Design a Churn Prediction and Retention Proposal for ConnectTel
Overview
What this challenge is about.
Analyze telecom customer data, design a churn prediction model, and propose a retention strategy. Earn a verifiable certificate.
The scenario
ConnectTel is a regional telecommunications provider offering mobile and broadband plans, where contract type and monthly billing patterns vary widely across its customer base. In a market where acquiring a new subscriber costs far more than keeping an existing one, a 15% yearly loss of customers is a direct threat to revenue.
The Brief
What you'll do, and what you'll demonstrate.
Design a credible, business-ready proposal for predicting customer churn at ConnectTel and converting those predictions into a targeted, budget-aware retention strategy.
Earning criteria — what you'll demonstrate
- Frame a predictive modeling task as a business proposal grounded in real organizational constraints
- Justify variable selection and a logistic regression design from exploratory analysis of customer data
- Choose and interpret classification measures (accuracy, precision, recall, ROC-AUC) appropriate to a retention use case
- Translate predicted churn risk and key drivers into a feasible, budget-aware retention strategy
- Communicate technical reasoning clearly to non-technical decision-makers
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Logistic Regression
Apply logistic regression to solve real industry problems and demonstrate production-level capability.
- Classification Metrics
Apply classification metrics to solve real industry problems and demonstrate production-level capability.
- Feature Selection
Apply feature selection to solve real industry problems and demonstrate production-level capability.
- Data Visualization
Transform complex data into clear, insightful visual representations.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Business Analyst
This challenge mirrors the analyst's core job: turning customer data into a defensible recommendation. You practice scoping a modeling approach, selecting variables, and presenting evidence-backed strategy to leadership under real budget constraints.
This challenge sharpens
- logistic-regression
- feature-selection
- data-visualization
Customer Retention Analyst
Retention analysts predict and prevent churn for a living. By designing a churn model and matching offers to risk segments within budget, you build the exact skills this role demands around measuring and acting on attrition risk.
This challenge sharpens
- logistic-regression
- classification-metrics
- feature-selection
Junior Data Scientist
This proposal exercises the modeling lifecycle a junior data scientist owns: exploring data, choosing features, fitting a classifier, and evaluating it with the right metrics, then explaining the result to stakeholders who decide on it.
This challenge sharpens
- logistic-regression
- classification-metrics
- data-visualization