The project's objectives are to analyze sales budget data, gather essential product and customer information based on a variety of factors, and create a dashboard for evaluating the business's success.
Do ETL : Extract-Transform-Load dataset Perform EDA through python Extract various information such as Sales, budget, variance Extract necessary information about Products and Customers Make necessary dashboard Find key metrics and factors and show the meaningful relationships between attributes
Business Intelligence
Python, DAX, and Power Query M
Jupyter Notebook, MS-Excel, Power-BI
- A sizable portion of the clientele is made up of people between the ages of 40 and 59
- The year 2016 saw an exponential surge in sales
- High quantity of products is ordered from Australia and United States
- Major Profit is contributed by the Bike Category
- The average order has a gap of 7 days between the day the order is ready for export from the factory and the date it was shipped
- Maximum profit earned in the months of June, November, and December
- High sales orders are seen on Wednesday and Saturday, when compared to other weekdays
- There is a high negative correlation between Price and number of Quantity ordered
- The average amount spent by men without permanent addresses is low, whilst the average amount spent by women without permanent addresses is higher
- Age range of 40-49 and 50-59 is shows high demand compared to other age group
- High salary range leads to increase in revenue
- Customers with a high school diploma and modest annual income buy more products than people with bachelor's degrees
- According to the customer segmentation described above, approximately 15% of our clients are high value clients, whereas the majority of our clientele are low value and lost clients
- Client retention in 2014 was subpar
- 2016 brought about a slight improvement in retention