Once it is clear that keywords are worth a second look, the next question is practical: where do better keywords actually come from? The honest answer is that they come from paying close attention to how real readers talk, not from a clever trick or a tool promising guaranteed rankings. This guide works through the sources worth using, and a simple worksheet for turning research into a genuinely relevant shortlist.
This builds directly on [how do Amazon keywords help book sales?](/resources/book-sales-growth/how-do-amazon-keywords-help-book-sales), which explains why relevance matters more than volume. Here, the focus shifts from theory to the actual work of finding better terms.
Start with reader language
The most useful keywords tend to come from the words readers themselves would use, not the words an author might use to describe their own work in more literary or formal terms. Reading reviews of comparable books, browsing relevant reader communities, and noticing recurring phrases are all reasonable, low-cost ways to gather this language.
For fiction, this often means genre and subgenre terms, mood or setting descriptors, and comparable-author phrasing readers actually use when recommending books to each other, rather than formal literary classifications.
For non-fiction, think about the problem, not just the topic
Non-fiction readers often search by the problem they are trying to solve rather than the abstract subject area. A book about time management might be more usefully found through phrases describing the reader’s frustration ("stop procrastinating", "get more done in less time") than through the topic label alone.
Example
Imagine a book on managing a small business’s finances. "Small business finance" is accurate but broad. "Cash flow problems for small business owners" more precisely matches what a worried reader might actually type, and signals relevance to that specific concern.
Themes and comparable search behaviour
Recurring themes in the book, such as grief, second chances, or a particular historical period, can be useful keyword sources when phrased the way readers search rather than the way a literary blurb might describe them. Looking at how comparable, already-successful books in the same space are categorised and described can offer useful pattern evidence, without copying their listings.
Amazon search suggestions as directional evidence
Typing relevant phrases into Amazon’s own search bar and noting the autocomplete suggestions can offer a reasonable sense of common phrasing. This is directional evidence of how people search, not proof of volume, competitiveness or ranking potential, and should be treated accordingly.
Reviewing existing metadata honestly
Before researching new terms, it is worth reading the book’s current keywords, categories and description with fresh eyes, as if seeing them for the first time. Common findings include keywords that describe the author’s hopes for the book rather than its actual content, or terms carried over from an early draft of the metadata that no longer fit the finished book.
What to avoid
Do
- Use language readers themselves would search for, based on genuine research
- Match keywords to what the book actually is and does, not an aspiration
- Revisit keywords deliberately, after a genuine reason to reconsider them
Avoid
- Use another author’s or brand’s trademarked name as a keyword
- Stuff irrelevant high-traffic words in hoping for extra reach
- Copy a competitor’s exact keyword set without checking genuine fit
From research to a shortlist
Once research has produced a reasonable pool of candidate phrases, the useful next step is comparing them against the book itself, honestly, rather than including every plausible-sounding term. A shortlist of genuinely accurate, specific phrases tends to do more useful work than a longer list padded with weaker fits.
Private worksheet
Book keyword worksheet
A private working space for turning reader research into a genuinely relevant keyword shortlist. There is no scoring here; the aim is honest fit, not a number.
Your answers stay in this browser tab only. Nothing is scored, sent or saved to an account.
A note on platform change
Common mistakes
- Guessing rather than researching. Keywords chosen from assumption rather than genuine reader language tend to miss how people actually search.
- Describing the topic instead of the problem. For non-fiction especially, the problem a reader is trying to solve is often more searchable than the abstract subject.
- Treating autocomplete as proof of volume. Search suggestions are directional evidence of phrasing, not confirmation of how many people search a term.
- Including trademarked or competitor terms. This can breach KDP content guidelines and rarely brings genuinely interested readers regardless.
- Padding the list with weak fits. A longer list of loosely relevant terms is generally less useful than a shorter, genuinely accurate one.
Frequently asked questions
Where should I look for keyword ideas first?
Reader language is the most useful starting point: reviews of comparable books, reader communities, and how readers describe the problem or theme in their own words.
Is it worth using a third-party keyword research tool?
Such tools can offer directional ideas, but their volume figures are estimates, not official Amazon data. Treat them as one input among several, not a definitive answer.
Can I use a bestselling author’s name as a keyword?
No. Using another author’s or brand’s name as a keyword can breach KDP content guidelines and is not a reliable way to reach genuinely interested readers.
How many keywords should I aim for?
KDP provides a set number of keyword fields; the aim is to use the available fields with genuinely relevant phrases rather than to hit a particular count with weaker fits.
How often should I revisit my keywords?
Only when there is a genuine reason, such as new evidence about reader language or a repositioning of the book, rather than on a routine schedule out of habit.