SQL_CASE_STUDIES

ID	CASE-STUDY	DESCRIPTION
1	DANNY'S DINER	- Datasets used: sales , members , menu - Functions used: SELECT, SUM, AND, WHERE, GROUP BY, ORDER BY, JOIN, DISTINCT, COUNT, CASE, LIMIT, CTE. - Tools used: MySQL WorkBench 8.0 - Key Takeaway: Developed SQL solutions in order to examine each customer's total spending, frequency of visits, favorite menu items, and pre- and post-membership buying trends. Generated actionable insights for customer engagement strategies by analyzing customer activity during key phases and calculated reward points based on spending rules.
2	BURGER BASH	- Datasets used: runner_orders , burger_orders , burger_names , customer_orders. - Functions used: SELECT, WITH, GROUP BY, JOIN, DISTINCT, CTE, AS, CASE, SUM, EXTRACT - Tools used: MySQL WorkBench 8.0 - Key Takeaway: Determined runner performance, burger popularity, and customer ordering patterns by analyzing order and delivery data. calculated important metrics to support logistics and customer service enhancements, such as total and unique orders, frequency of customization, and delivery distances.
3	Pizza Runner	- Datasets used: runners, customer_orders , runner_orders , pizza_names , pizza_recipes , pizza_toppings - Functions used: SELECT, GROUPBY, WHERE, CASE, SUMIF, SUM, AVG, ROUND, TIMESTAMPDIFF, MONTH, YEAR, EXTRACT, EXCEPT, NOT IN, IN, CTEs - Tools used: MySQL WorkBench 8.0 - Key Takeaway: Analyzed pizza order, delivery, and customer data to measure key metrics, evaluated runner performance, and optimized ingredient management. Developed SQL queries to address pricing, ratings, and operational challenges, delivering actionable insights which support data-driven business improvements.
4	Data Bank	- Datasets used: regions , customer_transactions , customer_nodes. - Functions used: SELECT, CTES, RANKING, CASE, DATE_FORMAT, MONTH, YEAR, TO_DAYS, EXTRACT, LAG, LEAD, MIN, MAX, SUM, MIN, DATEDIFF - Tools used: MySQL WorkBench 8.0 - Key Takeaway:To understand customer distribution, node reassignment behavior, and financial activity patterns, analyzed customer transaction and node allocation data from different regions. Calculated metrics such as transaction summaries, reallocation times, and monthly balances. Implemented a daily interest calculation method to estimate monthly data growth and created SQL solutions for running balance and monthly balance computations. These insights provided a foundation which supports forecasting, infrastructure planning, and optimization of customer data provisioning.