Complex DB Reports

1. Top Selling Products and Restock Plan

This report is meant to look at the best-selling items over the last year to figure out exactly when they will sell out and how products should be repurchased. By calculating how many copies of a specific vinyl, CD, or cassette are sold every day and comparing that speed to what is left in stock, the query will find items that will run out of stock in less than a month. It then automatically calculates the perfect order amount to keep the store supplied for the next three months without overspending, ensuring top money-makers are never missing from the shelves.

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Relational Algebra

RELATIONAL ALGEBRA

2. Slow Moving Products

This report is meant to look at items that are completely stuck in inventory. The query scans the database to find products that have either never been bought or have had zero sales for more than six months, while also checking if customers are still adding them to their wishlists or ignoring them completely. It multiplies the current unsold stock by the item's original price to show managers exactly how much cash is frozen in dead inventory, making it easy to see which releases need discounts.

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Relational Algebra

RELATIONAL ALGEBRA

3. Impact of Admin Discounts on Sales Numbers

This report checks if discounts and promotions created by product managers actually bring in more profit or just lose money. It monitors sales numbers 30 days before and 30 days after an administrator modifies a product to lower its price. By comparing the drop in profit margin per item against the increase in total orders, the query would prove whether the discount caused a big enough wave of new buyers to make the promotion successful or if it just hurt the overall revenue.

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Relational Algebra

RELATIONAL ALGEBRA

4. Customer Habits and Points Spending

This report tracks how registered users behave over a period of two years by looking at how they earn and spend their rewards points. It groups buyers based on the year they created their accounts and measures their total shopping history alongside their points balance to see who is saving up thousands of points and who is actively spending them. This helps find high-value users who hold a large number of unspent points, which represents a future discount cost and can indicate if customers stop buying completely once their free rewards are used up.

SQL

SQL CODE

Relational Algebra

RELATIONAL ALGEBRA