In the world of digital advertising, ensuring ad quality and effectiveness is crucial for advertisers and publishers alike. However, fraudulent activities can compromise these objectives, leading to wasted ad spend and inaccurate performance metrics. GIVT, which stands for General Invalid Traffic, is a term used to describe certain types of fraudulent or invalid traffic that can affect digital advertising campaigns. In this blog post, we will delve into what GIVT is, how it impacts advertising, and strategies to mitigate its effects.
What is GIVT?
GIVT refers to traffic that is considered invalid or fraudulent due to various reasons, such as non-human traffic, automated bots, or other deceptive practices. GIVT can artificially inflate ad impressions, clicks, or engagement metrics, skewing campaign results and potentially wasting advertisers' budgets.
Types of GIVT
- Non-Human Traffic: This includes automated bots, crawlers, or other software-generated traffic that mimics human behavior but lacks genuine human intent.
- Proxy Traffic: Proxy servers or virtual private networks (VPNs) can be used to disguise the true origin of traffic, making it difficult to determine its authenticity.
- Data Center Traffic: Traffic originating from data centers, which are often associated with non-human activity, can be categorized as GIVT.
- Click Farms: Click farms employ individuals to generate fraudulent clicks or engagement on ads, leading to artificial performance metrics.
Impact of GIVT on Advertising
GIVT can have significant implications for advertisers, publishers, and the overall digital advertising ecosystem:
- Wasted Ad Spend: Advertisers may unknowingly pay for impressions or clicks that do not originate from genuine users, resulting in wasted budget and diminished ROI.
- Inaccurate Performance Metrics: GIVT distorts key performance metrics such as click-through rates (CTR), conversion rates, and engagement rates, making it challenging to assess campaign effectiveness accurately.
- Damaged Reputation: Advertisers and publishers may suffer reputational damage if their ads are associated with fraudulent or low-quality traffic, potentially impacting their brand image.
Strategies to Mitigate GIVT
- Ad Fraud Prevention Tools: Implement ad fraud prevention tools and technologies that can detect and block invalid traffic in real-time. These tools use sophisticated algorithms and machine learning to identify and filter out fraudulent activity.
- Verification Partnerships: Collaborate with reputable third-party verification partners to validate and monitor ad impressions and traffic quality. Verification partners provide an additional layer of protection against GIVT and ensure transparency in the digital advertising supply chain.
- Ad Quality Standards: Adhere to ad quality standards set by industry organizations, such as the Interactive Advertising Bureau (IAB), to maintain a high level of ad integrity and minimize the risk of GIVT.
- Continuous Monitoring and Analysis: Regularly monitor ad performance metrics, traffic patterns, and anomalies to identify potential instances of GIVT. Analyze data and patterns to refine targeting strategies and optimize campaigns for genuine user engagement.
GIVT poses a significant challenge to the integrity and effectiveness of digital advertising campaigns. By understanding the concept of GIVT and implementing robust prevention measures, advertisers and publishers can protect their ad budgets, maintain accurate performance metrics, and ensure a trustworthy advertising ecosystem. Proactive measures, including the use of ad fraud prevention tools, verification partnerships, adherence to industry standards, and continuous monitoring, are essential for combating GIVT and fostering a more transparent and effective digital advertising landscape.
Shedding Light on SIVT (Sophisticated Invalid Traffic) in Advertising
In the realm of digital advertising, ad fraud continues to be a persistent challenge that affects the industry's integrity and impacts campaign performance. Sophisticated Invalid Traffic (SIVT) refers to a category of fraudulent or invalid traffic that is more advanced and deceptive in nature. In this blog post, we will explore SIVT, its impact on advertising, and strategies to combat this type of ad fraud effectively.
SIVT encompasses fraudulent or non-human traffic that employs sophisticated techniques to mimic genuine user behavior, making it difficult to detect. Unlike General Invalid Traffic (GIVT), SIVT is more advanced and requires specialized tools and techniques to identify and mitigate.
Types of SIVT
SIVT can manifest in various forms, including:
- Click Injection: This occurs when an app fraudulently generates clicks on ads just before or after a legitimate user interaction, attributing the click to the fraudulent source.
- Click Spamming: In this technique, a fraudulent entity floods an ad network with a large number of clicks in a short period, distorting the attribution and inflating the performance metrics.
- Device Hijacking: SIVT can involve hijacking legitimate devices, using malware or other malicious software to generate fraudulent ad interactions and traffic.
- Cookie Stuffing: This technique involves surreptitiously inserting additional tracking cookies into a user's browser without their consent, artificially inflating attribution and conversions.
Impact of SIVT on Advertising
SIVT poses several challenges to the digital advertising ecosystem:
- Budget Drain: Advertisers may unknowingly allocate a significant portion of their budgets to fraudulent clicks or impressions, wasting resources that could have been invested in genuine user engagement.
- Distorted Attribution: SIVT can manipulate attribution models, falsely attributing conversions or engagements to fraudulent sources, leading to incorrect performance analysis and optimization decisions.
- Advertiser-Publisher Trust: SIVT damages trust between advertisers and publishers when ads are associated with fraudulent traffic. This can result in strained relationships and a negative impact on the overall ad ecosystem.
Strategies to Combat SIVT
- Advanced Fraud Detection Tools: Implement advanced ad fraud detection tools that employ machine learning algorithms and pattern recognition techniques to identify and filter out SIVT in real-time.
- Collaborative Partnerships: Work with trusted third-party vendors specializing in ad fraud prevention and verification. These partnerships provide additional expertise, tools, and resources to combat SIVT effectively.
- Regular Auditing and Monitoring: Conduct regular audits of ad campaigns, traffic sources, and attribution models to identify patterns and anomalies that may indicate SIVT. Continuously monitor and analyze data to refine targeting strategies and optimize campaigns for genuine user engagement.
- Industry Initiatives and Standards: Stay updated on industry initiatives and best practices in ad fraud prevention, such as those established by industry organizations like the Interactive Advertising Bureau (IAB). Adhering to these standards can help protect against SIVT and ensure a safer advertising ecosystem.
SIVT represents a sophisticated form of ad fraud that challenges the transparency and effectiveness of digital advertising campaigns. Advertisers and publishers must be vigilant and implement proactive measures to detect and mitigate SIVT effectively. By leveraging advanced fraud detection tools, collaborative partnerships, regular auditing, and industry standards, stakeholders can protect their budgets, maintain accurate attribution, and foster a more trustworthy digital advertising landscape. Continued efforts to combat SIVT will contribute to a more sustainable and fraud-free environment for the entire ad tech industry.
According to our CTO (Anand Kumar)
We are witnessing 30-40% of traffic that is not created by human devices, and in most cases, sources are doing it on purpose to benefit. Amli filters billions of IPs and evaluates their percentage for real-time auto-blocking. Caching and managing such large IP addresses was a difficult challenge at first, but we managed it without utilizing separate servers and with 100 percent uptime. It takes us less than 1 millisecond to filter, with no additional strain on equipment. In the current circumstances, it appears positive for us because our ad response time is still less than 80 milliseconds.
Post validations are important components in filtering and eliminating SIVT sources; they begin with validation against request data and beacons (for example, impression and click) data that we receive directly from the users' devices.
Ad networks should avoid using direct payload and instead use their own encoding techniques that can only be decoded on their ad servers to avoid exposing any entities.
Following that, we validate the beacon headers data against the request data. It includes User-Agent, IP, Bundle, and Domain, among other things. Finally, our machine learning algorithms track the legitimate behaviour of others. Ad viewability is another element that we filter and exclude altogether or limit such sources where ad viewability is less than 1 second with the proper MRC-defined pixel. Our IVT filtration technology is the greatest, and I can guarantee it.