Zappos Scraper - Shoe & Apparel Product Data

Scrape Zappos category and search pages in bulk. Extract product name, brand, price, original price, discount, colour, rating, review count, stock level and image URLs to CSV/JSON. No API key, no login.

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What does the Zappos Scraper do?

This Actor turns any Zappos search into a structured dataset. You give it by category URL or by keyword, it walks the result pages one after another, and it writes one clean row per product into your dataset — ready to export as JSON, CSV or Excel, or to pull straight from the Apify API.

Zappos ships its search results as a hydrated __INITIAL_STATE__ payload in the page HTML. The Actor parses that state object, so every field the site renders is available without running a browser. Pagination is followed automatically until it runs out of results, hits your page limit or hits your Max items cap, whichever comes first. Every row is de-duplicated across the whole run, so you are never billed twice for the same product.

There is no API key, no login and no browser involved. That keeps runs fast and cheap, and it means you can schedule the Actor without worrying about credentials expiring.

Who is it for?

• Footwear and apparel buyers benchmarking assortment and price architecture.
• Brand managers monitoring how their products are merchandised and discounted.
• Competitive-intelligence analysts tracking a rival's catalogue depth by brand.
• Repricing teams that need current US retail prices with discount percentages.
• Data teams assembling product-catalogue datasets for search or recommendation models.

Use cases

• Export an entire Zappos category with brand, price and discount for assortment analysis.
• Track how deeply a brand is discounted across a season.
• Build a brand-by-brand price ladder for a footwear category.
• Monitor review counts and ratings as a demand signal per style.
• Assemble a labelled product-image dataset with colour and category metadata.

Why use this Zappos Scraper?

• 🔑 Keyless — no account, no API token, no cookies to paste.
• 📦 21 fields per product — everything the result page exposes, already typed.
• 📄 Real pagination — it walks page after page instead of returning the first screen.
• 🎯 Precise caps — Max items stops the run exactly where you want it, so the bill is predictable.
• 📊 Export anywhere — JSON, CSV, Excel or HTML, plus the Apify API and integrations.
• 💸 Pay per result — you pay for rows you actually receive, with no platform fees to calculate.

What data can you extract?

Every run produces one row per product, with these fields:

Field	Type	Description
productId	string	Zappos product ID
styleId	string	Style ID for the specific colourway
productName	string	Product name
brandName	string	Brand name
productType	string	Product category, e.g. Shoes
price	number	Current price as a number
priceFormatted	string	Current price as displayed
originalPrice	number	List price before discount
percentOff	number	Discount percentage
onSale	boolean	Whether the item is currently discounted
color	string	Colour name of the variant
styleColor	string	Full colourway description
rating	number	Average product rating
reviewCount	number	Number of customer reviews
stockOnHand	number	Units on hand when exposed by the site
isNew	boolean	Whether Zappos flags the item as new
imageUrl	string	Main product image URL
productUrl	string	Canonical product page URL
sourceUrl	string	Category or search URL this row came from
page	number	Result page the product appeared on
scrapedAt	string	ISO timestamp of extraction

Output example

{
  "productId": "10064056",
  "styleId": "6723789",
  "productName": "All-Pro Nitro 2 Basketball Shoes",
  "brandName": "PUMA",
  "productType": "Shoes",
  "price": 135,
  "priceFormatted": "$135.00",
  "originalPrice": 135,
  "percentOff": 0,
  "onSale": false,
  "color": "Multi",
  "styleColor": "Sea Illusion/Nitro Blue/Intense Mint",
  "rating": 0,
  "reviewCount": 0,
  "stockOnHand": 95,
  "isNew": false,
  "imageUrl": "https://m.media-amazon.com/images/I/81PBUITDHQL._AC_SR255,340_.jpg",
  "productUrl": "https://www.zappos.com/p/puma-all-pro-nitro-2/product/10064056",
  "sourceUrl": "https://www.zappos.com/men-shoes",
  "page": 1,
  "scrapedAt": "2026-09-11T08:40:12.004Z"
}

How to use

Option A — by category URL

{
  "startUrls": [
    {
      "url": "https://www.zappos.com/men-shoes"
    }
  ],
  "maxPagesPerSearch": 20,
  "maxItems": 2000
}

1. Open the Actor and fill in the category URL field.
2. Set Max pages per search and Max items to bound the run.
3. Click Start, then export from the Output tab.

Option B — by keyword

{
  "searchTerms": [
    "running shoes",
    "winter boots"
  ],
  "maxPagesPerSearch": 10,
  "maxItems": 1000
}

Paste one or more Zappos URLs into Start URLs and the Actor paginates each of them independently.

Input parameters

Parameter	Type	Default	Description
startUrls	array	–	Zappos category or search URLs, e.
searchTerms	array	[]	Keywords to search on Zappos when you have no category URL.
maxPagesPerSearch	integer	5	How many result pages to walk for each search.
maxItems	integer	500	Stop after this many products.
maxConcurrency	integer	4	Parallel requests.
proxyConfiguration	object	{"useApifyProxy": true}	Proxy used to fetch pages.

Tips for best results

• Category URLs such as /men-shoes or /women-clothing return far more rows than keyword searches.
• Zappos serves 100 products per page, so a 20-page run yields roughly 2,000 rows.
• Use Max items to cap the bill precisely; the Actor stops the moment the cap is reached.
• percentOff is populated only for discounted items — zero means full price, not missing data.
• stockOnHand is exposed for many styles but not all; treat nulls as unknown rather than zero.
• The same product appears once per colourway; de-duplicate on productId if you want one row per style.
• Schedule a weekly run on the same category to build a discount-history dataset.
• Keep Max concurrency modest — Zappos throttles bursts of parallel requests.
• Combine brand filtering in the URL with a high page count for deep single-brand exports.
• Pair with the AliExpress scraper to compare branded retail against marketplace pricing.

Integrations

Send results straight into the tools you already use: Google Sheets, Slack, Zapier, Make, Airtable or any Webhook. You can also schedule the Actor to run hourly, daily or weekly and have each run append to the same dataset, which is how you build a price or availability history rather than a one-off snapshot.

API usage

Run the Actor and collect results from any language. Replace <YOUR_TOKEN> with your Apify API token.

cURL

curl -X POST "https://api.apify.com/v2/acts/logiover~zappos-product-scraper/run-sync-get-dataset-items?token=<YOUR_TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{"startUrls": [{"url": "https://www.zappos.com/men-shoes"}], "maxPagesPerSearch": 20, "maxItems": 2000}'

Node.js

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: '<YOUR_TOKEN>' });
const run = await client.actor('logiover/zappos-product-scraper').call({"startUrls": [{"url": "https://www.zappos.com/men-shoes"}], "maxPagesPerSearch": 20, "maxItems": 2000});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Python

from apify_client import ApifyClient

client = ApifyClient('<YOUR_TOKEN>')
run = client.actor('logiover/zappos-product-scraper').call(run_input={"startUrls": [{"url": "https://www.zappos.com/men-shoes"}], "maxPagesPerSearch": 20, "maxItems": 2000})
for item in client.dataset(run['defaultDatasetId']).iterate_items():
    print(item)

Use with AI agents (MCP)

This Actor is available through the Apify MCP server, so an AI agent can call it as a tool. Point your agent at https://mcp.apify.com and it can run the Zappos Scraper on demand — for example: "Pull the first 500 products from Zappos and summarise the price distribution." The agent receives the same structured rows you would get from the UI.

FAQ

Do I need a Zappos account or API key?

No. The Actor reads publicly available pages only. There is nothing to authenticate and no credentials to rotate.

How many products can I get in one run?

As many as the search exposes. Raise Max pages per search and Max items together; the run stops at whichever limit it reaches first.

Why did I get fewer rows than I asked for?

The search ran out of products. That is normal for narrow queries — broaden the search or add more searches to one run.

Are results de-duplicated?

Yes. Each product is emitted once per run, even when it appears on several pages, so you are never billed twice for the same record.

Why are some fields empty?

Zappos does not publish every attribute for every product. Empty means the source did not show it, not that extraction failed.

What export formats are supported?

JSON, CSV, Excel, HTML and RSS from the Output tab, plus the Apify API and any integration you connect.

How fast is it?

It is pure HTTP with no browser, so a page of results typically takes a second or two. Raise Max concurrency carefully — the source rate-limits aggressive crawling.

Can I schedule it?

Yes. Use the Apify scheduler to run it on any interval and append each run to the same dataset for time-series analysis.

Does it work behind a proxy?

It uses Apify Proxy automatically. You can switch groups or supply your own proxies in Proxy configuration.

How often does the data change?

Zappos updates continuously. Re-run whenever you need current data; the Actor always reads the live pages, never a cache.

Is the output schema stable?

Yes. Field names and types are fixed, so downstream pipelines will not break between runs.

What if the site changes its layout?

Open an issue on the Issues tab and it gets fixed. The Actor is actively maintained.

Is it legal?

This Actor reads only publicly available pages on Zappos — the same content any visitor sees without logging in. It does not bypass authentication, does not collect private data and does not attempt to defeat access controls. You are responsible for how you use the output: respect the source's terms of service, applicable copyright, and data-protection law such as GDPR where personal data is involved. Scraping public data is generally lawful in the EU and the US, but the responsibility for the downstream use of that data sits with you.

Related scrapers

• AliExpress Scraper — Marketplace pricing for the same categories
• Newegg Scraper — Electronics catalogue data
• Amazon Product Scraper — Amazon listings and pricing
• Poshmark Scraper — Resale pricing for fashion