Superstore Sales Dataset

Dataset Details

• Row ID: A unique identifier for each record in the dataset.
• Order ID: A distinct identifier for each purchase transaction.
• Order Date: The date when the order was placed.
• Ship Date: The date when the product was shipped to the customer.
• Ship Mode: The method used to deliver the order (e.g., Standard, Express).
• Customer ID: A unique identifier for each customer.
• Customer Name: The name of the customer who made the purchase.
• Segment: The market segment to which the customer belongs (e.g., Consumer, Corporate).
• Country: The country where the customer is located.
• City: The city where the customer resides.
• State: The state or province of the customer’s residence.
• Postal Code: The postal code of the customer’s address.
• Region: The broader geographical region associated with the customer.
• Product ID: A unique identifier for each product.
• Category: The primary category under which the product is classified.
• Sub-Category: The specific sub-category of the product.
• Product Name: The name of the product purchased.
• Sales: The total sales revenue generated from the product.
• Quantity: The number of units of the product sold.
• Discount: The discount applied to the product during the sale.
• Profit: The profit or loss incurred from the sale of the product.

Additional Insights

• Profit Margins: Analyze the profit margins across different products, categories, and regions to identify the most and least profitable segments.
• Sales Trends: Examine sales trends over time to determine peak sales periods and seasonal variations.
• Customer Segmentation: Explore how different customer segments contribute to overall sales and profitability.
• Regional Analysis: Assess which regions perform better in terms of sales and profit, helping to focus marketing efforts and resource allocation.
• Predictive Modeling: Build regression models to forecast future sales or profit trends based on historical data.