The goal of this project is to analysis the sales budget data, extract necessary information about Products and Customers based on a combination of features and make a dashboard to review the performance of the company.

• 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

Retail & Sales

• 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