Objective: To analyze the sales data of a retail company in the United States and provide insights and recommendations to optimize inventory levels, and reduce inventory costs, sales performance, customer behaviour, and product trends.
The project includes data cleaning, data visualization, and data analysis using tools like Excel and Power Bi.
Dataset: The dataset was obtained from Kaggle.
The process involved in creating the US Superstore sales performance project:
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Data Collection: Collecting the sales data from publicly available source Kaggle.
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Data Cleaning: Cleaning and preprocessing the data to remove any duplicates, missing values, or inconsistencies. This involves techniques such as data profiling, data quality checks, and data transformation.
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Data Visualization: Creating visualizations of the data to explore patterns and relationships. This involves creating charts, graphs, and dashboards using Power Bi.
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Data Analysis: Analyze the data to gain insights into sales performance, customer behaviour, and product trends.
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Insights are drawn from the US Superstore sales performance project:
1. Total quantity ordered is 9800 with 4922 of total orders and $2.26M of sales
2. With 50.76% of overall sales placed by Consumers segmented by customers
3. Consumer prefered Standard Class of shipping mode
4. The top category is Office Supplies with the highest number of products sold from the Binders sub-category
5. The highest sales were made in the month of November with an overall of $350.16K of sales
6. California is the top-selling state with Los Angeles being the top city
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Conclusion:
By looking at the overall numbers and trends, the retail company can optimize inventory levels, reduce inventory costs, and improve supply chain efficiency, which will ultimately lead to improved customer satisfaction and increased profitability.