Customer Analytics
Customer segmentation with clustering: three actionable segments from 2,240 customers
Five clustering algorithms compared, one decision made with judgement: the model with the best score doesn't win - the one you can actually operate does. The result is three operationally usable customer segments and a campaign strategy that follows the budget. An MIT capstone with a reproducible companion repository.
A retailer runs campaigns across web, catalog and store for six product categories - and gets acceptance rates of just 1 to 15%. The reason is simple: every customer is treated the same. The question behind this project: can unsupervised learning read the natural customer segments out of the behavioural data, so the marketing budget flows where it actually converts? There is no target label - the structure has to be found.
The dataset
The basis is a public marketing dataset (marketing_campaign.csv) with 2,240
customer records and 26 attributes: demographics, category spend (wine, fruit, meat, fish,
sweets, gold), channel behaviour (web/catalog/store/deals) and responses to five campaigns. After
cleaning (median imputation for income, one extreme outlier removed), 2,232 customers
remain modelled; 17 behavioural features go into the clustering, demographics are kept
for profiling only. The dataset is a public educational dataset provided within the MIT capstone
program; dataset rights remain with the original publisher.
The approach: five algorithms, one usable structure
Exploratory analysis surfaced three patterns: income is the strongest spend driver, wine + meat make up ~75% of spend, and children sharply reduce premium spend. After feature engineering (8 new features), StandardScaler and PCA (10 components, 90.7% variance), five clustering algorithms were compared - judged on silhouette score and operational usability: K-Means, K-Medoids, Hierarchical (Ward), DBSCAN and Gaussian Mixture.
Results
Silhouette score at k=3 per algorithm:
| Algorithm (k=3) | Silhouette | Verdict |
|---|---|---|
| K-Means (selected) | 0.2888 | Balanced, interpretable, assigns new customers |
| K-Medoids | 0.2670 | Comparable, slower |
| Hierarchical (Ward) | 0.2661 | Confirms the same 3-segment structure |
| Gaussian Mixture | 0.1808 | Weaker separation |
| DBSCAN | 0.4820 | Rejected - 2,216 in one cluster + 10 noise points, can't assign new customers |
DBSCAN scored highest - but is useless in production (one dominant cluster, no way to place new customers). The K-Means silhouette of 0.2888 marks moderate, still operationally interpretable structure - not a case of cleanly isolated clusters. K-Means (k=3) was chosen because its three segments are reproduced by K-Medoids and Hierarchical clustering - the confidence comes from that reproduction, not from the size of the score. The best score doesn't win - the defensible, deployable solution does.
The three segments
| Segment | Share | Avg income | Avg spend | Campaigns accepted |
|---|---|---|---|---|
| Premium Enthusiasts | 25.4% | $76,319 | $1,420 | 1.01 |
| Moderate Mainstream | 27.8% | $57,292 | $721 | 0.40 |
| Budget Families | 46.8% | $35,475 | $99 | 0.17 |
All figures in US dollars, as per the source dataset.
From clusters to strategy
- Three-tier campaigns - Premium: exclusive/VIP, catalog + in-store; Mainstream: cross-sell/loyalty, omnichannel; Budget: deals & family bundles, mobile + email.
- Budget where it converts - Premium share up, Budget down.
- Channel fit - stop catalog mailings to Budget; scale catalog for Premium.
The projected financial upside (~$98K net benefit / ~196% first-year ROI) is a model calculation on stated assumptions, not a measured result.
Reproducibility & verification
The full, executed code (EDA, feature engineering, five-algorithm comparison, segment profiling) is in the companion repository: github.com/myBytesResearch/mit-capstone-mpianowski. The project was completed as a capstone in the MIT Professional Education Applied AI and Data Science Program (verifiable certificate).