This project analyzes a movie rental business's database to provide actionable insights for improving operations, marketing strategies, and inventory management. The dataset is hosted in the MAVENMOVIES database, and SQL was extensively used for exploratory data analysis (EDA), schema understanding, and answering business-critical ad-hoc queries.
Customer Insights: Identify customer details (names, emails) for targeted marketing campaigns. Analyze customer rental patterns to improve customer engagement.
Explore the rental inventory and classify movies based on rental rates and availability. Provide recommendations for expanding the movie collection based on popularity and rental rates. Revenue Optimization:
Analyze rental rates to identify trends and the profitability of various pricing categories. Determine the most rented movie categories and ratings to maximize revenue.
Help track and manage movie inventory effectively. Highlight gaps in the inventory and optimize stock levels.
Q15. Now, could you please write a query to pull all payments from those specific customers, along with payments over $5, from any customer?
Q16. We need to understand the special features in our films. Could you pull a list of films which include a Behind the Scenes special feature?
Q20. I’m wondering if we charge more for a rental when the replacement cost is higher. Can you help me pull a count of films, along with the average, min, and max rental rate, grouped by replacement cost?
Q21. “I’d like to talk to customers that have not rented much from us to understand if there is something we could be doing better. Could you pull a list of customer_ids with less than 15 rentals all-time?”
Q22. “I’d like to see if our longest films also tend to be our most expensive rentals. Could you pull me a list of all film titles along with their lengths and rental rates, and sort them from longest to shortest?”
Q25. “I’d like to know which store each customer goes to, and whether or not they are active. Could you pull a list of first and last names of all customers, and label them as either ‘store 1 active’, ‘store 1 inactive’, ‘store 2 active’, or ‘store 2 inactive’?”
Q26. “Can you pull for me a list of each film we have in inventory? I would like to see the film’s title, description, and the store_id value associated with each item, and its inventory_id. Thanks!”
Q28. “One of our investors is interested in the films we carry and how many actors are listed for each film title. Can you pull a list of all titles, and figure out how many actors are associated with each title?”
Q29. “Customers often ask which films their favorite actors appear in. It would be great to have a list of all actors, with each title that they appear in. Could you please pull that for me?”
Q30. “The Manager from Store 2 is working on expanding our film collection there. Could you pull a list of distinct titles and their descriptions, currently available in inventory at store 2?”
Q31. “We will be hosting a meeting with all of our staff and advisors soon. Could you pull one list of all staff and advisor names, and include a column noting whether they are a staff member or advisor? Thanks!”
