Jagmeet Singh

data-clean-rooms

Data Clean Rooms: The Key to Ethical and Efficient Advertising

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Third-party cookies have been the key for marketers to track individuals online and understand their preferences. This enabled the advertisers to run personalized ads and do accurate targeting. However, things are going to drastically change in the digital ecosystem by the end of 2023. Apple’s browser Safari and Mozilla’s Firefox have already done away with third-party cookies. Google has now announced its final decision to shut the doors for third-party cookies. Marketers are looking at alternatives to overcome this problem, and one of the prominent solutions that have emerged happens to be data clean rooms. What are Data Clean Rooms? Data clean rooms are software pieces that facilitate brands to run targeted digital advertising campaigns, cap the frequencies, measure campaign performances, and generate reports in a secure and user-friendly manner. This is done by uploading the brand’s first-party data and comparing it to the aggregated data in the data clean room. This includes data added by other companies. Users can access data in a data clean room, but none of the first-party or user-level information is shared with anyone outside the data clean room. Data clean rooms provide various services such as audience segmentation and overlap analysis, and they can facilitate measurement and attribution research of the campaigns without sharing any individually identifiable data. With data clean rooms, brands can access data for a wide range of tasks wherein the privacy rules are defined and implemented by the clean room provider. The data uploaded to a clean room is fully encrypted and anonymized throughout onboarding and audience building. With cookies going out, we are witnessing the rise of various data clean room providers. However, just like any other emerging technology, data clean rooms also have certain pros, cons, and risks associated with them. Let’s take a look: Pros of using data clean rooms Data clean rooms are privacy-friendly, and companies can use them to analyze their target audiences, ad targeting, and evaluation of campaign performance. Despite the user-level data being added to the data clean room, it is protected from exposure to other companies. Companies can get complete visibility of the campaign performance across different channels on some clean data rooms. The data that collaborating businesses add to a data clean room remains completely under their control, and not shared with any other users. Cons of using data clean rooms The data is not ID-based but aggregated. Hence it affects the precision of ad targeting. Before uploading the data to any data clean room, users need to standardize it in a single format to get visibility of the same. First-party and transactional data is gold for businesses and many enterprises might be reluctant to share it. Thus, it would adversely affect the outcomes and the functions that companies seek from a data clean room. There are data clean rooms specific to large platforms such as Google and Facebook. This would make advertisers manually compile and collate data from the different data clean rooms to get the complete picture. The data clean rooms don’t yet have any global standards or operational uniformity as they are only an evolving technology. Risks of using data clean rooms The first-party data is uploaded to the data clean rooms, and in the eventuality of a data breach, companies can face fines, loss of reputation, and clients. Manual management of data clean rooms can make them prone to errors, and accidentally granting access to people who are not authorized can’t be ruled out. There can also be incorrect queries and the exchange of data in an unsecured environment. There can be variations in the type and quality of data uploaded to a data clean room. For instance, one company might not share all of its customer data, and the other might share all of it. This would lead to unfair and inaccurate data exchange and analysis. Having looked at the pros, cons, and risks, we can still say that data clean rooms are one of the most promising solutions to the challenges faced by digital advertisers especially in a programmatic ad scenario in the absence of cookies. However, there are other safer and more convenient options as well. Alternatives to Conventional Data Clean rooms Contextual advertising is one of the most prominent alternatives to data clean rooms. Conventionally, cookies have been used to track and analyze user behavior and target advertising accordingly. However, with the GDPR and other privacy laws and concerns leading to a cookie-free digital world, contextual advertising can help marketers by working as a privacy-friendly source of data collection that is accurate, but non-invasive. It allows targeting of the audience through the context of a web page. For instance, a user visiting a fashion and lifestyle website would be shown products in those categories, and another browsing book will get ads related to books. This contextual approach to advertising ensures that the ad budget is spent more accurately as only the relevant people will be targeted. Unlike cookie-based behavioral targeting, businesses require fewer data and tools or technology for execution. AI systems can make trend and insights-based predictions that help in choosing the right channels and web platforms to advertise on. Contextual advertising also leads to greater personalization as consumers are likely to see ads relevant only to what they are currently looking for. 64% of the customers value the relevance of advertising, and it can lead to greater customer engagement and loyalty. Moreover, contextual advertising is more real-time as it is based on the current actions of a user, and not on the documented habits. For instance, someone might have been buying a lot of books and playing games during remote working or lockdowns, but with offices reopening the current preferences might be more about eating out or shopping from physical stores instead of online stores. Contextual advertising can help out with such information. Towards Transparency & Data Sanity Whether the advertisers go ahead with data clean room or contextual advertising, the key to digital growth in 2023 will be gaining efficiency with data

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ad-fraud

8 Things to Stop Believing In 2023: A Marketer’s Checklist

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2023 is coming with a storm of changes in the digital advertising world. Meanwhile, the bots are also ready to upgrade themselves to steal the advertiser’s money. According to the Statista report, the global cost of loss in ad budgets is going to reach $100 bn. in 2023. To ensure the advertisers are also ready with their armor to protect their ad campaigns next year, it is important to let go of a few beliefs that might have led to some mistakes in 2022. We are here with an exclusive marketer’s checklist to help digital advertising to stop believing in things that are pulling back their digital growth. By letting go of these thoughts’ advertisers can be prepared to “Advertise Fearlessly” in 2023. 1. Stop Believing That Programmatic Publisher’s Reports Are 100% One of the biggest disadvantages of programmatic ad is the lack of transparency. In this case, to give a clear picture the publishers provide a report including the ad placements and where the brand’s budget was spent. However, in most cases, the fraudulent publishers provide a skewed report to receive their payments. It is important to not trust the publisher’s report because they don’t know if the placement, they are claiming is true or not. For example, they claim that the ads are running on BBC. But there is still a glimmer of doubt about whether it actually running on BBC or not. Therefore, it is essential for advertisers to resort to validating their ad traffic and not trusting the publisher’s report. By understanding the amount of invalid traffic coming they can make payments based on the clean traffic and partner with confidence. 2. Stop Believing OEM Platforms Are Fraud Free OEM app stores are believed to provide high-quality users and significantly increase the visibility of an app. It also results in being an optimum platform to attract high installs. Due to fewer restrictions, these platforms can also be used by apps removed from Google Play Store to increase their market growth. The OEM app stores receive a security certification and clearance from the mobile manufacturer. However, it doesn’t have the required safety provided by Google Play store apps against potentially harmful apps. The publishers claim OEM traffic is fraud-free, however in reality they provide mixed traffic. When a person purchases a device, the OEM apps are pre-installed in them. In the pre-installed/pre-burned app case, the digital advertiser is paying the handset for those pre-burns. So they have incurred a cost here, and then when the affiliate/OEM partners get those apps opened, the advertiser again pays for the same install. This way, the digital advertising is under the impression that they are getting unique traffic. But the reality is that they are actually paying the customer acquisition cost for the same person twice. Therefore, it is essential for app advertisers to deploy third-party ad traffic validation solutions to safeguard their apps on third-party app stores. With an ad traffic validation suite, the digital advertising can verify the quality of installs and the devices from which it has been installed to ensure the traffic is genuine. 3. Stop Believing That Performance campaigns are fraud free One of the many misconceptions that digital advertising and agencies have is that no fraud happens on performance campaigns as they are targeted campaigns. It is believed that even though media campaigns are prone to ad fraud, performance campaigns cannot be skewed by publishers with invalid traffic. This is because they are paying for performance, and they are getting performance However, over time the bots have become sophisticated and can easily imitate human behavior. The advancement of the bots has reached a level where the events like filling a lead form, making a purchase and other events can also be spoofed. According to our findings, performance campaigns (CPC/CPV/CPL/CPS) attract up to 30-35% of invalid traffic across the industry. Therefore, it is important to do a full-funnel check of the performance campaigns to ensure they are not hampered by invalid traffic. 4. Stop Believing MMP Fraud Protection Is Enough There are a number of MMPs or attribution platforms that claim to detect invalid traffic on ad campaigns. However, this is a conflict of interest. MMPs revenue is generated from the number of attributions. And when the more numbers of fraud they detect on attributed sources, their revenue decreases. This causes a conflict of interest and therefore the real fraud is left undetected. According to mFilterIt findings, we have detected 50-60% fraud on the same ad traffic in which MMP has detected 20% fraud. In this case, the digital advertising is in the shadow that the fraud on their ad campaigns has been detected and prevented. But the reality is that they are still paying for the ad traffic coming from bots. On top of that, the MMPs have limitations in detecting invalid traffic at the impression level and have minimal checks that often miss out on sophisticated fraud patterns. Therefore, it is essential for marketers to partner with an ad traffic validation suite to ensure their ad campaigns are getting clean traffic. Moreover, the marketers must also ensure that their traffic verification partner’s solution is not limited to just detecting invalid sources at the impression level but also at the click stage, re-engagement and referral. 5. Stop Believing keyword blacklisting is enough for brand safety Brand safety is no more a choice, rather it has become a necessity for brands to stay protected in the digital landscape. One of the key reasons brands need a brand safety suite is to ensure their ads are not placed beside violence, hate speech, morbidity, and other derogatory content. Blacklisting certain keywords to ensure the ads are not displayed next to illicit content is one of the common ways to deal with such issues. However, it is not enough to ensure the safety of ads. One of the loopholes of keyword blacklisting is that it assumes that the platform knows the context of the content. It can be relevant for English-focused content,

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ad-fraud-detection-software

Can Ad Fraud Detection Stop Your Brand’s Growth? Know the Truth

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Ad Fraud is a term that has shaken the entire digital advertising ecosystem in the past decade. Every year, fraudsters are becoming sophisticated and smart in stealing revenue from advertisers. Whether it’s advertisers, publishers, or ad networks, no one is safe from the threats of cybercriminals. The legitimate publishers get their genuine clicks stolen by fraudsters. Whereas the ad networks have to see their performance suffer and campaigns fail. And the worst of all happens with the advertisers as they pay for both invalid users and organic downloads. In this fight between the fraudsters and the digital marketing ecosystem, myths make the advertisers question the ad fraud detection vendors while the fraudsters feed on their money. Here is another myth that is making noise lately. In this blog, we are busting this myth with some facts which will help you understand the importance of an ad fraud detection software. What is cooking? In the digital advertising world, publishers are spreading the word that they are not able to scale an advertiser’s business due to ad fraud detection solutions. However, the reality is different Publishers lose a high percentage of their revenue due to ad fraud detection solutions. For instance, if an ad fraud preventive service provider detects 70% of fraud coming from a specific source, then that particular publisher receives a payout on just 30% of the genuine traffic by the advertiser. Publishers & MMP union Some of the renowned Mobile measurement platforms (MMPs) also offer ad fraud detection services clubbed with their attribution services. They claim that they will detect fraud in the data attributed by them to an app advertiser and ensure that they receive clean traffic. However, there is a catch. The MMPs bill the advertisers based on the number of attributions. Hence, if the MMPs detect a higher number of frauds on the attributed data, they will lose revenue. Therefore, to ensure that their revenue is not impacted, the MMPs detect 10-12% of the fraud and the rest of the fraud remains undetected. This benefits the MMPs and the publishers as they can claim higher payout from the advertisers. As the MMPs detect low fraud, the publisher encourages the agencies, advertisers, and other stakeholders to use the MMP ad fraud detection as it will eventually benefit them. This is similar to a situation where the culprit is telling to choose where to go and file a complaint to keep themselves safe. And listening to this, the advertisers fail to detect the real fraud and end up losing huge revenue. What do advertisers miss? Real % of Fraud: Due to less fraud reporting by MMPs, the advertisers remain in the dark regarding the actual fraud numbers. This further affects the performance of the ad campaigns, and the advertisers end up losing money twice to invalid traffic. First, they lose money to the invalid traffic before ad fraud solution. Later, they end up losing money on invalid traffic that is not reported by the MMPs. Growth Opportunities: The advertisers use an MMP to measure the performance and get analytical data for their campaigns. However, due to less fraud reported by MMPs, the marketers stay under the impression that their campaign performance is good. Furthermore, as the marketer takes decisions based on this skewed data, they end up investing more in the wrong campaign. This further hampers the overall growth of the business. mFilterIt Vs MMPs In comparison to the fraud detection done by MMPs, we ensure holistic protection of advertisers from ad fraud. Our ad traffic validation suite enables: Full-Funnel Protection MMPs can detect the general bots, but they often miss to detect the signs of sophisticated bots in an ad campaign. As sophisticated bots can easily mimic human behavior, they are hard to detect and require an advanced solution. With our full-funnel approach, we detect sophisticated bot patterns in real-time to help advertisers take immediate action to curb the impact. Detect New Bots Across Domains Every day a new bot is coming, and it is hard to detect across all platforms and domains. MMPs often lose the time to respond to the threats as their systems and rules are updated once in 6 months or a year. Whereas we detect a new bot on any campaign, we ensure to flag it across all the advertisers/campaigns. This results in the protection of ad campaigns from the impact of the new bot. Proactive Reporting The attribution platforms provide a late ad fraud report to the advertisers. This means that if the ad fraud is detected by the 20th of a month, then the advertisers will receive the report on the 28th of that month. This further delays the process of taking preventative measures against fraudulent sources. Furthermore, it also affects the invoicing and closures at the end of the month, and even after taking so much time they fail to detect the right number of fraudulent sources. Whereas we provide D-1 data, which means that if the fraud is detected by the 20th of the month, the advertiser gets the report on the 21st of the month. This helps the advertiser to understand the possible impact of the ad fraud and take preventative measures immediately without wasting further ad spends on irrelevant traffic. Early reporting also helps publishers to optimize better and reduce the threat of fraud. Forget the Myth, Believe in Facts With the fast-growing world of digital advertising frauds are making their every move smarter, more discrete, and an illusion to the naked eye. To ensure your marketing efforts are not wasted, it is important to fight against fraudsters with an advanced ad traffic validation solution. A reliable ad fraud detection and prevention solution will help to detect fraud in real time and provide holistic protection against sophisticated bots without impacting your growth. The only party affected is the publishers as their pay-out decreases when an effective ad fraud tool is in place. It’s time to believe the data, not the myths. Get in touch with our experts for deeper insights. Reach out to learn

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Bots

Bots Are Evolving with Time. Is Your Brand Ready to Combat?

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According to Statista, in 2021 the number of mobile users has increased to 7.1 billion and it’s estimated to reach 7.26 billion by the end of 2022. With the growing numbers of mobile users, fraudsters are also evolving their bot attacks on mobile apps.   Bot traffic is non-human automated traffic that visits a website and mobile app. While there are good bots like search engines and AI-based assistants that are required to make the work smoother. However, there is also a wide range of bad bots that are malicious and are used to commit frauds like data scraping and account takeover.   With time, there has been an evolution in bots and fraudsters are making sure that they cannot be detected easily. In this blog, we are covering the signs to detect bot traffic on apps and what are the new trends observed in bot activity.   Global share of human and bot web traffic 2020, by industry – Statista Basic vs Sophisticated BOTs The first generation of malicious bots operated according to quite evident strategic rules. The basic bot traffic used to come from sources like data centers and IP addresses that behaved predictably. For instance, a basic bot performs an app install innumerable times using one IP address for a consistent duration. In simple terms, their actions were not like humans, and it was easy to predict once the bot pattern was identified.   However, in the last few years, cybercriminals have developed and created more sophisticated programs of bots. The sophisticated bots can replicate human activity and compromise the walls of safety on the internet. To make the bot activity look human-like, the cybercriminals use different residential IPs instead of single data centers.   This is an example of a sophisticated bot pattern. In this graph, there are multiple abnormal data patterns detected. About the above graph – In the case of A, there is a sudden spike in the percentage of installs, and then it disappears. Later in the case of B, there is a constant peak for a while which disappears in a few seconds. In the case of C, there is a regular peak for a continuous window. Signs to Identify Bot Traffic 1. Compare Conversion make & model Bot activity can be detected in installs by observing the incorrect conversion make and model. In the below example, there is a discrepancy in the devices from where the conversion is made and the actual model. As visible in the data, it is possible to indicate fake devices with conversion make & model. 2. High Volume of Installs Normal traffic is spread over, which means that the conversion time is not normally in the publisher’s control. However, in the case of bot traffic, the click-to-install rate is very high and quite streamlined. Below is an example of the high volume of installs due to bots:   Publisher M – Click to Install Trend 3. Detecting Old Android Versions In major cases, the malicious bots are found running heavily on old OS versions which are normally very small in percentage distribution.     Publisher M – OS concentration   Emerging Trends Captured in Bot Traffic 1. Abnormal is the new Normal According to the observation, almost all publishers generate on average 70% of installs within the first 2 minutes.     2. No Traffic is 100% Clean Traffic Since OEM inventory is blindly whitelisted by the attribution platform, the advertisers also end up paying for fraudulent traffic for OEM.     3. Similar Bots Across Multiple Domains Identical traffic patterns were observed for various clients for different domains from one source indicating BOT-generated traffic.     How mFilterIt Detects Bot Traffic Every day new bots are emerging and MMPs are not able to differentiate between clean traffic and sophisticated bot traffic. In this case, partnering with an app fraud detection and prevention solution like mFilterIt can protect your ad campaigns from bot traffic.   Our Ad Traffic Validation suite ensures to evaluation of the bot traffic based on different parameters. When analyzing the installation source, we have observed downloads from devices with older Android versions, a high volume of installs at small intervals, a discrepancy in the devices from where conversion is made, and the device model.   The different bot patterns were detected by us, and appropriate measures were taken to ensure clean traffic for the brands and save their money from getting wasted in invalid traffic.   Conclusion As advertisers are increasing their digital spending, fraudsters are also evolving with new types of ad fraud techniques to scam the brands. Fraudsters are using sophisticated bots to pass through the ad fraud detection systems in apps. Therefore, a full-funnel ad fraud detection and prevention solution like mFilterit is required to detect data anomalies and eradicate them at the earliest stage. Eliminating bot traffic will protect your app campaigns and help you focus on reaching a relevant set of audiences without wasting any ad spend. Get in touch to learn more about the bot traffic.

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OEM-Myths

Busting the OEM Myths

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During the first quarter of 2021, OEM app stores acquired nearly 42% of the global market share. OEM refers to original equipment manufacturers, and in mobile terminology, it caters to brands like LG, Redmi, Realme, etc. Such companies offer personalized app stores, and brands can obtain premium leads through OEM advertisements. Moreover, unlike Google Play Store, OEM apps are perceived as relatively safer and fraud-free. The cost of advertising on OEM stores varies on the type of placement. OEM advertising has two broad categories: pre-installed apps and app store promotion. Pre-installed apps are a gateway to new users as they are visible on the screen upon first-time phone usage. App store promotion is an icon & browser-based promotion, hot downloads, and recommended apps on an OEM app. In mobile terminology, OEM stores are even referred to as alternate Play stores, as they are pre-installed in addition to Google Play Store. OEM advertisements have opened doors to an untapped market, wherein brands can directly connect with the targeted traffic. Moreover, advertisers believe that they can generate higher ROAS and not worry about fraud, as the mobile manufacturer ensures exclusivity. OEM stores are mainly used for app installation. According to sources, OEM stores can boost app installs by 5x higher than the standard advertising methods. Does the App You are Promoting Require OEM Type Traffic? OEM app stores offer high-quality users and increase the visibility of the app. Moreover, it is an optimum platform for boosting installations. Apps removed from the Google Play Store can use the alternate app store to increase their market growth. The third-party app stores receive security certification and clearance from the manufacturer. Unfortunately, they may not take measures like Google Play Store apps when they encounter potentially harmful apps (PHA). Therefore, it is necessary to decide whether putting the app on an alternate store with lower protection levels against ad fraud can cause more harm than benefit. Look at the Data Points When receiving installs, carefully look at the app versions used. Many times, fraudsters may display fake installs using an archived app version. Moreover, sudden spikes in conversion levels should also match the click levels. If you find discrepancies, it is most likely due to ad fraud. Closely monitor and question the type of traffic. For example, is the correct targeting happening, or a plain install campaign? For, what is the percentage of the handsets traffic in installs vs. reality? Are the installs happening on older handsets? Besides this, do a simple click-to-conversion analysis and see what the graph looks like, and does that make any sense? Then, check your backend/KPI’s are they getting met or not. Issues with OEM Advertising Merely Runs on Faith Advertisers believe that OEM app stores offer brand-safe environments because they trust the OEM mobile manufacturing brand. The two-way communication between the advertiser and the brand ensures transparency in this relationship. Building a two-way trust helps in increasing the app installs, but how does it prove that they are legit? Do you have to leave that on trust too? Moreover, does it eliminate the possibility of duplicate/fake apps? So, are alternate app stores offering a brand-safe environment, or is it an illusion? Moreover, the recent malware release through the Netflix duplicate app on an alternate store is sufficient proof that “yes” is a questionable answer to any of these questions. If it were true, ad fraud elimination companies wouldn’t be working hard and fast to detect data anomalies. Nobody Goes Behind and Checks What is the Actual Source? Is the Actual Source and Claimed Source to be Same or Different? The transparency between the OEM and advertisers often leads to the belief that the actual and claimed source would remain the same. However, if advertisers go in-depth and review, they would find many discrepancies. For example, fraudsters display fake installs by cloning the SDK of an app and using different means of installation. Sources state that 13-18% of third-party app installations happen majorly through fake devices and other ad frauds. At times, users are unaware of the app install. Fraudsters also use cloned installs to display the “x” installation of the advertiser and achieve monetary gain. Moreover, cybercriminals even add malicious codes to these apps and conduct more ad fraud in the background. By default, these Sources are Whitelisted on MMPs. Commonly, an MMP receives click and impression attribution after a user clicks on an ad. The “install” attribution happens whenever a user opens an app for the first time. Such in-app OEM installs, impressions, and clicks are commonly whitelisted by MMPs. However, fraudsters register fake impressions and clicks with the MMP. Moreover, they even use spoofed SDKs for faking “install” attribution on the real device and report the same to the MMP. Another common method of stealing attributions for organic and inorganic installations on MMP is through click spamming, wherein fraudsters fire clicks until they claim the last click attribution. When analyzed, the install-to-attribution ratio goes beyond the 1:8 ratio. Therefore, the default misattributions by MMPs are causing analytic and reporting discrepancies. How did they Get Themselves Whitelisted and Start Mixing Traffic and Fooling Everyone? At present, the decision to recognize sources to include in the whitelist lies with the MMPs and includes numerous fraudulent attributions. Moreover, the whitelists are created by default, and the advertiser has no say in the whitelisting decision, even though the advertiser is making the payments. As such, the advertiser believes that the MMP is doing its due diligence and providing accurate attribution results. Moreover, the results provided by the MMP motivate the advertiser to increase the advertising budget and the payout to the fraudster. Similarly, outdated app or SDK version installation is typical fake installs used by fraudsters. Moreover, the fraudulent ad network may make the fake attributions appear organic to boost the installation’s legitimacy. Furthermore, cybercriminals may even use the older version of the SDK for displaying purchase rates. In reality, there is no purchase from the fraud ad network, and neither does

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mFilterIt is an AdTech and MarTech pioneer with a mission to build trust and transparency across the digital ecosystem. We safeguard your brand’s reputation, optimize your advertising endeavors for maximum impact, and help advertise fearlessly!

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