Power BI Project · Advanced · 4 to 6 hours · VIP
Customer cohort and retention DAX in Power BI
New vs returning, repeat rate, AOV, and share of segment, built with SUMX, DISTINCTCOUNT, and CALCULATE with ALL. The measures that separate row context from filter context.
The brief
A direct-to-consumer brand has 842 orders from 420 customers and wants to understand repeat behavior: how many orders come from brand-new customers vs returning ones, what share of customers ever buy again, and which segment actually drives revenue. Each answer is a DAX measure that forces you to reason about context: new vs returning needs row context inside an iterator, share of segment needs CALCULATE with ALL to expose the grand total, and repeat rate is a customer-level metric that a naive order-level count will get wrong.
Your role
You are the People-and-Growth analyst. Deliverable: a one-page Power BI report with new-vs-returning, repeat rate, AOV, and segment revenue, plus your top 3 findings.
The dataset
842 orders (Jan 2025 to Jun 2026) from 420 customers, with signup date on each row
powerbi-customer-orders.csv · 842 rows
Columns: order_id, customer_id, customer_signup_date, region, segment, order_date, amount
Setup
- Open Power BI Desktop (free) and load powerbi-customer-orders.csv.
- Note that customer_signup_date is denormalized onto every order row.
- Confirm order_date and customer_signup_date read as dates and amount as a number.
VIP project
The full brief for this Power BI project is for VIP members
The first Power BI project is free for every member. The dataset, the tasks, the answer key, and the graded skills check for this one are part of the VIP tier, along with the other 8 locked projects. 5 projects stay free, one for each tool, so you can try every tool before you decide.
VIP access comes with the bootcamp. Enroll once, and every locked project opens, alongside the live sessions, the replays, and the certificate.
Work like an AI-powered analyst
The modern analyst uses AI as a thinking partner, not a shortcut that skips the learning. Try these on this project.
- Ask ChatGPT or Claude why new vs returning needs an iterator with row context, and why plain SUM cannot express it.
- Have the AI explain why ALL(segment) is required in the denominator for a share-of-total measure, with a one-line example.
- Paste your repeat-rate measure and ask the AI to confirm it counts customers, not orders.
Finished it? Put it in your portfolio.
This is exactly the kind of output the bootcamp builds with you live, with mentor feedback and an AI badge and certificate of completion at the end.