Tableau Project · Advanced · 4 to 6 hours · VIP
Build a cohort retention dashboard in Tableau
Order-level data in, a signup-cohort retention triangle out. This is the LOD-and-table-calc project that separates a chart-maker from an analyst.
The brief
LutoBox, a meal-kit brand, keeps acquiring customers but cannot tell if they stay. You have 4,210 orders across 1,191 customers and one year of history. The founder wants a single view that answers: of the people who joined in a given month, how many are still ordering one, three, and six months later? That is a cohort retention analysis, and it lives or dies on getting the level-of-detail logic right: each customer's cohort is their first order month, computed with a FIXED LOD so it never shifts when the view is filtered.
Your role
You are the BI analyst. Deliverable: a published Tableau Public dashboard with a cohort retention heatmap and a short read of where retention is won or lost.
The dataset
4,210 meal-kit orders (Jul 2025 to Jun 2026) across 1,191 customers
tableau-box-orders.csv · 4,210 rows
Columns: order_id, customer_id, order_date, box_type, qty, revenue
Setup
- Install Tableau Public (free) and connect it to tableau-box-orders.csv.
- Confirm order_date reads as a date and revenue as a number.
- Plan the cohort calc on paper first: cohort month, then months-since, then retention.
VIP project
The full brief for this Tableau project is for VIP members
The first Tableau 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 to explain, in plain terms, why FIXED (not INCLUDE or a plain DATETRUNC) is what keeps a customer's cohort stable under filters.
- Paste your retention calc and ask the AI whether your denominator is the original cohort size or the prior month, and why that matters.
- Have the AI stress-test your read of the curve: what would make the month-1 to month-3 drop misleading?
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.