My Work Using Tableau to Visualize Data

Exploring Regional Sales with Tableau: A Dillard’s Case Study

As part of a Tableau project, I stepped into the shoes of a newly hired regional sales manager for Dillard’s, a department store chain headquartered in Little Rock, Arkansas. My goal? To better understand sales performance across the Midwest region and uncover insights that could inform a future sales strategy.

The dataset I worked with included 2015 sales data across multiple states, and I used Tableau Desktop to build a series of visualizations that made the story behind the numbers much clearer.

Top 10 Zip Codes by Revenue

To get started, I created a column chart to visualize the Top 10 ZIP codes by sales revenue in the United States for 2015. One ZIP code stood out immediately: 72113 (Maumelle, AR). This single location brought in nearly $80 million in revenue—far ahead of the rest.

Chart 1- (Top 10 Zip Codes – Column Chart)

This led me to question whether this ZIP code was a true outlier or a data error, which became important later in the project.

State-by-State Revenue Breakdown

Next, I zoomed out and looked at the data from a higher level—by state. Using Tableau’s geographic mapping tools, I built a heat map to show total revenue by state. Unsurprisingly, Texas topped the list with over $237 million in revenue, followed by Arkansas with $113 million.

Chart 2- (Geographic View by State -- Geographic Heat Map)

This helped me frame my ZIP code-level insights within a broader regional context.

Revenue by Zip Code in the Midwest Region

Because my area of responsibility covered Arkansas, Kansas, Missouri, and Oklahoma, I created a new map to visualize ZIP code revenue specifically for these states. While Maumelle again dominated the visualization, I wanted to see how the other ZIP codes performed on their own.

Chart 3- (Midwest Zip Code Map with 72113 -- Geographic Point Map)

To address this, I filtered out 72113 and re-ran the visualization.

Chart 4- (Midwest Zip Code Map without 72113 -- Geographic Point Map)

This adjustment made the regional differences much more visible and helped identify ZIP codes that could benefit from more strategic sales efforts.

Interactivity and Styling

To make the dashboard more interactive, I added filters that allowed the user to toggle between different years of data and also updated the color palette to create clearer contrasts across ZIP codes.

Chart 5- (Interactive Year Filter and Custom Color Palette -- Geographic Point Map)

This made the dashboard more dynamic and user-friendly, especially for decision-makers who might want to explore the data on their own.

Cleaning the Data at the Source

As it turns out, ZIP code 72113 was a bit of a red herring—it wasn’t a retail store at all, but actually the warehouse where all online sales were recorded. That explained the unusually high revenue and highlighted the importance of data validation.

To fix this, I applied a filter at the data source level in Tableau to remove 72113 from all charts moving forward. This kept the analysis focused solely on physical store performance.

Final Dashboard

To bring it all together, I created a dashboard combining all three updated visualizations. This made it easy to compare ZIP code performance, regional revenue, and identify trends at a glance.

Chart 6- (Final Dashboard View -- Dashboard Visualization)

Final Thoughts

This project was a great way to apply Tableau in a real-world business scenario. I practiced everything from data exploration and cleaning to geographic mapping and dashboard design. More importantly, it taught me how crucial it is to question the data, dig deeper into anomalies, and tailor visuals to specific business questions.

Let me know if you'd like to see the dashboard in action or if you have any feedback!

Thanks for reading!