Combating automated interactions in email marketing analytics 

Over the past few years, automated behavior has skewed campaign performance metrics, leading to misallocated marketing resources and a distorted understanding of strategy performance.

Automated clicks in customer engagement platforms substantially distort key performance metrics, misleading businesses about their marketing effectiveness. When automated systems like security scanners, link validation tools, and email security platforms generate artificial clicks, they inflate click-through rates (CTRs) and engagement metrics.

To understand the scope of the issue, it’s necessary to look at where these automated interactions are actually coming from. They originate from several sources:

• Security scanning tools: Email security solutions that validate links before delivery
• Link validation services: Systems that pre-fetch URLs to verify their safety
• Enterprise email protection: Corporate firewalls and security tools that scan incoming mail
• Email preview systems: Applications that render email content for display

The result is a significant distortion of marketing metrics that can lead to:

• Overestimation of campaign effectiveness
• Misallocation of marketing resources
• Flawed A/B testing results
• Inaccurate customer engagement profiles
• Compromised revenue attribution

Separating human from automated engagement is not always straightforward. In fact, overly aggressive detection can exclude legitimate engagements, while insufficient detection leaves metrics contaminated with artificial interactions.

A customer shared this experience:

This anecdote highlights how automated click detection requires nuance and precision. The customer found that many flagged "bot clicks" came from legitimate contacts at businesses where security systems like ProofPoint or Barracuda triggered automated interactions.

Addressing the impact of automated interactions starts with accurately identifying them, beginning with how we detect artificial opens.

Our open detection methodology uses a proven approach based on user agent identification, similar to the successful implementation seen in the Parcel system.

Quickly, the results showed effective identification of automated opens:

• Significant reduction in suspicious opens from known automated sources
• Preservation of legitimate human engagement signals
• Consistent performance across different email types and customer segments

After analyzing available data and constraints, we've developed a robust approach to automated interaction detection that balances accuracy with implementation feasibility.

• Immediate post delivery activity (clicks within seconds of delivery)
• Multiple clicks within milliseconds
• Identical clicking patterns across different profiles
• Clicking activity outside normal business hours or across unusual time spans

• Synchronized multi-link clicking
• Frequency of clicks and very high click-to-open ratios
• Clicking every link in an email

• User Agent (use of well-known user agents for identifying link validation)
• IP Address (datacenter ranges, VPNs, proxies)
• Geographical anomalies (IPs from regions inconsistent with the recipient location)

• MX host patterns (google.com, outlook.com, etc.)
• Recipient domain clusters showing similar automation patterns
• Email provider (Google Workspace, Outlook 365, etc.) correlation with suspicious activity

• Clicks without activation of tracking pixels without corresponding engagement
• Clicking on invisible or hidden links that humans wouldn't see
• Clicking links in a perfectly predictable order rather than based on content interest

We’ve developed a comprehensive and accurate detection methodology by combining each of these dimensions with advanced AI and ML.

Since we’ve added automated open detection, the results have been dramatic. Detecting automatic clicks has shown similarly good results. Our solution has reduced the impact of automated clients by over 70% across our customer base.

Accurate engagement metrics are essential for effective email marketing. Our multidimensional approach to detecting and filtering automated interactions provides customers with significantly more reliable data on which to base their marketing decisions.

Blending AI, ML, timing analysis, technical metadata evaluation, and email service provider intelligence, we've created a robust system that balances removing artificial noise and preserving genuine engagement signals.

This commitment to accurate analytics reflects our broader mission to provide marketers with the most reliable tools for understanding and improving customer engagement.