Every year, advertisers spend billions of dollars in a traffic-driven ecosystem. A measurable user action is needed in order to pay for the ad. For example, the user needs to click the ad, download an app, sign up for something, or make a purchase. Because advertisers pay for these actions, the quality of the traffic is a critical concern for marketers.
Fraudulent or invalid traffic poses an ongoing threat to the digital ad ecosystem. While some of the world’s advertising traffic is somewhat attributable to automated bots, erroneous attribution, or fraudulent user impersonation, digital ad verification companies have concluded that this is a persistent problem in digital advertising. It can vary widely depending on the vertical or region, but a little fraudulent traffic can really mess up campaign performance.
The affiliate ecosystem has a much broader scope and creates more complexity. Affiliate marketing is built on a framework of multiple publishers, sub-affiliates, media buyers, and traffic resellers. Each of these has its own traffic sources, landing pages, and tracking systems. This kind of structure allows for rapid growth in a campaign, but it also leaves the door wide open for invalid traffic to enter the system. Fraudulent clicks lead to financial loss and economic drain. Fraudulent clicks lead to financial loss and economic drain. Artificially creating clicks and leads as well as manipulating attributive events can create false performance signals and lead to missed budget allocations, poor optimization of advertising campaigns, arguments and misunderstandings between the advertisers and their partners. The costs of fraudulent clicks are only temporarily hidden as it is within aggregated performance data.
Because of this, the verification of the quality of web traffic is more important than ever. Fraud detection and traffic validation systems are designed such that they analyze the quality of the traffic coming to the systems, identify anomalies, and prevent fraudulent clicks before they reach the billing systems and attribution systems. By preventing fraudulent clicks before they reach the attribution systems, advertisers improve their fraud detection systems, and also traffic partners to save money from paying the fraud clicks and buyers to save their money.
What Is a Fraud Detection & Traffic Quality Platform?
Fraud detection and traffic quality platforms are platforms that specialize in the detection of fraud, in that they help determine the quality of the traffic that is coming toadvertisementst by evaluating the quality of user actions. Each of the click, advertisement, impression, installation, and lead generation actiongeneratesste signals, and these systems help determine the quality of actions and assess fraud detection.
Fraud solutions not only focus on finding bad acts, but also on dealing with them after they take place. Many modern systems aim to deal with bad action claims on active systems. These frameworks let you defend, capture, or reroute bad traffic before they form deal impact on how an active system performs.
Fraud detection solutions almost always work with a combination of many technical signals. One of the methods that is litigated in IP intel fraud is catalogues. These catalogues tell us the origin of traffic in a certain country. Use of Datacenter, proxy, or previously flagged fraud. Fingerprint devices are used to capture a unique user’s device, browser system, and configuration. These signals, when used together, tell us if it was an intervention made by real people or an automated system.
Behavior analysis is also one of the methods used in modern fraud solutions. Behavior models are more concerned about how people interact with landing pages or applications. An automated traffic representation is when a person spends a very short time on the site, clicks on something several times, andnavigatese in an agitated manner.
Fraud systems based on machine learning increasingly deal with detecting fraud. These models deal with previous traffic to find new patterns and update systems about risks that are associated with certastreams and user traffic. The system becomes more operational in the course of time in determining the fraud suspicion of activity that is subtle.
Traffic quality platforms are relied on by various businesses. Media purchasers, for instance, utilize them to check the validity of advertising traffic bought from networks or publishers. Affiliate networks deploy fraud detection to manage their publishers and safeguard their clients from publishers. Lead generation fraud traffic detection systems, on the other hand, are used to verify that the leads that were submitted came from actual users and not automated systems.
Performance traffic brands also use these systems to safeguard their advertising. It is almost impossible to check the quality of every interaction when numerous traffic sources are used. Automated fraud detection systems offer the necessary traffic quality analysis.
Types of Fraud in Affiliate and Performance Marketing
Click Fraud
Click fraud is the act of generating false clicks on antor to create a charge to an ad sponsor or to create a false justification of the success of an advertising campaign. In pay-per-click advertising, a fraudulent actor can click on an ad multiple times to create an invalid charge to the ad sponsor or to use up the advertising budget.
Fraudulent clicks can be created by using a bot network or a click farm. In affiliate marketing, click fraud can also be used to manipulate attribution models. When a click fraud occurs, the fraudulent source would get the credit for the conversion even if the fraudulent click was not the cause of it.
Lead Fraud
Lead fraud is the act of providing a lead that is not from a real person or a real interest. In lead generation campaigns, advertisers pay affiliates for leads when the leads correspond to completed forms, registrations, or sign-ups. Fraudulent actors can create leads by using automated methods or by recycling personal information. Even if the fraudulent leads can get past simple automated verification, they are usually useless to the advertisers. Fraudulent leads can have invalid phone numbers, email addresses from throwaway domains, and duplicative user identities. Advertisers end up paying for leads that are useless tformaking a sale.
Attribution Fraud
Attribution fraud is the manipulation of the way advertising conversions are credited. Performance Marketing uses attribution models that rely on fraudulent actors to try to include fake touches to the attribution fraud chain.
A common way to do this is called click injection. This is where fraudulent software simulates a click event right before a legitimate conversion happens. Another way is called click flooding, where a large number of simulated clicks are created, hoping to get attributed to a conversion that happens later.
Bot Traffic
Bot traffic is the automated behavior of a computer program that generates the activity of human users. While the activity of crawlers like search enginesise legitimate, impersonating the clicks, impressions, and submissions of forms to generate activity is considered malicious.
Most advanced bot systems that are responsible for this kind of activity are capable of simulating human browsing, mouse movements, scrolling, and delays between sessions. Because of this sophisticated behavior, traffic of this sort cannot be detected through a simple filter and must rely on a more comprehensive analysis of behavioral patterns and technical fingerprints.
Proxy and VPN Technology
Proxy and VPN users can hide their real IP addresses by rerouting their traffic through different servers. While many users may use these tools for privacy concerns, they are also commonly used for fraudulent traffic generation.
Fraudulent actors may rotate through broad ranges of proxy servers in order to generate the illusion of different users. Automated apps can easily circumvent basic fraud detection systems by IP addresses. Because of this, modern fraud detection systems need to analyze additional factors, such as device fingerprint, behavior, and connection patterns.
Key Features to Look for in Fraud Detection Platforms
To choose a fraud detection platform, a buyer must know the system’s capabilities to identify fraudulent traffic. Detection of fraud systems must be able to perform seamlessly and analyze traffic fraud detection in marketing campaigns that run on a large scaleacrossn different traffic sources.
- Detecting fraud that analyzes campaign traffic as it enters the ccampaign’sfunnels
- Detection of proxies and VPNs by analysis of global IP fraud
- Identification of devices by unique fingerprinting of devices
- Automated browsing fraud detection by the use of bots
- Detection of user interaction through behavioral fraud
- Ability to integrate with ad tracking, affiliate, and CRM fraud
- Management of fraud routing and automated traffic in the management of fraud
- Provision of analytic dashboards to the point of detection quality ,traffic, and history of fraud
Systems that use different levels of detection systems, fraud traffic systems, are more successful. In both the analysis of behavioral and technical fraud systems, traffic validation systems provide a more in-depth and dependable evaluation of traffic validation.
5 Best Fraud Detection & Traffic Quality Platforms for Affiliate & Performance Marketing
Various tools are available for spotting fraudulent traffic, as well as for safeguarding advertising campaigns from fraud. Certain platforms concentrate exclusively on the fraud detection side of the equation, while others incorporate traffic validation along with campaign management and automation. The platforms below illustrate different approaches to managing traffic quality in performance marketing settings.
- Hyperone
- FraudScore
- ClickFlare
- TrafficGuard
- AppsFlyer Protect360
Hyperone
Hyperone has created a useful tool for automating traffic and identifying fraud that will be helpful for affiliate and performance marketing ecosystems. This tool automatically analyzes the quality of advertising traffic as it flows through an advertising ecosystem.
Most fraud detection tools are used for post-analytics. Hyperone employs a unique technology that is integrated into the actual traffic routing system. This means that, prior to reaching a client or an advertiser, potentially fraudulent traffic can be filtered or redirected.
Hyperone encompasses a variety of automation tools that can assist complex affiliate structures. Features of the tool include automation of traffic routing based on customizable UAD (User-Agent Distribution) scenarios, real-time analytical traffic data visualization, and the ability to manage multiple company accounts for entities that oversee multiple campaigns or partner networks.
Hyperone has the ability to automate workflows for campaigns through lead distribution, which allows performance marketers to set rules on how traffic should be distributed to certain advertisers, offers, or campaign endpoints.
Regarding Hyperone’s fraud detection, a multi-layer analytical approach is used. This means incoming traffic will be analyzed based on IP intelligence, proxy detection, device fingerprinting, and behavioral interaction.
Traffic scoring models analyze risk indicators as they occur. If unusual patterns are recognized, traffic can be flagged, filtered, or redirected based on pre-configured routing rules. The system’s intelligent automation and fraud detection are combined to create an instant response to any irregularities in traffic quality.
Typical Use Cases
Hyperone is generally utilized by affiliate networks, traffic brokerage firms, media buying groups, and lead generation companies that handle large amounts of dispersed traffic. Businesses that operate over several ad sources typically require both traffic validation and automated traffic routing.
Due to its ability to facilitate API integrations and automated processes, the platform can be incorporated into pre-existing tracking and data workflow systems.
Strengths
Hyperone’s unique strength is the synergy of fraud detection and traffic automation. Rather than addressing traffic quality issues as a separate part of campaign management, the platform combines validation processes with traffic routing. This empowers performance teams to set and maintain quality standards and operational velocity.
FraudScore
FraudScore is an analytics platform that captures and processes interaction fraud across web and mobile advertising channels. It analyzes traffic flow, identifies fraud, and provides a quality score based on risk metrics from device, network, and behavioral indicators.
Performance marketing teams most often utilize FraudScore as a standalone verification tool.
FraudScore offers its users dashboards and reports that outline specific areas of traffic fraud by source, campaign, and publisher, thereby assisting marketers to better target those areas of traffic that are high risk.
FraudScore is designed to integrate easily with advertising tracking and affiliate marketing systems, allowing for traffic that has been flagged as suspicious to be automatically filtered.
FraudScore employs a combination of detection methodologies utilizing IP reputation databases, device fingerprinting, and browser signature analysis. Session length, interaction & navigation patterns are further indicators that behavioral models analyze.
Over time, the machine learning models that are part of the system develop more refined detection methodologies based on the analyzed historical traffic data. Each detected instance of fraudulent activity is assigned a risk score that translates to a probable violation.
Typical Use Cases
FraudScore is primarily employed by lead generation companies and affiliate networks. It is also an integral platform for media buyers to assess the quality of traffic purchased across several ad networks.
Strengths
FraudScore’s greatest strength is its reporting system. The platform’s detailed analytics on quality metrics help marketing teams detect bad traffic sources. From there, they can make appropriate changes to their campaign strategy.
ClickFlare
ClickFlare offers performance marketing tracking and built-in fraud detection. Although it began as an ad tracking system, it has evolved to include traffic validation features.
ClickFlare integrates fraud detection into campaign tracking, giving marketers the ability to assess traffic quality on their performance dashboards.
Marketers can use the ClickFlare platform for click tracking, conversion tracking, and campaign analytics, whichincludes fraud detection to assess and flag abnormal traffic interactions.
Marketers can also set up automated traffic fraud detection rules within ClickFlare to stop and reroute traffic that is deemed fraudulent.
ClickFlare fraud detection works with and analyzes proxy detection databases, IP reputation, and fingerprints. Automated activity is detected by analyzing user behavior sessions.
Because the system integrates closely with campaign tracking, it can analyze events throughout the entire attribution chain.
Typical Use Cases
ClickFlare is most widely used by affiliate marketers and independent media buyers who operate multiple ad networks. Having an integrated platform for tracking and fraud detection reduces the number of separate analytical tools needed.
Strengths
Improved campaign tracking integration is ClickFlare’s main strength. Marketers can evaluate performance metrics and monitor traffic quality signals without the need for external data exports.
TrafficGuard
TrafficGuard is a fraud prevention software for digital marketing that protects advertisers across the web, mobile, and app environments. The platform is designed to spot and stop fraudulent clicks and impacts so that campaign performance metrics are not affected.
Advertisers, agencies, and marketing teams using the platform protect themselves from bot traffic and invalid clicks.
TrafficGuard provides advertisers with real-time monitoring and analytics of traffic across various advertising channels. The platform detects clicks and interactions that are invalid and allows marketers to keep invalid traffic from their campaigns.
TrafficGuard combines behavioral modeling, device fingerprinting, and IP intelligence to protect advertisers. With the help of machine learning, TrafficGuard analytics detects fraud based on patterns of past traffic.
Real-time monitoring of TrafficGuard’s attributes detects fraud and determines the authenticity in real-time, so that legitimate traffic is not impacted.
Typical Use Cases
Agencies and brand marketers who are advertising across various sources of traffic typically rely on TrafficGuard. Digital advertising allows advertisers to manage and optimize their campaigns using the TrafficGuard platform and its dashboard.
Strengths
TrafficGuard’s coverage across multiple channels is one of its most significant advantages. The platform assesses the quality of traffic across a range of advertising ecosystems, including websites and mobile apps.
AppsFlyer Protect360
AppsFlyer Protect360 is an example of a mobile attribution solution focused on preventing fraudulent app advertising campaigns from claims regarding fraudulent app installs and attribution fraud.
Since mobile app marketing is unique and there is a variety of fraudulent activities, Protect360 was created for mobile attribution-specific fraud.
Attribution event fraud, such as app installs, app launches, aand in-appfraudulent activities are app launch event attribution fraud.
Attribution fraud is evasive, and fraudulent events are processed and excluded from attribution fraud for a more precise fraud event analysis.
Mobile advertising fraud uses a variety of fraud event detection methods, such as mobile advertising activity data fraud event and also behavioral data modeling, mobile advertising activity data fraud event and also behavioral data modeling, mobile advertising activity data, and Device ID fraud event timing analysis Mobile fraudulent activity.
Use Cases
Mobile app developers and mobile app marketing teams are Protect360 users as they run direct performance campaigns via mobile ad networks.
Strengths
Most users consider Protect360 as the most fraud detection attribute since it is focused on mobile app attribution fraud analyticsand evice fraud detection model for specific app marketing fraud analytics.
Fraud Detection vs Traffic Automation Platforms
In performance marketing, fraud detection services and automation products offer separate, but closely related, functions. Classical fraud detection tools look at traffic quality indicators and behaviors and flag things that look suspicious. These tools provide an analysis or set of analyses on an incoming traffic set and then create a risk assessment based on one or a myriad of detection frameworks.
On the other side of the coin, traffic automation tools are focused on the operational management of advertising traffic across different points of entry and defined campaign endpoints. Successful advertising automation allows marketers to set defined rules, logic, and establish campaign order of priority. Here, the focus of the automation is on the movement of traffic at high volumes, as opposed to the quality parameters described above.
In the last few years, we have been seeing the rise of hybrid products that seek to integrate both sides of the equation. These products take elements of fraud detection and integrate them into automated traffic management systems. Specifically, when fraud is identified, triggering automated traffic management systems allows these systems to clamp down on campaign attribution before fraud can do too much damage to a campaign.
This integrated method can streamline operational workflows of campaign managers since the workflow no longer has to break and leave the campaign management system to conduct separate analyses on the fraud detection side of things.
Unsustainable Ad Spend due to Fraudulent Clicks
When fraudulent clicks, leads, or installs occur, marketing budgets are spent on engagements that will never convert to real customers—and in the long-run, wasted engagements are a campaign killer for ROI.
Rodition decreases fraudulent, invalid, or intentionally misleading engagements, including clicks, installs, and leads; and, as a result, it increases ROI.
Rodition optimizes campaigns to deliver and sustain engagement on real and genuine advertising and marketing objectives and increases ROI on the advertising and marketing campaigns.
When fraudulent traffic sources are attributed to deceptive click leads that convert to “real customers” and are engaged in marketing objectives, marketers mistakenly direct budget to deceptive click leads that are, in reality, attributable to the performance of fraudulent participants.
By legitimizing traffic data and identifying events that are not fraudulent, Rodition allows marketers to effectively optimize campaigns and deliver data that is not misleading for their marketing objectives and to sustain meaningful advertising and marketing engagements, without the interference of fraudulent behavior.
Building trust with advertisers and traffic partners is crucial for fraud prevention. In affiliate systems, advertisers believe that leads or conversions come from poor-quality sources, leading to disputes. Traffic validation systems offer evidence to help validate traffic and resolve disputes.
Trends in Traffic Quality and Fraud Prevention
With new developments in digital advertising ecosystems, fraud detection is progressing, especially with new technologies. Artificial intelligence (AI) is being used to study patterns in behaviors across large datasets. AI-based models can find patterns and correlations that other systems cannot.
Another area of focus is behavioral fingerprinting, or studying digital user behavior as opposed to IP addresses and other technical means of identifying a user. Digital user behavior includes patterns of speed and sequence in digital navigation, user interaction, and other behaviors.
In addition, there is a new focus on real-time traffic scoring, or the practice of evaluating digital user behavior in real-time as opposed to in the past. In the past, systems analyzed risk behaviors after campaigns and documented fraud. New systems automatically suspend suspicious traffic.
Another technological advancement that impacts the way fraud is detected is server-side tracking technology. While traditional tracking methods are limited due to the growing number of tracking protections implemented by web browsers, server-side tracking methods provide better data sources for the traffic validation models.
Another change that is occurring is an increase in the level of automation in the management of affiliate traffic. Advertising teams have the option of adding fraud detection parameters to their automated routing mechanism,s which allows them to mitigate the level of manual traffic management due to fraud anomalies.
Conclusion
The need to manage the quality of traffic in performance and affiliate marketing is one of the biggest operational concerns. The combination of a loosely organized structure of affiliate networks and a large volume of digital advertising traffic creates a perfect storm for the flow of fraudulent traffic in campaign pipelines.
The combination of a traffic quality and fraud detection platform provides the necessary analytical framework to successfully analyze the entirety of an advertising campaign and defend against invalid interactions and the subsequent loss of the advertiser’s money. Through the analysis of technical signal fraud, behavioral attribution data, and patterns, the campaign performance metrics are restored.
The evolution of advertising ecosystems incorporates traffic validation methods. The ability to assess advertising fraud and verify traffic will mitigate the concerns of the advertisers, publishers, and marketing technology platforms.
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