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Turn noisy email data into real insights.

BetterMetric separates real human opens and clicks from bots, security scanners, and privacy proxies — so your campaign reports finally mean something.

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400,000+
emails sent weekly, tracked
180+
campaigns studied
~3M
contacts analyzed
The problem

You're not imagining it.

A meaningful share of the opens and clicks in your reports never came from a person. Left unfiltered, that noise quietly skews every decision built on top of it.

Security gateways click first

Proofpoint, Mimecast, and similar scanners open and click every link before a human ever sees the email — and they look exactly like engagement in your reports.

Privacy proxies inflate opens

Apple Mail Privacy Protection and image proxies pre-fetch tracking pixels automatically, so "opened" no longer reliably means "seen."

Scanners skew your funnel

When bot activity blends into your real numbers, every downstream decision — segmentation, send-time, subject lines — is optimizing for the wrong signal.

How it works

Clean data in three steps.

01

Add tracking in seconds

Paste your email's HTML or upload the file — BetterMetric rewrites the links you choose to track, right in your browser.

02

We separate people from bots

A hidden trap link, fingerprinting, and behavioral signals flag scanners and automated activity as it happens.

03

See a clear real vs. bot breakdown

Every open and click is labeled Real, Suspicious, or Bot — per campaign, per contact, with the reason why.

Why I built BetterMetric

I'm Moa, an Email & Lifecycle Marketing Manager, and email has always been one of my strongest marketing channels.

I've used email for acquisition, conversion, onboarding, retention, and pretty much everything in between. But over the years, I started noticing a problem that became harder and harder to ignore.

We were sending more than 400,000 emails every week, and our reports were full of opens and clicks that didn't always make sense. Some emails were opened almost immediately after being delivered. Some campaigns had unusually high engagement. Some contacts were clicking in patterns that simply didn't look human.

At first, we ignored it. The numbers weren't significant enough to worry about, and like many marketers, we continued using the data we had. But after a few months, I started asking myself: how much of this engagement is actually coming from people?

That question changed everything. I realized we were making marketing decisions based on email metrics without really knowing how much of those metrics represented real human behavior. And if we can't distinguish human engagement from automated activity, our open and click data can become misleading.

So I decided to build something for myself. I started digging deeper into email infrastructure, tracking systems, bots, security scanners, privacy systems, and the patterns behind automated engagement. I built, tested, and refined different approaches using more than 180 campaigns and approximately 3 million email contacts.

That research eventually became BetterMetric.

I wanted to build something simple: a tool that helps email marketers identify suspicious engagement and get a clearer picture of their real campaign performance. I didn't build BetterMetric because I wanted to create another complicated analytics platform — I built it because I've been an email marketer dealing with this problem myself. And now I want to share the solution with other marketers who might be facing the same challenge.

My hope is that BetterMetric can become a small contribution to the email marketing community — helping marketers spend less time questioning their data and more time using it to make better decisions. Because better email marketing starts with better data.

Turn noisy email data into real insights.

Join the first cohort of companies using BetterMetric.

Thanks — we will be in touch soon.

Limited to the first 50 companies in this early cohort.