Starbucks has never been on my radar. So then why do a Tableau project on Starbucks then? Starbucks is a great American brand that helped popularized coffee culture in the US. In 2024, I read that Starbucks sales were declining and that was apparently a big deal not only for the company of course but possibly also as an indicator of the greater health of the American economy at large. The objective of this project is to show insight into what Starbucks segments are experiencing, declining sales and profits.

For this dashboard, I used Microsoft Excel to scrape all the financial data from Starbucks income statements going as far back as 2017. I went as far back as 2017 because I wanted to see a couple years of data prior to COVID-19. The process of collecting the data was by far the most time-consuming part of the data prep as some financial tables did not format properly and had to be manually entered.

As much as I wanted to import the data into Python, I ended up doing all the data manipulation/modeling data in Excel. I did this for two reasons (1) It was just a more efficient workflow process to keep everything already in Excel before importing spreadsheets into Tableau. (2) I wanted to visually see the income statement in Excel as this is likely the norm in the FP&A industry.  Just for reference, each year’s income statement amounted to less than a hundred rows of data.

In Excel, the main function I used to manipulate the data was the TRANSPOSE function. I also did things like creating new columns with calculated cells etc. Some of the data tables I scraped from the financial statements were very sparse; it was simply easier to copy and paste instead of using a more sophisticated method of aggregating the data.

The most complex part of the data prep was modeling the data correctly in Excel. In a way, I was essentially making my own datasets. There were a couple of times when working in Tableau, I had to go back into Excel to correctly model the data.

The most interesting insight I gained from the data was learning that Starbucks International segment is Starbucks’ worst performing segment, with respect to net sales. More specifically, Starbucks saw its international licensed stores have its steepest sales decline from 2023 to 2024. Internationally speaking, Starbucks has its largest number of company-operated stores in China.  I googled “largest coffee shop in China” and found out that Luckin’ Coffee has recently surpassed Starbucks as the largest coffee shop in China. Coincidence? It appears Starbucks is facing a fierce direct competitor in China that it has never seen the like of in the United States.

As part of this project, I wanted to do a competitor analysis of other publicly traded coffee shops i.e. Black Rifle Coffee Company, Dutch Bros etc. but abandoned the idea for the fact that the market share Starbucks holds in the marketplace and coffee experience it offers its customers cannot be compared, in my opinion.

In creating this dashboard, I started out by displaying the total net sales, profits etc. for the entire company in a chronological order you would likely see in an income statement. I wanted to give the viewer context before diving into the Starbucks business segments.

Since this dashboard’s goal is to provide insight into Starbucks segments, I next introduce the segments in a way that makes them appear to be their own individual entity.  For the comparison bar charts, I have to give credit to Brittany Rosenau for her article in Medium showing how to make Comparison Bar Charts in a more visually appealing way.

I subsequently created the Average Net Sales Growth Rate bar chart to add more context to the YoY Net Sales Growth metric. It was in this bar chart that I created one of two calculated fields that I am most proud of. It took a little bit of tinkering to make the average net sales metric. The other calculated field I figured out how to write was a calculated field to specify the color of bars in bar graphs based on certain criteria. This can be seen throughout the entire Starbucks visualization.

The ‘Operational Expense as a % of Net Sales’ chart was created to help identify any possible instance where operational expenses might be increasing due to inflation and therefore would be reducing profit margins. On this chart, I wanted to add custom shapes but didn’t know what to add so I went with the stock shapes Tableau gives you.

I learned how to make a waterfall chart (bottom left) to visually show what net sales, operating expenses and operating profit appear like together in one chart. Like the Comparison Bar Chart, this is a visualization I had to refer to a tutorial because of the advanced logic /calculated fields involved in the building process. Unlike the comparison bar chart though, this type of bar chart has more practical usage and has been popularized by large consulting companies.  Despite this, this is the bar chart I thought about not including in the dashboard because it doesn’t quite flow with the story arc but decided to keep it at the last minute.

The Store Type Bar Chart (bottom right) ends the sales decline story arc in a cliffhanger. We found out which segment / store type had the greatest YoY sales decline, but this chart brings up many more questions.

Starbucks2024 financial statement states that China’s ‘competitive environment’ as one factor in Starbuck’s lower than expected performance. China is Starbucks' largest market outside the United States, so a decline in this market will have a significant impact overall for the company. It is just not China though. The Americas segment’s company owned stores also showed a significant decline in net sales as well.

It would be interesting to see in more detail what specific cities/states/regions of the United States had a greater sales decline than others etc. To answer those questions, you would probably want net sales store data to perform YoY same store analysis.