• Identify trends on genre level and compare different levels of genre categories directly with each other
• Deep-dive into any genre and identify trends on title or author level including big hits and long-tail candidates
• Compare international data from the U.S., and Germany with each other to spot upcoming trends early
• Follow trends over time and track their development to ensure their robustness and anticipate their trajectory
• Cater to your own intuition and experience by using 1-sentence summaries that allow recognizing common themes

We constantly extend our data scope. Depending on the data type, the scope may vary marginally. Currently, we cover the following data scope: 

• Countries: Germany, United States
• Titles: All titles generally available
• Authors: All authors generally available
• Genres: Crime, Thriller, Mystery, Children's (only story-telling sub-genres), Romance
• Genres hierarchy: We provide a three-level genre hierarchy: 
• Level 1, e.g., "Thrillers"
• Level 2, e.g., "Thrillers - Supernatural"
• Level 3, e.g., "Thrillers - Supernatural - Vampires"
• Data types: Publisher, author, author gender, title, topic, various success metrics, ratings, reviews, price, product variants, popularity (custom use cases may include additional data types)
• Sources: We collect data from dozens of sources including but not limited to major online shops, review communities, agent communities, individual publishers, distributors, agent and author pages, social media platforms, text aggregators, and industry associations. 
• Updates: Data is updated once per month

Our data runs through an extensive quality control process. We capture all potential issues in an automated and reliable way. Issues addressed are constantly being updated, since we continuously increase our data quality further, dive deeper into the data and explore more use cases.

Common issues include misspellings (e.g., Stefen King), different ways of writing names (e.g., H. P. Lovecraft vs. HP Lovecraft), data gaps in our input sources (e.g., no price available), wrong data in our input sources (e.g., miss categorized: "The Gift" in "Gift books" instead of "Thriller"), and many more.  

• Updates are generally done on a monthly basis
• Updates imply a data pull from all available sources
• Updated data doesn't overwrite previous data but is appended to it, building a transparent timeline and adding value to the data

We define "success" as a multi-layered metric that is user-centric and consists of two main aspects:

• It includes a reader perception component that measures how well a book is received by the readership based on ratings and reviews
• It includes an approximation of actual sales based on price, number and value of ratings, reviews, bestseller list entries, and more