The method

How a Map gets built.

Three lenses, scanned in parallel. One editorial pass to write the verdict. Read it like a developmental editor would read a manuscript report — because that's the standard we're trying to meet.

01 · Supply — what's already winning

Every Amazon bestseller in your subgenre. Their tropes, blurbs, cover language, review counts. The whole competitive landscape, read in full and structured for you.

We scrape the top of every relevant Amazon list monthly, then parse each blurb and cover for the tropes, archetypes, settings, and language patterns that are actually moving units right now. Then we cross-reference review counts, time-on-list, and price band so a hot trope on a $0.99 promo doesn't outrank a steady seller at $4.99.

What you see in the Map: ranked tropes by lane (hot / warming / soft / oversupplied), the top 3-5 comp titles in your specific cell, the patterns repeating in winning blurbs and covers.

02 · Demand — the tropes readers name

Reddit threads, Goodreads reviews, BookTok captions. The "I want a book that…" voice in the reader's own words. We mine it and score it.

Bestseller data tells you what worked; reader voice tells you which tropes readers name and praise. We track "looking for a book where…" threads across r/RomanceBooks and siblings, 4-and-5-star Goodreads reviews that articulate what the reader loved, and the BookTok captions where readers name what they want. Same readers, in their own words.

The Map's editorial pass sets the tropes readers name and praise beside what is already published.

03 · Vocabulary — the words readers use

The Goodreads shelves real readers create. Your blurb keywords, ad targeting, metadata. Words that find your readers, not words your editor invented.

If your blurb says "enemies-to-lovers" but readers are shelving the books they love as "I hated him then I didn't", your discovery is broken. We map the actual shelf-name and tag vocabulary readers use in your lane, then suggest the phrases to drop into your blurb, your Amazon keywords, and your ad targeting so the right people can find you.

04 · The verdict

Each signal gets its own pass. Then one editorial step compares them, looks for the agreements and the contradictions, and writes the Map — a structured document a developmental editor would recognise, not a wall of bullet points.

You get a complete pre-launch blueprint — thirty-nine sections covering a go/no-go opportunity score, the ranked trope stack with demand-vs-supply, FMC and MMC archetypes, the open market gap, comp titles three ways, cover direction, title and character-name ideas, the book-length sweet spot, reader vocabulary, the categories you can rank in, market economics, risk flags, and a step-by-step action plan. Same skeleton every time; the verdict changes.

Who it's for

  • Indie romance authors deciding what to write next — or whether to shelve a half-drafted project.
  • Series-planners working out the spin-off or the next lane.
  • Writers coming back after a break who need to know what's moved in the last six months.
  • Anyone who'd rather spend the week writing than scrolling Goodreads.

Who it's not for

  • People looking for a ghost-writer or an AI to draft the manuscript. We don't do that.
  • Authors who've already locked their next book — a Map is most useful before the outline.
  • Anyone outside romance and its adjacent lanes. The model is tuned for this market specifically; we'd rather say no than ship something thin.
Tropesmith is a research tool, not a writing tool. The taste, the prose, the book — that's still you. We just keep the field notes.

Build my Map →

Our data pipeline, plainly

Four signal layers, each counted.

A Map isn't a guess about what's selling. It's a triangulation across four independent reader-voice channels — each scraped and structured. Here's what feeds the engine.

Layer 1 · BookTok
113,500+
demand signals from BookTok
760+ hashtags tracked 145,000+ videos logged
Layer 2 · Demand signals
2,860,000+
demand signals analyzed
Goodreads + Amazon reviews, BookTok, Reddit Parsed by Claude into trope + heat + setting
Layer 3 · Reader praise
4,120,000+
Goodreads shelf signals
5,070,000+ reviews mined What individual readers shelve and recommend
Layer 4 · Supply scan
142,000+
titles tagged with tropes
1,190,000+ book-trope mappings To find the gap between demand and what's published
Where the signals come from

Demand signals come from Goodreads reviews, Amazon reviews, BookTok comments and captions, Reddit posts and Amazon Q&A. 95.6% come from Goodreads and Amazon reviews, 4.1% from BookTok comments and captions and 0.3% from Reddit posts (source shares counted 7 October 2026). Amazon tells us where the market is over-supplied — that's the gap your Map points you toward.

The demand signals figure was counted 2026-11-05 10:15 UTC. A demand signal is one specific thing a reader asked for or praised in a review, comment or post, picked out by Tropesmith's classifier. One review can give several. How we count

By the numbers

What Tropesmith has actually read

Every figure on this page is a row count against our own corpus, not an estimate. The counters above animate for visitors but ship their final values in the page source, so a search engine or an AI assistant reads exactly the same numbers you do. Here is the whole set in one place.

670,000+titles in the book registry
1,190,000+book–trope tags extracted
142,000+titles tagged with tropes
5,130+tropes in the book-tag set
5,070,000+Goodreads reviews read
4,120,000+Goodreads shelf signals held
2,860,000+demand signals analyzed
39%of demand signals tagged underserved or novel by Tropesmith's classifier (as of 7 October 2026)
113,500+demand signals from BookTok comments and captions
140+demand lanes scored
760+BookTok hashtags tracked
145,000+BookTok videos parsed
12,200,000+rows across the review, shelf, demand-signal and BookTok-video tables (demand signals are extracted from the reviews, so the rows overlap)

Counted by direct query against the Tropesmith corpus. Each of the 2,860,000+ and 113,500+ floors was counted 2026-11-05 10:15 UTC; the 39% share is as of 2026-10-07 17:24 UTC. Each other floor above was counted 2026-11-05 10:15 UTC. A demand signal is one specific thing a reader asked for or praised in a review, comment or post, picked out by Tropesmith's classifier. One review can give several. Each signal gets a rarity tag (common, underserved or novel) from the text of that review or post alone; the classifier is never shown what is on the shelf. A tag points at where to look. It does not prove that no book answers the signal. Shares are the tagged signals divided by all signals in the same scope. Method: How we count · how we count demand signals · how a Map is built.

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