Churn Prediction

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. ...

If you read part 1 of this series, ala Churn Prediction for E-Commerce with Predictive Modelling, you know I recently wrapped up a full end-to-end churn prediction project as part of my postgrad program. That article was the 30,000-foot view – the business problem, the segmentation insights, the high-level model results. With this one I simply walk you through the code. But instead of just dumping code snippets for you to copy pasta, I want to walk you through what I actually did and, more importantly, why it matters. ...

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. ...