Point at a Node
Give AllyHub the ASINs you want to follow — your own, competitors', or both — and the file you want the history kept in.
See whether your product is climbing or sliding in its category — build the rank history on your own file, run to run.
One category link becomes a full-tree product dataset in three steps.
Give AllyHub the ASINs you want to follow — your own, competitors', or both — and the file you want the history kept in.
For every ASIN it captures the current Best Sellers Rank, category, price, and rating, appends the row to your file with today's date, and notes any that Amazon doesn't rank.
Re-run it whenever you want the next data point — or save it as a Playbook to trigger again — and ask it to flag what changed since last time.
Below, in order: how deep the crawl goes, what shape it returns in, how you narrow it, which stores it works on, what you can do with the file, and what a second sweep adds.
Pull the current Best Sellers Rank for each ASIN that carries one, beside its price and rating — one row per product. Anything Amazon doesn't rank comes back without a number, not a guess.
AllyHub doesn't keep your history on its server. Point each run at your own spreadsheet and it appends today's rank, so the time series is a file you hold, not a subscription you rent.
Ask it to compare with your last run and it marks which ASINs climbed, slipped, or newly entered the ranking — the change, not just today's snapshot.
Follow a set of ASINs together — your own and your rivals' side by side, up to about 20 in one run — the whole race in a single file, not page by page.
Because Best Sellers Rank is public on the product page, AllyHub reads it for any ASIN — a competitor's as easily as your own — without an Amazon seller account.
Every run writes back as CSV or into your spreadsheet, the same columns each time, with no Amazon API and no code — ready to chart or diff however you like.
Analysts, launch teams, buyers, and sourcing scouts who need the category whole, not sampled.
The pain: someone asks how big this category is and you have an impression, not a number you trust. Work from the full node instead and the answer is countable — how many products, at what price tiers, with what review depth behind them.
The pain: you are about to commit a first order into a category you have only browsed. Size the field before the money moves, and read what it actually takes to get seen here rather than discovering it from a listing nobody finds.
The pain: assortment shifts every quarter and your evidence is last quarter's screenshot. Keep dated sweeps of the same node and shelf movement becomes something you can show a supplier, not something you argue about from memory.
The pain: the gaps you find are the ones you happened to scroll past, which means the best one is probably still sitting there unseen. Read the whole node and the thin spots surface because you covered everything, not because you got lucky.
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Questions that come up when a browse node turns out bigger than it looked.
A browse node is Amazon's own filing system for products. A category scraper returns what is filed under one as structured data, instead of the handful of listings a browse page puts on screen — so the output is something you can group and measure, not something you scroll.
Yes. Smaller nodes cost nothing on the free plan. Deep sweeps across a large tree, scheduled snapshots, and crawls that feed another system sit on the paid plans.
Complete, and size is not the variable. A bigger node takes longer and uses more credits, but it does not come back with less in it — the row count that lands is the node's own, not a tool's ceiling.
Yes, and the filter applies before the crawl rather than after it. Name a price band, a rating floor, or Prime-only when you start, and AllyHub simply doesn't collect what falls outside it — so the file that lands is already the slice you wanted.
Other category scrapers start every sweep from zero and charge the same for it. AllyHub keeps the map it built, so a repeat sweep is a re-run rather than a re-discovery. It builds on what it already knows, and repeat coverage gets cheaper as the history grows.