Amazon is an American technology multinational specializing in e-commerce, cloud computing, digital streaming and artificial intelligence. Founded in 1994 by Jeff Bezos, Amazon is one of the largest online retailers in the world, offering a range of products ranging from books and music to furniture and clothing. It also offers services like Amazon Prime, which offers unlimited streaming access to movies and TV shows, as well as free two-day shipping on millions of items.
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I aimed to generate insights about Amazon sales, including:
- The difference between order date and ship date
- Year-over-Year (YoY) and Month-over-Month (MoM) comparisons
- Analysis of item types, order priority, and regional sales
I obtained the database from the Kaggle website at:
During this project, I focused on improving my skills in Power BI by implementing the following functionalities:
- Time Intelligence for advanced date-based analysis
- Visually appealing cards to highlight key metrics
- A dashboard template designed using Figma
- Variance graphs to show differences and trends
- Metrics neatly organized into folders for better structure
The database does not contain information about CLOUD COMPUTING or AWS SERVICES, which represent a significant portion of Amazon's profits.
Additionally, some regions lack data for major countries. For example, the North America region only includes data for Mexico.
Therefore, this dataset does not provide a complete view of Amazon's global sales data from 2010 to 2017.
Differents insights can be retrived from the dashboard, however, i will quote some results obtained from the analysis.
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This analysis highlights the Sub-Saharan Africa region as the top performer in sales volume, with a strong preference for suits, irrespective of the sales channel (online or offline).
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The significant delay between order and ship dates in November and January suggests increased sales activity, possibly linked to seasonal demand such as holidays and promotions.
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This analysis reveals that cosmetics and clothes dominate sales, whereas meat and snacks contribute the least to overall sales, indicating a disparity in consumer demand across product categories.
You can do your analysis just clicking the link below: