GTM Engineering

I have spent around seven years working in e-commerce in one way or another. I have worked with brands doing a couple hundred thousand dollars a month. I have helped smaller brands that were barely starting up and just trying to get the foundations right. I have run my own e-commerce stores and I have also worked with high-end fashion brands across five countries. Most of my work was as a technical marketer building websites, marketing workflows, funnels and helping brands sell more stuff. The work also involved looking at people who bought once and never came back, people who abandoned carts halfway through, campaigns that pulled in customers who had no reason to stay, and the general pain of trying to make a store feel worth returning to. ...

I still get excited when I see a boring task and think, “I think I can do something about it, I can build something to automate this.” That is partly the GTM engineering mindset I’ve been in for a while now. And so much of the work is spread across things that do not really belong together. CRM fields, spreadsheets, call notes, dashboards, Slack messages, enrichment tools, random CSV exports, and someone asking for an update five minutes before a meeting. ...

My old churn analysis capstone post has somehow become one of the most popular posts on this blog over the last year. I did not expect that. At the time, churn looked like a neat data science problem to me. You get a dataset, clean it, engineer some features, train a model, check the metrics, maybe explain feature importance, and then recommend a few retention ideas. It is a good project shape. Business problem, dataset, model, metric, interpretation. Very neat, if I may call it that. Very portfolio-friendly. ...

So I was watching a video from this creator I follow and something felt off about it. His lips weren’t syncing right. Not a huge thing, but enough that I noticed. So I went and checked his profile. Turns out he hadn’t actually shown up in any of his own videos for like six or seven months. The whole feed was Heygen. Or something like it, I’m not totally sure which tool. ...

So, the idea for this came from a chat I had with Dhruv, the CEO of Middleware. He pointed out that I should think of myself as a ‘builder-marketer,’ not just a marketer, and that I should build stuff that proves it. I thought about it, and he had a point. A few years later, I would put the same feeling more directly: marketers need enough engineering instinct to build and debug their own workflows. ...

A stakeholder comes to your desk. They’re excited. “We need to use AI,” they say, “to improve customer retention.” You nod, open your editor, and you start thinking. Should I use XGBoost? Or maybe a neural network? How will I set up the pipeline? Stop. Right there. This is the single biggest mistake many of us make when we’re starting out: we jump straight to thinking about solutions and algorithms. ...