Metadata intelligence for publishers
Books succeed when readers can find them.
Kadaxis is an independent metadata intelligence firm. For fifteen years we have worked with the world's largest publishing houses, and the most distinctive independent presses, on one discipline: the keyword and metadata work that determines whether a book is found on Amazon.
Begin a conversation →- Fifteen years.
- More than thirty-five publishing houses.
- Hundreds of thousands of titles.
Approach
A single discipline, refined.
Readers search for books in language that is rarely the same as the language in publisher catalogs. For fifteen years we have specialized in closing that gap, combining retailer search data, editorial judgment, and proprietary research methods developed across hundreds of thousands of titles. Each engagement is list-specific. Nothing we deliver is templated.
Read about our methodology →Notes from the Field
Occasional writing on metadata, discoverability, and the economics of publishing online. Sent a few times a year. Read on site, or have it sent to you.
METHOD Keyword portfolio ordering
A keyword portfolio is an ordered coverage system. The first keyword is measured against the book's visible metadata. The second, against that metadata plus the first keyword. Every position is judged by what it adds over everything above it, so a lower-scoring keyword can outrank a higher-scoring one when it reaches readers the rest of the portfolio has missed.
METHOD Keyword portfolio allocation
Most keyword strategy is treated as a ranking problem: generate candidates, score them, keep the top N, ship the list. That works at the individual-keyword level but fails at the portfolio level. A keyword buys access to a search doorway: a cluster of reader intent, search results, substitute books, and commercial expectations. Portfolio strategy is deciding which doorways deserve scarce metadata space, which overlap, and which introduce risk.
RESEARCH Amazon's warm-start research makes launch metadata a ranking decision
Amazon has described a production search-ranking system that uses substitute products to give new products a warm start before they have sales history of their own. For publishers, the implication is direct: cover, title, description, comp-title language, and category placement help determine the behavioral neighborhood a new book enters at launch. That neighborhood can shape early visibility, sales velocity, and the book's ability to build its own ranking signals.
METHOD The discoverability funnel
Amazon accounts for roughly half of US print book sales and two-thirds of US ebook sales, and most US consumers now start their online product searches on Amazon rather than Google. The path from a book's metadata to a reader's purchase runs through a funnel with four sequential filters. Most candidate books drop out at one of them.
Kadaxis was founded in 2010 and named BookTech Company of the Year at the FutureBook Awards in 2016. We contributed to the Book Industry Study Group's 2018 keywords best-practices standard. After several years operating with a smaller list, we are taking on new publishing partners again, with refined methodology and proprietary tooling developed across hundreds of thousands of titles.
If you are a publisher considering metadata work, we would like to hear from you.
Contact Kadaxis →