Boots Products Scraper

A category URL in,
a priced catalogue out.

Point it at a Boots product page or a whole category and get the listings back as rows: name, price, currency, availability, brand, and the identifiers that make an export joinable to someone else's catalogue - sku, mpn and gtin. Fifteen columns, one row per product, and no proxy for you to set up.

one-time 500 free rows$0.002 per row afterone row per productCSV · JSON · Excel
How it works

Five steps,
and no setup.

One input and one control. There is no proxy to configure on this one, so the whole job is a list of links and a number.

  1. STEP 1Sign in to the platform.
  2. STEP 2Open the Boots Products Scraper.
  3. STEP 3Paste Boots product URLs or category and search URLs, one per line - or upload a CSV, XLSX, TXT or Parquet file.
  4. STEP 4Set a limit per query, or leave it at 0 to take everything, then click Get Data.
  5. STEP 5Download the result as CSV, JSON or Excel.

The limit is per query, not per run: five category URLs with a limit of two hundred is two hundred products from each, not two hundred altogether.

Why teams use it

Nothing to configure,
and the right keys.

No proxy to bring

Requests go out through our shared pool rather than your own address, and Boots is not one of the four retailers the platform flags as blocking datacenter traffic or rendering products client-side. There is no proxy field on this form and nothing for you to rent, which is the difference between starting in a minute and starting next week.

Identifiers, not just names

sku, mpn and gtin are separate columns. A product name is a label that two retailers will write two different ways; a barcode is not. Those three are what let a Boots export be matched against a supplier feed or a competitor's list without fuzzy string matching.

Price and currency kept apart

The price and the currency it is quoted in arrive as two columns, so nothing downstream has to strip a symbol out of a string before it can do arithmetic. Re-run the same category later and the two files line up column for column, which is the whole basis of tracking a price over time.

Data dictionary

Fifteen columns,
and an honest ceiling.

The names are the service's own declared list. The descriptions say what each column is for - not what it will contain, because that is Boots' decision rather than ours, and the note below explains why that distinction matters here more than usual.

query
The URL you submitted for this row - the product, category or search page it came from. The scraper writes it, so it is on every row, and it is what ties a row back to the input line that produced it.
service
Which retailer this row came from. This scraper shares one pipeline with several other product services, and the one you picked is stamped onto every row - useful the moment you keep more than one retailer in the same table.
name
The product name as the page declares it.
price
The price the page declares.
currency
The currency that price is quoted in, as its own column - so you are not parsing a symbol out of the price field.
availability
The stock status the page declares.
brand
The brand the page declares.
sku
The retailer's own stock code for the item.
mpn
The manufacturer part number. Together with gtin, this is what makes an export joinable to a catalogue that is not Boots'.
gtin
The global trade item number - the barcode identifier.
rating
The product rating, if the page publishes one. No scale is stated here, because we have not seen a value.
reviews
The review count - a number of reviews, not the review text. There is no review body anywhere in this export.
image
The product image the page declares.
url
The product link.
description
The product description the page declares.

Two things about the evidence behind this page. First, the mechanism: products are read from the JSON-LD a page publishes about itself - the structured data retailers embed for search engines. That sets a ceiling this page will not pretend past. Every column after service exists only if Boots declares that field, so a blank means not published, not run failed - and those two call for completely different responses. Second, the gap: we have never run this against Boots. We hold forty-five exports from the shared pipeline behind this service and not one of them is a Boots run - forty-four came back empty and the only populated one is a different retailer. That export is worth exactly two honest data points about the tool rather than about Boots: query and service filled on every row, because the scraper writes them itself; and six of the fifteen were empty on every single row. Expect gaps here too, expect them to fall differently because a different retailer publishes different structured data, and let your first free-tier run tell you which. That is also why this page prints no example values and no formats - with no Boots run behind them, we would be inventing them.

Common workflows

Three jobs people
run this for.

All of them start from a link you can copy out of your browser.

Pricing

Watch a category rather than a page

Submit the category URL instead of forty product links and the whole shelf comes back in one pass. Run it again next month and the two files line up column for column, so the diff on price is the analysis - no screenshots, no manual checks, nothing to keep in sync by hand.

Pricing · Category management
Catalogue

Match a range against your own list

Names never match cleanly between two retailers. gtin and mpn do, when they are published - which is exactly why they are separate columns here rather than buried in a description. Join on those first and fall back to names only for what is left over.

Merchandising
Availability

See what a range actually looks like right now

availability and price together describe a shelf as it stands at the moment of the run. Whether Boots publishes a stock status in its structured data is something your first run answers - and if the column comes back blank, that is the retailer not declaring it rather than the scraper failing.

Supply · Ops
Pricing

Pay per product row,
nothing else.

No subscription, no minimum, no per-seat licence. Your first 500 rows are on us - after that it is pay-as-you-go.

Free tier

500 free rows - $0

For every new account, one time. No credit card. All scrapers unlocked. Because it is one row per product, that is 500 products - comfortably enough to run a real Boots category and see for yourself which of the fifteen columns arrive filled before you commit anything.

$0 forever
Pay-as-you-go

$0.002 per row, after the free tier

Roughly $2 per 1,000 products, the same flat rate as every other scraper on the platform. The limit-per-query field is what keeps a broad category from costing more than you meant - set it while you are exploring and raise it once you know what a category returns.

Most popular
Enterprise

Custom - many categories, on a schedule

Volume pricing, SLAs, dedicated workers and tailored onboarding for teams tracking large ranges across several retailers at once. Tell us your numbers and we will quote.

Talk to us
10% off your first paid run.Use code LIVESCRAPER10 at checkout.
Sign up
Pairs well with

The same fifteen columns,
a different shelf.

These run on the same pipeline, so their exports stack in one table without any reshaping.

The legal bit

Is it legal to scrape
Boots product pages?

Short answer: yes, when you only collect what a product page publishes about itself - and that is all this service reads.

A product listing is public. Anyone can open it without an account and read the name, the price and the availability, and collecting publicly visible commercial information is long-settled practice. What this service reads is narrower still: the JSON-LD a retailer embeds in the page specifically so that search engines and shopping comparison sites can machine-read it. That is structured data published to be consumed.

This is also the cleanest kind of export we run, because none of these fifteen columns is personal data. A price, a barcode and a stock code describe a product rather than a person, so the data-protection questions that apply to a review or a profile do not arise here. Prices are a business's own commercial information - treat a competitor's the way you would want yours treated, and remember that a price you captured is a price at a moment, not a standing fact.

Boots' terms restrict automated access, so this remains a question of terms. We touch nothing behind a login, read only what an ordinary visitor's browser is served, run no third-party trackers on the data layer, and your exports self-delete after 30 days.

livescraper.app · principles
Public product pages only
No logins, no paywalls
Read from the page's own structured data!
No personal data in these fifteen columns
Exports self-delete (30 days)
The same listing any shopper sees.
Common questions

Things people ask before signing up.

The questions we hear most. Something else? Talk to us - humans write the answers, not bots.

What columns will the export contain?+
Fifteen: query, service, name, price, currency, availability, brand, sku, mpn, gtin, rating, reviews, image, url and description. That is the service's own declared list, and it is the same list every retailer on this shared pipeline returns.
Do I submit a product URL or a category URL?+
Either, and you can mix them in the same list. A product URL returns that one product; a category or search URL returns the products on it, up to the limit you set. One per line, or upload a CSV, XLSX, TXT or Parquet file instead of pasting.
Do I need a residential proxy for Boots?+
No. Requests go out through our shared pool rather than your own address, and Boots is not one of the four retailers the platform flags as blocking datacenter traffic or rendering products client-side. There is no proxy field on this form and nothing for you to rent.
Which columns will actually be filled?+
We will not guess, because we have never run this against Boots. Products are read from the JSON-LD the page publishes about itself, so a column arrives filled only if Boots declares that field - and a blank means not published rather than run failed. In the one populated export we hold from this shared pipeline, six of the fifteen were empty on every row, but that run is a different retailer and its gaps will not be the same as Boots'. Run the free tier on one category and read the header row.
What does the limit do?+
It caps how many products come back per query, not per run. Five category URLs with a limit of two hundred gives two hundred products from each, not two hundred altogether. Set it to 0 to take everything a category returns.
Does it return reviews?+
No. The reviews column is a count, not the review text, and there is no review body anywhere in these fifteen columns. If you need what customers actually wrote, that is a reviews scraper rather than a products scraper.
Can I put several retailers in one table?+
Yes, and that is what the service column is for. Every row is stamped with the retailer it came from, and because everything on this shared pipeline returns the same fifteen columns in the same order, two exports stack without any reshaping.
What does it cost?+
The first 500 rows on a new account are free and one-time; after that it is $0.002 per row - about $2 per 1,000 - pay-as-you-go with no subscription. Because it is one row per product, that is a straight count of the products you pull back. Credits do not expire and there is no monthly reset.

Point it at one category
and see.

Paste a Boots category URL, set a limit, and read the header row of what comes back. Your first 500 rows are free, and there is no proxy to arrange first.

Activates instantly · no card required

Export Boots product data from a category URL

Retail research usually starts with a shelf rather than a product. You want the whole of a category - what is stocked, what it costs, what it is called and how it is identified - and clicking through it by hand is the part nobody has time for. This service takes the link instead. Paste a Boots product URL or a whole category URL, one per line, and each product comes back as a row: name, price, the currency that price is quoted in, availability, brand, the three identifiers that make an export joinable, an image, a link and a description. Fifteen columns, one row per product.

The form has one input and one control, because there is nothing else to decide. The control is a limit per query - and the word per is doing real work: five category URLs with a limit of two hundred returns two hundred products from each, not two hundred in total. Set it to 0 to take everything. There is no proxy field, and that is not an omission: Boots is not one of the four retailers the platform flags as blocking datacenter traffic or rendering products client-side, so requests go out through the shared pool and there is nothing for you to rent or configure.

What makes this export useful downstream is that the identifiers are their own columns. A product name is a label two retailers will write two different ways, and matching on it means fuzzy matching. sku, mpn and gtin are exact, when a retailer publishes them - join on those first and fall back to names only for the remainder. Price and currency are likewise kept apart, so nothing downstream has to strip a symbol out of a string before it can do arithmetic, and two runs of the same category line up column for column, which is the whole basis of watching a price move.

One limit is worth stating plainly, because it changes what you should expect. Products are read from the JSON-LD a page publishes about itself - the structured data retailers embed for search engines - so a column arrives filled only if Boots declares that field, and a blank means not published rather than run failed. And we have not run this against Boots ourselves: of the forty-five exports we hold from this shared pipeline, forty-four came back empty and the only populated one is a different retailer, where six of the fifteen columns were empty on every row. That tells you something about the tool and nothing reliable about Boots, which is why this page prints no example values, no formats and no fill rates. Run one category on the free tier and read the header row - your first 500 rows cost nothing and need no credit card. See pricing for current rates.