Clustering is widely used for customer segmentation, anomaly triage, and exploratory analysis when you do not have labels. The challenge is that many clustering algorithms will always produce groups, even when the underlying structure is weak. If clusters change dramatically when you slightly perturb the data, they are hard to trust in production. Cluster stability evaluation addresses this by measuring how consistent your groupings...
Why “use” is the real success metric
Many data products fail for a simple reason: they are technically correct but behaviourally ignored. A dashboard that nobody checks, a churn model that never changes retention workflows, or an “AI assistant” that frontline teams bypass in favour of spreadsheets all point to the same gap—adoption. If you are learning product thinking alongside modelling skills in a data...
