A Data Scientist’s Guide to Predicting Customer Behavior: From Raw Data to Real Insights: Part 1
How a simple dataset about insurance customers revealed flawed data, surprising trends, and the hidden story of who is most likely to buy. In the world of data science, we’re often portrayed as digital wizards, conjuring predictions from complex algorithms. But the reality is far more like being a detective. We’re given a case file — a raw dataset — and our job is to dust for prints, interrogate the witnesses (the variables), and piece together a story that solves the mystery. Recently, I was handed just such a case: a dataset from an insurance company. The mystery was To figure out which of their existing customers were most likely to purchase a new insurance product. The company’s goal was simple: they wanted to target their marketing efforts more effectively. Instead of casting a wide, expensive net, they wanted to focus on the customers who were already raising their hands, even if they didn’t know it yet. Join me on this journey as we walk through the real-life process ...