Customer Retention

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. ...

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. ...

Customer churn isn’t just a marketing problem - it’s a business survival issue. In competitive industries like e-commerce, losing one customer often means losing several revenue streams, especially when one account can represent multiple users. This post is a breakdown of my churn prediction capstone project for the postgraduate data science program at UT Austin - also tied to my master’s in data science at Deakin U. The project was closed-source, so I can’t release the full notebook, but I’ll walk you through everything I did including code snippets, results, charts, what I learned, and where this project fits in my larger journey into machine learning and MLOps. ...