Cluster a Telco's Subscriber Base for a Pricing Refresh
Overview
What this challenge is about.
Clean telco data, run k-means and hierarchical clustering, validate with bootstrap, profile segments, and recommend pricing tiers. Get a verifiable certificate.
The scenario
The MVNO (around 350 staff, present in Spain and Portugal) has flat subscriber growth and a 6.4% monthly prepaid churn rate; pricing has not been refreshed in three years.
The Brief
What you'll do, and what you'll demonstrate.
Identify 3-6 behaviorally distinct subscriber segments and propose a refreshed prepaid tier structure that reduces cannibalization.
Earning criteria — what you'll demonstrate
- Apply preprocessing and feature scaling appropriate to behavioral data
- Compare multiple clustering algorithms and select with stability evidence
- Translate cluster centroids into named, interpretable personas
- Connect a segmentation to a concrete business action (pricing)
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Data Mining and Knowledge Discovery
Master · Data Engineering
Strong alignment
This challenge maps to Data Mining and Knowledge Discovery at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Clustering
Apply clustering to solve real industry problems and demonstrate production-level capability.
- Feature Engineering
Apply feature engineering to solve real industry problems and demonstrate production-level capability.
- Exploratory Data Analysis
Apply exploratory data analysis to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Scikit Learn
Apply scikit learn to solve real industry problems and demonstrate production-level capability.
- Segmentation
Apply segmentation to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Data Scientist
Behavioral segmentation tied to a pricing decision is a canonical junior-data-scientist first project in any consumer-facing business.
This challenge sharpens
- clustering
- segmentation
- exploratory-data-analysis
Applied AI Scientist
Stability analysis plus business framing mirrors applied AI work in product or consumer teams.
This challenge sharpens
- clustering
- scikit-learn
- segmentation
AI Product Manager
Translating cluster outputs into pricing tiers and revenue impact maps directly onto AI product manager interview cases.
This challenge sharpens
- segmentation
- exploratory-data-analysis
- feature-engineering