Your latest digital campaign report looks promising at first glance. But as your team digs deeper, you see results. Conversions are not matching engagement. Traffic from unknown sources makes up a substantial portion of the total traffic. Therefore, there is an important issue that comes into play here. What portion of this traffic is legitimate?
Bots, scripts, and click farms do not contribute anything meaningful to the campaign since they only lead to increased traffic with no actual benefit. You will need to be able to detect unusual traffic immediately when it appears and act accordingly.
This article explains the role of AI in detecting invalid traffic in AdTech.
What Is Invalid Traffic in Advertising?
In advertising, invalid traffic is any type of clicks, impressions, or activities that do not come from legitimate users with a genuine intent.
Invalid traffic is typically grouped into two categories. The first includes basic bots or accidental clicks that are easier to detect and filter. The second type is more sophisticated and challenging to detect since it involves bots programmed to act like actual users.
How Machine Learning Helps Detect Invalid Traffic
Here’s how machine learning is helping businesses detect invalid traffic effectively,
1. Identifies Unusual Behavior Patterns
Machine learning models study how real users interact with websites. When traffic behaves differently, it gets flagged.
Example: If hundreds of user lands on a page and leave within two seconds without any interaction, the system can detect this as suspicious.
2. Detects More Complex Bots
Bots try to mimic the behavior of humans, thus becoming difficult to detect. With machine learning, complex patterns are identified.
Example: In case a bot moves and clicks like a human does, Bot detection AI can notice anomalies like regular intervals between actions and similar devices used by various users.
3. Minimizes False Positives
One challenge in fraud detection is mistakenly blocking real users. Machine learning enhances the detection process by considering several signals at once.
For instance, if someone visits a site from an unknown location, it becomes suspicious. Nevertheless, if they behave like other users do, there will be no reason for classifying them as invalid traffic.
AI’s Key Indicators for Detecting Invalid Traffic
By merging these indicators, AI offers a precise assessment of traffic quality.
1. Abnormal Click and Impression Patterns
AI analyzes how often ads are clicked and how impressions are generated over time.
Example: A sudden spike in clicks within a short time frame, such as without a matching increase in conversions, can indicate bot activity. AI identify these irregular spikes and isolate them.
2. Session Duration and Engagement Quality
Authentic users will take time browsing through information, whereas bots will leave immediately or act erratically.
Example: Users who arrive at a website and leave immediately without any activity could be flagged by Bot detection AI as invalid traffic.
3. Navigation Behavior and User Flow
AI analyzes user navigation within the website, such as the sequence of pages and user paths taken.
For instance, when hundreds of users follow precisely the same path on a website, it implies script-based activity. This is a strong signal used in AI fraud detection in advertising.
4. Conversion Mismatch
AI measures user engagements against tangible business results such as registrations or sales.
For example, high traffic and click rates, but low conversions could be an indication of bot activity.
Challenges in Detecting Sophisticated Bot Activity
Identifying complex bots is an ongoing challenge; however, by using AI effectively, companies can be proactive.
1. Challenge: Huge Amounts of Traffic Data
The amount of data produced during advertising campaigns can be enormous, leading to problems identifying fraudulent traffic manually.
Solution: Employ AI algorithms for fraud detection that can analyze massive amounts of data and point out any anomalies.
Example: During campaign surge, an AI algorithm may identify an abnormal spike in CTR coming from one specific type of device.
2. Challenge: Complication of Fraud Across Channels
Fraudulent activities can occur across multiple channels like display, mobile, or programmatic ads, thereby creating complications in detecting them.
Solution: Employ AI-based centralized fraud detection that would provide insight on the traffic of all the channels.
Example: For instance, when the same bot network targets both mobile and web ads, then AI can make a connection.
3. Challenge: Lack of Transparency on Traffic Sources
Some sources of traffic may not be clear, which makes it impossible to identify their legitimacy.
Solution: Utilize AI to analyze behavior and assess traffic quality.
Example: Despite receiving traffic from a legitimate publisher, the use of AI can help identify any low engagement patterns or recurring trends indicating fraudulent traffic.
Ad Fraud Prevention and Traffic Quality in the Future through AI
The process of ensuring traffic quality has become difficult with the evolving nature of bots and their ability to mimic legitimate traffic. The need for fraud prevention is leading companies to adopt AI, and ultimately, the purpose is clear: trustworthy advertising data.
