Five customer segments, and the one that holds 65% of the lost revenue

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. Three principal components now explain 87% of the variance, and they read naturally as spend and service depth, commitment and fibre versus protection. The silhouette doubles:

Feature set	Components for 80%+ variance	Silhouette at k = 5
18 raw service flags	10	0.18
6 engineered features	3	0.37

Five algorithms, same k

Model	Silhouette	Davies–Bouldin	Bootstrap stability (ARI)
K-means, engineered features	0.37	1.06	0.99
Hierarchical (Ward)	0.34	1.11	0.79
Gaussian mixture	0.28	1.25	0.95
DBSCAN (19 clusters)	0.30	1.42	n/a

K-means wins on every internal metric and is the most stable: refitting with different random seeds reproduces the same segments (adjusted Rand index 0.99). Stability is the metric I would not skip; a segmentation that changes every time you refit cannot be used to run campaigns.

The five segments

Segment	Share	Profile	Churn	Share of churned revenue
Fibre newcomers	27%	17 months' tenure, month-to-month, all on fibre, $85/month	56%	65%
Budget newcomers	27%	12 months, $39/month, a third without internet	26%	14%
Premium fibre loyalists	17%	58 months, annual contracts, $103/month, most add-ons	19%	17%
Loyal DSL bundlers	16%	52 months, the longest contracts, protection bundles	6%	3%
Phone-only veterans	14%	46 months, no internet, $23/month	1.4%	0.2%

Churn ranges from 1.4% to 56% across segments, even though the clustering never saw the churn column. The fibre newcomers are 27% of customers and 35% of revenue, but 65% of the revenue lost to churn. That is where a retention budget goes first, and the profile tells you what to offer: they are on month-to-month contracts, so the obvious lever is a contract incentive.

A negative result worth keeping

I also tried training one churn classifier per segment instead of a single global model. On the same test customers: global model AUC 0.843, one model per segment 0.843, global model plus a segment feature 0.844. Splitting the data five ways costs more in sample size than it gains in specificity. Segments are a communication and targeting tool here, not a modelling shortcut, and it is useful to know that before building five pipelines.

Limits

The data is a point-in-time extract with no customer history. The engineered features embed judgement calls, such as which services count as "protection". DBSCAN's small clusters are real sub-groups but too small to act on.