Product Placement Analysis

This project explores the effectiveness of product placement in movies and TV shows using statistical methods and data visualization techniques.

I analyzed data from over 500 movies released between 2010-2022 to identify patterns in product placement effectiveness based on factors like screen time, context, and audience demographics.

Key Findings

• Products with 3-5 seconds of clear screen time showed the highest recall rates
• Contextual relevance was more important than screen time for positive brand association
• Subtle placements in high-emotion scenes created stronger brand connections than obvious placements

Methodology

I used Python for data analysis, with pandas for data manipulation and matplotlib/seaborn for visualization. Statistical significance was tested using chi-square tests and ANOVA.