Diagnose Churn Drivers for a B2B SaaS Workflow Tool
Overview
What this challenge is about.
You receive three CSV exports: 18 months of weekly product-usage events for about 1,800 accounts, the full support-ticket history, and account firmographics (industry, size, plan tier, contract value). Clean and join the datasets, perform exploratory data analysis (EDA, the systematic visual + statistical exploration of a dataset), and surface 3-5 churn-predictive signals with effect sizes you can defend. Validate signals against a 6-month holdout. Success looks like a 6-page analysis memo with charts the Chief Revenue Officer can present, plus a Jupyter notebook another analyst can rerun in under 10 minutes.
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.
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 rolesData 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