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Why is Ad-Fraud in Retargeting Campaigns Rising as We Move Towards the New Normal?

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Retargeting and re-engagement campaigns have a difference in intent but face similar ad-fraud challenges.   Covid-19 brought the entire world to a pause. At the same time, it also either forced or allowed businesses to get into new areas. Everything got to a standstill in digital space, like the physical world. However, digital was the first to resume as it inherited the new standard properties, which included social distancing, less human interactions, and a relatively sanitized and cleaned logistics chain.   The resumption of businesses and sales fulfillment meant brands are going heavy on retargeting and re-engagement campaigns. Brands had to tell their customers that they are open and, in many cases, selling new products and services. For instance, Amazon started focusing on ‘essentials’ versus ‘shopping’ items.   So did many other e-commerce players. Yet, there was a new breed of digital players who started afresh selling ‘essentials’ or the traditional offline players onboarded the digital journey. Many industry sectors like FMCG went digital, and brands like Pepsi also forayed D2C or Direct-to-customer.   In a typical retargeting / re-engagement ad fraud, affiliates resort to organic hooking where they falsely attribute already motivated users to a retargeting campaign and take the credit. They fire a volley of clicks and steal the attribution against a device ID, which organically engages with the campaign. As the intent is genuine and high, the performance of such campaigns results very high.   However, the pandemic situation paves the way for the new normal is an extraordinary one. Here, such campaigns are more susceptible to fraud.   The explanation for that is users are organically looking for such products and services via digital mediums. Not just the ones who are used to it, but even novices are exploring digital means to buy groceries, medicines, baby food, and other essentials.   So, while users are anyways moving towards digital to buy existing and new products and services, fraudsters in the ecosystem are keeping eyes and ears open to leverage from the situation. They need to poach organic users to misattribute them, jacking up the performance results. Performance Marketing has taken precedence over Brand Marketing as marketers are looking for inorganic means to resume. The first preference is for re-targeting and re-engagement so that transactions with the existing base are encouraged. Remember, the cost of a transaction with a new customer is always higher than the existing one. So, as brands are looking at the sales graph to go up, they also keep tight control of costs and inefficiencies.   Keeping a check on ad fraud in re-targeting and re-engagement campaigns is a priority for marketers. Digital marketers have become front-line business development warriors in this new normal against the supporting role in the pre-Covid-19 era. They need to reorient themselves and start thinking like astute business development folks where they focus on sales and keep a deep view of the entire sales enablement process and partners.   Retargeting is one of the most effective and efficient techniques for digital marketers to maximize the merchandise value and take the average transaction value up per active customer.   At the same time, re-engagement can attract inactive customers, thus adding to the funnel. However, there must be comprehensive and real-time monitoring of such campaigns and give credit to affiliates for the genuinely re-engaged customers. They learn about new products and services that the online seller is offering now.   Therefore, ad fraud in retargeting and re-engagement campaigns are rising as we move towards a new normal. Here is an interesting case study giving a deeper view of how mFilterIt retargeting ad-fraud solution works and what tangible benefits it gives to a customer.

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Ad Fraud to Grow in All Dimensions – Research

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A research study powered by mFilterIt shows that the average ad-fraud rate is likely to peg between 45-55% as Digital Advertising gets the ‘Essentials’ tag in the new standard business setup.   Market researcher techARC announced yesterday ‘The Ad-Fraud Report’ revealing some interesting insights about how Digital Advertising is becoming an essential element of businesses and the broad impact of this reorientation on Ad-Fraud.   Some of the key insights are enumerated below: – Even if an ad-fraud solution gives 1% better results than the competition, it would mean a lot of money. The ad-fraud average for Digitally Mature organizations is 25-35%. However, the absolute numbers will grow as marketers shift more and more budgets towards Digital Advertising. This means the money wasted due to ad fraud will increase. In this case, marketers would require a holistic and advanced solution that gives maximum protection.   The New Entrant sectors and organizations have a learning curve journey to aboard. For these advertisers, it is a must to have an ad-fraud protection solution in the digital tools’ checklist. With almost no internal capabilities and industry benchmarks available, the SIVT percentage, hence the ad-fraud rate, will be much higher, estimated to be 45-50% of the spending.   Digital Advertising has become essential for every organization in the new everyday business practices. As a result of the Covid-19 pandemic, marketers have curtailed 30-50% of the overall marketing spending. However, at the same time, many have doubled their digital spending.   Performance Marketing techniques are taking precedence in the Digital Marketing mix for organizations. No organization, even the lesser-knowns, is taking the long route of investing in building a brand and then expecting to create a pull. It is an aggressive push strategy at the moment.   Investing in Keyword and Search marketing is becoming more relevant and vital. It helps brands, especially the new ones, improve their discoverability as consumers – business and end-users- look for new products and solutions to cope with new standards in their respective domains. Brands must not allow this spending to go unchecked. There was an average of 30-35% wastage for some of the digitally mature brands in keyword spend. This also hurts the organic evolution of brands over digital.   Marketers are moving towards more immersive engagement, increasing dependence on video advertisements. Being available on relevant channels and not getting associated with postures entirely against the brand philosophy is a significant concern for advertisers.   The tools presently used to ‘handle’ ad fraud primarily come from Brand Marketing orientation and prove ineffective. Performance Marketing needs advanced machine learning capabilities which can penetrate deep into the digital advertising ecosystem to follow the trail and decipher what’s fraud and what’s not. The report based on mFilterIt’s data analysis and primary research findings prescribes a robust set of best practices for marketers to follow to extract the most out of this new normal and get their fundamentals right to have a real early mover’s advantage. To learn about the best practices and other industry insights, fill up the below form and free access to the report.   Read more: Ad-fraud in re-targeting campaigns.

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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.   Read More 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. Click here for a free trial!

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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 above strategies is to steal Organic traffic and ensure those 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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Protect Your Ad Campaigns from Invalid Traffic

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As a publisher, you need to make sure that your content is up-to-date, accurate, and easy to understand by your audience and advertisers (for running campaigns). After all, your audience and advertisers are the backbone of generating revenue. Invalid Traffic (IVT) can affect your relationship with the audience as well as advertisers. Quality content on a website will attract search engine bots and other unsolicited bots. Although search engine bots are good bots that help you get noticed and bring traffic to your site, malicious bots, on the other hand, are dangerous for your website as they contribute to a lot of invalid traffic, which in turn may lead to ad fraud. Good bots will declare themselves as bots, and website owners can exclude them from counts, etc. Bad bots will take a lot of steps to NOT be detected. Tools like Google Analytics will have options to remove good bots. But it doesn’t track bad bots. The same issue occurs on the Advertiser’s side as well. As an advertiser, when you run campaigns, your Ads may be seen by bots and invalid traffic can inflate your impressions and clicks on which you are paying. You can see clicks coming on your website and not converting to sales/leads. These can cost you spending from your marketing campaigns which are effectively wasted. Finally, many advertisers use thumb rules like Bounce Rates to measure the level of bots and quality of traffic. That is a wrong metric to consider since bots can easily fake as low a bounce rate as you require. Bots are the ONLY source that can actually generate extremely low Bounce Rates (even lower than your Organic traffic, which is actually a better metric to track fraud) So, what is invalid traffic? Invalid traffic is an artificial inflation of clicks or impressions on the website, which are never seen by a real human. These are generally generated by bots or automated tools to engage with ads and increase ad impressions. The clicks might get injected by publishers themselves on their site. Types of Invalid Traffic There are two types of invalid traffic: General Invalid Traffic: General invalid traffic is the acceptable form of invalid traffic. The crawlers, bots that come from data centers and search engines, and traffic from unknown but real browsers come under general invalid traffic. These are non-human hits, but each of them serves a particular purpose. They help the ecosystem to measure and improve. General traffic does not indulge in ads on the publisher’s website. Sophisticated Invalid Traffic: Sophisticated Invalid Traffic is the traffic that contains malicious bots. This traffic is generated to click or view ads to increase ad revenue. Fraudsters create sophisticated invalid traffic to manipulate devices, locations, and more. This type of traffic is very sophisticated and is not easily detectable. This invalid traffic is not easily detectable. To detect sophisticated invalid traffic (SIVT), one needs to use advanced analytics to identify and analyze fraudulent activities. Some common fraudulent activities include malware, spiders, bots that hijacked devices and sessions, falsely represented sites, cookie stuffing, and more. Fraudsters profit immensely from sophisticated invalid traffic. The most common method fraudsters use to gain revenue is through ad fraud on both the app and the web. On the mobile app, fraudsters imitate genuine clicks, installs, and post-install actions. Some of the most common types of mobile ad fraud are SDK spoofing, click spam, click injection, bots, etc. Fraudsters are also targeting high-premium campaigns like “re-engagement” where CPC rates are traditionally on the higher side. How advertisers can protect their ad campaigns? It is crucial to protect your ad campaigns from sophisticated invalid traffic (SIVT). Partnering with the right neutral platform-agnostic ad fraud detection solution for the web and app can help you understand the performance of your marketing campaigns. Along with partnering with the right measurement provider, one can also be working with publishers to understand the causes of invalid traffic on the website and apps. Contact mFilterIt today to learn about an invalid traffic detection solution or click here to request a demo.

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PepsiCo’s D2c is a Big Sign of Digital Commerce Becoming Mainstream for Brands

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PepsiCo announced two new direct-to-consumer offerings to shop directly online. So far, the approach of brands that existed prior to the digital era has been lukewarm about digital platforms. It’s been more about feeling better to be there as well as feel the FOMO. However, the actual sales expectations have not been much. Typically, such businesses don’t sell more than 10% online and still depend heavily on brick-and-mortar retail.   Well, they are not wrong in doing so. That is where the customer has been so far. For the online, the strategy has been to hook on some of the popular marketplaces with very soft objectives. It was more like just tick-marking one of the checklist items.   The COVID-19 situation has made brands realize that irrespective of whatever penetration they have in the offline channel, online can bring a much-required and reliable direct connection with the customer. This has many advantages for a brand.   The most important benefit at the moment is sales fulfillment. Brands come to know where the opportunities are in terms of demand and align everything at their disposal to fulfill that. Other advantages include building a direct connection with the customer, so learning a lot about them. Also, the supplies right up to the shelf level can be curated and connected with the demand. This way customers can be pampered more by exactly delivering what is being looked for.   Whether the brand will continue to engage with aggregate marketplaces or not, is too early to debate. However, it appears they will continue to be present on these marketplaces and built upon a direct online presence as well.   This is not the first journey for many brands. They have been digitally active primarily through social media platforms to engage directly with customers for their feedback, messaging, and communication. Now they are adding one more important layer of selling directly to the customer. This is going to disrupt the traditional channels where we have many layers of intermediaries.   Disney, for example, has its Direct to Customer & International program which is redefining the business of entertainment content. Even if the production house would be the same, the content would always be location-driven and the libraries Disney would be having in the US are entirely different than what they have in India, as an example. Much of it is done with the distribution network in each country which pushes what they feel will sell rather than what consumers want.   Going direct to the customer gives Disney the power to deliver content irrespective of any borders and actually sense the pulse of the customer, who is now a global citizen and wants no disparity between what could be consumed in India or the US.   Brands will have to reach the right customers and know a lot about them and their preferences. These newcomers will have to take a lot more cautious approach while going directly online in terms of challenges like ad fraud and brand safety. The digital marketing learning curve has just begun for these traditional brands while the aggregate marketplaces are digital-only/digital-first organizations that have a much-evolved understanding of the complexities of the digital maze which is behind the application or service.   PepsiCo and many other brands that will go Direct-to-Consumer in the next few weeks or at least months will have to proactively deal with ad fraud and brand safety. This is because their customer will come for a particular purpose on their platform, so discovering the right and genuine customer will hold the key to success.   On aggregate marketplaces, the customer can still find many other products (reasons) to connect even if the primary hook did not meet the expectations. More so, the brands that are digitally evolved are losing anywhere between 25-35% on account of ad fraud, there are chances that newcomers will lose more.

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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 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 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.

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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 on 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. Connect with me by writing to contact@mfilterit.com.

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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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