📘 Business Task

This case study explores the top 50 bestselling books on Amazon between 2009 and 2019. The goal is to identify trends and insights related to genre, pricing, ratings, and author recurrence. These insights can help publishers and retailers understand what drives book sales and make data-driven decisions in publishing and marketing strategies.

📂 Data Source

Dataset: Amazon Top 50 Bestsellers (2009–2019)
File: bestsellers with categories.csv
Tools: R, tidyverse, ggplot2, janitor

🧹 Data Cleaning

library(tidyverse)
library(janitor)

books <- read_csv("bestsellers with categories.csv") %>%
  clean_names() %>%
  distinct() %>%
  mutate(
    year = as.integer(year),
    genre = as.factor(genre),
    author = str_trim(author),
    name = str_trim(name)
  )

🔍 Analysis & Visualizations

🖊️ Top 10 Authors by Bestseller Appearances

books %>%
  count(author, sort = TRUE) %>%
  slice_max(n, n = 10) %>%
  ggplot(aes(x = reorder(author, n), y = n)) +
  geom_col(fill = "#6A0572") +
  coord_flip() +
  labs(title = "Top 10 Authors by Number of Bestsellers (2009–2019)",
       x = "Author", y = "Number of Books") +
  theme_minimal()

📌 Insight: Series authors like Jeff Kinney and Suzanne Collins appear most often, highlighting the commercial power of recurring characters and established franchises.

📚 Fiction vs Nonfiction Distribution

books %>%
  count(genre) %>%
  ggplot(aes(x = genre, y = n, fill = genre)) +
  geom_col(width = 0.6) +
  labs(title = "Fiction vs Nonfiction Bestsellers",
       x = "Genre", y = "Count") +
  scale_fill_manual(values = c("#FF6F61", "#3E92CC")) +
  theme_minimal()

📌 Insight: Fiction dominates the bestsellers list, but Non Fiction grows steadily post-2015, indicating a shift in reader interest toward real-world topics.

💵 Price vs User Rating

ggplot(books, aes(x = price, y = user_rating)) +
  geom_point(alpha = 0.6, color = "#2E4057") +
  geom_smooth(method = "lm", se = FALSE, color = "#FF5733") +
  labs(title = "Book Price vs User Rating",
       x = "Price (USD)", y = "User Rating") +
  theme_minimal()

📌 Insight: No strong correlation found — readers rate books highly regardless of price, suggesting content quality drives satisfaction more than pricing.

💬 Reviews vs Ratings

ggplot(books, aes(x = reviews, y = user_rating)) +
  geom_point(alpha = 0.5, color = "#1B998B") +
  geom_smooth(method = "lm", se = FALSE, color = "#EDC948") +
  labs(title = "Number of Reviews vs User Rating",
       x = "Number of Reviews", y = "User Rating") +
  theme_minimal()

📌 Insight: Popular books with high review counts tend to maintain high ratings, suggesting that visibility and strong content are reinforcing factors.

📆 Bestseller Trends by Genre and Year

books %>%
  count(year, genre) %>%
  ggplot(aes(x = year, y = n, fill = genre)) +
  geom_col(position = "dodge") +
  labs(title = "Bestseller Genre Trend Over Time",
       x = "Year", y = "Number of Bestsellers") +
  scale_fill_manual(values = c("#E69F00", "#56B4E9")) +
  theme_minimal()

📌 Insight: Fiction maintained a lead throughout the decade, while Nonfiction showed a noticeable rise around 2016–2019, aligning with cultural and political moments.

📌 Key Takeaways

Author recurrence is a major factor in bestseller status.
Fiction leads the market but Nonfiction is gaining ground.
Ratings are consistently high regardless of price.
Books with more reviews tend to have high visibility and success.

🧠 Recommendations

Authors should consider developing series or thematic continuity.
Publishers can invest in boosting review generation early in book launch.
Offer both Fiction and Nonfiction titles in top seasonal slots.
Marketing efforts should focus on boosting visibility to capitalize on strong content.

✅ Conclusion

This analysis uncovers useful business and behavioral patterns in reader preferences. Consistent authorship, quality storytelling, and strong visibility all contribute to making a book a long-term bestseller.

Amazon Top 50 Bestselling Books (2009–2019)

Sreerup Banerjee