Why Your Email Shows 10 Opens or 15 Clicks
If you have ever sent a marketing email, cold email, newsletter, or sales sequence and thought, “Wait, why did this person open my email 12 times?” you are not alone.
The first instinct is usually excitement.
Maybe they are interested. Maybe they forwarded it around. Maybe the timing was perfect. Maybe this is finally the lead that is ready to book.
Sometimes that is true.
But a lot of the time, those opens and clicks are not coming from a person at all.
They are coming from email security systems, privacy tools, link scanners, company firewalls, inbox providers, and tracking systems doing exactly what they were designed to do: protect the recipient.
That does not mean your email data is useless. It means raw email data needs interpretation.
A click is not always a click. An open is not always an open. And a dashboard that treats every event as human behavior can create a very distorted picture of what is actually happening.
Why Email Opens Are So Messy
Email opens are usually tracked through a tiny invisible image inside the email. When that image loads, the email platform records an “open.”
Simple enough.
But modern inboxes are not simple.
Apple Mail Privacy Protection can load email images automatically, even if the person never actually reads the message. Some corporate systems preload images to inspect messages. Some inboxes cache images. Some tools check the email before delivering it to the user.
So when your dashboard says someone opened an email 8 times, it might mean:
They actually opened it 8 times.
Or it might mean:
Their inbox provider loaded the tracking pixel. Their phone loaded it again. Their desktop client loaded it later. A privacy tool fetched the image. A security system scanned the message.
The platform recorded activity. But the activity was not necessarily human attention.
Why Clicks Can Be Even More Confusing
Clicks feel more meaningful than opens because a person has to click a link, right?
Not always.
Many organizations use security tools that automatically inspect every link in an email before the recipient can touch it. These tools are looking for phishing, malware, suspicious redirects, and unsafe domains.
So if your email includes a link to a website, calendar, quiz, case study, or landing page, the company’s security layer may click that link first.
This is common in healthcare, education, finance, government, enterprise SaaS, and large corporate environments.
The person may not have clicked anything yet. Their email server may simply be asking:
“Where does this link go?”
“Does it redirect?”
“Is it dangerous?”
“Has this domain been reported?”
“Should we rewrite this URL through a safe-link system?”
That automated check can show up in your email platform as a click.
Sometimes it shows up once. Sometimes it shows up several times. Sometimes it checks every link in the email within seconds.
That is how one person can appear to click 10 or 15 times without behaving like an actual human.
The Problem Is Not the Security Tool
It is easy to get annoyed at these bots, but they exist for a good reason.
Email is one of the most abused channels on the internet. Phishing attacks, fake invoices, credential theft, malware links, spoofed domains, and impersonation attempts all flow through inboxes every day.
Security systems are trying to protect people from clicking something dangerous.
So when an email server opens an email or clicks a link before the recipient does, it is not necessarily “bad data.” It is part of the delivery environment.
The real issue is when marketing and sales dashboards treat that security activity as buyer intent.
That is where teams get confused.
They see inflated engagement, chase the wrong leads, overestimate campaign performance, or assume a message is working better than it is.
The Better Question: Is This Human Enough to Act On?
For operators, founders, marketers, and sales teams, the goal should not be to eliminate every noisy signal. That is impossible.
The goal is to separate raw activity from useful activity.
Instead of asking: “Did this email get opens and clicks?”
Ask: “Which opens and clicks look like they came from a real person?”
That changes the whole measurement system.
A single click five minutes after delivery may be more meaningful than six clicks within three seconds. A quiz visit followed by time on page may matter more than a security scanner touching every link. A reply is stronger than an open. A second visit from the same person later in the day is different from duplicate events in the same second.
Raw counts are easy. Trustworthy attribution is harder.
That is where the real work begins.
How We Think About This at All Great Things
At All Great Things, we build GTM systems, outbound engines, dashboards, and automation layers where the point is not just collecting data. The point is knowing what data deserves attention.
This comes up constantly in outbound.
A campaign can look like it is performing well because the dashboard shows high opens and clicks. But when you inspect the event stream, a chunk of that engagement may be automated.
So the system needs to classify events instead of blindly counting them.
Here is the basic idea:
Some activity is attributed to likely human engagement.
Some activity is classified as automated, security-driven, or too noisy to trust.
Some activity is kept in the raw totals but excluded from the follow-up queue.
That distinction matters because it prevents a sales team from treating bot behavior like buying intent.
The More Technical Side
Under the hood, this usually means looking at several signals together.
The first signal is timing.
If an open or click happens immediately after delivery, especially within the first few seconds, that is suspicious. Real people can click quickly, but security systems often act almost instantly.
The second signal is the event type.
Some platforms flag machine opens directly. For example, privacy-protected opens or known automated opens may come through with metadata that tells you not to treat them like normal human behavior.
The third signal is the user agent.
A user agent is the technical fingerprint of the browser, email client, or system requesting the link or image. Some user agents clearly belong to security systems, link scanners, or gateway tools.
The fourth signal is click pattern.
If one recipient appears to click three or more different links within 30 seconds, that usually does not look like normal human behavior. It looks like a scanner checking the email.
But there is another pattern that is just as important: repeated clicks on the same destination.
We recently saw a case where one contact appeared to click a quiz link 10 times in a few minutes. At first glance, that looked like a very hot lead. But the raw events showed multiple duplicate clicks on the same URL, including repeated events in the same second.
That is not the same thing as 10 real visits.
So the better system normalizes the URL, strips tracking noise, detects same-destination bursts, and deduplicates events that are likely repeated webhook records or automated checks.
In plain English: if the same link fires repeatedly in a short period, do not count every single event as a separate human action.
Why This Matters for CRM and Reporting
Bad attribution creates bad follow-up.
If your CRM says someone clicked 15 times, your sales team may prioritize that person. If those clicks were automated, the rep is acting on false confidence.
This is especially important for teams building CRM workflows, lead scoring, lifecycle automation, and outbound dashboards.
A good CRM is not just a place to store contacts. It should help operators understand what happened, what probably matters, and what action should happen next.
That is one reason we publish resources like the Open Source CRM Index. CRM systems are becoming more flexible, more technical, and more automation-driven. But the value still comes down to signal quality.
More data does not automatically mean better decisions.
Better filtering does.
What Teams Should Do
If you are running email campaigns, do not panic when you see strange opens or clicks.
Instead, build a simple mental model:
Opens are directional, not definitive.
Clicks are stronger than opens, but still need filtering.
Fast clicks right after delivery may be automated.
Many links clicked in a few seconds is probably a scanner.
Repeated identical clicks may be duplicate tracking noise.
Replies, form fills, booked calls, and meaningful page behavior are stronger signals.
The goal is not to ignore engagement data. The goal is to stop treating every event equally.
Final Thought
Email engagement is not dead. It is just noisier than most dashboards admit.
The teams that win are not the ones staring at raw open rates. They are the ones building systems that can separate human intent from machine activity.
That is the difference between “we got 400 clicks” and “we found 37 people worth following up with.”
If you are building outbound, analytics, CRM workflows, or a GTM system that needs cleaner signal, that is the kind of work we focus on at All Great Things and in my work as a technical GTM operator. You can read more on my Jason Mellet profile, explore our analytics and KPI articles, or browse our startup growth guides.
