How to accurately measure email engagement despite bot clicks and platform limitations?
Michael Ko
Co-founder & CEO, Suped
Published 26 Jun 2025
Updated 18 Aug 2025
7 min read
Email engagement metrics are crucial for understanding how your audience interacts with your campaigns. We rely on these numbers to measure success, identify trends, and make informed decisions about future strategies. However, in today's complex email ecosystem, accurately measuring engagement can feel like navigating a minefield, primarily due to the prevalence of bot clicks and the inherent limitations of various email platforms.
Many email service providers (ESPs) attempt to filter out non-human interactions (NHI), but their methods aren't always perfect. This can lead to distorted data, making legitimate human engagement appear much lower than it truly is. For instance, I've seen situations where click rates plummeted from a healthy 1.5-2% to a mere 0.2-0.3% overnight, despite high open rates and engaged contacts.
The challenge is that some of these bot clicks originate from security scans or proxy opens within organizations (like universities or businesses) whose employees are genuinely interested and engaged with your content, even making purchases. Excluding these interactions entirely can give you a false, demotivating picture of your email performance. The goal is to accurately distinguish between automated activity and actual human interest without penalizing engaged subscribers.
The pervasive problem of bot clicks
Automated security software, often deployed by large organizations and internet service providers (ISPs), is a primary source of artificial engagement. These systems scan email content, including links, for malicious threats before delivering them to the recipient's inbox. This pre-scanning triggers clicks that appear legitimate but are, in fact, non-human. This phenomenon is why your click-through rates might look inconsistent or surprisingly low, despite your emails being delivered to an active audience.
The challenge intensifies because these Microsoft and other security scans often come from IP addresses associated with data centers or known security providers. While ESPs try to identify and filter these, the methods are not foolproof. Some legitimate users might be behind these robot clicks, making it difficult to decide whether to exclude them from your metrics or not.
For example, you might find that a significant percentage of contacts flagged for bot clicks are actually purchasers or highly engaged individuals from educational or business domains. This situation makes a blanket exclusion problematic. It's a binary choice between including all clicks (even artificial ones) or excluding potentially engaged human interactions, leading to a misleading view of your campaign's true impact.
The impact of bot clicks
Inflated metrics: Artificially boosts click-through rates, making campaigns appear more successful than they are.
Skewed segmentation: Leads to misidentifying engaged subscribers and potentially excluding valuable contacts.
Misleading A/B tests: Invalidates results, hindering effective optimization of subject lines, calls-to-action, or content.
To gain a more accurate understanding of email engagement, you need to go beyond standard ESP reporting. One effective method is to create your own bot detection mechanism. A popular technique involves embedding invisible, or honeypot, links within your email footer. These links are not visible to human eyes but are readily clicked by automated security scanners.
When a honeypot link is clicked, it indicates a probable bot interaction. You can then configure your system to add a specific tag or update a field on the contact record, marking them as a bot. To account for accidental human clicks, implement a threshold, such as requiring multiple honeypot clicks before classifying an interaction as bot-driven. You can also send the bot clicks to a dedicated list, allowing you to differentiate performance.
Furthermore, analyze click patterns. Bots often click all links in rapid succession or exhibit perfect consistency, opening and clicking every email within seconds of receipt. Humans, however, display more varied and natural behavior. By observing these patterns, you can build smarter segments that truly reflect engagement.
Shifting focus to more reliable engagement metrics
Given the unreliability of opens (due to Apple Mail Privacy Protection) and clicks (due to bot activity), it's essential to shift your focus to metrics that truly indicate human engagement. Conversion rate is a paramount indicator. If your emails drive purchases, sign-ups, or downloads, those are undeniable signs of effective engagement, regardless of bot interference.
Beyond conversions, consider website activity as a key metric. Use tracking tools like Google Analytics to measure:
Time on site: How long users spend on your landing pages.
Pages per session: The number of pages they visit after clicking an email link.
Engagement with specific content: Interactions with videos, forms, or downloadable assets.
These downstream metrics offer a more robust and reliable measure of your email's impact on audience behavior. Instead of solely fixating on potentially inflated click rates, look at what happens after the click. This approach gives you a clearer, more actionable understanding of your campaign's true effectiveness and helps you refine your strategy to drive meaningful results.
Focus on actionable metrics
Conversion rate: Track purchases, sign-ups, or form submissions that directly result from email clicks.
Website engagement: Analyze metrics like bounce rate, time on page, and pages visited post-click.
Reply rate: For transactional or one-on-one emails, direct replies are a strong indicator of engagement.
Refining audience segmentation and automation
The key to accurate measurement despite bot activity is intelligent audience segmentation. Rather than relying on your ESP's possibly overzealous bot filtering, consider building your own segments based on robust behavioral data. This ensures you're not excluding genuinely engaged contacts who happen to be behind a corporate firewall.
For example, combine email engagement data with on-site activity. If a contact has a bot click but also made a purchase, visited multiple pages on your website, or interacted with other parts of your business, they are clearly a human. Bots don't typically place orders or browse websites extensively.
Traditional segmentation
Relies heavily: On ESP's default bot filtering and click data.
Risk of exclusion: May inadvertently exclude engaged human contacts masked by bot activity.
Inflated metrics: If bot filtering is off, metrics can be artificially high.
Refined segmentation
Integrates data: Combines email behavior with website activity and purchase history.
Accurate targeting: Ensures automated re-sends and campaigns reach truly engaged subscribers.
By developing segments that truly capture genuine engagement, you can make more accurate decisions about which contacts to target with re-engagement campaigns or exclude from future sends. This strategic approach ensures your marketing efforts are based on authentic data, leading to improved email performance and better resource allocation.
Empowering email engagement measurement
Measuring email engagement in the face of bot clicks and platform limitations requires a proactive and thoughtful approach. It’s no longer enough to rely solely on the default metrics provided by your ESP. By understanding the nature of artificial interactions, implementing your own detection methods, and prioritizing more reliable downstream metrics, you can gain a far more accurate picture of your audience's true engagement.
Focus on what matters: actual conversions, website activity, and other indicators of genuine human behavior. This will not only improve the accuracy of your reporting but also lead to more effective email marketing strategies that drive tangible business results, ensuring your efforts are always aimed at truly engaged subscribers.
Views from the trenches
Best practices
Implement honeypot links in your email footers to identify probable bot clicks without affecting legitimate users.
Use UTM parameters with your links to segment bot traffic in web analytics, preventing skewing of website data.
Prioritize downstream metrics like conversions, website time on site, and pages viewed, as these are harder for bots to fake.
Build custom segments that combine email activity with on-site behavior and purchase history to identify true human engagement.
Common pitfalls
Blindly trusting ESP's bot filtering: It can be overly aggressive, excluding real, engaged human contacts.
Over-relying on inflated click rates: This can give a false sense of campaign success and lead to poor optimization decisions.
Not differentiating between bot clicks and bot addresses: A real human can still have their clicks flagged by security scanners.
Excluding valuable customers from automated flows: If bot filtering removes active purchasers, you miss re-engagement opportunities.
Expert tips
If your ESP's bot filtering seems too aggressive, consider turning it off and developing your own internal tracking mechanism to maintain control.
Focus on consistent, trending click numbers over time rather than isolated campaign metrics, as trends provide a better baseline for assessment.
Use behavioral segmentation: Bots behave with perfect consistency, while human behavior is naturally varied and unpredictable.
Always introduce purchase data into your logic. Bots don't buy products, so purchasing activity is a strong indicator of human engagement.
Marketer view
A marketer from Email Geeks says Klaviyo's bot click exclusion impacted their client's click rates, dropping them from 1.5-2% to 0.2-0.3%, despite engaged contacts and high open rates.
2025-03-31 - Email Geeks
Marketer view
A marketer from Email Geeks says they came across a similar problem with another ESP and marketing automation platform. They hope that human and non-human engagement can be accurately tracked within ESPs.
