Genre revenue mix
Which movie categories contribute the largest shares of recorded revenue?
Top five genres are shown individually; all remaining genres are grouped as Other.
Exploring movie rental demand, customer value and global market opportunities through interactive business intelligence.
A snapshot of recorded rental transactions, customer reach and revenue in the cleaned dataset.
Which movie categories contribute the largest shares of recorded revenue?
Top five genres are shown individually; all remaining genres are grouped as Other.
Adapted from the original Rockbuster Stealth business analysis report.
Rockbuster Stealth is exploring a move from its traditional movie rental business toward online delivery. To guide strategy, its management needs a clearer view of rental revenue, customers and geographic demand.
Identify commercially strong films and genres, understand rental behavior, locate customers and high-spending markets, and support a focused online launch strategy.
Which films, customers and markets should Rockbuster prioritize when developing its online rental business?
Explore how movie titles and genres perform on rental volume, recorded revenue and average payment.
Choose a genre, metric and ranking direction to examine high- and low-performing titles.
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Compare all 16 genres by total recorded revenue, rental count or average payment.
Compare the film’s configured rental allowance with the actual time between rental and return. These are different measures.
Distribution of observed rentals and their recorded payment value across duration and price groups.
Duration groups are based on valid elapsed times. Missing return dates cannot contribute an actual elapsed duration.
The film-level rental duration describes the contractual rental allowance. The overview shows the average across distinct rented films, while the grouped chart describes rental transactions.
Calculated as the difference between return and rental timestamps. This describes historical customer behavior and should not be interpreted as a contractual allowance.
Switch between countries and cities to examine geographic revenue, demand and customer distribution. Customer totals are unique within each group.
Compare markets and identify opportunities for further evaluation.
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Location is based on customer addresses, not the physical location of each rental transaction.
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Compare historical spending and rental frequency to understand the customer groups worth further investigation.
Each dot represents one customer. Explore how total rentals relate to recorded lifetime spending; hover for the exact values.
Customer IDs are anonymous identifiers for this report; no names, addresses or emails are shown.
Lifetime value here is cumulative recorded spending over the available rental history, not predicted future customer value.
Explore available rental months, film ratings and film lengths with multiple chart metrics.
The available dataset covers only the observed monthly periods; do not extrapolate this chart to unobserved years.
Compare rental demand across film classifications and runtime categories.
Recommendations derived from the original report’s business questions, to be interpreted alongside the live exploratory metrics above.
Use film revenue and rental demand together when considering online catalog selection. High rental counts and high revenue are not interchangeable, and the sample period limits generalization.
Assess countries and cities using recorded revenue, unique customer counts and rental activity. Investigate delivery feasibility, licensing, and market conditions before selecting launch regions.
Study high-frequency and high-spending customers, then test retention offers or personalized recommendations rather than assuming historical spend guarantees future demand.
Extend the time series, incorporate online delivery costs and licensing fees, and distinguish payment amounts from profit. The present dataset does not establish future profitability.
Rockbuster’s online strategy should balance popular content, meaningful customer segments, and geographic demand—using recorded performance as evidence rather than a guarantee of future results.
Method: descriptive aggregations of a cleaned rental-level dataset in Python/pandas; browser charts consume precalculated JSON. No predictive financial model is claimed.