How Tropesmith Works — Romance Market Analysis in Minutes
The method

How a Map gets built.

Three signals, 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 — what readers ask for

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

Bestseller data tells you what worked; reader voice tells you what's missing. We track every "looking for a book where…" thread across r/RomanceBooks and siblings, every 4-and-5-star Goodreads review that articulates what the reader loved, and the BookTok captions that drove the latest reading-list spikes. Same readers, but their wishlists run ahead of the bestseller list.

That delta — where readers are asking for X but the shelves are full of Y — is the most useful signal we ship. It's also the hardest to game.

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 — around twenty 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, weighted by how loudly readers actually speak.

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

Layer 1 · Virality
24.4B+
BookTok plays observed
Weighted highest 580+ hashtags, 80,800+ viral videos
Layer 2 · Explicit demand
1,970,000+
structured reader asks
Reddit + Goodreads + BookTok Parsed by Claude into trope + heat + setting
Layer 3 · Reader praise
1,750,000+
Goodreads shelf signals
2,870,000+ reviews mined What individual readers shelve and recommend
Layer 4 · Supply scan
21,500+
bestsellers analyzed
158,000+ book-trope mappings To find the gap between demand and what's published
Live BookTok signals feeding the engine
#bridgerton 1229M plays #polyamoryromance 722M plays #catherinekennedy 716M plays #enemiestoloverstiktok 631M plays #elsiequinn 598M plays #isleofwesberrey 585M plays #militarythriller 582M plays #coldwaterbay 546M plays #catmystery 385M plays
How the weighting works

Each demand signal is scored on strength (how many readers, how recently, how unambiguously). BookTok virality wins on volume — a video with millions of plays compresses thousands of reader endorsements into one data point. Reddit asks score next: lower volume, but each post is an explicit reader request. Goodreads adds depth per book. Amazon tells us where the market is over-supplied — that's the gap your Map points you toward.

Pipeline runs continuously. Numbers reflect the live corpus.

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.

260,000+titles in the book registry
158,000+book–trope tags extracted
21,500+titles tagged with tropes
4,850+tropes in the book-tag set
2,870,000+Goodreads reviews read
1,750,000+Goodreads shelf signals held
1,970,000+demand signals ingested
777,379reader requests classed underserved or novel
30,307of those, read from BookTok
138demand lanes scored
580+BookTok hashtags scanned hourly
80,800+BookTok videos parsed
24.4B+BookTok plays across tracked videos
6,680,000+reader signals in total

Counted 2026-08-21 18:49 UTC by direct query against the Tropesmith corpus. Method: how we read reader demand · how a Map is built.

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