Businesses rely on online ads to reach their target audiences and promote their products or services. However, with the increasing prevalence of online advertising, a sinister has brought with it a concerning companion -Ad fraud. Ad fraud is like the invisible pickpocket of the digital world. It's sly, it's sneaky and it can steal your money before you even know it's happened. But unlike a real pickpocket, ad fraud doesn't just target your wallet. It can also target your reputation.
Imagine you're a business owner and you're paying thousands of dollars a month for user acquisition campaigns. But instead of reaching your target audience, your ads are being clicked on by bots and displayed on fake websites.
In this blog, we will learn mainly about Mobile Ad Fraud, exploring what it is, how it works, its impact on your performance marketing strategies to combat ad fraud. We'll also take a closer look into the sphere of mobile ad fraud, which presents unique challenges and requires tailored solutions.
What Is Ad Fraud?
Ad fraud, short for advertising fraud, refers to the deceptive and fraudulent activities carried out by individuals or organisations to generate illegitimate or false ad interactions, impressions, clicks or conversions. The primary aim of ad fraud is to manipulate online advertising campaigns for financial gain, often at the expense of advertisers, publishers and even consumers!
Common Types of Ad Fraud
Online Ad Frauds:
Click Fraud: This involves repeatedly clicking on ads with malicious intent. It may be carried out by competitors looking to exhaust an advertiser's budget or by publishers trying to increase their earnings.
Impression Fraud: In this type of fraud, impressions (the number of times an ad is displayed) are falsely inflated. Bots and automated scripts generate fake impressions, making it appear as if the ad received more exposure than it actually did.
Conversion Fraud: Advertisers often pay based on conversions, such as sign-ups or purchases. Fraudsters may use fake accounts or stolen information to trigger conversions, costing advertisers money for fraudulent actions.
Ad Stacking: Multiple ads are stacked on top of each other in a single ad placement. Although only the top ad is visible to users, impressions for all stacked ads are recorded, resulting in inflated statistics.
Mobile Ad Fraud
While ad fraud is a pervasive issue in online advertising, it's equally prevalent in the mobile advertising landscape. With the increasing use of smartphones and mobile apps, fraudsters have adapted their tactics to target the mobile ecosystem. Mobile ad fraud encompasses various deceptive activities aimed at manipulating mobile ad campaigns for financial gain. Here are some common types of mobile ad fraud:
Click Spamming: In mobile advertising, click fraud can take the form of click spamming. Fraudsters generate a high volume of fake clicks on mobile ads, often using click farms or automated bots. This artificially inflates click-through rates (CTR) and can lead to wasted ad spend.
Install Fraud: Mobile app marketers often pay for app installations. Fraudsters create fake instals, either through automated scripts or by using fraudulent app install farms. This results in advertisers paying for instals that have no real value.
In-App Ad Fraud: Fraudulent apps may serve ads to users in deceptive ways, such as placing ads behind other app elements or generating fake ad impressions. This makes it difficult for advertisers to verify the authenticity of their ad placements.
Device Emulation: Some fraudsters use emulators or virtual devices to mimic real mobile devices. They interact with ads to generate fake engagement metrics, such as clicks and instals, giving the appearance of legitimate user activity.
SDK Spoofing: Fraudsters can manipulate the software development kits (SDKs) of mobile apps to falsify user data. This can lead to incorrect user demographics and targeting, which negatively impacts campaign performance.
Click Injection: In this method, fraudsters monitor the installation process of legitimate apps on a user's device. As soon as an app is installed, they inject fake clicks to claim credit for the installation. This type of fraud can be challenging to detect because it occurs in real-time.
Attribution Fraud: Fraudsters manipulate the attribution process to take credit for legitimate instals or actions that should be attributed to other marketing channels or sources. This can lead to inaccurate data and wasted ad spend.
Fake User Engagement: Fraudsters simulate user engagement with ads, including actions like swipes, taps and interactions within an app. This artificially inflates engagement metrics, making the ad campaign appear more successful than it actually is.
Ad Stacking in Mobile: Similar to ad stacking in website ads, this technique involves stacking multiple mobile ads on top of each other within a single ad placement. Only the top ad is visible to users, but impressions for all stacked ads are recorded, leading to inflated statistics.
Bots: In its broadest sense, a bot can be described as a self-operating software program created to execute predefined actions online. Although bots traditionally handle straightforward tasks, they have evolved to handle more intricate operations, encompassing both positive and negative applications.
Click Farms: The term "click farms" pertains to physical venues where devices are established to generate a large volume of clicks, typically for dubious purposes.
Click Hijacking: Click hijacking involves manipulating or intercepting legitimate clicks on mobile ads to redirect them to unintended destinations, often for the benefit of fraudsters.
Click Redirection: Click redirection involves misleading users by taking them to different destinations than advertised, often leading to malicious or unwanted content.
CPA Fraud (Cost-Per-Action): In CPA fraud, fraudsters generate fake mobile actions (like sign-ups or purchases) to fraudulently claim commissions from advertisers.
Device Farm: Device farms employ multiple devices to simulate genuine user behaviour, enabling fraudulent engagement with mobile ads and apps.
Device ID Reset Fraud: Fraudsters reset or manipulate device IDs to disguise multiple fraudulent installations as unique users, leading to inaccurate tracking and attribution.
Display Fraud: This involves artificially inflating the number of ad impressions or interactions, deceiving advertisers into paying for non-existent or exaggerated engagement.
Duplicate IP: Fraudsters use multiple devices on the same IP address to mimic distinct users, tricking advertisers into believing their campaigns are reaching a wider audience.
Emulated Devices: Mobile fraudsters employ emulated devices or simulators to impersonate genuine mobile users, generating fake engagement and interactions.
Mobile Malware: Malicious software designed for mobile devices can compromise user data, engage in ad fraud, or perform other fraudulent activities.
Phone Farms: These are facilities with numerous smartphones used to generate illegitimate clicks, instals or interactions with mobile content, often for profit.
Purchase Fraud: Fraudsters make fake or unauthorised purchases within mobile apps, causing financial losses to both users and businesses.
Why does mobile ad fraud matter?
The significance of mobile ad fraud lies in its far-reaching consequences. For advertisers, it translates into immediate and lasting impacts on their marketing efforts, including budget losses, compromised data quality and resource depletion.
However, the adverse effects of fraud extend beyond advertisers alone. They reverberate throughout the entire marketing ecosystem, affecting all parties involved, not just by draining advertising budgets but by causing harm across the board.
Countermeasures Against Ad Fraud
To combat ad fraud effectively, advertisers and the digital advertising industry as a whole can implement the following strategies:
Ad Fraud Detection Tools: Employ advanced ad fraud detection tools and services that use machine learning algorithms to identify and block fraudulent activities in real-time. Here are some advanced ad fraud detection tools that use machine learning algorithms to identify and block fraudulent activities in real-time: Appsflyer P360, Forensiq by Impact, mFilterit, TrafficGuard. These tools use a variety of techniques to detect fraud, including: Device fingerprinting, Behavioural analysis, Machine learning and more.
Fraud Prevention Protocols: Implement industry-standard protocols like Ads.txt and Ads.cert to verify the authenticity of publishers and prevent domain spoofing.
Traffic Source Analysis: Regularly analyse traffic sources and patterns to detect irregularities, spikes, or anomalies that may indicate fraudulent activity.
Ad Verification Partners: Partner with third-party ad verification companies that specialise in monitoring and ensuring the quality and legitimacy of ad placements. Here are some third-party ad verification companies that specialise in monitoring and ensuring the quality and legitimacy of ad placements: Integral Ad Science (IAS), Moat by Oracle, DoubleVerify, The Media Trust. These companies also offer a variety of ad verification services, such as Viewability verification, Ad fraud detection and so on.
Ad fraud remains a pervasive threat in the world of online advertising. As technology continues to evolve, so do the tactics employed by fraudsters. However, with vigilance, the right tools and collaborative efforts among stakeholders, the industry can make significant strides in reducing ad fraud's impact. Protecting digital advertising from ad fraud is not only crucial for advertisers' financial health but also for maintaining the trust and integrity of the entire ecosystem.
At PixelPulse Digital, we are dedicated to combating ad fraud and ensuring a safe and transparent digital advertising environment. We are committed to industry best practices, we strive to protect our clients' campaigns and investments, contributing to a healthier and more trustworthy online advertising landscape.
Connect with us at PixelPulse Digital to boost your User Acquisition efforts so that we can help you drive more engaging users at scale.