A few years ago, I built a churn model for a B2B SaaS product. Logistic regression, binary label, 30-day prediction window. It performed fine. The business used it. I moved on.
What bothered me was a question the model couldn’t answer: how long does a customer actually stay?
Customer segmentation is one of those problems that sounds straightforward until you actually sit down with the data. In this post I’ll walk through an approach I built for segmenting customers based on their HTTP traffic patterns — the kind of traffic data that tells you not just how much a customer uses a service, but how they use it.
In 2020, I was handed a PDF — an ILO working paper titled Spotting Export Potential and Implications for Employment in Developing Countries (Cheong, Decreux & Spies, 2018) — and asked to turn it into a working algorithm.
The paper describes a methodology developed by the International Trade Centre to identify a country’s unrealized export opportunities, and then estimate how many jobs realizing those opportunities would create. Across six developing countries. At the product-market-sector level.