Email marketing is one of the oldest, most profitable channels in tech. Yet somehow, its analytics still feels unfinished.
I realised this after building Email Calculator, a small side project to help me make sense of email performance. I kept running into the same problem: most ESPs give you lots of numbers — opens, clicks, CTRs, conversions — but almost none of them answer the questions that actually matter.
I’d ask myself: Did this campaign generate leads? Did it drive revenue? How does this campaign really compare to the last one? Usually, the answer was hidden somewhere in a spreadsheet I’d cobbled together, or worse, in multiple dashboards across different tools.
It struck me as strange. Email marketing reportedly has one of the highest ROIs of any channel, yet the analytics layer feels like it stopped halfway. Most ESPs are great at sending emails at scale — that part works perfectly — but understanding outcomes? That’s still surprisingly manual.
The Hidden Gap in Email Architecture**
If you look at most ESPs, their workflow is basically:
campaign → send → track events → dashboard
Everything is focused on sending emails and recording low-level events. That’s fine for operational reporting, but it leaves a huge gap for actual business intelligence.
What we really want is more like:
email activity → leads → revenue → ROI
This requires combining campaign data with form submissions, CRM data, and website events. That’s why most teams end up exporting CSVs, building manual calculations, or maintaining a private “real dashboard” that nobody else can trust.
It’s not just inefficient — it’s fragile. And for anyone trying to build data-driven email strategies, it’s exhausting.
Why I Built Email Calculator
I started Email Calculator to explore what happens when you treat analytics as the primary layer, instead of an afterthought.
The idea is simple: pull all your campaign metrics into one place, connect them with lead or form submissions, calculate ROI, and compare campaigns over time. You can do it manually or programmatically via API. The goal is a clear, consistent, “single source of truth” for email performance.
I’m still iterating, but one thing became obvious fast: the hardest part of email marketing today isn’t writing good copy or sending at scale. It’s understanding what actually worked.
You can check it out here: Email Calculator
Lessons for Developers and Teams
For developers, the fragmentation is familiar. Most software eventually splits into layers: execution, data, and analytics. Email marketing solved execution early — the sending layer — but the analytics layer is still catching up.
The questions I keep asking myself are:
Would teams trust an external analytics layer over their ESP dashboard?
Do developers want API-first marketing analytics?
How would a proper “single source of truth” change the way people measure email performance?
I don’t have all the answers yet, but I’ve seen enough to know there’s an opportunity to do analytics differently.
Email marketing is an old, mature channel. Its sending infrastructure is reliable. Its audience is massive. Yet when it comes to making sense of outcomes, teams are still manually stitching together spreadsheets and dashboards. That feels like a hole worth fixing — and I’m trying to figure out the best way.
If you’ve built tools, dashboards, or processes to make email analytics meaningful, I’d love to hear how you approached it. The more stories I hear, the better I can understand the real gaps — and hopefully build something genuinely useful.
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