $1000. Inaccurate reporting: These bots can alter your reporting data, making it nearly impossible to track the actual performance of your campaigns. For Example, if a bot is boosting your impressions by 50%, your CTR will appear to be much higher than it is. Damage to your brand: So, if your brands are revealed to internet bots, it might damage your brand reputation. For example, if a bot clicks on your ad for a product the customer is not interested in, the user will consider your ad spam. How to Detect Bots? Bots often use IP addresses that are associated with known botnets. Here some of the most common methods include IP address analysis: Bots often use IP addresses that are associated with known botnets so by analyzing the traffic by these IP addresses you can easily identify the bot traffic. Behavioral analysis: Bots frequently engage in unusual behavior, such as rapidly clicking on adverts or viewing several pages in a short amount of time. So, by examining the behavior of your traffic, you may detect bot traffic. Traditional Bot detection tools: They can only detect basic bot patterns and don’t have the capability to identify sophisticated bot patterns like click spamming or lead punching. Limitations in traditional bot detection tools However, there are some limitations of using traditional bot detection tools. Over time, bot patterns have become increasingly sophisticated, enabling them to mimic human behavior and carry out complex tasks such as completing sign-up forms or generating leads. Traditional bot detection tools often fall short in identifying these advanced bot activities due to their limited capabilities. Some key limitations include: Only Impression-level detection: Many tools rely on impression-level analysis, which may not be sufficient to identify advanced bots. This approach often overlooks nuanced behaviors that occur across multiple impressions or sessions. Limited to Pre-bid monitoring: Many fraud detectors focus on pre-bid detection because it is easier to conduct checks because they only need to consider two factors: geolocation and browser, and the success rate is only 2%. Key Features to Look for in a Bot Detection Tool Utilization of the Latest Technology: One of the key features to look out for in a bot detection tool is whether it is using the latest technology to detect ad fraud. When looking for a bot detection tool, evaluate their technology. Many traditional ad fraud solution depend on outdated methods like 1×1 pixel tracking, offering limited visibility. In contrast, to identify sophisticated bot patterns the bot detection must use the latest technology. We use cutting-edge technologies like VAST and JavaScript to assess over 70 parameters of bot traffic for effective identification. Deeper and Comprehensive Checks: The bot detection tool will be able to provide deeper insights with a post-bid analysis. With a comprehensive full-funnel check the success rates are boosted by 30%-40%. This approach is reliable and eliminates blind spots in detecting bot activity or ad fraud. Omnichannel Coverage: An advanced ad validation solution provides robust protection across multiple platforms, including programmatic advertising, Connected TV (CTV), Over-The-Top (OTT) platforms, and Made for Advertising (MFA) websites. This comprehensive coverage effectively addresses all potential avenues for fraudulent activities. Real-Time Insights: The platform detects and assesses fraudulent behavior in real-time, allowing advertisers to take proactive measures to minimize losses and protect their advertising budget. 100% Transparency: A critical feature of an advanced fraud detection tool is source-level transparency, this provision of detailed, source-level data enables advertisers to trace fraudulent activities back to their origins, ensuring accountability and enabling more targeted counter measures. How mFilterIt help to detect sophisticated bots? mFilterIt is known for its effective bot detection and ad fraud prevention capabilities. Ad fraud detection (Valid8) by mFilterIt offers real-time detection, granular insights, and comprehensive protection across the entire campaign lifecycle. By utilizing machine learning, behavioral, and heuristic checks, it ensures the detection of advanced-level bots leading to fraud-free campaigns, making it ideal for advertisers seeking

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Pixel Tracking Guide: How to Track Conversions Easily

Blog, Ad Traffic Validation / mFilterIt Experts

Tracking conversions is essential for measuring the effectiveness of your marketing efforts, and one of the most reliable ways to do this is through a conversion pixel. A conversion pixel is a small piece of code embedded in your website or landing pages, which tracks specific actions users take, like making a purchase or subscribing to a newsletter. This helps you evaluate your ads’ performance and refine future campaigns. How to track conversions using a pixel: 1. Install the Pixel: The first step is setting up a pixel on the platform you’re using, such as Meta Ads, Google Ads or mFilterIt Visit Pixel. These platforms typically provide easy-to-follow instructions for adding the pixel to your website. You’ll either add the pixel code directly to your site’s header or use a tag manager to simplify the process. 2. Define Your Conversion Goals: After adding the pixel, you need to define what actions you want to track as conversions. This could include activities like making a purchase, submitting a lead form, or completing a registration. Most platforms let you set up multiple conversion events to capture various types of user actions. 3. Test the Pixel: Once the pixel is installed, it’s important to test it to ensure it’s working correctly. Tools offered by platforms like Meta allow you to check if the pixel is firing as expected. If you find any problems, double-check your website’s code or try reinstalling the pixel. 4. Analyze and Optimize: When users complete the defined conversion actions, the pixel sends the data back to the platform, giving you valuable insights into your conversion rates. Use this information to see which ads or traffic sources are performing best and adjust your campaigns to optimize results. Tracking conversions with a pixel is a powerful way to evaluate return on investment (ROI), refine targeting strategies, and ultimately enhance the success of your marketing campaigns. Connect with us, to start your conversion tracking.

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Protection from Brand Bidding with AI and Automations for Brands and Ad Networks

Blog, Affiliate / mFilterIt Experts

Competitors or affiliates may bid on your brand keywords, potentially driving up costs and diluting your brand’s presence. A comprehensive affiliate monitoring tool needs to be in place to safeguard your affiliate marketing spend and protect your brand reputation. Identify affiliate and competition bidding on your brand keywords along with whitespaces across locations for own and competition keywords. Brand bidding fraud costs approx. $1.3 billion each year as competitors bid on branded keywords. Over 60% of brand terms are targeted by competitors, inflating cost-per-click (CPC) by up to 30% and reducing conversion rates by 5-10%. This not only drives up advertising costs but also dilutes brand recognition, as 50% of users may click on a competitor’s ad when searching for a brand name. This leads to confusion and lost customers. Businesses must actively monitor and optimize their paid search campaigns while implementing robust brand safety solutions to mitigate these risks. What is Brand Bidding? Brand bidding refers to a practice that falls under paid search marketing. It’s a digital marketing strategy where brands bid on their own brand keywords in search engine advertising platforms like Google Ads and when someone searches for your brand name, your company’s sponsored listing is more likely to appear at the top of the search results page. But this turns into a brand reputation threat and leads when aggressive affiliate bids, potentially aiming to capture traffic and drive sales through affiliate programs or competition bids on your brand keywords. Why is affiliate monitoring needed for Advertisers? Affiliate monitoring ensures the integrity of campaigns, protects brand reputation, and optimizes performance. Concerns in Brand Bidding: – Campaign Integrity: Monitoring affiliate activities ensures that campaigns are running as intended. It helps to identify if affiliates are engaging in practices like brand bidding, misleading advertising, or promoting the brand inappropriately. – Protecting Brand Reputation: When affiliates are bidding on brand-specific keywords (e.g., your company’s name or a competitor’s), it can confuse customers or misdirect them to other sites. Without monitoring, there’s a risk that your brand reputation could be damaged if affiliates don’t align with your brand values or make misleading claims. – Optimizing Performance: By closely tracking affiliate performance, advertisers can identify high-performing affiliates and cut out low-performing ones. This ensures that advertising spend is allocated effectively, improving overall return on investment (ROI). – Organic poaching: A major concern that brands should be careful about. It occurs when affiliates or competitors bid on a brand’s keywords, capturing traffic that would have naturally come to the brand’s website. This misappropriates organic leads and increases costs as the brand must pay commissions to affiliates for traffic that would have come organically. Ensures unauthorized affiliates are not bidding on brand keywords or misdirecting customers. Prevent unnecessary commissions and preserve your organic search efforts. Prevent affiliates from hijacking brand visibility, ensuring the brand stays front and center for potential customers without competitors benefiting from organic traffic. Stopping organic poaching ensures that their customers are consistently directed to the brand, which enhances trust and customer loyalty. Case Study 1: Brand Bidding for a Popular Shoe Brand Problem statement: Brand was facing challenges in ensuring effectiveness of keyword the brand bidding on across multiple geographies and wants to identify competitor bidding on their brand keyword. mFilterIt Solution: mFilterIt analysis and insights into the effectiveness of brand bidding, we conducted a comprehensive analysis of a well-known shoe brand across major Indian cities. We meticulously monitored 35 keywords related to the brand, including brand keyword variations across 35 cities. This allowed us to understand the search queries users were employing to find the brand. The brand garnered a significant search volume across time slots. This indicated strong interest in and demand for the brand. A substantial 50% of the total searches were Google Ads for the brand’s keywords. This demonstrated the competitive landscape and the efforts of various entities to capture search traffic. We identified 28% were competitors count which were actively bidding on the brand’s keywords. This highlighted the intense competition for visibility and market share. 22% of the total came from affiliates and coupon websites who were also bidding on the bidding keywords. This revealed that affiliates were also using brand keywords and running ads. mFilterIt Impact Here are some key observation and Findings: – Organic Poaching by aggressive Affiliate Bidding: Affiliates & coupon websites were particularly active in bidding on brand keywords, potentially aiming to capture traffic and drive sales. They were capturing organic users. This is called organic poaching. The brand was having to pay commissions to affiliates where the customer would have come organically. – Competition on Brand Keywords: Competitors were actively bidding on the brand’s keywords, which meant that competition keyword strategies needed to be built within the marketing approach. AI based Optimization in Brand Bidding with mFilterIt LOCOKS LOCOKS (Location & Campaign Optimization Keyword Strategy) with AI-ML powered automation of brand bidding process can make identifying and bidding on brand keyword more efficient and controlled. It also prevents overspending on keyword bidding and can schedule when to start or stop the bidding and limit the budget spent for a particular time slot. – Geo Based Tracking: Our tracker runs in different cities and analyses the different sources of traffic related to a set of keywords coming from various sources – Intelligent Reporting: Identify when competitors invest more or less and on which keyword. Discover key periods when keyword competition increases or decreases and take action on the findings – Time Based Tracking: Tracks on-the-basis of day – parting, hourly tracking, ad scheduling strategies of competitors and affiliates – Seasonal Sales/ Discounts: As most frauds occur during the flash sales such as Black Friday, Prime day etc., we track the user journey to check the source. Case Study 2: mFilterIt LOCOKS Solution for Optimizing Competition Keywords and Brand Visibility Problem Statement: An advertiser was struggling with white spaces in their campaign where both competition keywords and brand keywords were not targeted. They wanted to identify such whitespaces and explore

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Ai in Programmatic Advertising Fraud Detection to Deliver Performance and Sustainability

Blog, Programmatic / mFilterIt Experts

The rise of programmatic advertising has shifted the focus towards accuracy and automation. It surged from $9.75 billion in 2023 to $12.46 billion in 2024, an annual growth rate of 27.8% and is expected to continue expanding, reaching $28.12 billion by 2028 at a compound annual growth rate (CAGR) of 22.6%. However, with AI coming into the picture, performance programmatic platforms are prone to ad fraud even more. The need for optimization of programmatic media buying with comprehensive ad fraud solution across the advertising funnel is the necessity to yield results. More and more advertisers are pushing for a stronger and harder success KPIs in programmatic advertising. The shift from visibility only to performance-first is underway. With new and upcoming programmatic platforms selling inventory on impressions, it is today evident that impression fraud is 10-15% of campaign spends in the MENA region, as per mFilterIt reports. There is a ROI uplift of 7-10% when advertisers identify and block for Made-For-Ad sites and Ad Frequency cap violations. For advertisers, an ad traffic validation tool is the need of the hour to weed out fraud, optimise programmatic traffic and improve the hard KPIs of their campaigns. Also, programmatic platforms & ad networks have started providing ‘Certificate of Verification’ to advertisers to ensure their ad inventories are validated. Let’s dig deeper into the explore how programmatic ad fraud detection can help elevate performance of ad campaigns and what are the key challenges. Why Programmatic Ad fraud prevention? Protect your brand with programmatic ad fraud prevention. Ensuring the invalid traffic is blocked from malicious sources not only safeguards advertising budget but also protect brand reputation. Safeguard Your Programmatic Ad Campaigns from Fraud-Explore Our Expert Solutions. Here’s how mFilterIt guides with trust and transparency in programmatic advertising: Impression fraud Impression Fraud analysis is better at the post-bid stage than pre-bid, measuring performance beyond viewability metrics. In pre-bid analysis, i.e. before the ad is served, fraud can be identified based on only two parameters, IP and User Agents. Also, the time for analysis is limited to 10 milliseconds. This results in a meager 2% fraud identification. This is where a post-bid analysis trumps a pre-bid impression validation. Now that we have several more parameters fraud detection is done on deterministic and heuristic measures as well. This results in the detection of higher invalid impressions of 15-20%. This results in improved ROI on Ad spending. Post-bid impression analysis is a more beneficial method for detecting ad fraud. Made for Ad sites Advertisers spend an average of 15% of their programmatic budget on MFA sites, but some may spend as much as 42%. While 35% of programmatic spending is wasted on low-value environments like MFA sites, according to a recent study by ANA (Association of National Advertisers). By focusing on robust ad fraud detection advertisers can combat the various forms of fraud that undermine their campaigns across digital advertising platforms. Prioritizing impression validation is essential for maximizing return on investment and maintaining trust in the advertising ecosystem. MFA Sites not only drain budgets but also pose a challenge to a brand’s safety. Limited reach and exposure, no real user engagement misleading clicks, click fraud, artificially inflated metrics, poor conversion rates, low-quality/intent traffic and brand un-safe content tarnished brand image and lead to budget drainage. mFilterIt identify ad placement on MFA sites with Deep Content Analytics: A multi-faceted analysis using NLP & image & video analysis to identify brand unsafe content. Advanced AI-ML Sophisticated Algos: AI –ML driven analytics for extraction of meaningful insights, patterns, and information. Regional & Contextual Understanding: Local language, cultural nuances and domestic norms lead to overall risk assessments. Extensive MFA Repository A collection of websites & metric measurement is gathered with regular updates & feedback loop Fig. 1: The site has multiple ad-stacked ads with high refresh rates. It’s also brand-unsafe promoting gambling. Ad Frequency Cap Violations The most common and often neglected issue is Frequency Capping (F-Cap) violations along with bots spamming impressions for burning media budget. 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. A quick succession of impressions generated from the same google advertising ID. Distribution for a genuine user could be distributed throughout the day. These impressions were not only coming so excessively but were also being shown quickly. Multiple Impressions in a Short Period. A single GAID generates multiple impressions quickly. Impression Injection from subnets which reflect that the usage of device farm to fire multiple impressions. Subnets divide a larger network into smaller, more manageable sections. IP Repetition with Same IP, different users. It reflects high chances of fake impressions being injected with different GAIDs. Same IP, Different Impressions. This issue is not limited to IP repetition, but it extends further with the same IP generating multiple unique GAIDs and different impressions. Viewability & Attention metrics Instead of focusing on a single data signal, check on attention metrics along with viewability encompassing a range of data points. These are processed by a machine-learning model to estimate the probability that a specific media environment and ad creative will capture the attention of a hypothetical audience member. However, Viewability only itself does not help in taking decisions when it comes to effectiveness or attention. Multiple factors need to be measured, monitored and acted upon swiftly. The Viewability and Attention Model encompasses several key factors that determine the effectiveness of an ad in capturing audience attention. Viewability refers to the percentage of an ad that is actually visible to users and the duration it remains in view. It must also include: Viewability Metrices % of ad viewability and number of second viewed based on IAB standards Display ads should be at least 50% of the ad’s pixels are visible in the browser window for at least one second Video ads must be at least 50% of the ad unit

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Mobile Ad Fraud: Challenges for Advertisers in the USA

Blog, Mobile Ad Fraud / mFilterIt Experts

As mobile app ads have become more pervasive, advertisers are facing growing concerns around app installation fraud and the complexity of detecting fraudulent activity, especially in markets like the USA, where the stakes are high. The U.S. market mobile ad fraud, with estimated losses of around $1.2 billion. The focus is on safeguarding the organic traffic stolen, preventing APK fraud and referral fraud along with full-stack fraud prevention that can help optimize ad campaigns and build trust and transparency across the digital advertising ecosystem. Let’s dive deeper the unveil the various aspects of mobile ad fraud and how to combat them. Challenges of Mobile Ad Fraud in the USA The mobile app ecosystem is growing and evolving and expanding across the global especially in the BFSI industries, the rise of Fintech apps and lending apps has also raised the stake of fraud prevention in app ecosystem. Most app fraud prevention apps don’t cover the sophisticated and dynamic nature of ad frauds that lead to fake installs and thereby low return on investment. Here are some of the major challenges: Organic hijacking via Click Spam: Theft of organic traffic is one of the biggest hurdles in mobile app advertising. Deceptive techniques to mimic legitimate installs and generate traffic that appears organic, resulting in inflated numbers that distort performance metrics. It leads to skewed insights for advertisers who rely on authentic user data and affects the return on investment (ROI). Click Fraud: Validating traffic with comprehensive click fraud prevention is a must for advertisers to excel in the competitive landscape and ensure that budget is spent on valid clicks only. The deterministic, heuristic and behavioral checks with google approved mFilterIt click tracker can help combat fraud like no other. APK installs: Fraudulent mobile app ads are created to mislead users into downloading fake apps or counterfeit APK files. This compromises devices or artificially boosts install metrics. Detecting APK fraud is essential for ensuring that advertising budget is spent effectively and that users are protected from malicious content and bring true performance to their campaigns. Referral fraud: Fake referrals or incentivized clicks driving traffic inflate numbers that affect campaign efficiency. Fraudulent end users use the coupons codes multiple times either by cloning the app or by using VPN/Proxies etc. creating multiple device environments in the same device. By exploiting referral programs, fraudsters generate fake installs and impressions, tricking advertisers into paying for traffic that doesn’t convert. Implementing mobile ad fraud detection systems can protect advertisers who rely on mobile app ads to drive real user engagement. How can advertisers combat mobile ad fraud? Make Payout for validated traffic and work with Trusted Publishers Validate traffic and pay for only genuine engagement. Identify the publishers the bring in influx of invalid or fraudulent traffic to your campaign and work with only trusted published to protect integrity of your ad campaign. Encourage Good practice by Ad Networks to give a ‘Certificate of Validity’ Ad traffic validation could also support ad networks to authenticate and validate based on the performance to safeguard the interest of advertiser and builds clean and transparent digital advertising ecosystem. Ask MMPs the right questions Do not trust the MMPs blindly, a third-party validation removes the suspicion around traffic validation as fraudsters bypass MMP fraud detection. Mobile Measurement Partners (MMPs) have become pivotal for marketers and businesses, especially in tracking app installs, user engagement, and campaign performance. However, recent developments highlight the limitations of solely depending on MMPs for ad fraud detection. How deploying independent Third-Party validators build transparency? The most effective way for advertisers to combat mobile ad fraud is by using independent third-party validators—an unbiased, external layer of protection. Validate the fake traffic and interactions associated with an ad campaign. mFilterIt offers a comprehensive ad fraud detection system powered by advanced artificial intelligence and machine learning algorithms. It can identify suspicious patterns with deterministic, heuristic, and behavioral checks. It enables advertisers to identify and block fraudulent activities before they drain their budgets and ensure that only genuine traffic is counted, reducing the risk of fraudulent interactions, like click fraud, bots, and fake impressions. As an essential step in the fight against ad fraud and invalid traffic, it is important to validate before advertisers, ad networks and agencies collaborate with publishers. Monitor and verify each install or click with Mobile ad fraud detection solutions. It helps in identifying APK fraud, referral fraud, and protects organic traffic from being stolen. Proactive fraud prevention using data-driven strategies preserves the integrity of mobile advertising campaigns and ensures it delivers true value. Impression Integrity: Start with checking up impression integrity with Impressions validation, ad visibility and post-bid validation. Click Integrity: Weed out invalid or fraudulent traffic and bots with click fraud prevention. Install Validation: Check if the installs are by genuine customers or bots also follows up tracking soft KPIs and events triggered such as registration, logins or signups. Re-engagement & Post-back Blocking: Hard KPIs such purchases, deposits, and transactions also need to be validated for efficient re-engagement and post-back blocking. Advertisers and developers need to adopt robust ad fraud detection systems with advanced algorithms and machine learning tools to identify suspicious patterns and block fraudulent activities. As an essential step in the fight against fraud, validate before collaborating. Trusted ad networks and ensured transparency in mobile ad transactions. Here are some benefits of proactive mobile app fraud prevention: Weed out fraud to improve ad campaign efficiencies Enabling brands to take better-quality business decisions Show funnel visibility & transparency basis performance Optimizing the publisher ecosystem Case Study: How FinTech App identified high volume of Fake Installs Problem Statement Fake app installs were significantly inflating the user acquisition costs and reducing the efficiency of marketing campaigns. They needed to identify sources of such fake installs and block them. High volume of fake install adversely affects the overall return on investment (ROI). The Challenges Inability to accurately measure genuine user engagement and conversion rates. Due to a lack

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Is lack of Accurate Stock Availability Metrics Impacting your Ecommerce Revenue?

Blog, Digital Shelf Analytics / mFilterIt Experts

Product availability is the key issue for brands looking to expand their presence across the fast-paced E-Commerce landscape. With the expansion of new categories that cater to shoppers’ last-minute needs, quick commerce platforms are expanding their horizons. There is a need to measure and monitor stock availability performance across platforms and geographies. Customer loyalty is affected by availability issues as the industry figures suggest that 20-30% of customers may switch to competitors permanently after encountering product unavailability. Also, Tier 2 and 3 cities may face upto 20% more disruptions due to logistical challenges caused by lack of accurate and high-quality availability metrics. Let’s dive deeper and assess the core issues that brands face on ecommerce and quick commerce platforms. It is important that digital first customer-facing product-oriented brands today understand that reducing out-of-stock occurrences can lead to a lift of revenue by 5-8% with real-time business intelligence and insights. Are you getting the right numbers on e-commerce and quick commerce platforms? Every quick commerce and e-commerce platform has its own set of challenges, but getting out-of-stock poses a major challenge for brands looking to strike hard at the moment customers is looking for them. This not only puts them out of the race but also lose a loyal customer looking for your brand. What should brands do to maintain stock availability? Keep track of every SKUs in real-time, drill deeper to find out which product is out-of-stock or about to be out-of-stock on which platforms and at which dark store under a pin-code. In-depth analysis and actionable insights are the only way to keep you ahead in this fast-paced race on online shopping platforms. A brand stock-out or going out of stock (OOS) can occur due to various reasons like supply shortage, poor inventory management, inaccurate forecasting of demand or unexpected demand surge, etc. The key is digital commerce intelligence on stock availability and real-time alerts on Out-of-stock status. The availability monitoring should not be limited, it should be a more granular update such as on certain pin codes where is your product available? Where they stand vs competition? On which platform brand need to stock it up? For instance, a shopper looking for a specific product of the brand might search on multiple platforms as well. This means brands must monitor their presence across platforms at the pin-code level and on the platform’s dark stores. Here are some key metrics that brands must track within the stock availability monitoring: Brand availability trends versus competition Availability share versus competition City-wise availability trends – monthly, weekly, daily, and hourly Platform-wise & geography-wise analysis Heat map to identify new geography to target Tracking Bottlers’ (Sellers) performance Maintaining Out-of-Stock product lists & real-time alerts This is where mScanIt, digital commerce intelligence comes in handy as it covers all aspects of product availability monitoring across platforms and geographies. Leading brands from FMCG, electronics, beauty and personal categories trust us as it covers more than 150 e-commerce and quick commerce platforms across the world, drilling deeper within city-level analysis along with global coverage of multiple geographies. Case study: How a global leader in the beverage industry improved availability across platforms and geographies Problem Statement A global leader in the beverage industry faced challenges in ensuring consistent product availability across digital platforms in key AMESA (Africa, Middle East, and South Asia) markets. In September, product availability in the KSA region on prominent platforms like Quick Market, Carrefour, and Nana was inconsistent, reported at: Quick Market: 28% Carrefour: 57% Nana: 86%. This lack of availability led to: Missed sales opportunities A weakening of consumer trust and brand loyalty Limited visibility in a competitive digital landscape The company aimed to bolster its market presence by enhancing availability through real-time monitoring, identifying supply chain inefficiencies, and ensuring sellers maintained optimal stock levels. How mFilterIt helped the brand boost its brand presence to match the competition across geographies To address these challenges, the company partnered with mFilterIt to implement the Digital Shelf Monitoring, mScanIt a cutting-edge solution designed to monitor product availability across multiple platforms and geographies along with other KPIs such as keyword share, product page content, Feedback analysis of rating & review and product pricing and promotions. Here’s how our capabilities lead the way. Real-Time Monitoring with Regular updates on product availability and stock levels across Quick Market, Carrefour, and Nana. Alerts for out-of-stock (OOS) items, enabling immediate corrective actions. Gap Analysis helps identify bottlenecks in the supply chain impacting stock availability. Provided detailed insights into seller performance and platform-specific challenges. Actionable Recommendations helped develop region-specific strategies to enhance stock levels, such as improving coordination with bottlers and distributors. Prioritized high-demand SKUs to maximize availability during peak shopping periods. Performance tracking with continuously targeting improvements in availability and visibility. The results with digital commerce intelligence and shelf monitoring By the end of November, significant improvements were observed across platforms: Quick Market: Availability rose from 28% to 51% (+82%). Carrefour: Achieved 100% availability, up from 57% (+75%). Nana: Improved from 86% to 94% (+9%). Fig. 1: Product Availability Across Platforms September to November These improvements translated into: Enhanced customer trust and satisfaction by ensuring products were consistently available. Strengthened market share and sales across key platforms, solidifying the brand’s position in the AMESA region. Through mScanIt, digital commerce intelligence and shelf monitoring – availability analysis, the beverage leader transformed its operational approach, leveraging data-driven insights to achieve exceptional results in a competitive market. Optimize Customer Journey with Digital Commerce Intelligence Do not limit to just stock availability tracking! It’s essential to consider various analytical metrics to optimize the customer’s journey across e-commerce and quick commerce platforms. Each stage requires a tailored approach to ensure that the brand’s presence is effectively communicated, and customer engagement is maximized across the digital shelf. This journey can be optimized at three broad levels with digital commerce intelligence: Awareness and Interest Stage Brands must track visibility and ensure that products are accurately featured throughout the marketplace. This phase boosts awareness and

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Full-Funnel Ad Fraud Protection: Beyond Impressions & Viewability

Blog, Ad Traffic Validation / mFilterIt Experts

Ad Spending in the US market will reach $421 billion in 2024. Out of a market worth $143 billion, TV and video Advertising stands out as the largest market there. Accurately measuring an ad campaign’s effectiveness is essential. It is the marketer’s job to protect against waste and improve campaigns’ ROI. Adopting ad fraud detection tool driven by AI-ML tech can achieve this. According to the Statista Report, by 2029, it has been estimated that 87% of advertising revenue in the United States will be attributable to programmatic advertising. Need for Full Funnel Protection When it comes to combating ad fraud, one must understand that the focus on impressions alone will not work. It is imperative to adopt full-funnel protection, focusing on detecting ad fraud and preventing it within the entire customer journey from the first impression to post-click interactions. Here’s why- Impression Validation is not enough. Full funnel protection is a must as impression validation is not enough. It’s not a foolproof indicator of ad fraud. Here’s why: Bot-generated views and impressions: Bots can be programmed to simulate human behavior, including scrolling and pausing on pages, to ensure ads are “viewed.” These bots can inflate viewability rates without generating any real user engagement. Low-Quality Traffic: Even if a human views an ad, the traffic may come from low-quality sources like click farms or websites with minimal user engagement. These impressions may not lead to conversions or meaningful interactions. Click-Through Rates (CTR): While viewability indicates if an ad was seen, CTR measures if it was clicked on and engaged with. A high viewability rate and a low CTR can be a red flag for low-quality traffic or ad fraud. Low conversion rates: Ultimately, advertising aims to drive conversions, such as sales or sign-ups. A high viewability rate without corresponding conversion increases is always suspicious Data is limited Trackers easily bypassed by using Safe-Frames / iFrames Due to the huge volume of impressions, only sampling is executed but with limited time for analysis (~20ms). Limited data for analysis covering only IPs and user agents. Impressions are the easiest to spoof! Deeper Fraud Checks Fraud is not only inflated impressions. There are several ad techniques used by publishers such as ad pixel stuffing, domain spoofing, fake clicks, etc. A comprehensive validation process needs to be in place covering deterministic, behavioral, and heuristic checks for a multilayer approach to fraud prevention across the funnel. Full-funnel checks address these deeper issues to guarantee that the figures reflected in the campaign metrics translate to valuable interaction from the target audience. Combined Power of Click & Down the Funnel Analysis Embrace the power of click validation and ensure the click turn visits with genuine engagement. Seek validation beyond those that bypass walled garden restrictions, and ensure a more reliable environment on advertiser’s own pages. For lower volume, allows census analysis and makes sure that more time is available to track user behavior patterns and generate genuine engagement. Track bot patterns vs huma to detect sophisticated fraud like mouse movements and cover all campaign types with down-the-funnel analysis mFilterIt vs Competition – Pre-Bid Analysis vs Post-Bid Analysis There are several ad verification tools on the market. However, they differ in their effectiveness in providing ad verification services. This will examine the relative advantages for us against its competitors in both pre-bid and post-bid analyses. mFilterIt: A Full-Funnel Approach We detect fraud in post-bid analysis; thus, fraud detection is our strong suit. It employs techniques for the detection of fraud that reach beyond the most basic advertisement viewability measures. Impression Fraud Detection- We detect up to 15-20% fraud in campaigns. When it comes to validation at the impression level, the focus is on both real human impressions, impressions on Made-for-ad sites, and frequency cap violations. Also, viewability scores are measured to counter fraud throughout the life cycle of the campaign. Click Validation- Validate click with visit lead intent to check on campaign performance. Visit Intent Scoring is a must for improved channel management and retargeting campaigns Getting a lead is not enough to ensure a swift automated process in place for lead validation and lead quality assessment for setting priorities. Competition: A Narrow Focus on Viewability & Pre-Bid Validation Impression Fraud analysis is better at the post-bid stage than pre-bid, measuring performance beyond viewability metrics. In pre-bid analysis, i.e. before the ad is served, fraud can be identified based on only two parameters, IP and User Agents. Also, the time for analysis is limited to 10 milliseconds. This results in a meager 2% fraud identification. This is where a post-bid analysis trumps a pre-bid impression validation. Now that we have several more parameters fraud detection is done on deterministic and heuristic measures as well. This results in the detection of higher invalid impressions of 15-20%. This results in improved ROI on Ad spending. Post-bid analysis is a more beneficial method for detecting ad fraud. As viewability is not a measure to detect fraud, most of our competition focuses on advertisers spending an average of 15% of their programmatic budget on MFA sites, but some may spend as much as 42%. While 35% of programmatic spending is wasted on low-value environments like MFA sites. By focusing on robust ad fraud solution advertisers can combat the various forms of fraud that undermine their campaigns across digital advertising platforms. Prioritizing impression validation is essential for maximizing return on investment and maintaining trust in the advertising ecosystem. Understanding the Scope and Limitations of Industry Accreditations Limited Geography Some accreditations are largely limited to the US Market. However, competitors use these globally as a selling point. These accreditation bodies have recently come out to express their discontent with traffic validation platforms using it globally. Brands and agencies must check the geo-limitations of accreditations before onboarding the platform. Service-Specific Accreditation A part of these accreditations is granted on a service-by-service basis, rather than offering blanket accreditation for entire platforms. Each service or tool must be evaluated separately for its compliance with standards. Exclusion in Major Platforms Notably, brands must do a platform-wise analysis

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Why eCommerce Brands Need Digital Commerce Intelligence

Leave a Comment / Blog, Digital Shelf Analytics / mFilterIt Experts

“Let’s BlinkIt”, or “Look it up on Amazon” These phrases have become common in our everyday lives. This reflects how online shopping; and digital commerce intelligence are getting ingrained in our habits and their convenience has become an integral part of any shopper’s routine. To match the changing and ever-evolving customer behavior and needs shopping platforms are also expanding their inventory across categories covering every aspect of shopper’s needs from food and beverages, groceries, fashion & accessories, and home decor to last-minute needs with quick commerce. enhancing UI/UX to connect with the audience, penetrating beyond tier-1, and tier-2 cities. The eCommerce is expanding, enhancing customer experience, and even coming up with new exclusive niche categories. These are exciting times for brands and shoppers. However, as the eCommerce game is moving into the next level brands also need digital commerce intelligence to stay ahead of the competition. Intelligence across platforms and geographies helps effortlessly elevate business by strategically refining every touchpoint with data-driven decisions to optimize your customer journey across digital commerce marketplaces and product categories. Why Do e-commerce Brands need Digital Commerce Intelligence? For instance, with digital commerce intelligence – Availability analysis for a popular noodle brand, we discovered it has the lowest availability amongst the competitors, among quick commerce platforms on Blinkit it has the lowest availability of 25%. In Delhi, 61% of the listings are out of stock! Availability is almost 0% on the pin codes 110017, and 122001. Actionable insights such as these on various KPIs help brands identify gaps, track platforms, and geography with customized scheduled ed platform scans, monitoring on monthly, weekly, daily, and even hourly on certain KPIs such as stock availability, product page content, delivery turnaround time and keyword share, etc. can help a brand grow in fiercely competitive eCommerce and quick commerce landscape. Optimize Customer Journey with Digital Commerce Intelligence Every step on the e-commerce platform can be elevated to boost sales, get new shoppers, have more visibility on the digital shelf, and enhance customer experience. The biggest impact of eCommerce intelligence is that it helps optimize your customer journey to stay ahead of the competition. Phase 1: Generate Interest and Awareness In the first phase generating eCommerce competitive analysis helps generate awareness & Interest by optimizing discoverability, keyword share, and viewability of the own, competition, and other products in the market across multiple platforms. Lists out the keywords that the client should start bidding on and also the keywords where the client can stop their bidding by comparing the parameters like traffic, CTR, CPC, organic, and sponsored discoverability. Also covers discoverability and placement rank on category pages (commonly known as Browse Share). Enhance PDP page content (images, text), as per ASCII guidelines, platform guidelines, and best practices. Review, edit, and rebuild the content to meet the specified benchmarks to improve your content. Also, keep track of the Banner Visibility of own and competition brands. Analyses the eCommerce Ad banner position, content, and themes/ cohorts of advertising content such as offers, discounts, branding banners, etc. Phase 2: Optimize Consideration & Evaluation Process To ace the race, you need to be on the shelf! Availability and pricing are the keys to winning across the e-commerce marketplaces. Key track of your stock availability across sb-brand, categories, sub-categories, and product variants against the competitors’ own products. Analyze the market via competitive pricing analysis, tracker, and monitoring of pricing trends. Be vigilant for MAP violations norms and discount threshold violations. Keep track of delivery turnaround time on eCommerce platforms. Analyze the customer feedback, ratings, reviews, and Q&As on various parameters of the products of own and competition. Phase 3: Monitor Purchase & Sales Monitor the purchase phase with sales analysis. Comprehensive sales tracking and analysis that covers “What” the sales are, “Why” is the sale, ‘What’ it is, and “How” to improve the sale. Case Study – Digital Commerce A global leader in the beverage industry, in optimizing their performance across platforms and geographies with a digital shelf tracker to monitor their products across multiple KPIs like Availability, Keyword Share, Pricing & Promotion, Product Page Content, and Ratings & Reviews across the Middle East & North Africa region. In the Kingdom of Saudi Arabia (KSA) region they mainly focused on enhancing its market presence by monitoring availability and optimizing content pages. Concentrating on the beverage category, this case study delves into the challenges faced by leading beverage brands in ensuring product availability across various platforms in September, October, and November. Additionally, it explores the efforts to optimize product page content on key platforms, with a spotlight on Carrefour and Nana. Stock availability gives the brand an added edge over the competition across platforms. Intelligence on stock availability helped the brand re-stock on time and identify the demand across locations. With a comprehensive global dashboard, they were able to identify and plug the availability gaps, optimize performance, and ensure that their products were consistently accessible to consumers. Brand Availability Trends Availability share versus competition City-Wise Availability Trends – monthly, weekly, daily, and hourly Platform-wise & geography-wise analysis Heat map to identify geography to be a target Track Bottlers (Sellers) performance Out-of-Stock product lists & alerts Maximize the impact of product pages to ensure that the product content resonates effectively with diverse audiences. Content analysis enabled brands to refine and tailor product descriptions, images, and other elements to align seamlessly with the unique requirements of each platform. By leveraging data-driven insights, the brand was able to identify and rectify any inconsistencies on PDP, ensuring cohesive and compelling brand content across platforms. This holistic optimization not only enhances user experience but also boosts visibility. Image analysis of product images Comprehensive Perfect Page Analysis (Title, content, and product reviews) High-traffic keyword and content theme recommendations Final Thought – Optimize Your Brand Performance Digital commerce intelligence helps brands explore new possibilities in expanding e-commerce, Quick commerce, and D2C marketplaces. It can monitor product performance across different platforms and geo-locations. It doesn’t matter if you are a small brand or multinational conglomerate, actionable insight, and analysis can catapult

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Ad Fraud in Performance Vs. Brand Campaigns

Leave a Comment / Ad Traffic Validation, Blog / mFilterIt Experts

Digital Marketing spending is growing at a 9% CAGR globally, and digital media today has become a non-negotiable medium to reach out to consumers/audiences. For a marketer, the 2 obvious choices are to run either performance or brand campaigns to reach, act, convert, and engage with their target audience. While Performance campaigns are directly associated with results achieved, Brand campaigns’ focus is to ensure visibility and recall. Brand campaigns rely largely on viewability (impressions), while performance campaigns focus on down-the-funnel metrics. Performance marketing focuses on CPI, CPV, cost per sale, conversion rate, etc. on the other hand, the share of voice through mentions, sentiments, tags, etc., measures brand campaigns. Marketers and advertisers spend a large portion (>50%) of their digital advertising budget on these two campaigns. Our research suggests that it takes 6 to 8 impressions for someone to build a recall value of your brand. The regular reappearance of the brand ensures higher recognition of the solutions it offers. Viewability, according to IAB is, 50% of the ad’s pixels are visible in the browser window for a continuous 1 second. For larger ads (those greater than 242,000 pixels), 30% of the ad’s pixels are visible in the browser window. The same applies to video ads but for a minimum of two seconds. Ad viewability is the topmost layer of an ad metric. Fraudsters use fake impressions, bot impressions, ad stacking, and pixel stuffing for impression fraud. Meanwhile, performance campaigns work down the funnel and measure clicks, visits, events, and conversions. Clicks are important because they define the website traffic from online advertising. Visits account for the number of people who viewed the URL associated with the ad. Similarly, events could include installs, add-to carts, registrations, signups, conversions, etc. A close look at click-to-visit ratios and a visit-to-conversion ratio will give you the efficacy of your performance campaign. Cybercriminals impact these through click fraud, lead generation fraud, CPA fraud, influencer marketing fraud, cookie stuffing, click farms, and domain spoofing. The impact of ad fraud also influences programmatic, affiliate, and retargeting campaigns. The result of ad fraud is higher ad budgets, lower ROIs, diminished brand safety, fraudulent analytics, and infiltration of cybercriminals in customer data systems and ad servers. Ad Fraud in Brand Campaigns Impressions are the measure of brand recognition through online ad campaigns. Most digital brand advertisements are based on cost-per-mille (CPM), a.k.a., cost per thousand impressions. Total impressions determine the campaign cost in a CPM advert. The impressions also determine the reach of the advertising channel and total ad viewers in a specific channel. Ad fraud in impression-based campaigns happens when a fraudster opens a fake website, joins an ad exchange, loads ads on a fake website uses bots for page loading & impressions, and sells the impression inventory to the ad exchange. The common methods of impression fraud include the following: Pixel Stuffing Loading a 1×1 pixel ad on a page counts as an ad served but is not visible to the human eye. Ad Stacking Piling one ad on top of the other and keeping the original ad at the top. The impression counts for all ads, even when the top ad blocks ads below it. Fake Websites Using bad bots to generate impressions on fake websites created solely to sell inventory that does not have human visitations. Bot Inventory on Genuine Websites Fraudsters use bots to fulfill the “most required inventory” needs of the advertisers for acquiring credit and financial gain. Auto Impressions Running in-app ads (even on inactive apps) on mobile devices to auto-generate impressions. Determining ad fraud in impression-based campaigns is challenging because the analytics reveal more data than performance-oriented ads. You only have the option of comparing CTR with impressions. High impressions mean an advertisement has significant exposure. Typically, campaigns with high impressions experience a high click-through rate (CTR). Under the unlikely circumstance that you have low CTR and high impressions, the ad is possibly incurring fraudulent activity in the background. Businesses thinking that programmatic or retargeting can resolve issues about brand campaigns should know that it’s not true. Fraudsters have spoofed domains, penetrated customer data systems, and used bots to act as a target for remarketing lists. So, ad fraud is prevalent in brand campaigns. Furthermore, brands should optimize programmatic campaigns by incorporating inclusion lists consisting of URLs where the ads should be placed. This fear of programmatic ads landing on sites built for ad fraud has become a common affair. Fake websites distort the analytics of brand campaigns. The unexplainable ad impressions can only account for invalid traffic as only 36% of the online traffic is human. Moreover, sometimes programmatic campaigns declare results higher than the population of a location. So, ensuring that ads are delivered to humans is a serious concern. Ad Fraud in Performance Campaigns All marketers and advertisers rely on analytics for creating brand strategies. Infiltration of ad fraud into the data falsifies the results, gives false hopes, increases the marketing budget, and doesn’t reach a large proportion of the human audience. Popular researchers quote that ad fraud would exceed $50 billion by 2023. Moreover, nearly 40% of advertisers think that ad fraud is a significant downside of programmatic ads. For example, fake clicks display that the campaign achieved higher performance than expected, but in reality, engagements with bots will not bring home any business. Ad fraud is still happening even after optimizing the campaign with geolocation, remarketing lists, and pre-bid programmatic placements. Fraudsters use the following methods to target performance campaigns of brands: Click Spamming Executing clicks on behalf of real users without their consent in the background and claiming credit for obtaining financial gain from advertisers. Click Injection Using malware in apps to stay alert about “install broadcasts” and obtaining the last-click attribution through click firing before the new app installation. SDK Spoofing Tricking advertisers to believe that their ad will appear on premium apps, whereas it appears on fraudulent apps through SDK spoofing. Lead Generation Fraud Filling lead forms using real or fake user information with

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