A few weeks ago, I stared at an email dashboard, wondering why some emails get opened while others don’t. What makes someone click a link in an email? Most importantly, is there a way to predict and improve this?
I had no idea.
But the idea that data might hold some answers stuck with me. What if I could look at email performance through numbers instead of guessing? What if patterns existed—patterns I couldn’t see yet but could uncover with the right tools?
I don’t know if this will work or if data analysis will provide the answers I want. But I’ve decided to find out. This isn’t a guide from an expert; it’s the start of a journey to see if data analysis has anything valuable to offer email marketing.
Where Do You Even Begin?
Since I knew absolutely nothing about data analysis, I started with the most accessible tool I could think of: Excel. There was no fancy code, no SQL queries, no machine learning, just a spreadsheet, and an open mind.
My goal? To figure out if data could tell me anything useful about email marketing.
The first step was just understanding what was in front of me. I opened an email campaign report, and suddenly, there were numbers everywhere: send times, open rates, click-through rates, subject lines, and bounce rates. It felt overwhelming at first. What was I supposed to do with all this information?
So, I started simply: sorting, filtering, and just looking for anything that stood out. I noticed that some emails had higher open rates than others. But why? Was it the time they were sent? The subject lines? The audience? I had no clue, and that was both frustrating and exciting.
I wasn’t finding answers yet, but I was finding questions. From what I’ve read, knowing what to ask is the first real step in data analysis.
Is There a Pattern? Or Am I Just Seeing Things?
At this stage, I’m still in the exploration phase. I’m staring at rows of data, wondering: Is there a pattern here, or am I just imagining things?
I’ve read that pivot tables can help summarize data meaningfully, so I’ve been playing around with them. But I have no idea if I’m doing it right. I can group email open rates by different factors, like the day of the week or the time they were sent, but I don’t know if these differences mean anything yet.
For example, emails sent in the morning might perform better. But is that true, or is it just a random coincidence? Right now, I have no way of knowing. I need a way to dig deeper.
That’s when I heard about SQL and Python tools that could help analyze data more systematically. And suddenly, my curiosity grew even more potent.
What’s Next? SQL, Python, and the Whole Shabang
So here’s where I stand: I don’t have answers yet, and I’m not even close. However, I have a growing list of questions and want to find out if data analysis can help answer them.
That’s why I’ve decided to learn SQL and Python next. Right now, I don’t know how to write a single line of SQL code. I have no idea how to use Python’s data analysis libraries like Pandas. But I do know one thing:
If there’s something hidden in this email data, I want to uncover it.
For now, my approach is simple:
✅ Learn how to look at data in new ways
✅ Figure out if data analysis can improve email marketing
✅ Stay open-minded because I have no idea where this will lead
I might discover that data analysis is a goldmine for email marketing. Or I might find it unnecessary, and marketers already know everything they need just by intuition. I have no expectations only curiosity.
Right now, I’m standing at the very beginning of this journey. I don’t have insights or breakthroughs yet I just have a strong desire to explore. If you’ve ever wondered whether data analysis is worth diving into, my advice is: just start. You don’t need to be an expert. You don’t need fancy tools right away. Just ask questions and see where they take you.
I’ll keep experimenting, learning, and sharing what I find. Who knows? Maybe this whole thing will lead to a game-changing discovery or maybe it won’t. Either way, I’ll keep going.
Stay tuned I’ll let you know what I discover.
