Suped

Summary

Email Service Providers (ESPs) utilize a combination of technical and behavioral analyses to distinguish between human and bot email opens and clicks, although achieving complete accuracy remains a significant challenge. Their primary methods include scrutinizing IP addresses for suspicious origins, known bot networks, or unusually rapid activity. They also analyze user-agent strings to identify automated clients and assess the speed of interaction, as bots frequently engage almost instantly upon delivery. Furthermore, ESPs look for behavioral anomalies like repetitive patterns and the non-execution of JavaScript. However, the introduction of Apple Mail Privacy Protection (MPP) has fundamentally altered open tracking, as it pre-fetches all images, making automated opens indistinguishable from genuine human interactions and rendering the open rate largely unreliable as an engagement metric. While ESPs continuously refine their proprietary algorithms to filter out bot activity, their efforts operate on a best-effort basis, acknowledging that sophisticated bots can increasingly mimic authentic user behavior.

Key findings

  • Multi-faceted Detection: ESPs employ a multi-faceted approach to identify bot activity, combining IP address analysis, user-agent string checks, interaction speed evaluation, and behavioral pattern recognition.
  • IP and User-Agent Clues: Key indicators for bot activity include rapid-fire interactions from single or multiple IP addresses, known bot network IPs, user-agent strings that reveal non-human browsers, and activity originating from data centers or proxies rather than residential ISPs.
  • Interaction Speed as an Indicator: Very fast clicks or opens, especially those occurring within milliseconds or seconds of email delivery, are strong indicators of automated behavior rather than genuine human interaction.
  • Behavioral Anomalies: ESPs also look for behavioral anomalies such as repetitive patterns, consecutive events for the same message, and the absence of JavaScript execution, which is a common characteristic of many bots.
  • Apple MPP Impact: Apple Mail Privacy Protection (MPP) significantly impacts open tracking by pre-fetching email images via proxy servers, making automated 'opens' indistinguishable from genuine human opens and rendering the open rate largely unreliable as an engagement metric.
  • Inherent Limitations: Despite these methods, ESPs acknowledge that no approach achieves 100% accuracy in distinguishing human from bot activity, operating on a best-effort basis to provide reasonably accurate reporting.
  • B2B vs. B2C Bot Patterns: In B2B environments, a higher percentage of non-human clicks are still 'dumb' scans occurring shortly after delivery, sometimes with randomized delays. In contrast, B2C interactions are more likely to involve advanced bots that mimic user behavior with delayed and varied interactions.

Key considerations

  • Open Rate Unreliability: Due to Apple Mail Privacy Protection (MPP), which pre-fetches all images, the open rate is no longer a dependable metric for true human engagement. This necessitates a shift to other indicators like clicks and conversions for measuring engagement.
  • Sophistication of Bots: Bots are evolving beyond simple immediate scans, mimicking human behavior by clicking later, from similar geo-locations, and using varied user agents, which makes their detection increasingly complex for ESPs.
  • Data Accuracy Imperfection: Marketers must recognize that engagement data will never be 100% accurate. The best approach is to analyze metrics relatively and correlate engagement data with financial data and ROI for a more comprehensive understanding.
  • ASNs as Limited Indicators: While some non-human clicks originate from specific Autonomous System Numbers (ASNs), like those linked to Microsoft, relying solely on ASNs for bot identification is cautioned against due to their broad and often inaccurate nature.
  • Binary Distinction: ESPs typically provide a binary human/non-human distinction for clicks, but this distinction may not be real-time. This delay can be a strategic choice to prevent sophisticated bots from evading security checks.

What email marketers say

13 marketer opinions

Distinguishing between human and bot email engagement remains an ongoing, complex challenge for Email Service Providers (ESPs). While they leverage a range of technical analyses-including scrutinizing IP addresses, user-agent strings, and the speed of interaction-the sophistication of bots continues to evolve, making 100% accuracy elusive. ESPs are in a continuous race to refine their proprietary algorithms to filter out non-human interaction, operating on a best-effort basis to provide actionable data. This dynamic landscape means marketers must interpret engagement metrics with increasing caution, often correlating them with broader business outcomes rather than relying solely on raw open or click data.

Key opinions

  • Advanced Bot Mimicry: A significant challenge for ESPs is the increasing sophistication of bots that mimic human behavior, including delayed clicks, varied geo-locations, and diverse user agents, moving beyond simple immediate scans.
  • Composite Detection Methods: ESPs utilize a comprehensive set of indicators, including analysis of IP addresses, user-agent strings, interaction speed, whether JavaScript is executed or redirects are followed, and patterns in consecutive events.
  • Partial Accuracy in Reporting: ESPs operate on a 'best-effort' basis, acknowledging that achieving 100% reliability in distinguishing human from bot activity is not possible, and their reporting aims for 'reasonable' accuracy.
  • Engagement Data Reinterpretation: Given the influence of bots and Mail Privacy Protection, traditional engagement metrics like open and click rates are increasingly unreliable indicators of true human interest, requiring marketers to reinterpret data through correlation with business outcomes.
  • Strategic Non-Real-Time Filtering: To prevent evasion, some ESPs do not provide real-time human/non-human distinctions for clicks, introducing a delay to enhance security checks.

Key considerations

  • Prioritize Outcome Metrics: Marketers should prioritize metrics directly tied to business outcomes and ROI over raw open or click rates, as these traditional engagement metrics are increasingly distorted by bot activity.
  • Dynamic Detection Landscape: The constant evolution of bot technology means that ESP detection methods are continually refined, requiring marketers to understand that the accuracy of reporting is a moving target.
  • Nuanced Data Analysis: Interpreting engagement data requires a nuanced understanding of potential bot influences, recognizing that patterns like rapid clicks or certain IP origins might signal automated rather than human interaction.
  • Caution Against Sole ASN Reliance: While some bot activity may cluster around specific Autonomous System Numbers, relying solely on ASN data for bot identification is not advised due to its broad and often inaccurate nature.

Marketer view

Marketer from Email Geeks explains that ESPs do not achieve 100% reliability in distinguishing between human and bot opens/clicks, operating on a best-effort basis to provide reasonable reporting. Non-human clicks often occur very shortly after delivery, making them somewhat plausible to "fake up" in reporting, while opens are much harder to reliably distinguish.

5 Mar 2024 - Email Geeks

Marketer view

Marketer from Email Geeks shares that a significant portion of non-unique HTTP requests for click tracking links originate from a single ASN and a small number of user-agents. He suggests that a decent starting point for distinguishing can be achieved with a few rules, but more comprehensive rules are needed to reach 95%+ coverage.

4 May 2025 - Email Geeks

What the experts say

2 expert opinions

The primary challenge for Email Service Providers (ESPs) in discerning human email opens from automated actions stems directly from Apple Mail Privacy Protection (MPP). This feature pre-loads all images through proxy servers, rendering automated 'opens' functionally identical to genuine recipient interactions. Consequently, ESPs find it nearly impossible to reliably differentiate between a human open and one triggered by MPP or a bot, effectively invalidating the open rate as a reliable metric for measuring true subscriber engagement.

Key opinions

  • MPP Pre-loading: Apple Mail Privacy Protection (MPP) automatically pre-loads all email images through proxy servers upon delivery, regardless of user interaction.
  • Open Indistinguishability: Due to MPP's pre-loading, automated 'opens' are indistinguishable from genuine human opens at the ESP level, making accurate differentiation nearly impossible.
  • Open Rate Reliability Loss: The pre-fetching behavior of MPP effectively invalidates the open rate as a reliable metric for true subscriber engagement, as every email is technically 'opened' by the proxy.

Key considerations

  • Rethink Open Rate Reliance: Given MPP's impact, marketers must reconsider the reliability of open rates and shift focus to other engagement metrics, such as clicks, conversions, and direct replies, to gauge true subscriber interest.
  • Focus on Deeper Engagement: With open data compromised, emphasize metrics that indicate deeper user interaction beyond a simple 'view,' like click-through rates, website visits, and purchase behavior, to assess campaign effectiveness.

Expert view

Expert from Word to the Wise explains that due to Apple Mail Privacy Protection (MPP), which pre-loads all images via proxy servers, automatic opens are indistinguishable from human opens at the ESP level, making it extremely difficult for ESPs to reliably differentiate between the two. This invalidates open rate as a reliable metric for engagement.

27 Jan 2025 - Word to the Wise

Expert view

Expert from Spam Resource explains that Apple Mail Privacy Protection (MPP) pre-fetches and pre-loads email images, resulting in automated 'opens' that are indistinguishable from genuine human opens. This makes it nearly impossible for ESPs to reliably discern whether an open was a true recipient interaction or an automated action by a bot or privacy feature.

6 Sep 2023 - Spam Resource

What the documentation says

4 technical articles

Email Service Providers (ESPs) actively implement sophisticated, often proprietary, systems to distinguish between human and bot-generated email opens and clicks. While most ESPs confirm they proactively filter out automated activity to provide more accurate engagement data, specific methodologies vary. Common techniques include analyzing IP reputation, inspecting user-agent strings for known bot patterns, evaluating the speed and sequence of interactions, and leveraging client-side script execution, like JavaScript, as a critical differentiator for confirming genuine user engagement.

Key findings

  • Proprietary Filtering Systems: Major ESPs like Mailchimp, SendGrid, and Constant Contact confirm they employ automated, built-in systems to filter bot activity from email open and click data, aiming for more accurate human engagement metrics.
  • Diverse Technical Indicators: Some ESPs, such as Mailgun, detail specific methods including IP reputation checks, analysis of user-agent strings for known bot patterns, and scrutiny of interaction speed and sequence.
  • JavaScript-Based Detection: The non-execution of client-side scripts like JavaScript can be a key indicator for distinguishing automated bots from human users, a method employed by some ESPs.
  • Enhanced Data Accuracy Goal: The overarching goal of these diverse filtering efforts is to ensure that the reported engagement metrics, particularly click-through rates, accurately reflect genuine human interaction rather than automated actions.

Key considerations

  • Undisclosed Methods: While ESPs confirm their use of bot filtering, the specific algorithms and techniques employed by most remain proprietary and are not publicly detailed.
  • Continuous Evolution: ESPs must continuously update and refine their detection mechanisms to counter the evolving sophistication of bots and maintain data integrity.
  • Focus on Human Signals: The core of these systems is to identify patterns unique to human behavior, such as JavaScript execution or realistic interaction speeds, to differentiate from automated activity.

Technical article

Documentation from Mailchimp explains that their systems automatically filter out a significant amount of bot activity from email open and click tracking data to provide more accurate engagement metrics. While specific methods aren't detailed, it implies proprietary algorithms are used to identify and exclude non-human interactions.

6 Jul 2024 - Mailchimp

Technical article

Documentation from SendGrid states that they proactively filter out common bot activity from their click tracking data. This ensures that the reported click-through rates reflect human engagement more accurately, indicating that their system has built-in mechanisms to differentiate between automated and genuine user interactions.

2 Nov 2023 - SendGrid

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