Set up the database
Created a car schema in PostgreSQL with four tables and primary keys, then loaded the data.
Two years of car sales stored in PostgreSQL and brought to life in Tableau: which makes, states, dealerships and customers drive the revenue?
Every sale in transactions points to one customer, one dealership and one vehicle.
Created a car schema in PostgreSQL with four tables and primary keys, then loaded the data.
Joined the four tables on customer_id, dealership_id and vin.
Why: one joined view lets a single sheet use fields from any table.
Four headline numbers: Total Sales, Total Transactions, Unique Customers and Average Price per Vehicle.
Top Makes, Top States, Top Dealerships, Top Customers and Most Popular Colours, each ranking by sales or by number of transactions.
A Monthly Sales Trend and a Sales and Revenue Rate sheet to show how sales moved over time.
Arranged the sheets into one dashboard, with the KPIs on top and the rankings below.
Figures calculated from the PostgreSQL data.
Washington brings in $82.7M, about 52% more than second-placed South Carolina ($54.6M). Michigan follows with $50.1M.
Mazda earned $18.1M from 339 sales, ahead of Genesis ($15.4M) and Buick ($14.9M).
The top three dealerships are close: Thornton, Thomas and Kennedy ($28.5M), Martin Group ($27.9M) and Willis, Salazar and Sanchez ($27.7M).
SkyBlue appears in 182 sales, followed by HotPink (169) and Maroon (165).