Analysis of sales data from a furniture store using SQLite

I utilized Anaconda Prompt to create a .db file based on the database information on the furniturestore_sql.txt file. I then connected Juptyer Notebook to the newly created furniture.db database file. I created a table titled suppliers on Jupyter Notebook of the furniture suppliers and the countries in which they are based. The id column in the suppliers table corresponds to the supplier_id in the sales table.

In terms of data analysis, I generated the following customized reports:

• Total sales of products organized by highest to lowest.
• Total purchases by customers and limited to customers who purchased more than $5,000 worth of furniture.
• Data from the sales and suppliers tables joined and limited to more than $1,500 in sales.
• Categorization of customer value by total purchases wherein those with more than $5,000 are marked 'High,' those with more than $2,000 are marked 'Medium,' and all other customers are marked 'Standard.'
• Average purchases made by customers organized by their state or province and limited to states with customers who average more than $2,500.
• Total sales by furniture supplier

Impact and Applications

• By analyzing the data, we can leverage these insights to inform the strategic decisions of the furniture store. For example, we can better anticipate a greater need for inventory of certain types of furniture, allocate greater marketing funds to states with moderate or low average sales, and tailor digital marketing and discount offers based on the customer segmentation.