Understanding click metrics: Navigating variations and automated interactions

Whether you’re a content creator, email marketer, or newsletter publisher, understanding how your audience engages with your email is essential to building strong connections. However, in today’s world of automated interactions and security filters, accurately interpreting click data has become increasingly challenging.

In this article, we’ll help you understand the complexities behind click metrics, including the rise of non-human interactions like bots. We'll dive into how click tracking works, why metrics vary across platforms, and give insight into how beehiiv filters non-human clicks. Finally, we'll outline beehiiv’s click-tracking naming conventions, and offer other suggestions for evaluating success.

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Click tracking explained

Click tracking is the process of recording when someone clicks on a link within your email content. This is done by replacing the original URLs in your content with a special, trackable version of the link. When a recipient clicks on a link, they're first directed to a tracking server, where the click event is logged, including details such as the time of the click, the recipient’s email address, the device used, and the link that was clicked. After logging the data, the email is then redirected to the intended destination.

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Why click rates vary across platforms

Accurately filtering non-human interaction (NHI) clicks is important to ensure engagement metrics accurately reflect the behavior of real readers. However, the methods for detecting and filtering these non-human clicks vary significantly between email service providers (ESPs). Each platform employs its own proprietary algorithms and filtering techniques, ranging from analyzing user-agent strings to detecting abnormal click patterns. Heck, some ESPs don’t do any filtering at all!

This wide-ranging variation in filtering methodologies highlights the challenge of evaluating click performance across different platforms. The metrics you see at one ESP most likely will be processed differently than another, even for identical content and audiences. There is no ‘industry standard’.  At beehiiv, we’re often asked, "Why were my click rates higher with another service compared to here?" Of course, there’s no one single answer, but knowing that we employ some of the most accurate filtering technologies in the industry leads us to point to the probable differences between platforms.

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Filtering non-human interaction (NHI) clicks

As mentioned, some platforms might not filter out non-human interactions as effectively, or at all, resulting in inflated click rates that make engagement appear higher than it really is. Below are just a few of the techniques used at beehiiv to give you the most accurate click metrics:

• Time interval analysis: Bots often click links in rapid succession, far quicker than a human could. This timing data is used to identify and filter automated interactions.
  
  
• User-agent analysis: beehiiv analyzes the 'user-agent' strings that bots use when clicking links. Many security bots have recognizable user-agent strings or contain hard-coded keywords, allowing us to filter them out from your metrics.
  
  
• Click pattern analysis: Bots tend to click all the links in an email or follow a predictable order. By analyzing these patterns, we can more accurately identify automated clicks.
  
  
• Autonomous System Numbers (ASN): We identify ASNs, often identified as ISPs or data centers,  that have been confirmed to be all bots related.

Like any forward-thinking service provider, beehiiv understands that security measures constantly change, including those that identify and analyze links in your email.  It’s a dynamic ongoing process.  So too are the tools and processes beehiiv uses to eliminate NHI and provide you with the most accurate data available. 

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Navigating industry naming conventions for click metrics

The email industry lacks a standardized set of naming conventions for engagement metrics, which can lead to confusion, especially when trying to compare data from different email service providers. Each platform may use slightly different terms to describe similar click metrics, often with subtle variations in what each term means. That’s why it’s important to understand exactly what you’re being provided when speaking to various click metrics. Below, is a list of terms used within beehiiv’s product, along with definitions to help clarify their meaning.

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beehiiv’s click metric terms

• Unfiltered Clicks or Unfiltered Email Clicks: The total number of links that are clicked within an email, including all detected interactions without applying filters to remove non-human interactions (NHI) or multiple recipient clicks. Many refer to this metric as ‘raw’ clicks, as well, some in the industry refer to this as ‘total clicks’, which again is why clarification is often needed.
  
  
• Total Clicks or Total Email Clicks: The total number of links that are clicked within an email, excluding non-human interactions (NHI) or automated clicks. This count does include multiple clicks by the same recipient.
  
  
• Unique Clicks or Unique Email Clicks: The total number of individual recipients who clicked on any link within an email at least once, counting each recipient only once regardless of how many times they clicked on that specific link.  This metric doesn’t count multiple clicks by the same recipient or NHI clicks, so it’s often viewed as a more accurate measure of unique audience interest. Some may refer to this metric as Aggregate Unique Clicks or Adjusted Click Rates.

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Best practices for evaluating success

While still an interesting metric, ‘clicks’ have lost their luster to a certain extent based on the challenges described above. To get a further understanding of your content’s performance, we recommend using a holistic approach that combines multiple data points, such as:

• Engagement Trends: Look at engagement over time to identify patterns and trends, rather than focusing on individual metrics for a single campaign.
  
  
• Interaction and impact: Measure how many readers take meaningful actions, such as taking a survey, purchasing a product, or sharing your content with others.
  
  
• Audience Feedback: Ask for and gather direct feedback from your audience to understand their preferences and improve your content strategy.

By relying on a broader set of metrics, you can gain a more comprehensive view of your audience's behavior and make better decisions that aren’t swayed by oft-tangled data points.

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Conclusion

The rise of automated bot clicks and non-human interactions presents both challenges and opportunities. At beehiiv, we're committed to helping you navigate these complexities by providing filtered click data that more accurately represents your readers' engagement. By understanding the nuances of click metrics and how we handle them, you'll be better equipped to make data-driven decisions that strengthen your audience connection.