INTRODUCTION

I analyzed the Sample Superstore dataset in this report to extract key insights and trends. This dataset provides information on sales performance in different categories and subcategories across various cities and states in the United States. The objective is to identify:

• The most and least-performing categories and subcategories

• The most used shipping mode

• The states with the highest and lowest profits

• Total sales and profit for each region

DATA OVERVIEW

• The dataset contains 13 columns and 9,977 rows providing information on sales, profit, and discounts across different regions, states, and cities. Key columns in the data:

• Ship Mode: Different methods used to deliver customer orders

• Segment: Customer groups (Consumer, Corporate, Home Office)

• Country: The country where sales took place

• City & State: Locations where sales were made

• Region: The different regions of the country

• Category & Sub-Category: Groups and types of products sold

• Sales: The revenue generated from product sales

• Quantity: The number of units sold

• Discount: The discount applied to each product

• Profit: The net profit or loss made from each sale

INITIAL INSIGHT

• After a first look at the data, I noticed the following:

• Shipping Mode: Standard Class is the most used shipping method.

• Category Performance: Sales were highest in Technology, but it had the least profit. Office Supplies made the most profit despite not having the highest sales.

• Loss-Making Products: Some products recorded losses instead of profits.

OBSERVATION

I used pivot tables in Excel to create charts that revealed deeper insights into the data. Each visualization helped to highlight key trends. The first visualization is a column chart that shows that Standard Class was used the most, with 5,955 shipments, while Same Day shipping was the least used, with 542 shipments.

The second chart is a clustered column chart that shows that;

• Technology had the highest sales but the least profit.

• Office Supplies made the most profit even though it had lower sales than Technology.

• Furniture had the least sales but the second-highest profit.

The third chart is a bar chart comparing the total profits across subcategories;

• Copiers ($55,617) and Phones ($44,515) had the highest profits.

• The least subcategories made losses instead of profits, with Bookcases losing $34,725.56 and Tables losing $17,725.48.

The fourth visualization is a column chart showing the number of sales in each subcategory reveals;

• Binders (1,522 sales) and Paper (1,359 sales) had the highest number of sales.

• Machines (115 sales) and Copiers (68 sales) had the least sales.

The fifth visualization is a column chart showing;

• California ($76,257) and New York ($74,015) made the highest profits.

• Texas (-$25,750) and Ohio (-$16,959) had the highest losses.

The sixth visualization is a bar chart that shows that California and New York had the highest sales and the highest profits.

However, the states with the least sales Wyoming (1 sale) and West Virginia (4 sales) were not the same as the states with the lowest profits.

The visualization shows a clustered column chart comparing total sales and profit for each region:

• The West Region had the highest sales and profits.

• The Central Region had the lowest sales and profits.

CONCLUSION

• Standard Class is the most used shipping method, showing that most customers prefer cost-effective delivery.

• Technology products have the highest sales but lower profits, meaning they may have high costs.

• Office Supplies give the highest profit despite not having the highest sales.

• Some products, like Bookcases and Tables, made losses, which means their pricing or discount strategies may need to be reviewed.

• California and New York perform well in both sales and profit, while Texas and Ohio struggle with losses.

Suggestions for Further Analysis

• Profitability Factors: Investigate why some subcategories, like Bookcases and Tables, lose money despite high sales in other areas.

• Impact of Prices: Check if high prices are affecting profit margins in the Technology category.

• Shipping Costs and Profitability: See if shipping costs play a role in profit margins.

• Regional Market Trends: Find out why California and New York perform well while Texas and Ohio struggle.

References

• HNG Internship: Learn more about the internship program here.

•Dataset Source: Kaggle