U.S. Sales and Product Performance

Star Schema

Overview

Geography

Products

This project takes a deep dive into U.S. sales data from 2013 to 2016 using Power BI and a SQL Server database (WideWorldImportersDW). My goal was to understand how the company performed financially during this period, which products drove the most value, and how different states contributed to overall sales.

I started by building a data mart in Power BI using a star schema. It included a fact table for sales and dimension tables for cities, products, and dates. With this structure in place, I could explore trends over time and compare results across locations and product categories.

One of the first things that stood out was the steady growth in both sales and profits. Despite the broader economic uncertainties in the years following the 2008 crisis, the business showed consistent improvement up to 2016. A particularly interesting moment came in early 2016 when the company launched its line of refrigerated products. From the moment they hit the market, these items gained traction fast. Sales grew month after month, suggesting a strong fit with customer demand. Meanwhile, dry goods held their ground and continued to perform reliably.

When I looked at individual products, the Air Cushion Machine in blue was the top earner in terms of revenue. But from a profitability standpoint, the real winners were some of the anti-static and double-sided bubble wrap items. This reminded me that the best-selling item isn’t always the most profitable one. I also noticed a pattern in customer preferences—blue was clearly the most popular product color, followed by black, white, grey, and light brown.

The geographic side of the analysis revealed something unexpected. States like California, which has the largest population in the country, didn’t bring in the most revenue. On the other hand, Texas—with fewer people—ranked much higher in terms of sales. It became clear that population size alone doesn’t drive performance. Other factors, like age distribution, lifestyle, and even how products are marketed, likely play a much bigger role.

If I were advising the company in mid-2016, my recommendations would be pretty straightforward. First, keep investing in the refrigerated product line—it’s clearly gaining momentum and meeting a market need. Second, double down on promoting high-margin items. These products might not be flashy, but they’re incredibly valuable for the bottom line. And third, take a closer look at underperforming states with large populations. There’s probably untapped potential there, and with the right strategy, those areas could see stronger results.

This project was a great reminder of how much value you can uncover when you combine a solid data model with the right tools. With Power BI, I was able to go beyond basic reporting and surface insights that could really shape business strategy—from product development to regional expansion.