Goodreads Reader Behavior Analysis

Project Overview

This project analyzes Goodreads platform data to understand how readers engage with books, what factors influence book popularity, how genres perform, and how author reputation affects audience behavior.

The objective was to uncover patterns in book performance, reader reviews, rating behavior, and genre preferences to identify what drives engagement in digital reading communities.

Dataset Overview

• 12,540 Books
• 18,800 Users
• 1.14 Million Reviews
• Book Metadata
• User Review Data
• Ratings Information
• Genre Categories
• Author Data

Data Cleaning & Transformation

• Performed data cleaning in Power Query.
• Corrected date formats.
• Removed duplicate records.
• Replaced empty values with Null.
• Filtered unnecessary rows.
• Created additional supporting tables.
• Split columns for improved analysis.
• Built relational data model.

Key Metrics

• Total Books → 12.54K
• Total Users → 18.80K
• Total Reviews → 1.14M
• Average Book Rating
• Review Volume
• Author Rating Performance

Key Insights

• The Hunger Games was the most reviewed book with 157K reviews.
• Fantasy was the most common genre and had the highest average page count.
• Children genre received the highest volume of ratings.
• Fiction books received the highest number of 5-star ratings.
• J.K Rowling had the highest number of ratings overall.
• Stephen King emerged as the most prolific author.
• Angie Thomas recorded the highest average rating performance.
• 20 hidden gem books were identified with strong ratings but low exposure.

Reader Behavior Analysis

• Books with ratings near 4.0 generated the highest review volume.
• Extremely low-rated and extremely high-rated books generated fewer reviews.
• Widely read books tend to maintain balanced ratings.
• Less popular books showed more extreme positive or negative ratings.

Business Value

Book marketplaces can use this analysis to improve recommendation systems, identify under-promoted books, understand genre demand shifts, and optimize marketing strategies around high-engagement titles.

This analysis demonstrates how customer behavior data can drive product discovery and improve digital platform engagement.

Recommendations

• Promote hidden gem books to improve discovery.
• Expand non-fiction while maintaining strong fantasy catalog.
• Continue leveraging bestselling franchises.
• Promote books around 4.0 ratings to maximize community engagement.

Conclusion

The analysis revealed clear relationships between reader behavior, book ratings, review volume, genre popularity, and author reputation. Reader engagement is strongest around widely relatable books rather than rating extremes.