This was my final project for IBM's Unsupervised Machine Learning course, on the same Telco Customer Churn dataset I used for a churn classifier. The question was different: without using the churn label at all, can we split 7,043 customers into groups that a marketing team would recognise, and does that split say anything about risk?

It does. One segment, which I call the fibre newcomers, is 27% of customers and 65% of the revenue lost to churn .

Feature engineering mattered more than the algorithm

The obvious input is the 18 raw service flags (phone, internet type, each add-on). Run through PCA, they need 10 components to explain 80% of the variance, and K-means on them reaches a silhouette of 0.18 at k = 5: weak, overlapping clusters.

I replaced them with six engineered behavioural features: tenure, contract length in months, monthly charges, the number of protection services, the number of streaming services, and a fibre flag.…