How can I detect and segment bot clicks in email campaigns?

Key findings

• Invisible links: Placing hidden or invisible links within an email (e.g., 1x1 pixel images) can act as a honeypot, attracting bot clicks without being seen by human recipients. A high click count on such a link indicates bot activity.
• IP address analysis: Bot clicks frequently originate from data center IP addresses, which are often associated with security scanning or spam filtering services rather than individual users.
• Behavioral patterns: Bots often exhibit rapid, consecutive clicks on multiple links in an email immediately after it's sent. They may click every link, irrespective of content relevance, or click a single link multiple times within seconds.
• User-agent strings: Analyzing the user-agent string (a piece of data sent by the client when it accesses a web page) can reveal if the click originated from a known bot or security scanner. For more details on this, explore how to identify bot user agents.
• Geographic location: Clicks from unusual or unexpected geographic locations, especially those without corresponding human activity, can indicate bot traffic.

Key considerations

• Avoid simple subtraction: Directly subtracting bot clicks from total click numbers can be misleading, as some bot interactions precede genuine human clicks or open the email for legitimate security scanning before it reaches the inbox. This can lead to underreporting real engagement.
• Segmentation: A better approach is to segment bot-identified recipients into a separate group. This allows for sending campaigns to both segments but reporting on the non-bot segment for more accurate engagement metrics. This helps in understanding the impact of bot clicks on email engagement measurement.
• ESP features: Many Email Service Providers (ESPs) offer built-in features for bot filtering or click fraud protection. Utilizing these tools can help automate the detection and exclusion of suspicious activity from your reports, improving data accuracy. ActiveCampaign, for instance, provides options to filter suspected bot clicks in their reporting.
• Long-term monitoring: Bot behavior can evolve. Continuously monitoring and analyzing click patterns and data sources is essential for adapting detection strategies and maintaining data integrity.
• Impact on deliverability: While bot clicks themselves aren't always negative for deliverability, a surge in such clicks might sometimes indicate issues with content that triggers aggressive scanning, or it could highlight areas where you need to prevent bot clicks from hurting your reputation.

Key opinions

• Honeypot links are effective: Many marketers successfully use 'blank' or invisible links (honeypots) to capture bot clicks, which helps in confirming the presence of automated scanners. This is a recognized method for filtering out bad signups on opt-in pages as well.
• Don't subtract directly: A common sentiment is to avoid simply subtracting bot clicks from total clicks, as it risks eliminating actual human engagements that might occur after a security scan.
• Segmentation is key: The preferred method is to identify and segment users exhibiting bot-like behavior into a separate audience group. This allows for sending to everyone but reporting only on the genuine human segment. This strategy helps excluding these clicks when building segments.
• Common bot sources: Marketers frequently observe bot clicks originating from Microsoft (Outlook) domains and public education organizations (.edu), indicating institutional security scanning.
• Impact on metrics: Bot clicks distort campaign metrics, making it challenging to gauge true subscriber engagement and campaign effectiveness, as discussed in the context of mitigating their impact on email marketing metrics.

Key considerations

• Refining honeypots: For invisible image links (honeypots), resizing them to 1x1 pixel and potentially using CSS to hide them further can prevent accidental human clicks while still capturing bot activity.
• ESPs with filtering: Leveraging email marketing platforms that offer built-in bot filtering or click fraud protection features is highly recommended for automating detection.
• Dynamic segmentation: Marketers should consider dynamic segmentation strategies where users are tagged as bots and remain in that segment until a specified period of time has passed without further bot-like behavior.
• Continuous monitoring: The nature of bot behavior can change, requiring ongoing vigilance and adaptation of detection methods to ensure continued accuracy of campaign data.
• Understanding impact: Recognizing that bot clicks primarily affect the accuracy of analytics, rather than being inherently bad for deliverability in all cases, helps marketers prioritize their mitigation efforts. For a broader understanding, refer to whether email bot clicks are bad.

Key opinions

• Segmentation is superior: The most effective strategy involves defining specific behaviors as clickbot-y and then moving these contacts into a dedicated segment. This allows for accurate reporting on non-bot engagement without discarding the data entirely.
• Honeypots for detection: Experts support the use of tiny (e.g., 1x1 pixel) invisible images with links as a reliable way to detect automated click activity, preventing accidental human interaction with the trap link.
• Security system influence: Many bot clicks are not malicious but rather a byproduct of inbox security systems, such as Proofpoint, scanning emails for phishing or malware before delivery. This means some bot clicks are a precursor to legitimate delivery.
• Data center IPs: Automated scanning services frequently originate from data center IP ranges. Identifying these IPs can help distinguish bot activity from human interaction. For more insight into this, see how data center IPs are associated with bot clicks.
• Behavioral anomalies: Rapid, sequential clicks across multiple links or immediate clicks on specific links upon receipt are strong indicators of bot activity, distinguishing them from typical human engagement patterns.

Key considerations

• Refine honeypot design: When using honeypots, ensuring the image is truly hidden (e.g., 1x1 pixel, potentially with CSS to prevent mouseover detection) is critical to avoid accidental human clicks and maintain data purity. This relates to how honeypots can be used effectively.
• Implement continuous segmentation: Bot segments should not be permanent. Contacts should remain in a clickbot segment only for a defined period, or until no new bot-like behavior is detected, allowing for their eventual re-entry into the general audience if their behavior normalizes.
• Distinguish from spam traps: While both affect data, bot clicks (especially from security scanners) are different from spam traps (which indicate list hygiene issues). Understanding the distinction is vital for proper remediation.
• Utilize existing data: Leverage existing ESP features or third-party tools that are designed to filter or flag bot activity, such as those that track user-agent strings or click timing.
• Address underlying causes: If bot clicks are unusually high from certain domains (like .edu or Microsoft), it indicates the presence of robust security systems. While these are not directly avoidable, understanding them helps interpret data more accurately.

Key findings

• Security scanning: Automated scripts are commonly used by security systems to check emails for phishing links, malware, and viruses. These scans often result in clicks (and opens) recorded before a human recipient interacts with the email. This is a primary cause of artificial engagement.
• Preloading content: Some ISPs and email clients preload links or entire email content for faster viewing or enhanced security. This preloading generates clicks and opens without actual user interaction, skewing engagement metrics.
• Data center IPs: Bot clicks frequently originate from IP addresses associated with data centers. These are typically used for automated testing, security scanning, and spam filtering rather than individual email users.
• User-agent analysis: Analyzing user-agent strings is often cited as a technical method to identify bot activity, as these strings can explicitly name the scanning service or bot responsible for the interaction. Learn how ESPs distinguish human vs. bot clicks.
• Distorted metrics: Documentation confirms that bot clicks inflate reported engagement rates, leading to inaccurate assessments of campaign performance and potentially misguiding optimization efforts. This issue is relevant to avoiding false email click data.

Key considerations

• Leverage ESP features: Many email marketing platforms offer built-in bot filtering or click fraud protection capabilities, which should be enabled to automatically exclude suspected bot activity from reports.
• Segmentation over subtraction: Documentation often implies that while bots may click links, the underlying contact is still legitimate. Therefore, segmenting bot activity for separate analysis is preferred over outright removal from all click data, to prevent the loss of potential actual customer data.
• Behavioral analytics: Beyond basic IP or user-agent checks, advanced behavioral analytics can detect bot patterns, such as multiple clicks on all links in milliseconds, which are highly unlikely for human interaction. This helps identifying artificial opens and clicks.
• Campaign design: Some documentation suggests designing emails with bot-detecting traps, like honeypot links, to deliberately identify and isolate bot activity without affecting legitimate users.
• Focus on deliverability: While bots skew metrics, they are often part of the deliverability process (e.g., security scans). Marketers should focus on understanding this context to ensure their legitimate emails reach the inbox, rather than solely fixating on click count deflation.