Point at a Node
Tell AllyHub the product and the market you're sizing, and name the data sources you want it to rely on if you have preferences.
Get a market size you can defend — TAM, SAM, and SOM with a source behind every number and a model you can open.
One category link becomes a full-tree product dataset in three steps.
Tell AllyHub the product and the market you're sizing, and name the data sources you want it to rely on if you have preferences.
It builds TAM, SAM, and SOM top-down and bottom-up, pulls each input from your sources, cites every figure, and lays it out as an editable model.
Flex the assumptions, check the two methods against each other, and export the model. Save the setup as a Recipe so your next sizing starts from your method.
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.
Get the market broken into three layers — the total market, the slice you can serve, and the share you can realistically win — each a number, not a hand-wave.
AllyHub sizes it both ways — segmenting an industry total from the top and building up from price times customers — so the two methods cross-check instead of resting on one guess.
Each figure carries a clickable source and the steps that produced it, so a skeptical reader can trace the number back to where it came from rather than taking it on faith.
Move an input — adoption rate, price, addressable population — and the whole model updates, so you can pressure-test the size instead of defending a single fixed figure.
Point it at the authorities you trust — a statistics bureau, an industry association, a public company's filings — and it pulls the inputs from there, not from whatever a model half-remembers.
Take it as a working .xlsx where every assumption is a cell you can change — hand it to an investor and they can audit the math, not just read a number.
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.