Overview
What this challenge is about.
You receive a CSV with company size, industry sub-vertical, country, product features adopted, monthly active users, and lifetime value. Standardize features, decide on a clustering approach (k-means, hierarchical, or HDBSCAN — defend your choice), and pick the number of clusters via a quantitative metric (silhouette, gap statistic) plus a business sanity check. Deliver a 4-page segmentation playbook a marketer can act on next quarter.
The Brief
What you'll do, and what you'll demonstrate.
Discover and describe 4-6 actionable customer segments from unlabeled firmographic and usage data.
Earning criteria — what you'll demonstrate
- Apply unsupervised learning to a real business segmentation task
- Use feature scaling and dimensionality reduction appropriately
- Choose cluster count with both quantitative and qualitative criteria
- Translate clusters into named, marketing-actionable segments
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
Data Scientist
Customer segmentation is a recurring data-scientist deliverable at any subscription business, and shipping a named-segment playbook is the kind of artefact hiring managers ask for in interviews.
This challenge sharpens
- unsupervised-learning
- clustering
- exploratory-data-analysis
Applied AI Scientist
Defending an unsupervised method choice with both quantitative metrics and business sanity checks is exactly the discipline applied AI scientists are evaluated on.
This challenge sharpens
- clustering
- dimensionality-reduction
- feature-scaling