Diagnose Churn Drivers for a B2B SaaS Workflow Tool
Overview
What this challenge is about.
Clean and join product usage, support tickets, and firmographics to surface 3-5 churn signals with EDA. Earn a verifiable certificate.
The scenario
The company (around 220 staff, 1,800 active accounts) competes against three larger incumbents; a 4-point reduction in churn would unlock roughly USD 6M ARR over the next year and is the single biggest lever for the Series C narrative.
The Brief
What you'll do, and what you'll demonstrate.
Identify the 3-5 strongest 90-day churn predictors in product, support, and firmographic data, with validated effect sizes.
Earning criteria — what you'll demonstrate
- Run a complete data-wrangling pipeline on real, messy multi-source data
- Apply EDA techniques to surface non-obvious relationships
- Validate findings on a holdout to avoid pattern-fishing
- Communicate quantitative results to a non-technical executive audience
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Applied Data Analysis and Practical Data Science
Master · Data Engineering
Strong alignment
This challenge maps to Applied Data Analysis and Practical Data Science 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.
- Exploratory Data Analysis
Apply exploratory data analysis to solve real industry problems and demonstrate production-level capability.
- Data Wrangling
Apply data wrangling 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.
- Statistical Validation
Apply statistical validation to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Data Storytelling
Apply data storytelling 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
End-to-end EDA on multi-source SaaS data plus delivering an executive memo mirrors the first 90 days of a junior data scientist at any product-led growth company.
This challenge sharpens
- exploratory-data-analysis
- statistical-validation
- data-storytelling
Data Engineer
Cleaning and joining three sources into a documented Parquet table builds the pipeline-thinking and schema-discipline that data engineers practice daily.
This challenge sharpens
- data-wrangling
- python
- feature-engineering
AI Product Manager
Translating product-usage signals into a churn narrative an executive can act on is the analytical core of the AI Product Manager role.
This challenge sharpens
- data-storytelling
- exploratory-data-analysis
- statistical-validation