01 The Problem
The rental company had years of transaction data in raw tables but no way to see who its best customers were, which movies made money, or which stores underperformed.
02 The Approach
Focus First
Picked one theme — customer behavior and revenue — and built everything around it.
Data Joining
Combined 7 tables into one warehouse so every rental carried full context.
Running Totals
Added window functions for total spend and revenue per customer, film, and store.
Query Writing
Wrote 8 SQL queries answering specific business questions using joins, grouping, and subqueries.
Dashboarding
Connected the warehouse to Tableau for at-a-glance dashboards.
03 The Result
A small group of high spenders drives a disproportionate share of revenue
Best customers stood out once spend was tracked across the warehouse.
“Mid Spenders” are the biggest group and drive the most total revenue
The volume segment — not only VIPs — carried the business.
Sci-Fi, Sports, and Animation lead in revenue
Clear winners for inventory and promotion decisions.
7–8 day rentals earn the most per rental
Longer holds outperformed short turns on revenue per rental.
Tuesday afternoons are the busiest
A concrete window for staffing and ops planning.
Both stores earn ~$33.7K
Revenue looks even — cost differences may still affect profitability.
04 Business Impact
Loyalty Program
Target Mid Spenders directly.
Inventory Shift
Stock more high-performing categories.
Pricing Change
Encourage longer rentals.
Staffing Fix
Schedule around the Tuesday peak.
05 What I Learned
The hard part wasn’t the SQL — it was picking one clear business question and sticking to it.