Set Revenue-Maximizing Prices for Glow Naturals Skincare
Overview
What this challenge is about.
Clean sales data, run regressions, and project revenue from price changes for Glow Naturals. Earn your verifiable certificate.
The scenario
Glow Naturals is a Seoul direct-to-consumer skincare brand that markets organic formulations entirely through its own online store, competing on price and advertising visibility against larger Korean beauty labels. With one year of operating data and no pricing analytics in place, the founders are deciding prices for their three flagship products by instinct.
The Brief
What you'll do, and what you'll demonstrate.
Determine a revenue-maximizing price for each of Glow Naturals' three top products by estimating the price elasticity of demand from one year of sales history.
Earning criteria — what you'll demonstrate
- Clean and validate a real-world daily sales record and justify each preparation decision
- Specify and estimate a multiple linear regression and interpret its coefficients correctly
- Derive and explain price elasticity of demand from regression output
- Test and interpret core regression assumptions, specifically heteroskedasticity and multicollinearity
- Communicate a data-driven pricing recommendation with explicit uncertainty to a non-technical audience
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.
- Linear Regression
Apply linear regression to solve real industry problems and demonstrate production-level capability.
- Hypothesis Testing
Apply hypothesis testing to solve real industry problems and demonstrate production-level capability.
- Data Cleaning
Apply data cleaning to solve real industry problems and demonstrate production-level capability.
- Elasticity Estimation
Apply elasticity estimation 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 Analyst
This challenge mirrors the daily reality of an analyst turning messy business data into a defended recommendation. You practice cleaning a real record, modeling a relationship, and communicating findings — the exact pipeline analysts run before any stakeholder decision.
This challenge sharpens
- data-cleaning
- linear-regression
- hypothesis-testing
Pricing Analyst
Estimating elasticity and projecting revenue under price scenarios is the core craft of pricing analytics. The challenge builds the habit of quantifying demand sensitivity and attaching uncertainty to price moves, which is what pricing teams demand before changing a sticker.
This challenge sharpens
- elasticity-estimation
- linear-regression
- hypothesis-testing
Marketing Analytics Associate
Disentangling the effect of advertising spend from price and competitor moves is a staple of marketing analytics. You learn to control for confounders in a regression and read coefficients responsibly, which transfers directly to measuring campaign impact.
This challenge sharpens
- linear-regression
- data-cleaning
- elasticity-estimation