¶ Click Flooding Filter
Click flooding is a deceptive practice in mobile advertising where fraudsters generate large volumes of fraudulent clicks to manipulate attribution systems. By claiming credit for organic conversions, these spammers inflate click counts and distort conversion data.
¶ Impact on Mobile Ads
Click flooding adversely affects ad campaigns by reducing conversion rates and prolonging Click-To-Conversion Times (CTIT). It poses significant challenges for campaigns lacking conversion data, iOS campaigns with unreliable CTIT metrics and those susceptible to click spam or cookie dropping.
¶ Prevention and Detection
To counter click flooding, adopt real-time click-level blocking mechanisms and analyze clickstream data to identify anomalies. Implement advanced filters tailored to detect and prevent click fraud, ensuring accurate attribution and optimized ad spend.
¶ Use Cases for Click Flooding Filters
- Real-Time Fraud Prevention: Detect and block fraudulent clicks in real-time.
- Campaigns Without Conversion Data: Protect ad campaigns that lack sufficient conversion tracking metrics.
- iOS PPC Campaigns: Address unreliable CTIT metrics in iOS campaigns.
- App Install Campaigns: Prevent click spam in app-focused mobile advertising.
- eCommerce Ads: Block cookie dropping in eCommerce campaigns to maintain attribution integrity.
¶ Unveiling Hidden Mobile Ad Fraud Tactics
Fraudulent practices such as click flooding and click spamming introduce several hidden challenges:
- Extended Session Times: Fraudulently inflated time between the first click and the final conversion.
- Prolonged CTIT: Artificially delayed Click-To-Install Times to misattribute app installs.
- Cookie Dropping: Unauthorized placement of cookies to claim unearned credit for conversions.
- Mobile Ad Fraud: Broad deceptive activities that generate fake clicks or misattribute legitimate ones.
¶ Case Study
Click Flooding and Click Spamming are classic attribution Fraud where the fraudster abuses the attribution window. High-volume offers (or games) are especially vulnerable to these tactics.
These are real and show ARPU (Average Revenue Per User) values but are misattributed from Organic traffic. Our filter only flags the conversions with a CTIT/Session-Time above the 1h mark. So in the case of mixed traffic, the good conversions are not flagged.
Typical traffic patterns for these are low Conversion Rates (below 0.15%), and higher amounts of session time (above 1h). Our filter flags Click Spam when more than 35% of the installs show a session time above 1h. This threshold can also be configured but has proven to be a good setting.
Checking the Click Data would show high amounts of Click Flooding reasons, Excessive Duplicate Clicks.
Argument with Partner:
The average CTIT / Session-Timwe compared to other partners is a significant outlier which is a clear Attribution Fraud Pattern, and these users originally belonged to Organic Traffic. We expect that most installs have a Session Time below the 1h mark.
¶ Conclusion
Mobile ad fraud, including click flooding and spamming, threatens the integrity of digital advertising. These schemes distort conversion data, inflate click counts, and increase costs, undermining campaign effectiveness.
Fraudulent activities such as extended session times, prolonged CTIT, and cookie dropping result in misattributed conversions and wasted ad budgets. Combating these threats with robust detection and prevention strategies is critical for ensuring transparency, accuracy, and efficiency in mobile advertising.