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Brand safety

Protect your Brand Reputation with Safety & Brand Infringement Suite

Blog, Brand Safety & Suitability /

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

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Affiliate Fraud

Affiliate Fraud Uncovered: Protecting Your Brand and Ad Spends

Blog, Brand Safety & Protection /

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

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

Blog, Digital Shelf Analytics /

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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ad fraud

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

Blog, Digital Advertising /

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

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trademark infringement

Brand Infringement: Protect Your Brand Across Digital Sphere

Blog, Brand Protection /

Since the advent of digital platforms and unmatched reach, the way brands engage with their audience has been completely transformed. As businesses expand their digital footprints, they are exposed to new challenges and risks that come under the umbrella term brand infringement.    Infringement occurs not just on names of the brand but also on social media handles, site domains, and ad campaigns. According to recent reports the global losses due to brand infringement are expected to reach over $4.5 trillion in 2024, IP theft globally is estimated to cost companies more than $1.2 trillion (source – Intellectual Property Helpdesk).   The numbers clearly show the alarming situation, and it is therefore important for every business to protect their brand and digital assets from impersonation by using AI-ML-driven identification tools & automated takedown management software.   Ultimately, ‘Brand Safety is the Brand’s Responsibility’ to protect the brand from impersonation and customers from fraudsters’ fraudulent activities to ensure the business’s ongoing success.   Let’s delve into the understanding of brand infringement or trademark infringement, its types, and takedowns to prevent it.    What is brand Infringement?  Brand Infringement or trademark infringement occurs when an unauthorized party uses a brand’s name, logo, or elements of its identification without permission, leading to potential confusion among consumers. Customer trust can be undermined, and this misuse damages the brand’s reputation.  For instance, a trademark is a term, symbol, or expression used by a company to differentiate its product from competitors. In the context of business services, a service mark is equivalent to a trademark.   Types of Brand Infringement?      Fake Websites & Helpline Numbers- They mimic legitimate businesses to deceive customers. They are tricked into taking away sensitive information, it is mostly done under popular bank names.     Similar Domain/ Typosqautting – This is done by creating nearly identical web addresses just like the brand’s official domain but with slight variations.    IP infringement- It occurs when someone uses a brand’s trademarks like its logos, or protected content without its permission. Often operated on fake websites to sell these goods and products. Phishing Websites – This is a form of cyberattack that gathers sensitive information like login credentials, bank account numbers, or other financial information.    Deep & Dark Web Monitoring– It involves scanning the hidden part of the internet where illegal activities occur including stealing of brands’ data and misusing them. Fake Brand Communication like Emails- A consumer may receive emails that look like from some famous brands. These fraudulent emails appear to be genuine, but they are sent by scammers.   Fake Social Media Handles and Posts – For example, a counterfeit fashion brand may create an online post to sell fake products using images and logos from a luxury brand, this is how a brand’s intellectual property is misused.    Counterfeit Products – To imitate or create unauthorized replicas of genuine products can deceive consumers into believing they are purchasing a legitimate product. Fake Promotion & Offers – Fake posts and advertisements on discount deals or offers that trick consumers, making them believe that they are getting a real discount deal, and often get trapped and lose their money.  How to protect the brand from the risk of trademark Infringement?   The brands must use comprehensive monitoring detection mechanisms to deal with infringement across the web, social media, and other media platforms. mFilterIt gives a comprehensive solution suited to the above-mentioned types of brand infringement.  Three Pillar Approach   Identification – By scanning various platforms such as websites, social media, YouTube, and IM apps to identify potential brand infringements and trademark violations.   Classification – Identifies and shares potential threats of trademark violation using AI and ML techniques with clients to review for their confirmation.  Action – The URLs of websites, YouTube channels, and IM handles found infringed are added to a blacklist for further actions by the clients.   Platform Coverage for Brand Infringement Detection    Figure 1.0   In the above-mentioned figure 1.0 we can check your performance across these platforms.   Infringement Protection via Takedowns   Inhouse Takedown – Without relying on any formal legal complaints documentation from any law enforcement agencies.  Network collaborations with Internet Service Companies – Collaboration with various individuals to protect brands on various marketplaces websites, domains, cyberlockers, video platforms, mobile apps, and search engines.   Continuous Monitoring – Across the various platforms to combat infringement and safeguard the brand’s digital integrity.   Let’s understand this with a use case of the banking sector   A major Indian bank wanted to protect its online presence to prevent fraud from happening to its customers. It was nearly impossible for them to monitor everything manually.  Bank needed a solution that could easily scale up with their needs and monitoring to protect their online presence.    mFilterIt Brand Protection Solution uses sophisticated technology of open-source intelligence to scan the internet for monitoring potential threats. Through machine learning (ML) a large amount of data is collected such as images, videos, and texts, and there is a confidence score to determine how likely these were fraudulent.   The total number of cases – 2,37,565 (Impact period to Jun)   Successful Takedowns- 231,018 of these threats.   The action was taken across 10 different online platforms on average it took 48 hours to resolve a case and remove the threat. 95% rate of successful takedowns with precision.  Conclusion   As brands navigate the complexities of the digital world, brand protection is no longer optional, it is essential. With social media, anything can be accessed with just a few clicks, be it gaining a reputation or losing it.   Our comprehensive solution combines trademark protection and concerns related to its violation to provide your brand with all-encompassing online protection. By leveraging AI-ML comprehensive monitoring brands can empower themselves to survive in the world of fraudsters and imitators.  Get in touch to learn more about the Brand Infringement.

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sentiment analysis to decode customer feedback across digital marketplaces in uae

Sentiment Analysis to Decode Customer Feedback Across Digital Marketplaces in UAE

Blog, Digital Shelf Analytics /

The UAE e-commerce is expected to reach $17 billion by 2025. Further, by 2027, it is expected that global retail e-commerce sales will cross $8 trillion indicating a 39% growth, according to a Statista Report. In such a growing market, sentiment analysis and customer feedback are essential for business success. Unlike brick and mortar where customer feedback is easier to take, in an ecommerce platform customer satisfaction is more difficult to assess. Moreover, it helps brands sell more on e-commerce platforms if it has a good volume of reviews. Building a good volume of reviews and thereby analyzing them is therefore the need of the hour.  According to the Global Statshot Report, 32.5% of customers make online purchase decisions based on customer reviews.  There is a need for every e-commerce player to have access to business intelligence in real-time and in actionable form to ensure the success benchmarks are achieved. Business intelligence needs to be granular to the last detail and scalable by geographies and platforms.     Concept of Sentiment Analysis   In online shopping, customers give reviews and ratings on different social media platforms. Customer sentiment analytic tool analyzes these to discern positive, negative, or neutral emotions. Does the customer express positive, negative, or neutral emotions?   This analysis gives insights that help in the growth of the businesses and find out what makes a product more successful and what creates dissatisfaction among the consumers, which helps to improve customer experience, and brand image, and address negative sentiments before it spread.  Sentiment Analysis Metrics that Brands Must Track:   Monitor Rating & Reviews  Customer Feedback Trend Analysis   Product sentiment tracking across categories, sub-categories, product variants, brands, and sub-brands vis-à-vis competition  Identify Q&A Sentiment themes, with real-time actionable Insights and analytics  Keywords graphically highlighted with word cloud  Identify sentiment themes that are working, gaps, or lags in terms of pricing, quality, packaging, flavor, delivery, etc.    Heat map to reflect sentiment intensity  Why Sentiment Analysis Is Important in E-commerce   Enhanced Service Outcomes and Customer Experience It brings direct insight into real-time perceptions of customers about the brand, its services, and interaction. It not only reveals the surface level of feedback but also gives an idea about the customer’s feelings and sentiments upon interaction. They can alter service strategies, product experiences, and more based on how customers react.   Comprehensive View of Customer Sentiments It gives an overview of a company’s snapshot view of total customer sentiment. It does not just analyze whether the feedback is positive, negative, or neutral, but aggregates sentiments from multiple sources to give a whole view. Whatever the customer feedback source, the sentiment analysis tools will let you decode the sentiment behind each word or comment.   Understand the Customer’s Mood, Tone, and Attitude With sentiment analysis, businesses manage to perceive customers’ moods, tone of voice, and attitude. That insight is rather important for personalized and empathetic support. It will help an organization with the right kind of emotions displayed by customers for their needs and expectations. This also helps a business organization perceive how customers will feel while interacting with your brand.    Enhance Customer Satisfaction with Data-driven Reactions Sentiment analysis will allow the business to understand where to improve based on customers’ feelings and act based on data to offer customer satisfaction. It acts like a compass for direction, guiding businesses on how to plan, focus their efforts, and appropriate resources to areas where they are most needed.   Identify Customer Pain Points Sentiment analysis gives companies insight into customer’s pain points and what usually irks them. It analyzes feedback and sentiment about aspects, features, touchpoints, or journey stages of a product or service. Companies can find out where improvements should be made. This way, they can take up all the concerns and make necessary improvements to enhance customer experiences.   Knowing the emotions underlying customer feedback lets you identify, very specifically, where things may be going awry-be it a glitch in the system, a baffling process, or even a feature that’s more frustrating than useful.    Plan Market Positioning with Informed Insights Knowing exactly where to place your product in the market holds the key to success. Making an informed decision, however, isn’t easy, and a misjudgment may sink your business. Fortunately, constant monitoring of customers’ sentiments may provide you with just what you need. You will get a better understanding of the features that customers would like to see in your products.   Sentiment Analysis in Improving Customer Experience   There are also many instances when customer sentiment analysis helps enrich the overall customer experience of organizations through tools and techniques.  Proactive Resolution of Issues Sentiment analysis is a potent tool for spotting negative sentiments in customer feedback. By sentiment analysis, a business would be well prepared to proactively address issues, resolve complaints, and avoid any potential escalations before this gets bigger and affects a large chunk of the customer base.   Once any negative sentiment is spotted, immediate and remedial action can be taken by a business to restore the situation and provide satisfaction to resolve it. This proactive approach helps in preventing further dissatisfaction among the customers and builds trust and loyalty.   Continuous Product & Service Improvement They help in identifying areas to improve, insights to develop products, and enhancements based on the sentiments of the customers.   Sentiment analysis gives ways through which organizations make continuous improvements. Businesses can review their customers’ feedback and find common themes and trends emerging from their sentiment analysis. Themes may be related to product features, customer surveys and interactions regarding the service, or overall customer satisfaction.   Efficient Resource Allocation Effective allocation of resources can be planned by prioritizing actions based on sentiments. Critical issues that require the most resources must be focused on.   Resource allocation is also an important business management process. Sentiment analysis plays a major role in this matter. It prioritizes feedback, according to urgency and importance, for businesses to optimize resource allocation. This way, they can go ahead and perform an action based on priority through customer sentiments.   Customer Journey Optimization Organizations

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Programmatic ad

Programmatic Ad fraud: The bane of the Branding Campaigns

Blog, Brand Safety & Protection /

The global programmatic advertising market size is expected to grow at a CAGR of 22.8% from 2024 to 2030. Programmatic advertising encompasses a wide range of digital channels, from walled gardens like Google and Facebook to connected television (CTV) and OTT. This variety of channels’ capacity increased the reach of advertisements to even broader groups of audiences.    However, this wider scope of innovation has also opened the door to significant fraud, with programmatic ad fraud becoming a growing concern for marketers worldwide. With the programmatically run advertisements at $546billion in 2023 according to a Statista report, the volume of ad fraud and low-quality impressions stand at 15-20% according to mFilterIt reports.   To make their digital campaigns a success advertisers need AI-ML driven ad fraud analytics to improve ROAS (return on ad spends) and reduce budget wastages.  Are reputed ad platforms, trade desks and walled gardens also affected by programmatic ad fraud?   Programmatic ad fraud broadly refers to the malicious practice of manipulating online ad campaigns through automated software, to generate revenue.   Nowadays new ad platforms, trade desks and reputable walled gardens are coming into the limelight with advertisers having a positive perspective and confidence about them. This confidence stems from the fact that they have positioned themselves as platforms having a clean inventory with superior quality traffic making a differentiated mark from traditional affiliates.   As ad fraud analytics uncovers, it is not the case. These platforms are bigger black boxes than affiliates, and the fraud rates are similar with new tactics uncovering every day. Advertisers need to place a check on these platforms through independent third-party validation tools which can provide a full-funnel analysis.   New tactics that are coming up in the programmatic ad ecosystem are:   Frequency Cap Breaches – Frequency capping is when a brand sets a limit on the number of times its advertisement is displayed to a given user within a specific time frame. Breaching this leads to oversaturation, which means the ads are shown to the same users instead of reaching a broader audience. This not only wastes ad spending but also lowers the reach of the ads and the campaign’s overall efficacy, which is in violation of the objective of the branding campaign. mFilterIt has identified approx. 25% of f-cap violations depending on the campaign.  Inaccurate OTT Viewability Scoring – Viewability metrics are crucial for determining if an ad has been viewed by a human or a bot. Uncertain data from inaccurate metrics might make it challenging for advertisers to assess the success of their efforts. Without accurate viewability, brands may end up paying for impressions that their intended audience never saw.    Low-Quality Impressions –These impressions come from content that mainly attracts visitors who are not genuinely interested in the brand or product. It comes from sites specially designed to generate clicks from clickbait sites resulting in limited reach and exposure. Such sites have poor conversion rates causing brands to waste budget and inefficient ad placements.  These are mostly generated by Made-For Ad Sites (MFAs). The ANA report on ad fraud states that MFA websites represent 21% of impressions and 15 % of ad spend overall.   How do these affect programmatic advertising?   According to ANA, the current $88 billion open web programmatic media ecosystem is rifted with waste.  In addition to revenue losses, there are other ways ad fraud can harm brands. These include:   Damage to the brand reputation as ad fraud can cause advertisements to appear on unsuitable websites and consumers may associate the brand with such negative connotation. Inadequate campaign performance ad fraud distorts data and analytics, producing false campaign performance benchmarks. As a result, decisions are made using erroneous data, which leads to ineffective marketing strategies.    Diminished customer trust potential customer numbers can be lost for brands in the future as well.    Reduced campaign ROI significantly due to fraudulent activities, as a portion of the ad spend was diverted on non-genuine interactions.  mFilterIt Solutions to combat these threats   The simplest and most efficient way to protect the interest of your brand is to use ad fraud detection tool to combat the new tactics of fraud in programmatic ad ecosystem:   In the case of F-Cap Violation using a creative serving hub, that sets the views per user per day to a certain limit with real-time monitoring to ensure no user sees the ad more than that set limit, helps to maximize reach and engagement.    Further, the viewability metrics can be checked by implementing a viewability attention score based on how much attention users give to ads. This can provide a more apt picture of whether the ads are truly seen and engaged by real users or not.    Low-quality impressions through Made-for-Ad Sites can be avoided by detecting and blacklisting these MFA sites. Suppose an ad network red flags a site as MFA, the specialized algorithms will automatically blacklist this site from the ad placement list.    Final thought   Programmatic ad has offered brands the chance to reach audiences with scalability but also opened doors to sophisticated forms of ad fraud that undermine their efforts to outshine the digital ad market. But at the same time, advanced AI-ML solution provider mFilterIt empowers the brands to fight this complex battle proactively.   

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click fraud

Click Fraud: How to Protect Your Digital Ad Budget

Blog, Ad Fraud /

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 cost 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. For advertisers, understanding these tactics is crucial to protect their investments and ensuring that their marketing efforts reach genuine and interested audiences.  Measuring Quality of Clicks- Need of the Hour The measurement of quality of clicks on ad campaigns is still on the backburner. Therefore, the quality of clicks is currently going unmeasured. This is causing significant ad budget to remain unoptimized or wasted across the industry.   The current impression-centric approach to traffic validation leaves out many click and post-click parameters indicating poor quality traffic. Clicks are measured on trusted advertiser domain vs impression tracking on publisher domain. PPC campaign formats are not within scope & this makes the poor-quality clicks go undetected. Walled Gardens don’t allow impression-level tracking: a major chunk of ad spends are not evaluated.  Click Fraud affects both Web and App Inventory. Therefore, validation should be done on both aspects.  Why Impression Validation Alone Isn’t Enough There is a need to supplement impression-level checks with click measurement.  General Constraints  Impressions are measured in Publisher domain and data is limited for analysis    Trackers can easily bypass by using Safe-Frames / iFrames.  Traffic Validators often, due to the huge volume of impressions, only end up sampling the data instead of per transaction validation  Tech Constraints   Limited data is available for analysis i.e. only IP + User Agent  Impressions are the easiest to spoof!  Time available for analysis is Limited i.e. 20ms approx.  How Does Click Fraud Work?  Click fraud happens when publishers artificially increase the number of clicks a PPC or CPC advertisement receives with bots. Invalid clicks do not bring about any desirable visit or event, such as generating leads or sales. Instead, they serve only to enrich fraudsters and drain the budgets of legitimate businesses. Malicious intent is at the heart of clicks fraud. Scammers use fraudulent clicks to show improved interaction on the ad and inflate their revenue from ads.    The advertisers cannot rely on data from digital advertising campaigns and website metrics as it is plagued by fraudulent traffic. The other output also is the damaged reputation of the business.   Ways in Which Click Fraud Happens  Click Farms Click farms are organized operations in which low-paid employees physically click on advertisements or perform specific tasks to mimic genuine user behavior. Click farms pose a major threat because they can simulate true patterns of users’ behavior that standard detection algorithms cannot detect as fraudulent activities. They are commonly used to increase visibility for ads or to deplete a competitor’s ad budget.  Click Injection Click injection is a more advanced kind of click fraud mainly targeting mobile applications. In this case, harmful apps installed on the user’s device insert false clicks into the user’s journey at a certain moment before app installation usually takes place. This creates an impression that the app installation resulted from that fake click thus enabling the perpetrator to take credit for it.  Click Spamming Another form of fraudulent clicking is called click flooding or click spamming. In this case, scammers produce many clicks using bots or automated scripts to spam an advertiser’s network with false traffic. The idea behind it is to create a situation where there would be so many clicks on the system to even get any attributed to true conversions.  This kind of fraud is especially dangerous because it can corrupt data, making it difficult to accurately analyze campaign results. Click spamming may result in an over-inflated CTR or skewed conversion numbers, leading to misinformed decisions and wasted ad budgets.  Pixel Stuffing Pixel stuffing is a sneaky form of click fraud where ads are crammed into tiny, often invisible, pixels on a webpage. These pixels are so small they are almost not visible to the naked eye and yet they count as ad impressions and clicks whenever a user visits the page even if that user interacts with the ad unknowingly because advertisers will be charged for clicks.  This tactic is particularly insidious because it exploits the trust that advertisers place in ad networks to display their ads in visible and relevant locations. Pixel stuffing results in advertisers paying for impressions and clicks unlikely to lead to conversions which has a significant impact on return on investment (ROI).  Geotargeting Click Fraud Geotargeting click fraud involves manipulating the location data associated with clicks to make them appear as though they are coming from a specific geographic area. This type of fraud is particularly common in campaigns that target users in certain locations, as advertisers are often willing to pay a premium for clicks from these regions.  Scammers can, however, use VPNs, and proxy servers, or manipulate the location settings on devices to generate fraudulent clicks that seem to come from high-valued areas. This leads to advertisers paying more for clicks that are not genuinely from their target audience thereby significantly reducing the effectiveness of geotargeted campaigns.  How to Prevent Click Fraud   Advertisers are always encouraged to look at the data in their campaigns at a granular level. More than often the signs are visible. The first step in combating click fraud is to determine the major signs that should raise red flags. If you see the following issues within your analytics data, click fraud may be occurring:  Atypical clicking behaviors High traffic rates with low conversions  High bounce rates  Interactions from odd

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how click fraud drains your ad budget?

Click Fraud: A Silent Budget Killer

Blog, Ad Fraud /

Imagine the plight when the ad spends which were allocated to acquire new users and expand reach is now being cannibalized by nefarious affiliates. Brands and agencies often consider well-reputed platforms like Google, Facebook, and other platforms to give them good-quality traffic.   In 2022, advertising spending that was wasted due to invalid traffic was $54.63 billion on the global level. The Statista projected that by 2027, the spending would reach $870.85 billion.  Click Spam and Fake Attribution are all instances of click Fraud, and they contribute to 74% of Click Fraud.    What is Click Fraud?   Click fraud, also referred to as pay-per-click fraud, is a kind of fraud that artificially inflates traffic statistics for online advertisements. In the typical pay-per-click advertising model, advertisers pay a fee for each click on their advertisement, hoping that they have attracted a potential customer.   Click fraud creates the illusion that many potential customers are clicking on the ad. However, the advertiser is unlikely to make any real human visits from these clicks as they are done by bots. It is done to increase the revenue of the publisher, and it drains the advertiser’s budget.   Types of Click Fraud?  Click Fraud ID is dominant both in the web and app ecosystems. Various types of click fraud can be seen in the digital ad ecosystem.   Sophisticated Bots- Bots are automated scripts acting as users, they go on targeted websites and create fake impressions. Bot activity comes from devices infected with malware viruses.  Click Farms – A click farm is a network of bots or a fraudulent operation of publishers that employs large groups of people to manually click on paid online ads.   Ad stacking – It is a type of mobile ad fraud in which the fraudsters stack or hide multiple ads on top of one another beneath the primary ad.   Install Hijacking– This kind of click fraud aims to make an application installation appear legitimate.  This is accomplished by installing a fraudulent app covertly. The fraud app overtakes tracking codes and attributes these installs as one that occurred because of it.   Device ID fraud– This method is used on device farms with multiple devices. The device downloads an app and clicks on real ads by using a script to click on actual ads. The gadget is then reset after that. This keeps happening over and over. There are IP address switches involved to gain the legitimacy of the act.   Incentivized traffic– Traffic created by users who visit websites in exchange for various rewards such as money gifts, discounts, whitepapers, or game tokens. It increases website traffic and provides insightful customer data.   Red Flags of Click Fraud?  If your PPC ads regularly exhibit any of the following signs, you may need to consider reducing your exposure.   High Bounce Rate   Unprecedented increase in impressions and clicks   High traffic rate but low conversions. Unusual clicks from some obscure country   Anomalies in performance data  Brands need a multi-level protective mechanism to track and validate click impressions on ads. Advertisers must strengthen their defenses and prevent wastage of ad budget.   How can it be Prevented?  By implementing an active ad fraud detection system that keeps an eye on clicks and impression integrity. With AI-ML advanced technology,mFilterIt assists advertisers in real-time click fraud detection. The sophisticated algorithm aids in locating anomalies in the click data.   Let’s understand this with a case study of a major automobile player running a Google search campaign to attract new customers through various meta platforms.   Use Case   The Client faced challenges despite a healthy advertising spend, the conversion ratio (lead generation) was suspiciously low.   The key initiative to resolve this issue involves the process of:   Blacklisting is aimed at filtering out fraudulent clicks and leads.  Ensuring cleaner traffic   Initially, the fraud rates were high with click rates at 21.44% and lead fraud at 15.56%.   After the blacklisting process began there was a significant drop in fraud rates.   Click fraud has reduced by 13% and lead fraud has reduced by 11%.   Conversion Ratio trends showed an upward trajectory from 3.82% to 6.71%.   The impact on the brand blacklisting process enabled the brand to save $0.47 million due to reduced fraudulent activities and improved conversion rates.   The case highlights the importance of monitoring and managing digital advertisement campaigns to mitigate click fraud and optimize performance.   Final thought   Unchecked click fraud has the potential to damage a brand’s online reputation and gradually reduce trust in digital advertising campaigns. mFilterIt provides a strong answer to this widespread problem by successfully detecting and guaranteeing that advertising dollars are spent on genuine interactions. By removing invalid traffic, and blacklisting process with maximization of return on investment, it is an indispensable instrument to counteract click fraud.   Get in touch to learn more about Click Fraud.  

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why-is-digital-shelf-optimization- important?

From Invisible to Unmissable: The Power of Digital Shelf Analytics

Blog, Digital Shelf Analytics /

In 2023, the global e-commerce retail reached $5.8 trillion as per a Statista report.  Further, by 2027, it is expected that global retail e-commerce sales will cross $8 trillion indicating a 39% growth.  With the growth of e-commerce, every marketer must pay attention to digital shelf analytics. It is your secret weapon for ensuring your products shine in the vast online landscape. You can outperform the competition and generate more sales by refining just about every aspect of product listings ranging from search engine rankings to customer reviews.    There is a need for every e-commerce player to have access to business intelligence in real-time and in actionable form to ensure the success benchmarks are achieved. Business intelligence needs to be granular to the last detail and scalable by geographies and platforms.   What is Digital Shelf Optimization?  The term digital shelf precisely means online shelf, where the shoppers search for the required product, compare it with different products at different platforms based on pricing and quality, and finally decide what to buy.   Digital Shelf Optimization refers to the process of tuning and presenting these online components in such a way that it makes the product dynamically visible and is easily accessible to potential buyers.  Why Digital Shelf Optimization is important?  The digital shelf is where brands compete for customer attention. With traditional marketing converting to e-commerce at a lightning pace with the help of a digital shelf analytics, that helps a brand to create a strong presence on the digital shelf is critical for success. Effective Digital Shelf Optimization ensures that your products are not just listed online but are positioned in such a way as to attract and convert customer searches into sales.   It typically covers the stages of awareness and interest, consideration and evaluation, and the ultimate purchase decision. A holistic outlook of the entire customer journey is critical for optimizing your customers’ e-commerce journey.    How to Win the Digital Shelf  It is important to consider the vast range of analytical metrics while devising a strategy to sell on e-commerce platforms. Analytical requirements are real-time and actionable insights is the need of the hour. The following are a few of the important metrics every e-commerce player should track:  1. Discoverability Analysis  Discoverability is all about Share of Shelf/Search. The brand should track Share of Shelf across the competition in the category across a wide range of e-commerce platforms (both apps & web) and locations.    Enhance brand discoverability based on keywords  Monitor and measure the digital share of shelf performance  Slice and dice through various platforms, cities, product categories, etc.  Brands can identify the right keywords and take appropriate actions.  2. Banner Analysis  To monitor the positioning of the category page Share of Voice and Share of Shelf banner with analysis of the keywords and images on the banner.    It can be analyzed based on the Home Page, Category Page, and Keyword.  Banners spread by themes and theming by brand,   Display banner and word cloud of keywords on the banner  SOV across brand and sub-brand  3. Pricing Analysis   Identify and monitor pricing and discounting trends across e-commerce platforms on own brands vis-à-vis competition. Get real-time industry-dedicated market insights to build improved pricing & discount strategies to improve revenue.  Real-time industry ASPs & discounts vis-s-vis your own brand.   Category averages along with current Brand price & discount metrics & trends.  In sighting into highest & lowest ASPs & Discounts based on platform, city & category.  Identify MAP violations & pricing violations by platforms using OEM codes.  4. Availability Analysis   The Availability tool helps the Brand understand the availability trends across platforms. Competition analysis enriches insights into geographies that can be targeted to increase reach and capture new customers.  Availability tracking by Platform, Location & Subcategory of own brand vis-à-vis competition,   Insights based on availability into potential geographies to target customers,  Monitor the Out-of-Stock (OOS) Status across products, regions, and platforms.  Availability Trend Analysis along with continuously Out-of-Stock Products.   Sales Analysis   The sales analysis tool maps brand sales data across platforms, products, and key variables like availability, pricing, discount, sentiment, ranking, etc. and identifies parameters that are impacting your sales.  Map brand sales time trend across platforms for own brand  Provide In-depth weekly sales insights to compare with changes in sales, availability, pricing, rating, organic & sponsored rank.  Provide In-depth weekly sales insights to monitor changes in sales, availability, pricing, rating, organic & sponsored rank.  Conclusion  Regarding brands, digital shelf analytics is important for remaining competitive in the shifting world of e-commerce. It helps in important positive ways when it comes to the customers finding the products, ongoing optimization efforts, keeping an eye on the competition, and ensuring the very best first impression for the brand’s online mention. These take in optimizing product display pages, monitoring reviews and ratings, managing items that are out of stock, paying for promotions, and maintaining consistency. With the help of advanced Digital Shelf Optimization strategies paying attention to more product visibility, the brands can not only ensure their digital presence and escalate sales but also build loyal trust.  Get in touch to learn more about the digital shelf optimization.

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