Ad Traffic Validation

Click Fraud Decoded

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If you are a digital advertiser, you have been stung by click fraud many times. But there are ways to identify and prevent it. Today, let us discuss what click fraud is, different types of click frauds, and how to prevent them? What is Click Fraud? In general terms, Click Fraud is associated with PPC fraud, where bots click on your campaign and exhaust your marketing budget. This results in many fake clicks and visits to your website and is generally associated with impaired performance. However, this is true for PPC/CPC-based traffic. In the case of performance marketing (where the advertiser pays on an end-goal like sale/lead etc.), Click Fraud takes another color. Now, the typical approach to Click fraud won’t work since the publisher will not get paid for all the fake clicks (since there is no performance). So in performance campaigns, Click Fraud gets changed to stealing Organic traffic (which has the best performance in general for any advertiser). When clicks are dropped randomly to steal Organic traffic is called Click fraud. It is used in the “Last click attribution models” scenario to make the fake click the “last” click before the conversion to steal it effectively. Since Organic traffic does NOT have any click, the fake click ends up winning, and thus the publisher ends up getting attributed to the performance generated out of this. It can occur in both the app and the web. It is done to steal credit for an install or a re-engagement event in-App campaigns. In Web campaigns, it is done to steal credit for a lead/sale by using Cookie-stuffing / Click injections. Types of Click Fraud As many consumers are moving online for their purchases, advertisers have increased their ad budget spending to target any new potential customer. Due to this reason, many PPC fraudsters are upping their game. They use different click fraud techniques to steal the advertiser’s ad spend. These frauds are mentioned as below: Click Spamming: The most common SIVT (sophisticated Invalid traffic) method used to spoof the performance. In this type of fraud, a random click is fired to capture the organic sale and the click-to-sale time difference is more. Recently, a leading health and pharma app company was facing the issue of fake installs; mFilterIt was assigned the task of fraud analysis for unearthly mysteries of performance spent drain. After the analysis, the company found out that Click Spam and Non-Play Store ad-fraud for acquiring new users contributed more than half of the total fake installs. This shows that even the best digitally evolved organizations experience ad fraud. Click Injection: In this type of fraud, a click is injected where a malicious publisher (apps) on the phone notices that the “ABC app” is 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 done to the wrong media source instead of the deserving source. The app’s users generally don’t use on their phones constitute junk apps. Fraudsters can fraudulently use these apps to generate clicks on the user’s device and steal credit for an inorganic install. This method has severe implications on advertisers’ ad spending. Automated Clicks Using BOTs: Fraudsters have created a sophisticated click fraud system using BOTs. They use fake IP addresses to avoid traceability. This type of fraud is often targeted through data stored in cookies on the web. BOTs browse histories, demographic information, and past purchases before targeting a particular ad. This impacts the advertiser as they lose their money by paying for fake visits instead of genuine customer visits. How Can I Prevent Click Fraud? Advertisers need to be aware of various click fraud warning signs. These warnings are as follows: Meager conversion rates (click to conversion rates). High bounce rate (in case of cookie stuffing, publishers will try to open the advertiser website in a hidden iframe to get the cookie dropped, resulting in high bounce rates) Reduction in organic traffic when inorganic sources are scaled up. mFilterIt offers sophisticated technologies that help advertisers detect click fraud in real-time. The advanced algorithm helps to identify abnormalities in the click data.

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How Conversion Rate Links with Ad Fraud?

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What is the Conversion Rate? When advertisers run ad campaigns, their ads are displayed thousands of times on the internet. Conversion rate (called CVR sometimes) refers to the number of conversions compared to the clicks received (a more performance-based metric, generally, advertisers use clicks as the base instead of impressions). CVR can be either a heavily watched metric or a wholly ignored metric depending on what the advertiser is paying for: If an advertiser is paying on the CPC model, CVR will be the most critical metric. Advertisers will spend ages trying to increase the CVR and optimizing their strategies and targeting to get better CVRs. If an advertiser is paying on Conversion (CPS / CPL / CPI etc., models), then the CVR is generally the most ignored metric. The logic is that if you are not paying for clicks, then why bother with how many clicks came or what the CVR for a source was? This dichotomy doesn’t make sense since the basic premise of HOW the advertising is being run remains the same. Only the payout model has changed. Irrespective of whether the payout is on click or conversion, the sequence that is required is Impression Click Conversion This means that the final comparison metric for any publisher to run an ad campaign is CPM only. The publisher will typically continue a campaign if the campaign makes sense with typical CPM metrics. Whether the payout is on conversion or click or impression, for that matter, is immaterial. Consider the below metrics: An advertiser pays $0.50 for every install of their app. Consider a CVR rate of 0.1%. This implies that to get 1 install, a publisher has to trigger 1000 clicks. The effective CPC earning for the publisher: $0.0005 / click. Let’s go one step back. To get 1000 clicks, how many impressions will it take? Let’s say the CTR is 1%. This means that for 1000 clicks, the publisher needs 100,000 impressions to be served. The effective CPM rate here: is $0.000005 per 1000 impressions The question to be considered here is, does the above make commercial sense for a publisher? Is the CPM rate this low to justify a publisher’s running this campaign when there are multiple other campaigns available at better CPM rates? The main reason for this crazily low CPM rate or even CPC rate is the extremely low CVR of 0.1%. The only way this business model makes sense for a publisher to run an ad campaign at this CPM rate is AdFraud. When advertisers ignore the CVR rate and assume that if they are paying for conversion and clicks don’t matter, they turn their back to a critical metric that can identify fraud in their campaigns. Why low CVR indicates AdFraud One key element that is generally missed when running a conversion-linked campaign is AdFraud types: Click Spamming (app-based) Cookie Stuffing (web-based) The point of both strategies is to steal organic traffic and ensure that the end conversions that were already occurring organically are rehashed as inorganic conversions. The advertiser pays for his traffic. These frauds work to take advantage of the last-click attribution model. When a conversion happens, the last click is searched for. If the last click comes through an inorganic source, it is attributed as the conversion source and gets paid for it. As a publisher, I can keep firing clicks repeatedly for different users and device IDs (in the background). If any of those users go organically to trigger a conversion, I will get paid for it. Obviously, for this to succeed, I will have to fire millions of clicks and then hope that some of these trigger organic conversions, which I will then steal. But that means that my CVR will be extremely low. Here is an example of an advertiser who ran a subscription-based campaign in the Middle East. Android Campaign : IoS Campaign: So, in 6 days, this source triggered 15m clicks across Android and IOS. This is amazing. The total population of UAE (target market) is ~10m! So, if this source is to be believed, this source covered the entire country in 6 days and then started over again!! From any logic, there is no way this traffic makes sense. A genuine publisher can’t waste so many clicks and impressions and earn little. So many users can’t click so many times before installing the app. A 0.01% ratio means that users clicked on the ad 10,000 times before installing the app. Would you ever have the time or energy to click 10,000 times before installing an app? So, understanding the CVR metrics for your sources and tracking them regularly is essential irrespective of whether your payouts are linked to impressions/clicks or not. They hold essential trends for you to understand fraud in your campaigns. Also, remember that an excellent performing source (in terms of ROI / ROAS, etc.) doesn’t mean it doesn’t have fraud. The excellent performing source, which has a crazily low CVR, is most likely stealing organic traffic from you. So, as a thumb rule: Terrible performing sources are bad Excellent-performing sources are most likely also bad! And that is the key message from this article that any performance marketer should take back.

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Why Shouldn’t You Be Doing Video Marketing Without Ad Fraud Bot Management?

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In the post-Covid-19 scenario, video marketing will take center stage in content-driven campaigns and needs to be done optimally. Marketing is now performing a more prominent than before role. Earlier marketing would complement sales functions to generate business. With digital becoming the medium of the entire business process due to social distancing and work-from-home trends, marketing will now open up doors and create avenues of sales using high-quality multimedia content to reach prospective customers – businesses and individuals. However, there are some points that an advertiser needs to bear in mind before going heavy with videos. Video is a costly affair both in terms of the creation of content as well as promoting it. Even the edits come at a cost. It’s not like a text message which can be edited at times without even people noticing it. Since video is very costly and every second counts, the messaging must be very sharp and precise. In digital video marketing, we use the term ‘thumb stoppers‘. That’s what videos must have! As the messaging is very precise, the target audience has also to be very sharp, which means advertisers will have to spend a higher CPx (click, view, completed view). Most advertisers prefer a CPCV as no one wants to pay for the half-viewed message, especially in the business domains. CPCV is the costliest model among the CPx stack for video advertising. From an ad fraud point of view, an advertiser needs to be entirely sure of the genuineness of the engagement level before going heavy on video marketing. Advertisers must verify the engagement levels claimed by channels and mediums they plan to engage, or their agency proposes to engage. The engagement is verified, starting from the number of followers, views, clicks, and even comments. These are manipulated using BOTs to pep up the KPIs without tangible benefits. Without proper monitoring of ad fraud, there is an even bigger chance of falling to the Brand Safety issues. Many agencies, as well as where advertisers aren’t aware enough, display ads on channels that go entirely against the brand’s philosophy. The ads are displayed on YouTube channels which the brand would never want to endorse. In this scenario, the brand does not only lose money but its reputation is also impacted adversely. The brand could get affiliated with porn, obscene, violence, and other unwanted content, and the funniest part is that its money is being used for crushing its reputation. Brands across sectors will go heavy on video content and its promotion. This means platforms like YouTube will increasingly get more share of the advertising mix from brands, especially on the digital front. Without being too heavy on videos, the overall ad fraud rate is anywhere between 25-35% for brands depending on how much optimization they are doing to manage the ad fraud. As brands start consuming ad inventories over video, the overall waste on ad fraud could increase substantially. It could go as high as 50% of the performance marketing spending in some cases. Hence, brands need to put in place an efficient, robust, neutral, yet easy-to-integrate ad-fraud solution for video marketing and spending with a complete view of how it’s being consumed. Talk to mFilterIt ad-fraud and brand safety specialists today to optimize your returns on video marketing.

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How to Tackle Click Injection?

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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

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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 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 / Expert Opinion

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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How Could Ad Fraud Land You Up Dating BOTs?

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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 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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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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