How can I identify bot user agents in my email click data?

Key findings

• User agent patterns: Many bots or automated systems have distinctive user agent strings that do not start with common browser indicators like Mozilla. Examples include AHC/2.1, python-requests, Slackbot-LinkExpanding, and various Microsoft Office/Exchange strings.
• Security scanner activity: User agents like Barracuda Sentinel or those associated with Amazon CloudFront (often linked to Outlook protection) indicate automated security checks rather than human clicks.
• API and crawler behavior: User agents containing terms like python-requests, Wget, or Social News Desk RSS Scraper often indicate automated scripts or crawlers. This is discussed more in our guide on automated scripts and crawlers.
• Impact on metrics: Bot clicks can significantly inflate email marketing metrics, making it difficult to assess true engagement and campaign performance. Learn how to mitigate the impact of bot clicks.
• IP address analysis: Beyond user agents, examining the IP addresses of clicks can reveal bot activity, especially if they originate from known data centers or suspicious networks. Our blocklist checker can help identify potential issues related to IP reputation.

Key considerations

• Data granularity: Accessing granular data including user agent strings and IP addresses is essential for accurate bot detection. Many email service providers (ESPs) offer this level of detail in their reporting.
• Pattern recognition: Look for unusual click patterns such as simultaneous clicks from multiple, disparate user agents, or clicks occurring within milliseconds of an email being sent.
• Maintaining a blacklist: Developing and maintaining a list of known bot user agents and suspicious IP ranges can help filter out irrelevant data from your analytics. This is similar to how email blacklists work.
• Segmentation: Segment your email click data to exclude bot activity when analyzing campaign performance, ensuring a clearer picture of human engagement. More information on how to detect robot clicks is available from Badsender.
• Continuous monitoring: Bot user agents evolve, so continuous monitoring and updating your detection methods are essential to stay effective.

Key opinions

• Mozilla-centric view: A common initial observation is that any user agent string not beginning with Mozilla is likely a bot, indicating a practical, rule-of-thumb approach to initial filtering. This is a good first step to avoid false email click data.
• Specific bot queries: Marketers frequently question the nature of specific user agents, such as python-requests, indicating a need for detailed understanding of common bot signatures.
• Data source complexities: There is a recognition that bot clicks originate from various sources, including security scanners and potentially automated systems within legitimate organizations like law firms or universities, suggesting diverse origins for bot activity.
• Skewed engagement: Bot clicks distort key performance indicators (KPIs) like click-through rates, making it challenging to gauge the true effectiveness of email campaigns. This impacts email reputation metrics.

Key considerations

• User agent blacklisting: Marketers should consider maintaining an internal blacklist of known bot user agents to filter them from analytics. This contributes to filtering bot clicks from newsletter reports.
• Timestamp analysis: Analyzing the timing of clicks, especially rapid bursts or clicks occurring simultaneously from diverse IPs, can help identify bot activity. More on this from Yocto.agency about click surge.
• IP address verification: Collaborating with IT or deliverability experts to check suspicious IP addresses against known bot networks can provide deeper insights.
• ESP features: Utilize any bot filtering features provided by your email service provider (ESP) to automatically remove artificial clicks from your reports.
• Honeypots: Consider employing invisible links or honeypots within emails as a sophisticated method to trap and identify bot clicks without affecting human users. This is covered in our article using invisible links to identify bot clicks.

Key opinions

• Script identification: Experts confirm that user agents like python-requests signify automated scripts. These scripts are typically used for web-based interactions, similar to command-line tools.
• IP-based research: Advanced bot detection often involves researching IP addresses associated with suspicious user agents, leveraging extensive IP data to identify malicious or automated sources. This is a critical component of how ESPs distinguish human vs. bot activity.
• Outlook/CloudFront interaction: The presence of user agents like Amazon CloudFront linked to Microsoft Outlook's protection domains highlights that even legitimate email providers perform automated link scanning, which can be misconstrued as user clicks.
• Holistic view: Experts emphasize that identifying bots requires combining user agent analysis with other data points, such as IP addresses, click timestamps, and referral data, to form a comprehensive detection strategy. This comprehensive approach is vital for maintaining a strong email domain reputation.

Key considerations

• Dynamic bot lists: Due to the evolving nature of bots, maintaining a continuously updated list of known bot user agents and IP ranges is critical for effective filtering. Our blocklist monitoring service can help.
• Behavioral analysis: Beyond static indicators, analyzing behavioral anomalies (e.g., clicks from the same IP at impossible speeds) can reveal sophisticated bot activity. Campaign Cleaner offers insights into distinguishing bot clicks from human interactions.
• Collaboration with data teams: Working closely with data analysts or IT teams to implement robust logging and analysis tools is recommended for comprehensive bot detection.
• Industry resources: Leverage publicly available user agent databases and industry forums to stay informed about new bot signatures and detection techniques. Resources like useragents.me provide regularly updated lists.
• Automated filtering solutions: Implement or integrate with systems that automatically identify and filter bot clicks, ensuring cleaner data for reporting and analysis.

Key findings

• Standard bot identifiers: Many automated services, like Slackbot and Snap URL Preview Service, clearly state their bot nature within their user agent strings, often linking to their robots.txt files or developer documentation.
• Programmatic user agents: User agents referencing programming languages or libraries (e.g., python-requests, Java, Apache-HttpClient) almost certainly indicate automated activity. This aligns with guidelines on minimizing bot clicks in email marketing.
• Email client/security suite UAs: Specific user agents from email clients (e.g., Microsoft Office/Outlook) or security services (e.g., Barracuda Sentinel) indicate automated scanning for security purposes. Learn more about handling bot clicks from Microsoft/Outlook domains.
• Mobile OS user agents: User agents for mobile operating systems (e.g., iOS, Dalvik/Android) followed by dataaccessd often indicate background data fetching processes, not direct user clicks.

Key considerations

• User agent databases: Consult comprehensive user agent databases to cross-reference and identify known bot signatures. Resources like WhatIsMyBrowser.com can be valuable for exploring common user agents.
• Regular updates: Bot user agents are constantly evolving. Regularly updating your recognition patterns and leveraging real-time data from platforms that identify new bot threats is crucial.
• API documentation: Refer to the documentation for APIs and services you integrate with to understand how their automated processes generate user agents.
• RFC compliance: While not directly about bots, understanding how email authentication protocols like DMARC, SPF, and DKIM influence email flow and potential scanner behavior can be beneficial. Our guide on DMARC, SPF, and DKIM provides a good starting point.