Analysis
Customer Lifetime Value Model for SaaS Scale-up
Overview
What this challenge is about.
You are provided with a dataset of 5,000 customers with features: acquisition channel, monthly spend, tenure, number of support tickets, product usage metrics, and churn flag. Your task is to calculate historical CLV, build a predictive model for future CLV using regression or machine learning, and create at least 3 customer segments with distinct characteristics. Deliver a report with model performance, segment profiles, and recommendations for targeting high-value customers. Success is a model with R-squared > 0.7 and actionable segments.
The Brief
What you'll do, and what you'll demonstrate.
How can TaskFlow predict customer lifetime value and segment customers to prioritize enterprise sales efforts?
Earning criteria — what you'll demonstrate
- Calculate historical CLV using cohort analysis
- Build a predictive model for CLV using regression or machine learning
- Segment customers based on predicted CLV and behavioral attributes
- Translate CLV insights into marketing and sales strategies
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 mappings coming soon.