Introduction
In the landscape of email marketing, a new challenge has emerged: the increasing prevalence of automated email clicks. These clicks, generated not by eager consumers but by non-human bots, are quietly but significantly altering the way email marketing campaigns are perceived and evaluated. This phenomenon is not just a mere curiosity; it has profound implications for how marketers interpret engagement metrics and make strategic decisions.
The impact of these automated clicks on email marketing cannot be overstated. Traditionally, metrics such as click-through rates have been essential in assessing the success of email campaigns. However, with the rise of the bots, these metrics are no longer as reliable as they once were. Bots can inflate engagement statistics, leading to skewed data that misrepresents actual user interest and engagement. This distortion poses a significant challenge for marketers aiming to gauge the true performance of their campaigns and to understand their audience.
The purpose of this article is threefold: First, to examine the reasons why robots, or automated systems, are interacting with email links. Understanding the ‘why’ is crucial in developing effective strategies to address this issue. Second, we aim to explore how to detect these automated clicks. Differentiating between genuine customer engagement and bot activity is key. Finally, we will discuss mitigation strategies. It’s not just about identifying the problem but also responding effectively to ensure the integrity and effectiveness of our email marketing efforts, as well as providing the best possible user experience to our audience and subscribers.
Understanding Automated Email Clicks
Automated email clicks are instances where a link within an email is activated not by a human user, but by an automated system, often referred to as a bot. These bots are programmed to simulate the action of clicking on links, a process usually invisible to the email recipient.
Many types of bots exist but in general fall into variations of Security and Spam Detection Bots.
These bots serve dual purposes – ensuring the safety of email recipients and maintaining the integrity of email communication. They are designed to automatically click on links within emails to scan for potential security threats, such as malware or phishing attempts, and to assess the legitimacy of an email for spam detection.
This is not quite new. What is different today is their more prevalent use, and many sectors are much more likely to use them:
- Government and Public Sector: Government agencies, which often handle sensitive data, are ripe targets. Schools and public libraries are also important users.
- Financial Services: Banks, investment firms, and insurance companies deal with quite sensitive financial information. They are prime targets for phishing and other cyber-attacks.
- Healthcare: Hospitals, healthcare providers, and insurance companies (yes, again) handle sensitive patient data. Using these bots to prevent phishing attacks and protect patient confidentiality is more or less standard.
- ESPs: One of the primary goals of Email Service Providers is to protect their customers from all sorts of attacks and only present to them emails they assess to be legitimate.
This is not an exhaustive list and robots can be encountered anywhere (Legal, E-commerce, etc). That said if your audience is primarily from these sectors, your mailings are more likely to encounter them.
Their goal at the end of the day is to help by preemptively identifying harmful content and filtering out spam. The use of advanced email scanning and link-checking bots are essential tools in the grand scheme of cyber security strategies.
As email marketers, we should not fight them, on the contrary, we should embrace them as allies in our efforts to maintain a secure and trustworthy permission-based email ecosystem. But they do create some complications…
The Impact of Automated Clicks
The impact of automated clicks is many fold, most notably in skewing data and complicating the interpretation of campaign metrics, and can significantly affect user experience as well.
Email Marketing Metrics
Email opens have long been considered unreliable for gauging engagement accurately. As noted in many places, open statistics are mostly dead and should be used more for relative comparison rather than absolute numbers. With the increasing prevalence of automated clicks, click stats are becoming just as questionable.
Automated email clicks have a significant impact on the way email campaign metrics are interpreted and on the decision-making process in email marketing. When bots click on links, these interactions are often indistinguishable from genuine user engagement, leading to inflated click-through rates. This distortion can mislead marketers into overestimating the success of their campaigns.
This diminishes their reliability for triggering automated marketing workflows or for serving as benchmarks in performance analysis.
User Experience
Indirectly, automated clicks can impact the end-user experience and the overall reputation of an email sender. If users receive emails triggered by bot interactions (like follow-up emails after a bot-initiated click), it can lead to confusion and irritation, damaging the sender’s reputation. If email content is adjusted based on skewed data from bot clicks, it might not resonate with the actual human audience, leading to a decline in user engagement and interest.
One particular challenge is the issue of unwanted unsubscriptions or premature confirmations in the double opt-in process. Bots might inadvertently click on unsubscribe links or prematurely confirm subscriptions, leading to inaccuracies in subscriber lists.
The widespread occurrence of automated clicks in email marketing today presents various challenges. We need to adopt a more careful and nuanced approach in analyzing email metrics and adjust our strategies to better align with the actual behaviors and preferences of our audience.
In order to do that we need to be able to distinguish between real interactions and automated clicks.
Identifying Bot Clicks
While bots are increasingly sophisticated, there are several strategies that email marketers and software developers can employ to identify and filter out the automated interactions created by bots.
User-Agent Analysis
One common method involves analyzing the ‘user-agent’ string of the clicker. Security scanners and spam detection bots often have identifiable user-agent strings when accessing links, which can be detected and excluded from statistics. For example, email security services like Barracuda, Proofpoint, Cisco, and others often use specific strings that include their names. However, some bots mimic popular browser strings to evade detection, making this approach somewhat unreliable.
Implementing filtering based on known user-agent strings used by bots is a relatively straight forward tactic. It becomes slightly more complicated when robots are not readily identifying themselves and keeping up with the constant maintenance that this approach requires.
This method is also somewhat reactive as one would have to have pre-existing knowledge that a specific user-agent string is that of a bot. As new agents are identified (including using some of the other methods) the list of user-agents would have to be updated accordingly.
Behavior Analysis
Behavioral patterns can also reveal bot activity. Bots usually exhibit different behaviors compared to human users. Let us examine some patterns that may be indicative of bot activity.
Techniques for behavior analysis include:
Time Interval Analysis
Monitoring the time between clicks. Bots often click links in rapid succession, far quicker than a human would. Time Interval analysis can fall into two broad approaches or a combination of both.
Interval Between Consecutive Clicks: This approach measures the time between one click and the next from a specific recipient. It’s effective for catching bots that click links in rapid succession. For example, if each links in an email are clicked a few seconds apart from each other, it’s highly likely it’s a bot. This method is especially useful for emails with multiple links. Also a simple lever – the click interval time – can be given to a user to tune the sensitivity of the detection. Shorter intervals would be less aggressive while longer intervals would be more aggressive detection which could result in more false positives.
Interval from First to Last Click: This approach considers the total time taken from the first click to the last click of links in a particular email campaign for a recipient. It can give you a broader view of user engagement. If the time from the first click upon receiving the email and the last link click is relatively short say less than a minute, it is more likely to be a robot interaction, in particular when combined with a high click to link ratio.
Click Pattern Analysis
In addition to timing analysis, the actual click patterns can also be revealing.
Total Links Clicked: This metric is useful for emails that contain many links. As discussed a human is highly unlikely to click on all the links in an email. A robot is much more likely to do so. It is also easy to provide a click to total link tuning parameter to raise or lower the sensitivity of the bot detection. The higher the ratio the more certainty that those clicks are coming from a robot.
Sequential Click Patterns: Bots may follow a predictable pattern, like clicking links in the order that they appear in the email, which is much more uncommon for human users.
Click Frequency and Parallelism: Rapid, repeated, or concurrent link clicks from the same source often suggest bot activity.
Redundancy Checks: Conversely, visits from different IP addresses hitting the same unique group of target links can also be indicative of a bot activity.
Geo Tracking: Monitoring the geographical origins of the clicks and cross-reference with expected user locations. Anomalies here can indicate bot interactions.
Email Interaction
Analyzing interactions with other email elements. If only links are accessed, it might indicate a bot. Interactive Emails are somewhat a new thing and most email platforms do not support them. Those that do require some additional steps to make it work. We may not quite be there yet to use as a widespread method of detection.
User Engagement
Measuring the time spent on the target / landing pages. Bots typically spend very little time on a page after clicking a link, unlike humans would. User engagement measurements would really be implemented outside of the email platform and those metrics would have to fed back into it.
The various clicks patterns described above are ripe to be analyzed by AI-powered systems. They can can analyze vast datasets quickly and identify patterns that might be invisible to the human eye. As bots evolve, so too can the AI system, continually learning and adjusting its criteria for what constitutes suspicious activity. It would a dynamic solution for a dynamic problem.
Honeypot Links
Another possible approach is to embed invisible ‘honeypot’ links in emails. These links are not visible to human users but can be detected and clicked on by bots. Any interaction with these links can be used to identify and disregard bot activity from your metrics. This is quite similar to one of the approaches used for bot detection on forms. The drawback to that is having hidden content is somewhat frowned upon and can raise the spam suspicion.
Having explored various methods to detect automated clicks, let’s now focus on how what we can do to mitigate their impact in email marketing campaigns.
Mitigation Strategies
Most of the mitigation strategies are a direct response to a specific detection method. As none of the detection strategies individually are entirely fool proof, usually a combination of two or more of them will be used together to minimize the impact of robots while still not generating too many false positives.
It is important to balance bot detection efforts with user experience. Overly aggressive filtering can hinder genuine user engagement. Once we have identified a click with a reasonable degree of certainty as being automated, there are several approaches we can adopt.
At the risk of mentioning the obvious, the initial step is to flag the interaction as an automated click. This flagging allows us to then take the step of excluding these clicks from our overall statistics. By doing so, our data will more accurately reflects genuine engagement of our audience. Some systems may choose to discard the click altogether.
For actions triggered by clicks, such as sending follow-up emails or other automated sequences, integrating a manual or intelligent review step may be needed. Before launching these automations, the non-flagged clicks should be reviewed to confirm their authenticity. This step acts as a safeguard, ensuring that automated processes are initiated only by real user interactions.
For critical actions like double opt-ins or unsubscriptions, adding an extra layer of confirmation can significantly mitigate the impact of automated clicks. Instead of relying solely on the click, introduce a form that asks the user for explicit confirmation. Doing so also allows the use of all the standard form mitigation techniques, such as CAPTCHAs, to validate human interaction. For the unsubscribe links if it is determined that the click was by a bot, one could optionally apply that determination to all the other clicks in that click group.
Also, specifically for unsubscribe actions, RFC 8058 offers a mechanism for handling one-click unsubscribes. It is important to note that this standard does not eliminate the need to include an unsubscribe link in our automated email communications.
Lastly, we would be remiss to discount the periodic review of our stats. This review can provide valuable insights about the health of our campaigns, lists, audience, and more. This gives us an opportunity to understand the pulse of our audience and guide our future actions.
By implementing these strategies, we can significantly reduce the impact of automated clicks on our email marketing campaigns, ensuring that our efforts are as effective and accurate as possible in reaching and engaging our real audience.
Conclusion
It is clear that automated robots, as integral parts of the modern email security infrastructure, are here to stay. As email marketers, we need to embrace their presence, understand their role, and work within this new context. Recognizing and responding to automated clicks is not just a technical challenge but an opportunity to refine our marketing practices to provide the best possible experience to our subscribers. Ultimately, our goal is to ensure that our email marketing efforts remain relevant, effective, and aligned with the the genuine interests and needs of our audience.
