Frequently Asked Questions

From real Amazon best-seller list scans — the same 499 Kindle Store categories you can browse on the category index. Each snapshot is a research scan with its window dated on the page, refreshed periodically rather than tracked live. No estimated search volumes: every number shown (median reviews, weak-competitor share, breakout books) is observable on Amazon and re-checkable.

Publisher Rocket estimates search volume — its core product, and genuinely strong at it. Amzanalyst doesn't estimate volume at all: Amazon exposes no book search volume, so every number out there is a model. We measure what's observable instead — how defended a category's best-seller list is (median reviews, weak-competitor share, breakout books) and what actually holds page one for a keyword.

The 100 free sign-up credits cover 20 fresh keyword lookups at 5 credits per new keyword. A keyword anyone queried recently hits the shared cache at 1 credit, and replaying your own recent lookups is free. Free-tier accounts also carry a small daily credit cap, so the fresh lookups spread over a few days.

Scraping is billed per page (1 credit per ASIN + 5 credits per page, ~10 reviews per page) on top of the flat 150-credit AI report. A single book typically lands at 156–176 credits total: about 6 credits to scrape a 1-page overview sample (~10 reviews), up to 26 for a five-star-bucket sample. Bigger pools scale linearly — the sample book report's pool (5 ASINs, 254 reviews) would run about 300 credits end to end.

Both — the same review-analysis engine runs on either. A book's reviews are just another review pool: sentiment and pain-point clustering, severity-ranked findings, every conclusion anchored to verbatim quotes. Book reports add an author-facing read of it — the sample report applies the full pipeline to a narrative-history title.

No store linking required. Amzanalyst works only with publicly available review data — you submit an Amazon product link, and we fetch and analyze the buyer reviews for that ASIN. We never ask for Seller Central access, API keys, or your store credentials.

Sign up and get 100 free credits valid for 30 days, no credit card required. Scraping is billed per page, so 100 credits covers scraping several products. AI insight reports are billed separately at a fixed rate per report — generating one requires a subscription plan or credit pack. No hidden fees, and unused scrape credits are refunded when a task over-reserves.

Yes. We publish complete, anonymized sample reports produced by the same pipeline your own reports use — sentiment and pain-point analysis, core demands, competitor benchmarking, and listing suggestions, every conclusion anchored to verbatim buyer quotes. You can also download the evidence CSV sheets to check the underlying data.

Yes. Submit the parent product link and scraping covers the review pool of the listing including its variants; the report attributes reviews back to their real ASINs so you can compare variants side by side. Multi-ASIN comparison runs are also supported — submit several competitor ASINs in one run and get one merged benchmark report.

Yes. Reports export as PDFs whose first page is conclusions and recommended actions, and every finding is ranked by priority with verbatim buyer quotes as evidence — so non-Chinese-speaking factories and overseas R&D teams can act on it directly, without you re-summarizing anything.

No. Buyer reviews are public on the product page — no Seller Central account, API key, or store linking is needed. You only need a product link (or the ASIN); we detect the marketplace automatically and fetch reviews from the publicly available pages for that ASIN.

The reliable way is to let a structured pipeline do it: scrape a star-balanced sample of the listing's reviews, run sentiment and pain-point clustering over the verbatim text, then rank findings by severity with quotes as evidence — instead of eyeballing the average rating. Amzanalyst automates the whole flow: submit an ASIN and get a decision report in minutes.

Pasting works for a handful of reviews, but it breaks down past that: you first have to scrape and clean hundreds of reviews yourself, the sample you paste is whatever you could grab, and generic AI tends to summarize rather than cite — fabrications go unnoticed. Amzanalyst keeps every conclusion anchored to verbatim buyer quotes, ships the full labeled review data as CSV, and ranks findings by severity instead of vibes.

Yes. Submit your ASIN together with competitor ASINs in one run — you get one merged report comparing sentiment structure, pain-point themes, and rating distribution across all of them, each conclusion still anchored to verbatim quotes. Variant comparison works the same way when you want to compare sizes or colors of the same listing.

The name is Amzanalyst — "amalyzer", "amz analyst" and "amazon analyzer" are frequent misspellings, and they all lead to the same tool. Amzanalyst is a review-intelligence platform for Amazon sellers and KDP authors: it clusters buyer reviews into severity-ranked root causes anchored to verbatim quotes, alongside category and keyword research you can verify yourself.