25 May 2023 • 10 min read
25 May 2023 • 10 min read
In today's digital world, where data plays a crucial role in decision-making, marketers heavily rely on accurate and reliable data to analyze user behaviour, optimize campaigns, and drive business growth. However, there’s a hidden threat lurking among the huge amounts of data – data skewing.
Data skewing, as defined by OWASP, refers to the automated, repetitive clicking, requesting, or submission of content, which influences metrics related to applications. These metrics can include counts and measurements of frequency or rate and may be visible to users (such as likes, poll results, and reviews) or hidden (such as application usage statistics and business performance indicators). Skewing can impact both individuals and the application owner.
While data skewing may seem similar to ad fraud, where both of the attacks involve fake clicks, they are different in terms of intention.
Ad fraud aims to deceive advertisers by falsifying the performance of ads, making them appear more effective than they actually are. On the other hand, data skewing floods websites or apps with bot traffic, faking visits and clicks, which leads website owners to believe that their web pages are performing exceptionally well, potentially leading to misguided marketing decisions.
Data skewing is bot activities that generate multiple requests to a specific web page or app. These bots simulate a large number of visits, clicks, and engagements, artificially inflating the metrics associated with the targeted content. By doing so, data skewing tricks website owners into perceiving a higher level of user activity, which can prompt them to invest in ads or make other marketing-related decisions based on misleading data.
The consequences of data skewing can be far-reaching, impacting both marketers and individual users.
Failed A/B Test: A/B testing is a common practice used by marketers to compare the performance of different designs or campaigns. However, data skewing can skew the results of such tests, leading to inaccurate conclusions and misguided optimizations.
Incorrect Product Ratings: Product review platforms rely on user engagement and feedback to rate and recommend products. Data skewing can manipulate clicks and votes, leading to incorrect product ratings, which can misguide potential buyers.
Misleading Ad Quality Scores: Ad companies use quality scores to determine the effectiveness and relevance of ads. Data skewing, by generating fake clicks, can inaccurately inflate an ad's quality score, potentially leading to poor targeting and wasted advertising budgets.
Misinformed Purchase Decisions: Data skewing can result in products receiving artificially high ratings or positive reviews. This can mislead individual users into purchasing products that may not live up to their perceived quality.
Loss of Trust: Data skewing undermines the trust that users place on online platforms, rating systems, and user-generated content. When users become aware of manipulated data, it can reduce their confidence in the authenticity and reliability of online information, making it harder to make informed decisions.
Wasted Time and Resources: If users rely on skewing data to make decisions, such as selecting a service provider or making a purchase, they may end up wasting their time and resources on offerings that do not meet their expectations.
To identify potential data skewing attacks, marketers and website owners should be vigilant for the following symptoms:
Peaks in Traffic: Sudden and unexplained spikes in website traffic, especially if accompanied by other unusual patterns, can be an indication of data skewing.
Unusual Increase in Users: If the number of registered users or active sessions experiences a rapid and unexplained surge, it is essential to investigate the possibility of data skewing.
High Bounce Rate: Data skewing often leads to an artificially high bounce rate, as bots do not engage with the website beyond generating visits and clicks.
To mitigate the risks associated with data skewing, it is crucial to implement preventive measures. Here are a few strategies that can help:
CAPTCHA: Implementing CAPTCHA can help differentiate between genuine human users and automated bots. CAPTCHA presents challenges that are easy for humans to solve but difficult for bots, thereby reducing the likelihood of data skewing.
Traffic Management: Employing intelligent traffic management solutions can help identify and filter out suspicious or non-human traffic patterns. Implementing traffic filters and anomaly detection mechanisms can aid in identifying and blocking data skewing attempts.
Block Harmful IPs: A simpler way to prevent data skewing is to regularly monitor and analyze website logs to identify IP addresses associated with data skewing. By blacklisting these harmful IPs or implementing IP blocking mechanisms, you can prevent further skewing attempts from the same sources.
Data skewing poses a significant threat to the integrity and accuracy of marketing analysis. Its deceptive nature can lead to misguided decisions, affecting marketers, individual users, and overall trust in online platforms. By knowing how it works, recognizing its symptoms, and implementing preventive measures, websites and mobile apps can safeguard the reliability of data and ensure a more transparent and trustworthy digital ecosystem.
GeeTest CAPTCHA v4 tackles data skewing with two powerful features. Firstly, its dynamic challenges and behaviour analysis effectively distinguish between real users and bots, ensuring only genuine traffic is measured. Secondly, its advanced algorithms filter out fake visits and clicks, providing marketers with accurate insights into real traffic performance. With GeeTest CAPTCHA v4, marketers can rely on GeeTest Dashboard's real-time traffic analysis, providing valuable insights for making informed marketing decisions based on authentic data. Say goodbye to skewed metrics and embrace reliable analytics with GeeTest CAPTCHA v4, empowering marketers to achieve success in the dynamic digital landscape.
Content Marketing @ GeeTest
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