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brand-safety

Importance of Brand Safety in Performance Marketing

Leave a Comment / Blog, Brand Safety & Protection /

Your brand is what other people say about you when you’re not in the room”- Jeff Bezos. The steady increase in fake news, extremist content, and ads appearing next to inappropriate content has led to a significant brand safety issue. Advertisers today are trying to address the fundamental question: Are we placing our ads in a brand-safe environment? No advertiser would want their branded content or commercial offers placed next to nasty or negative news stories or unsavory websites or apps. With the rise of social media platforms, people spot and share campaigns that have gone wrong in no time and are spread among the masses before you get to know them. Brands that advertise on fake or negative content websites are viewed by people as poorly, and consumers get a negative opinion of the brand. They believe that they will not buy your product or service since you are supporting them. In other words, they will hold you accountable for the inappropriate placement of ads. Therefore, it becomes the responsibility of advertisers to protect their brand name and reputation from negative or inappropriate content when advertising online. But many advertisers are aware of Brand Safety in brand campaigns. But ignore the perils of brand safety in Performance campaigns. Performance campaigns have a different payout model and the end metric that the advertiser optimizes against. But none of these have any relevance for the end customer. A brand unsafe placement has the same problem in Performance Marketing and Brand Marketing. Let’s look at different issues of Brand Safety in Performance Marketing. Different Types of Brand Safety Issues in Performance Marketing: Misleading Ads Placement Fraud: Misleading Ads by some fraudsters or fake websites makes end customers feel that the advertiser is misleading them to get traffic (clicks/ leads), etc., thus impacting the advertiser’s brand image. Publishers try popups, spamming a user repeatedly, showing ads that the user is forced to click upon, with no option to cancel, etc., all generate clicks and some performance, but they impact the brand. They indicate to the end customer as if there is the desperation of the brand, which may not be the correct indication to show to customers. Incentivized Campaigns: Fraudulent Affiliates run non-incent marketing campaigns over incent platforms where the user downloads or uses the app for a particular incentive rather than an actual interest in the app. This can be via incentives for filling up leads, etc. These campaigns bring low-quality users, resulting in low engagement; they again put the brand in an unsafe environment. A brand would not want to show it is ready to get installs/leads by any means to the customer. It impacts the premium-ness of the brand. Apps Placed on Third-Party Stores: Publishers often extract APK files of Advertisers’ app and place them on alternate third-party app stores. However, it contains potential malware that acts as a Trojan horse when placed. Your app carries the malware inside it. Technically, all privacy-related/fraud-related aspects are driven via your app only (since, for the customer, the malware is part of your app itself). This again impacts the advertiser’s brand image and can be a PR disaster if this ends up perpetuating financial fraud, for example. Google Search Misleading Ads on Brand Keywords Fraud: Publishers (and especially affiliates) can start bidding for brand keywords of the advertiser, and who misleading ads, which when clicked take the user to the advertiser website itself. These misleading messages can be extremely high discounts or talk of false promises. For the user, he searched on Google for a brand keyword and saw the Ad that had the brand Ad-URL, and when he clicked on it, it went to the Brand             website only. So, this is a safe and trusted journey as per the user. However, the ad placed is by an affiliate and contains false discounts or promises. This can make a customer unhappy since he will not get the same                discounts finally. Coupons and Cashback Tracking: In this type of fraud, fraudsters copy coupons and change the product details like name, expiry date, etc., and distribute them among the people via email, social media, etc. Fraudsters deceive the user with a false offer or communication by sharing these false promise coupons. The customer is deceived into using that coupon code, and the publisher still gets paid the total amount. The false offers can be brand-impacting by promising huge discounts that otherwise are unavailable. How mFilterIt helps advertisers with Brand Safety issues: mFilterIt is the only solution provider that helps its customers ensure brand safety for their performance campaigns mFilterIt Brand Safety Suite: Brand Safety (Incent Tracker): An automated tracker that tracks 100+ incent / porn, etc., apps daily and ensures that the customer app is not present. If the brand appears on the blacklisted locations, our automated system clicks on the ad to identify the publisher. Non-Play Store Tracker (mAppVault): A tracker which looks at APK files of your app present in alternate app stores and flags off cases where your app has been modified with malicious code. Search Keyword Solution: The solution keeps scanning for misleading and unofficial Google / Bing search ads where the brand is misused to make false promises. The solution ensures that brand unsafe placements are detected and actioned against immediately before it becomes a problem for the brand. Coupon/Referral Tracker: The solution tracks coupon codes for your brand and tracks which publishers are running them at different placements. This provides transparency to the brand and allows the advertiser to quickly understand the source of performance for different partners and takedown partners running brand-unsafe activities. Get in touch to learn more about the Importance of Brand Safety in Performance Marketing.

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click-injection

How to Tackle Click Injection?

Leave a Comment / Blog, Ad Traffic Validation /

In the click injection, Click is injected where a malicious publisher(apps) on the phone notices that the “ABC app” is being used by the customer and fires a click in the background. As the user is browsing on the “ABC app”, the click has been sent and the order captured. Hence, the attributes are manipulated, and payment is made to the wrong media source instead of the actual (and deserving) source. There are two levels of attribution: Click to Install Attribution: If a user clicks on an ad, we need to track the validity of that click that led to the installation or conversion. For example, a 7-day or 14-day attribution is considered a standard attribution window in many performance campaigns. If a click has been performed within the set attribution window, the click is valid for attribution, and the publisher that fired the click will be attributed to the install. Install to Event Attribution: The subsequent events after the installation are tracked, including add-to-cart, sale/purchase, booking, etc. The attribution window can also be defined from installation to the sale/purchase event. For example, many performance campaigns, from installs to a sale event, can vary from 24 hours to 30 days, depending on the advertiser’s marketing strategy. Steps Fraudsters Use in Click Injection: Fraudulent app installed on phone. When a new app (Advertiser app) is installed, fraudulent apps and other apps also get notifications through installation broadcast. This broadcast is essential to create a tight connection between different apps. The malicious app installed in the phone keeps performing its unsuspicious action until it listens to an Install Broadcast. Fraudulent apps push manipulated clicks. This click seems genuine as it has the device’s id and other records of the targeted device. Ads attribution services start tracing clicks in reverse chronological order and therefore determine the Fraudulent app’s click as the last-touch click and attribute this event to this fraudulent app. In this process, both genuine publishers and advertisers suffer losses. Genuine publishers do not get paid for their genuine efforts, and advertisers end up paying to the wrong channels. Many apps on the Play Store have been caught doing this. The case of Cheetah Mobile is classic in this, where all apps of CM (which were very popular and had millions of installs between them) would inject clicks to steal organic/inorganic installs from other sources. Further, users may unintentionally install a malicious app that performs non-suspicious operations, such as auto-change wallpapers, flashlights, cat-voicing, etc. It would appear harmless to them. These malicious apps are usually available on unverified Android sources for free. Such apps have permission to inject a click to run another application and listen to the ‘install broadcast’. How to Prevent Click Injection? Through Data Analysis: To detect click injection, mobile measurement partners need to track timestamps for when a user started an install (click-time) and when an install is finished on the device (conversion time). With access to this information, we can prove the user’s intent to install came before the fraudulent claim. Therefore, those claims can be detected before attribution, meaning that ad spend is safe from click-injection fraud. If we analyze the data pattern of a click injection, we can find that click-to-install time will always be less than expected. This generally works only to identify the more extreme and obvious cases of click injections. Users may take their own time installing and opening the app, which means that even if the click is injected, the time when the user opens the app can be outside the limit set. Use Google Play Store APIs (Only for Android): Google released Play Store Referral APIs, which provide timestamps of the time of click and download of the app from the App Store. These are more accurate and effective in ensuring the detection of click injections. Unfortunately, it works only on Android and not on IOS. Machine Learning and Artificial Intelligence: These methods seek for accounts, customers, suppliers, etc., that behave ‘unusually’ to output suspicion scores, rules, or visual anomalies, depending on the method. These methods can identify fraud with very high degrees of accuracy. Be Transparent with Publishers/Affiliates: As an advertiser, demand better transparency from your publishers or affiliates. Request publishers to identify all third-party traffic sources. If a publisher seems reluctant to identify his traffic sources, that indicates possible malicious activity and something to look out for. Implement Third-Party Fraud Monitoring: As fraudulent practices continuously evolve, it is challenging to identify all types of advertising fraud and block them in real-time. Implementing a third-party detection system will allow you to identify and block fake activity. Impact of Click Injection Click Injection creates a negative loop where the advertiser continues to pay someone else for the users they would have already acquired organically (or at least through other marketing channels). It captures organic traffic, brands it without the user’s knowledge, and then claims credit for it. It ruins the accuracy of a marketer’s data and impacts accurate decision-making. Few Exceptions: Coupons Sites/Deal Sites: A user adds a product to the cart but then figures out if there are any coupons/cashback available and clicks on the affiliate website later. Retargeting Sites: A user adds a product to the cart but changes his mind and keeps browsing some sites sees the ad and later decides to buy the product, so the time to add to the cart to click is more. mFilterIt’s Role: With its machine learning-based algorithms, mFilterIt tracks the characteristics of each device as per what it should be. The solution includes various situations and environments to detect and protect from various types of fraud. We combine cutting-edge machine-learning technology and a dedicated team of data scientists who endeavor day in and day out to help app advertisers flush frauds from their ecosystem, thus increasing their ROI. Get in touch to learn more about the Click Injection.

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Identifying Click Spam Deterministically

Leave a Comment / Blog, Ad Traffic Validation /

Within the gamut of techniques resorted by fraudsters to ad fraud, koi dikh is the most common SIVT (Sophisticated Invalid Traffic) method used to spoof the performance. Being the most common technique, 40-50% of the marketing dollars lost due to ad fraud is eaten up by the fraudsters through Click Spam. So how do we tackle Click Spam deterministically? Two main tests are carried out on any campaign to identify Click Spam and its impact. i) Click-Install Time Series ii) Outlier Publishers i) Click-Install Time Series Analysis: In this first essential step, the behavior of click to install is analyzed to understand the pattern over some time. The time gap between the click and the install cannot be comprehensive in any genuine traffic source. A user will click a source and then install an app. It cannot be that a user views a campaign and installs it later after a considerable gap.   On the contrary, in bogus traffic sources, the installs will show abnormal plotting, which interprets as users installing apps after an interval once they click a campaign or an advertisement. Logically, this is never possible. Even if one may argue that the user would have seen the campaign on the go and later decided in spare time about installing the app. Or, a scenario where the user discovers an app while surfing for something and later in the evening decides to install the app discovered during the day. Yes, all these scenarios are real and can result in abnormal distribution on a time series analysis. But this cannot happen in large volumes. These are unique and isolated behaviors that cannot be generalized to the masses.   ii) Outlier Publishers: Data can tell almost everything. The Click to Time analysis cannot determine between genuine and fake installs. There are other factors to consider before establishing Click Spam sources. For this, it is essential to identify the outlier publishers.   A baseline analysis is done by studying the click rates of different publishers running a campaign. Logically, the app should target similar users showing more or less the same behavior. This means the publishers should also get some behavior in their campaigns. A baseline analysis helps understand the expected genuine clicks/installs on a campaign. Historical data analysis is also helpful in establishing a baseline. Once the baseline is established, the click rates achieved by various publishers are plotted. It is understood that the publishers cannot exactly fall on the baseline. Hence, a range of tolerance is defined using a proprietary algorithm that factors several parameters. If the publisher falls within this range, it still delivers valid traffic. However, if the publisher shows performance way beyond this range, it is detected as an outlier, resorting to click spam to spoof the performance. There is no magic wand with any publisher to achieve substantially different results than other publishers. Conclusion: The campaign analysis helps determine the click spam fraud rate and impact unambiguously. Together, these two tests identify the sources fetching invalid traffic, which is a direct dollar loss for the advertiser. Only by blending the analysis of Click to Install time with the identification of an Outlier Publisher, mFilterIt deterministically pinpoints the fake sources, resorting to Click Spam to fake performance and getting paid for non-performance tricking the advertisers. Let’s engage in a detailed conversation on the Click Spam ad fraud technique and how it’s impacting brands bleeding their marketing dollars. Get in touch to learn more about Click Spamming.

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decoding

Decoding mFilterIt

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Many times, team mFilterIt is asked one basic but important question. What does the name mFilterIt stand for? In the journey so far, we have seen ourselves evolving by widening our horizons and thus creating an impact growing exponentially year after year. Today, mFilterIt is in its 3.0 version. The story began with making the mobile ecosystem clean and working on various challenges the mobile ecosystem faced. Apps were being built and deployed in millions for which brands were paying to discover users. This is even happening now. The second era for mFilterIt began with the thought of offering holistic solutions. While it is a fact that digital is becoming synonymous with mobiles, yet web is relevant. There are a lot of B2B2C transactions like lead generation for Banks which takes the web route with a direct selling agency in between predominantly. So, to offer a holistic fraud-free digital experience, the web became necessary, and the ‘m’ in our name became more of marketing, while the focus on mobile did not reduce. The relevance and purpose of going digital have changed. Businesses are no longer available on digital for marketing presence and amplification. It is the default business platform for new-age businesses while legacy businesses and sectors are catching up. The mFilterIt team’s conversations with its customers and other partners are now getting beyond marketing, essentially everywhere where there is an element of fraud, and mFilterIt could save money. This is mFilterIt 3.0, where ‘m’ has acquired three meanings:’ mobile’, ‘marketing’, and ‘money’. The proprietary technology of mFilterIt is used to filter the fake and bogus things taken away from the digital landscape to result in a trustworthy ecosystem where the organizations are getting what they see and spend. mFilterIt is confident of its solutions, which can decide between the angel and the evil, signified by suffixing It with Filter. It also adds a flavor of casualness, underscoring the ease of integration that has been the secret sauce of mFilterIt based on the KISS (Keep It Simple, Stupid!) principle. If the solution is not easy for any advertiser to implement, it is no good. These three distinct phases that can identify in the concise but impactful journey of mFilterIt have been filtering ‘mobile’, ‘marketing’, and now ‘money’. With the kind of Digital Transformation journeys different businesses are undergoing, ranging from services to manufacturing, the meaning of ‘m’ would keep on enriching, and our technology will also scale to keep filtering-It the evils of various fraudulent techniques implemented to achieve quantitative KPIs without any intent to complement it with quality. The future is unpredictable, but one can pick up early trends to see how future opportunities could evolve. At a time when we are at the cusp of the 4th industrial revolution or what is known as Industry 4.0, perhaps ‘machines’ is another flavor of ‘m’ that could be attributed to mFilterIt. One can foresee a lot of similarities in terms of potential threats in Industry 4.0 and the Smart and Connected world where brands could use mFilterIt technology. There will be an increasing demand to ‘tame’ and identify BOTs which can do a lot of harm in such scenarios. For imagination purposes, think of a machine’s operational plan compromised with a BOT which could over or underutilize it. Similarly, a BOT could loop electricity on and off for homes and public places. Examples can keep going on. mFilterIt is a listening organization and works in an agile work environment where products keep on improving and adding to their capabilities. Our R&D and product development teams are continuously working on repurposing and re-engineering the company’s core competencies to increase the impact, which results in growth and strengthens the key business parameters. mFilterIt will keep this blend of robustness and agility as guiding factors to be recognized as a thought leader in the space working with the entire ecosystem to build, nurture, and protect a trustworthy digital space where everyone across the value chain gets rewarded for the good by creating a genuine and pure ecosystem which takes the entire digital experience notches up.

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Brands Vs BOTs: Importance of Decoding BOT Fraud

Leave a Comment / Blog, Ad Traffic Validation, Brand Safety & Protection /

Alan’s Turning remarkable theory formed the basis of computer science today. His famous test ‘The Imitation Game’ was based on whether a machine can fool us into believing that it was a human. The objective of the game was that the interrogator while sitting in a separate room had to identify which of the other two was the person and the machine. The interrogator knows the person by labels ‘X’ and ‘Y’ and does not know which of the other person and the machine is ‘X’. Alan Turning’s argument was that if a human cannot tell the difference between a computer and a human then we should call the computer intelligence. Alan Turning’s test is turning out to be true in today’s world. Almost half of the online traffic is BOT generated. This has led to adulterating the quality and genuineness of engagement driven by various platforms such as financial services, healthcare, travel, and e-commerce among others. It has not left any industry unaffected. In the advertising industry, due to fake BOT traffic, advertisers are losing millions of dollars each year. Fraudsters are becoming more advanced in their workings. They find new ways and activities to inject fake clicks or use bots to generate their revenue. The ability of bots has increased in the past few years to mimic human online behavior. As the line between humans and BOTs blurs, our suspicions are raised; so how do we get to know that real humans are clicking on our ads or installing our apps? The answer to this question is very complicated as there is no clear way to know whether the real human is clicking on the ads or not. How does BOT fraud occur? Fraud publishers use BOTs to send multiple clicks to the landing page or to fill multiple leads to earn money from advertisers. BOTs avoid traceability by changing the IP address presented at the time of the transaction from the original IP address of the device, which is either hidden or tampered with. In the absence of any fraud check, the advertiser ends up paying for fake clicks or installs. 2 Different Kinds of BOTs BOTs are trained to do multiple things at the same time. There are two kinds of BOTs: Good BOTs: They are used to gather information. BOTs in such disguises are called web crawlers. Good BOTs are used to interact with customers in an automatic form. Bad BOTs: Bad BOTs or malicious bots are self-propagating malware that infects its host and connects back to a central server(s). The server functions as a control center for the network of BOTs. These BOTs can gather passwords, obtain financial information, relay spam, log keystrokes, launch DoS attacks, etc. How to make sure that you are paying for genuine traffic? Paying for genuine traffic is never easy when it comes to performance marketing campaigns. Since the Alan Turning test, not much has changed apart from the real human interrogator, now we have technology solutions that act like an interrogator and help us identify the BOTs traffic from a genuine one. mFilterIt ad fraud solution helps in identifying invalid traffic due to ad fraud in your campaigns by using different kinds of algorithms. Get in touch to learn more about the Importance of decoding bot fraud.

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ad-bot-fraud

How Could Ad Fraud Land You Up Dating BOTs?

Leave a Comment / Blog, Ad Traffic Validation /

Unaware of the complexities in tech, users end up interfacing with machines. Ad fraud is seen from a very myopic and transactional view by the entire ecosystem. Due to this insensitive nature of advertisers and publishers, an ordinary user of the service or application suffers. As per media reports, the latest buzz in the app world is Gleeden, a French dating and social networking service primarily marketed to women. Its success in India is also skyrocketing. With over 8 Lakh users in India, the app witnessed over 300% increase in subscriptions compared to the previous couple of weeks. That’s a joy ride for the app! BOT-driven users and traffic have been degrading the quality and genuineness of engagement driven by various platforms offering e-commerce, financial services, healthcare, travel, social networking, dating, and whatnot. This is literally ‘burning’ money of the entire digital value chain, including the investors who put money into growing ventures to help them scale up. But what is more damaging and consequently far-reaching is the overall experience of any user who is seriously looking at the service or value offered by the app or service. Imagine apps and use cases like dating, etc., where users come up with more of an emotional reason and look for satiating very intangible feelings. If the users on these platforms are either BOTs or the profiles are not validated, which aren’t, the whole reason for being on the platform is jeopardized. Some people also get extremely serious about these services, and the engagement could be beyond a superficial connection. In that case, a person is emotionally drained and heart-wrenched upon learning that the engagement has either been with a BOT or an imposter. This is a considerable brand safety issue where the credibility and reputation of the service go for a toss. Retail or financial services need to be careful about ad fraud and brand safety. Still, it is also equally important for platforms like dating and social networking apps to have a clean and trusted user base leading to genuine engagement. Digital platforms cannot do without inorganic growth. They will have to continue spending on Performance campaigns to get the platform discovered and potentially acquire users. However, it needs to be done with precaution to ensure that we are not paying for something that is fake and can rip apart the platform’s reputation at any stage – from acquisition to re-engagement. There is an old saying, “Precaution is better than cure,” A cure is always expensive and unsuccessful in reversing the damage. Ad fraud is one such classic example where even increasing budgets on damage control will not yield the desired results because one single bad experience makes its eternal mark in the minds of a prospect or a user. That’s the extent of damage ad fraud can cause to the safety of a brand. Get in touch to learn more about Ad Fraud on Dating Bots.

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app-ad-fraud

App Ad Fraud Continues to Be On the Rise in India

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India witnessed mobile ad fraud of over Rs 573 crore during Q3 2019 over fake installations. A recent report by Sensor Tower ranked India as the country with maximum app installs in 3Q (Jul-Sep) 2019. It reported 5 billion app installations for India out of 29.6 billion app installs globally. This is excellent news for the country. However, at the same time, it also means an increase in ad fraud. As per mFilterIt internal analysis, over 273 million fake apps installation during July 2019 in India alone. This translates to a loss of over Rs 573 crore in Performance Marketing spending. Over 15% of the total app installs come through publishers, with an average fake user rate of 35%. Publishers are essential stakeholders in the value chain as they hold and influence particular communities that are potential users of several apps. This makes the engagement of app makers inevitable with the Publishers. At the same time, it is not that all Publishers resort to ad fraud and acquire fake users for the advertisers. Some Publishers get 100% validated genuine users to the Advertisers. For marketers, the key to success is engaging with a neutral ad-fraud solution that can validate the KPIs claimed by Publishers in an unbiased way. With too many apps available to users and the app ‘real estate’ becoming increasingly precious, it becomes equally essential for advertisers to engage with genuine users who not only install an app but also keep the engagement on. With the valuation models changing for businesses, the user base no longer remains the only factor to gauge success. How engaging the users are with an application is the most critical part. There is an increasing challenge of Brand Safety, which comes with ad fraud. The organic traffic stealing misaligns the brand positioning and raises doubts about the performance of organic marketing, which does not come cheap. Also, organic performance is much more robust and has long-term implications for the brand. To conclude, advertisers must engage with Publishers and even have a reward system for the best partners. However, the performance cannot be judged by looking at attribution results alone. There has to be a neutral third-party validation that brings transparency to the system. That’s the most straightforward resolution of the issue. Get in touch to learn more about Ad fraud in India.

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app-ad-fraud

Apps Ad Fraud: Stealing an App Install after Install

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With the push towards higher and higher KPIs and engagement checks by advertisers for their App Install campaigns, it has become more and more difficult for publishers to generate revenue simply on the trading game. The alternative: Resort to Ad Fraud. Till recently the Click Spamming fraud whereby fraudulent publishers would fire thousands of fake clicks continuously to capture organic traffic was the way to go for publishers to generate revenue and at the same time provide fantastic quality and meet KPI benchmarks for advertisers. We have recently come across new fraud in the App Install (CPI/CPR) advertising campaigns driven through affiliate networks where Organic and Inorganic installs driven through other networks/publishers are being captured and converted to your name! It is an amazing process of simply stealing an install attribution right at the very last stage of the attribution cycle : Capturing the Install AFTER the Install has been done!! When an app is installed and opened, only then does an attribution platform tracking get enabled. This is part of the Android OS restrictions whereby an app is not allowed to execute simply upon being installed. However, after an app is installed (organically or inorganically), and BEFORE it is opened by the user, there is a small time. Typical studies done by us indicate an average gap of 10 seconds between an install and actually, the app is opened for the first time. This increases substantially for larger-sized apps (since users will typically start doing something else while the download is happening). Now, many publishers have malicious apps that detect the installation of an app on the device (Android actually has a basic API to allow other apps on the device to know about a new app install!) and trigger a ‘fake’ click from the background AFTER the install but BEFORE the user opens the app. Simply by this one fake click, the install has been STOLEN from organic or even other inorganic channels! The reason? Attribution platforms attribute the installation based on the last click received. In this case, the last click was received by this fraudulent publisher overwriting the organic attribution or even the inorganic attribution of some other network! Since the fraud publisher did not have to fire thousands of fake clicks to capture the installation, the CR% (which was a good indication of Click Spamming fraud) will no longer work. Since this will capture both Organic as well as Inorganic installs, the quality of users acquired will be average. So the normal indicators of Click Spamming no longer work. Size of this Fraud : We estimate Click Spamming to be swindling $15m of Ad Spending each year within India. This is an estimate based on the detection we have done for many of our clients and is only an estimated number. Solution: We at mFilterIt detected this fraud in the Indian market as recently as 1 month ago and can track and detect these frauds deterministically as part of our Ad Fraud solution mFilterIt. Many of our customers benefit from this solution and save thousands of dollars in ad spending which are being wasted on paying for Organic traffic or incorrectly captured traffic. mFilterIt is now validating more than 1m installs daily and working with many of the top app advertisers in the country. We aim to provide value and savings to our clients on their Ad Spends which are getting wasted on fraudulent activities in the advertising world. Get in touch to learn more about the Ad fraud in App install.

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Lead Predictor & Call Center Optimiser by mFilterIt

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mFilterIt has launched its Lead Predictor and Call Center Optimiser tool which will help advertisers “predict” the conversion of a lead in real time!! We will be able to identify which leads are punched-in, fake, or bots as the lead is filled up and block them from triggering the call center itself! Preventing a lead that is fake or punched-in to even reach customer care and hence save costs for the advertiser. The Background : When advertisers run lead campaigns, they generally pay on call center-validated leads. This is done to safeguard against fraud, since only when a lead’s contact number is reachable, the lead will be paid for. Unfortunately in this process, while the advertiser has safe-guarded (but only to some level) the payment of fake and dummy leads, the call-center costs would shoot up. Further, the actual frauds that are currently being done in lead campaigns like : Punched-in leads: leads filled by publishers of genuine users but without the users showing any interest or even being aware of the product or Fake call-center leads: where publishers fill leads with phone numbers belonging to their own call-center users, who will accept the calls but will never actually convert for the brand to bypass the normal scrutiny, since the call from the brand’s call center will always be complete, but no end-gain will come out of it. End impact on the advertiser : 1-Lower final conversion ratio 2-Higher Call Center Costs 3-Higher payouts to Publishers for fake leads How we do it! mFilterIt Lead Predictor and Call Center optimization tool will detect these cases in real-time, which can be used by advertisers to prevent fraudulent leads from even reaching the CRM and further the call center. This means : 1-Immediate lead validation 2-Improved focus on actual genuine leads 3-Lower call center costs 4-Higher ROI and Conversion Rates 5-Lower payouts to publishers And proof point of how good we are? In multiple campaigns, our false-positive rate (leads predicted to be fraudulent end up actually converting for the customer) is less than 0.5%. All this with almost zero tech efforts, a start time of less than 30mins, and many more features of our lead platform like : 1-Lead Data Enrichment to enhance the lead information for better ROI of genuine leads. 2-Email Verification to prevent fake/mistyped email IDs from going into your digital marketing database and resulting in hard bounces and IP reputation issues. 3-mTrackIt, our Publisher Management tool, removes the need for cookies of publishers and eliminates all manual operational activity of onboarding publishers. Many large brands have already shifted their lead campaign to our technology. Reach out to us and see how we can improve your ROI on your lead campaigns from Day#1 with Zero Tech efforts and maximum returns. Get in touch to learn more about the Lead Predictor and Call Centre Optimizer.

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app-privacy

3 Major Threats From App Piracy That Brands Cannot Ignore

Leave a Comment / Blog, Ad Traffic Validation /

Do you know? 85% of apps can be decompiled and modified to be injected with malicious code triggering undesired behavior of an app with ulterior motives. APPs have become the default interface for users to interact digitally with people, services, and platforms. Globally, an estimated 3 million apps are available on Google Play Store. The common man’s perception is an app is a distinct and infringeable digital asset of an organization. People consider it genuine, especially when it is on a platform like Google Play Store or Apple App Store. However, the fact is that an app can be pirated and can result in App fraud. Techniques like decompiling an app and modifying the package with malicious code lines make an app vulnerable. Essentially three main threats emanate from a pirated app. 3 Main Threats from a Pirated App Compromised Privacy: Irrespective of any such app available over a Play Store or otherwise, if a user inadvertently installs a pirated app considering it to be a genuine version, there is a higher probability of that app being able to access personal data, including contacts, SMS, pictures and other sensitive data that must store on a Smartphone. Ad-Fraud: Compromised apps are used as a medium for fraudsters to control a Smartphone, a publishing medium to fake traffic, users, or events. With malicious code lines put along with the app or digital ads, the fraudsters commit ad fraud by getting impressions, app and even trigger clicks, etc., to fake KPIs agreed with an advertiser whose campaigns are being run. At the same time, ill-practiced publishers steal the organic traffic of mobile apps/browsers to credit any activity a user does to earn the attribution without doing any hard work. In this case, such a publisher reports ‘stolen’ traffic as theirs and credits the attribution to get paid for something they never did. This also demotivates the digital marketing team as organic traffic earned after painstaking efforts is tagged as inorganic. Brand Safety: Another important ramification of a pirated app version is the damage it causes to the image and reputation of the brand. Since the app is compromised, it cannot guarantee its behavior will align with the tenets of a brand, its philosophy, and its guidelines. This means a spectrum of issues. In its simplest forms, the brand, through this rogue app, could be seen as promoting theft of data, infringing on privacy, displaying obscene content, and several similar issues. Since this app is not in the control of the actual brand, it would not act as a responsible digital asset representing it. App Piracy Cannot Be Ignored Unfortunately, app piracy has not been getting its due mindshare from the ecosystem, including governments. There is a need to have strict regulatory guidelines about app piracy for the various damages it could result in, ranging from hampering an individual’s privacy to hurting national interests. While having a national consensus around app piracy is essential, brands cannot and should not wait for the government to intervene. Marketers, every organization, institution, and entity having an app, must keep a vigil on the pirated versions of their apps available either over the Play Store or through non-play store platforms. Android RAT tools like FatRat and other powerful tools like Metasploit help to pass through the security layers of Android by circumventing the security policies and can even bypass an Antivirus and Firewalls, allowing attackers access to a Meterpreter session. These publicly available tools add to the vulnerability of an app where even app permissions are compromised. So, while a genuine version of an app will be genuinely seeking 10 permissions from the device, a pirated version might be taking entirely different or some more critical permissions, which are not required by the app. Still, fraudsters modify them for their ulterior intentions. How Can mFilterIt Help? mFilterIt helps its clients monitor any pirated version created over several alternate app stores and identifies the modification – addition or deletion of permissions fiddled with such duplicated versions. Below are some of the examples to highlight.   In all the above examples, mFilterIt scanned the pirated versions of these popular apps on various APK Stores and identified the modified permissions. This helped the clients take necessary actions and understand the motive behind creating such pirated versions, which ranged from infringing piracy of legitimate users and using these apps for ad fraud. Monitoring pirated app versions is essential for every organization. However, its importance becomes paramount for sensitive domains like government, security, BFSI, healthcare, etc. Consumers need assurance and trust that the app they are installing on their devices is the verified version of the organization or any other entity they are engaging with. There should be a public repository of identified pirated app versions, and consumers must be made periodically aware of fake apps. Get in touch to learn more about the threat of app piracy.

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