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 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.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
Career paths this builds toward
Canonical rolesBusiness 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