Welcome to Super Store Data Analysis! Today we will be exploring the importance of analyzing data for a super store.In today's fast-paced world, it's more important than ever to make informed decisions based on data. By analyzing sales trends, customer demographics, and inventory management, businesses can identify areas for improvement and capitalize on new opportunities. So buckle up and get ready to learn how data analysis can take your super store to the next level!
- Objective: To analyze Superstore sales data and derive actionable insights.
- Tools: Python, Pandas, NumPy, Matplotlib, Jupyter Notebook.
- Dataset: The dataset used for this analysis contains historical sales data from Superstore.
- Python Programming Skills: A fundamental understanding of Python programming is essential. You should be comfortable with basic data types, control structures (such as loops and conditional statements), functions, and libraries.
- Data Analysis Libraries: Install the necessary Python libraries for data analysis, including:
- NumPy: For numerical computing and working with arrays.
- Pandas: For data manipulation and analysis.
- Matplotlib: For creating static, animated, and interactive visualizations.
- Data: You'll need the data you want to analyze. Data can come from various sources, such as CSV files, Excel spreadsheets, SQL databases, web scraping, or APIs. Ensure you have access to the data you need for your analysis.
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Analysis based on segment
By looking at the pie chart, we can understand that our customer base is primarily made up of Consumer and Corporate segments, which make up over 80% of our customers.
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Analysis based on region
From the above graph, it is clear that most of our customers are from the West and the East region, therefore we can send promotional emails to these regions.
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Analysis based on category
The above graph suggests that even though the sale in each category is almost equal, most profit was earned in Office Supplies and Technology. Therefore we can introduce new and expensive products in these segments to increase the revenue. Furniture made the least profit.
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Analysis based on sub-category
By looking at the graph, it is quite obvious that copiers, accessories, and phones earned a huge profit. New and expensive phones could be showcased for sale. Tables, supplies bookcases incurred loss, I suggest dropping these items from the catalog. The discount given to Binders was huge, it should be reduced.
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Analysis based on states
The graph shows that a huge quantity of products were sold in the states of California and New York. Hence these states consist of potential customers to whom we can sell more products.
The graph shows that discount and profit were inversely proportional to each other. More the discounts more the losses. It clearly shows that states with huge losses also have huge discounts. Except for the states of California, New York, and a few others. These states earned a huge profit. The reason may be discounts or the people. It is predicted that more products could be sold in these states. Texas and Pennsylvania have incurred severe losses, therefore the discounts should be removed from these states and other loss-incurred states as well.
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Analysis on ship mode
On analyzing the graph, we can infer that the most preferred Ship Mode is ‘Standard class’. The least preferred is ‘Same Day’.