Email Marketing Arquivos - E-goi

Not Every Click Comes from Human Hands

Iúri Ferreira — Wed, 08 Jul 2026 10:42:37 +0000

Not Every Click Comes from Human Hands

That assumption is no longer valid, especially in B2B environments or databases containing corporate domains. Today, a significant proportion of clicks recorded in email campaigns are generated not by people, but by automated security systems. The problem is not merely statistical, it is structural, because these clicks directly interfere with marketing decision-making.

The Real Role of Modern Email Security Systems

Corporate security filters do not exist to observe user behaviour, they exist to prevent risk. To achieve this, they adopt an active approach. When an email enters an organisation, these systems analyse its content and test every possible destination.

This includes clicking links, following redirects, loading landing pages, and evaluating scripts. This process happens automatically and, in many cases, immediately after the email is delivered, before any employee has even seen it.

From a security perspective, this behaviour is entirely rational. From a marketing perspective, however, it creates a scenario where the first click is rarely human.

Why a False Click Is More Destructive Than a False Open

A false open creates an illusion. A false click creates action. This distinction is fundamental.

When a security system clicks a link, the event is recorded as genuine interest. From that point onwards, the marketing system reacts. Segments are updated, workflows are triggered, and contact statuses change. All of this happens without any human decision.

When the real recipient interacts later, that interaction loses analytical value. The event has already occurred, the workflow has already been executed, and the system has already “learned” something that never actually happened.

This is why automated clicks are particularly dangerous. They don’t just inflate metrics, they corrupt processes.

The Cascade Effect on Marketing Automation

Most modern automation workflows are built around a simple principle: reacting to user actions. When those actions are not human, the entire sequence that follows loses its meaning.

“Thank you for your interest” workflows may be triggered without any real interest. Lead nurturing sequences can move contacts through stages before they have consumed any content. In more serious cases, unsubscribe mechanisms can be triggered by bots, removing valid contacts from the database.

These issues are rarely detected immediately because reports continue to show activity. The problem only becomes visible when business results fail to reflect the positive metrics.

How to Distinguish Human Behaviour from Automated Activity

Although sophisticated, security systems leave clear signs of their activity. The most obvious is timing. Clicks that occur one or two seconds after an email is sent do not correspond to normal human behaviour. No one reads an email, makes a decision, and clicks within that timeframe (unless they’re The Flash).

Another common indicator is inconsistency. A single contact clicking several different links, including promotional links, social media links, and the unsubscribe link, at exactly the same moment is not expressing genuine interest; it is performing an automated scan.

Another clear signal is perfect synchronisation. When the email open and click share the exact same timestamp, it is almost always a machine processing the email as a technical object.

The Impact on Testing and System Learning

When automated clicks are treated as valid engagement signals, A/B tests stop measuring what they are intended to measure. Subject lines, content, or CTAs may appear to be winning simply because they were analysed more quickly by security systems.

The platform learns the wrong patterns, reinforces poor decisions, and gradually optimises campaigns for machines rather than people. This is one of the main reasons why experienced marketing teams increasingly notice a disconnect between positive campaign metrics and real business impact.

Conclusion

Clicks are no longer automatically reliable engagement signals. Continuing to treat them as such means building decisions on an unstable foundation.

In the next article, we’ll conclude this series with a practical approach to reorganising metrics, redefining KPIs, and measuring what truly matters in an imperfect environment.

Key Takeaways

Automated clicks are common in B2B environments and have a direct impact on marketing automation and decision-making.
The problem is not technical, it is analytical.
Without proper filtering and interpretation, marketing systems end up optimising for machines rather than people.

“Thanks for your interest” emails may be sent even though no genuine interest existed.
Lead nurturing sequences can progress before the recipient has consumed any content.
In more serious cases, unsubscribe workflows may be triggered by security bots, removing perfectly valid contacts from your database.

These problems often go unnoticed because campaign reports continue showing healthy levels of activity.

The issue only becomes obvious when business results fail to match the impressive-looking metrics.

How to Distinguish Human Behaviour from Automated Activity

Although email security systems have become increasingly sophisticated, they still leave identifiable patterns.

The most obvious is timing.

Clicks occurring one or two seconds after an email is delivered simply don’t reflect normal human behaviour.

No one receives an email, reads it, decides to click, and does so within two seconds, unless they’re The Flash.

Another common indicator is inconsistency.

When a single contact clicks multiple unrelated links, including promotional links, social media icons, and the unsubscribe link—all at exactly the same moment, that isn’t genuine engagement.

It’s an automated security scan.

Perfect synchronisation is another giveaway.

If the email open and click share the exact same timestamp, it’s almost always a machine processing the email rather than a person interacting with it.

The Impact on Testing and Machine Learning

When automated clicks are treated as genuine engagement signals, A/B tests stop measuring what you think they’re measuring.

Subject lines, content variations, or CTAs may appear to outperform alternatives simply because they were processed more quickly by security systems.

As a result, the platform learns the wrong behaviours.

It reinforces the wrong decisions.

It optimises future campaigns for machines instead of people.

This is one of the reasons I regularly see experienced marketing teams becoming increasingly frustrated by the growing disconnect between strong campaign metrics and disappointing business outcomes.

Conclusion

Clicks are no longer automatically reliable indicators of engagement.

Continuing to treat them as such means building marketing decisions on an unstable foundation.

In the next article, we’ll conclude this series with a practical framework for reorganising your metrics, redefining KPIs, and measuring what truly matters in an imperfect environment.

Key Takeaways

Automated clicks are common in B2B environments and directly affect marketing automation and decision-making.
The challenge isn’t technical, it’s analytical.
Without proper filtering and interpretation, your marketing platform will gradually optimise campaigns for machines instead of people.

Iúri Ferreira

Leiriense, colecionador de moedas de 2€ e apaixonado por marketing. Trabalha nas áreas de CRM, automação e lifecycle marketing há 3 anos, sempre com o objetivo de criar comunicações mais relevantes e aproximar marcas e pessoas.

New KPIs, honest estimates and conversion-driven decisions

Iúri Ferreira — Thu, 05 Mar 2026 13:00:00 +0000

The problem is not measuring. The problem is what we choose to measure, how we interpret those signals, and what decisions we make based on them. This article proposes a more mature approach, focused on real impact rather than comfort metrics.

The need to redefine the KPI hierarchy

For years, email marketing metrics were organized by technical proximity, not by proximity to value. Opens came first because they were easy to measure. Clicks came next. Conversion was often relegated to separate reports.

Today, that hierarchy no longer makes sense. In a modern context, the metric closest to real value should lead. Conversion becomes the main reference point. Clicks, when filtered and contextualized, function as intermediate indicators. Opens become merely auxiliary signals, useful for identifying trends but insufficient for making isolated decisions.

When a metric becomes a target, it stops being a metric

This is a classic problem in measurement systems. When a metric becomes a target, the system starts optimizing for the number rather than for the phenomenon it was meant to represent.

In modern email marketing, this manifests clearly. Subject lines are optimized to generate automatic opens. Content is adjusted to maximize quick clicks. The result is a system that produces good numbers and poor outcomes.

The logic of open-rate proportion as a pragmatic response

In an ideal scenario, it would be possible to segment by device before every send and interpret metrics cleanly. In practice, this is not always possible. This is where estimation approaches come into play.

The open-rate proportion strategy starts from a simple premise: if Apple MPP generates near-universal opens, its presence among opens can be used to estimate its presence in the database. By isolating these opens as technical events, we can recalculate a rate that is closer to human reality.

The result is not mathematically perfect, but it is analytically honest.

Why analytical honesty is a strategic advantage

An inflated rate may shine in presentations, but it creates misaligned expectations and poor decisions. A lower estimate, but one closer to reality, allows strategy, messaging and investment to be adjusted accordingly.

Mature teams prefer imperfect numbers that explain behavior over beautiful numbers that hide the truth

Operational adjustments that make a difference

In environments dominated by Apple MPP, it makes sense to abandon flows based on “did not open” and focus on signals of real action. In Gmail, opens can still be useful, provided geolocation is treated with caution. In B2B contexts, it is essential to introduce mechanisms to detect automated clicks, ignore immediate events and protect critical processes such as unsubscribe with additional confirmations.

These adjustments do not eliminate the problem, but they significantly reduce noise and restore coherence to the system.

Metrics as a management tool, not a vanity report

When analysis stops focusing on isolated events and starts observing behavioral patterns over time, metrics regain strategic value. Purchase frequency, funnel progression, retention and cumulative conversion become more relevant than any isolated rate.

At this point, email marketing stops being a channel evaluated by dashboards and becomes a management instrument.

Conclusion

Metrics remain essential, but they are no longer self-explanatory. They require context, interpretation and maturity.

When we stop asking “how many opened” and start asking “what changed in customer behavior”, email marketing definitively moves from vanity to impact.

Key takeaways

Conversion should lead the interpretation of results.
Honest estimates are preferable to false precision.
Clicks and opens only create value when interpreted in the right context.
Mature metrics support decisions, not egos.

Iúri Ferreira

Are email marketing metrics lying to you?

Miguel Gonçalves — Thu, 22 Jan 2026 13:00:00 +0000

Today, the same metric mixes human behavior with automated privacy and security processes. The number still exists, but it no longer answers the question we’ve always asked of it. This article explains why the open rate has lost reliability and why insisting on its traditional interpretation creates more noise than clarity.

The classic tracking model no longer reflects behavior

Open tracking is based on a simple technical event, the loading of an invisible image (pixel). For a long time, this event was aligned with human reading behavior. When someone opened an email, images were loaded and the system recorded an open.

That alignment has been progressively destroyed. The introduction of proxies, caches, preloading, and protection layers has changed the relationship between the technical event and human action. The pixel still fires, but it no longer consistently represents a conscious decision by the user.

The problem is not the mechanism itself, but the context in which it now operates.

Apple Mail Privacy Protection and the loss of meaning of email opens

With Mail Privacy Protection, Apple made a clear decision: prevent tracking of reading behavior. To achieve this, it began automatically loading all images shortly after email delivery, regardless of whether the user actually opens the email.

In practice, this turns the open rate into a device distribution metric. The higher the percentage of Apple users in a database, the higher the apparent open rate will be, even if no one reads the message. The timing of email opens loses its meaning, and the distinction between ignored emails and read emails disappears entirely.

Opening an email stops being a choice and becomes a technical side effect.

Why Gmail should not be grouped into the same category

It is common to group Apple MPP and Gmail under the generic label of “proxies,” but this leads to analytical errors. Gmail’s image proxy works differently. Images are only loaded when the user actually opens the email. Google hides the real IP address and serves images from its own infrastructure, but it does not anticipate human behavior.

This means that, in Gmail, opening an email remains a psychological signal of interest. It is not perfect, because geolocation becomes imprecise, but it retains behavioral value. Treating Gmail and Apple MPP as equivalent destroys useful information.

The problem is not the data, it is the interpretation

The real issue arises when events of completely different nature are aggregated into a single KPI. Human email opens, automated opens, and security system opens all carry exactly the same weight in reporting.

The number is mathematically correct, but analytically weak. The metric stops explaining behavior and merely reflects technical architecture.

A curious paradox: worse measurement, better communication

There is a positive side effect that is rarely discussed. The same systems that distort metrics ensure that emails arrive visually flawless. Images load by default, layouts remain consistent, and the user experience improves.

The mistake is not accepting this evolution. The mistake is continuing to evaluate it using metrics designed for a context that no longer exists.

Conclusion

The open rate has not disappeared, but it has lost its status as a central metric. Today, it only makes sense when interpreted in light of the device, the context, and the objective of the campaign.

In the next article, we move into an even more sensitive territory, where even clicks no longer represent human intent.

Key takeaways

Email opens are no longer synonymous with interest
Apple MPP turns email opens into technical events
Gmail retains behavioral value
Metrics require contextual interpretation