Retailers with both physical and digital channels hold an extraordinary advantage: they can see what customers browse, what they buy in-store, and how they behave online. This Reddit user joining an IKEA-like enterprise already has RFM analysis in place. That is a solid foundation, but it is only the beginning. Our take is straightforward: stop looking for the perfect ML project to start and instead ask what business question matters most right now.
The user's data set, clients, products, Google Analytics, and a survey from a subset of customers, is rich but fragmented. The temptation will be to chase unsupervised learning first, perhaps clustering customers into segments. Resist that urge for a moment. Clustering without a clear action is a map with no destination. Instead, start with a supervised problem that ties directly to a measurable outcome. For example, predict which online browsers are most likely to purchase in-store within the next week. That uses Google Analytics browsing data, product views, and historical purchase records. It is a binary classification problem, straightforward to evaluate, and its output can inform targeted promotions or in-store staffing. The business will feel the impact immediately, which builds trust for more complex projects later.
Once that first model is in production, layer in the survey data. Surveys are notoriously sparse and biased, but they become powerful when used as a validation signal rather than a primary feature. Compare the predicted segments from your browsing-and-purchase model against survey responses about preferences or satisfaction. If they align, you have confidence in your model. If they diverge, you have discovered a gap worth investigating. This approach keeps the machine learning grounded in human behavior rather than abstract patterns.
For resources, the user does not need another textbook on gradient boosting. They need a framework for sequencing work. Read *The Signal and the Noise* by Nate Silver for the discipline of probabilistic thinking. Study the *Machine Learning for Retail* case studies published by Google Cloud and AWS, not for code, but for how they frame problems around inventory turns and customer lifetime value. And most critically, spend time with the merchant and marketing teams before writing a single line of Python. The best model in retail is the one that answers a question someone is already asking. Start there, deliver a clear yes-or-no prediction, and let the business pull you toward deeper insights.