Mastering Data Analysis Through Continuous Practice: A Tableau Report on Sample Superstore Data

Introduction

In this article, I present a data visualization project on Sample Superstore data using Tableau. The project aims to explore the performance of different product subcategories. We will delve into key objectives, insights gained, and recommendations derived from the analysis.

Understanding the Data

The Sample Superstore data comprises 21 columns and 9994 rows, focusing on Product Subcategory, Order Date, Sales, Profit, and Discount for analysis.

• Subcategory: The product subcategory.
• Order date: The date a customer places his/her order.
• Sales: The total amount paid by a customer for a purchased product.
• Profit: The total profit gained from the sale of a product.
• Discount: The discount given to each product.

Methodology

Outlined below are the key objectives, insights, and recommendations gained from this analysis:

Key Objectives

i. What is the total revenue from each product subcategory, highlighting those with over $200,000 in sales.

ii. Overall sales trends over the years per product subcategory.

iii. Profit generated across each subcategory, emphasizing loss-making ones.

iv. Average discount across each product subcategory, identifying those with a discount > 20%.

To better illustrate the insights gained from this analysis, the following Tableau dashboard presents a visual summary of the key findings.

Sample Superstore dashboard focusing on subcategories

Insights

i. Total Revenue from Each Subcategory:

· Phones lead in sales ($330,007), followed by Chairs. Five subcategories have sales exceeding $200,000 (Phones, Chairs, Storage, Tables, and Binders).

· Notably, some subcategories generated revenue of less than $50,000, including Supplies, Art, Envelopes, Labels, and Fasteners. Fasteners have the least revenue across all subcategories ($3,024).

ii. Sales Trends (2014–2018):

· Utilizing Sparklines, an upward sales trend is observed for most product subcategories, indicating increased demand.

iii. Profit Across Subcategories:

· Copiers and Accessories yield the highest profits, while Supplies, Bookcases, and Tables incur losses.

· Surprisingly, despite Tables being among the highest selling products, it had the highest loss among all subcategories ($17,725).

iv. Average Discount Across Subcategories:

· Four subcategories received an average discount of more than 20%. Binders had the highest discount (37%), followed by Machines (31%), Tables (26%), and Bookcases (21%).

· Despite Tables and Bookcases having a higher discount compared to other subcategories, they still generated losses.

· Notably, Accessories, with an 8% discount (second lowest among all subcategories), was one of the most profitable products.

Recommendations

I generated the following recommendations based on the insights:

• Evaluate pricing strategy for Tables and Bookcases to align discounts with profitability.
• Investigate reasons behind losses in Supplies and consider adjustments in product offerings or pricing.
• Monitor trends in high-selling categories like Phones and Chairs to capitalize on market demand.
• Optimize discounts for Binders, Machines, Tables, and Bookcases to balance profitability.

Conclusion

Continuous practice and analysis enhance mastery. Through this project, some valuable insights were gained, providing a foundation for informed decision-making in product management and pricing strategies.

Explore the dashboard yourself here to gain a deeper understanding. Feel free to leave comments or questions for further discussion.