This data analysisis project aims to provide insigths into the sales performance of e-commerce company over the past year. By analyzing various aspects of thhe sales data, we seek to identify trends, make data-driven recommendations, and gain a deeper understanding of the company's performance.
Sales Data: The primary dataset used for this analysis is the sales_data.csv file, containing detailed information about each sale made by the company.
- Excel
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- SQL Server
- Data analysis
- PowerBI
- Creating reports
In the initial data preparation phase, we performed the following tasks:
- Data loading and inspection.
- Handling missing values.
- Data cleaning and formatting.
EDA involved exploring the sales data to answer key questions, such as:
- What is the overall sales trend?
- Which products are top sellers?
- What are the peak sales periods?
Include some interesting code/features worked with.
SELECT * FROM TABLE1
WHERE cond = 2;The analysis results are summarized as follows:
- The company's sales have been steady over the past year, with a noticeable peak during the holiday season.
- Product Category A is the best-performing category in terms of sales and revenue.
- Customer segments with high lifetime value should be targeted for marketing efforts.
Based on the analysis, we recommend the following actions:
- Invest in marketing and promotions during peak sales seasons to maximize revenue.
- Focus on expanding and promoting products in Category A.
- Implement a customer segmentation strategy to target high-LTV customers effectively.
I had to remove all zero values from the budget and revenue columns because they would have affected the accuracy of my conclusions from the analysis. There are still few outliers even after the omissions but even then we can still see that there is a positive correlation between both budget and number of votes with revenue.
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- SQL for Bussines by Werty.
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