---
title: "Brand protection"
description: "Brand protection data from Amazon, eBay, Temu and lookalike stores: unauthorized sellers, trademark misuse and screenshot evidence for takedown requests."
canonical: https://www.zenrows.com/use-cases/brand-protection
updated: 2026-10-18
---

# Brand protection

Brand protection is the work of finding unauthorized sellers, trademark misuse, impersonation stores and copied content online, then collecting the evidence to get them removed. Zenrows fetches marketplace searches, seller pages and suspect websites from protected sites, returns the HTML and a screenshot from the same visit, and runs your daily sweep as one Batch job.

## Sample output: one enforcement case

```json
{
  "case_id": "BP-2026-10-0417",
  "found_on": "marketplace",
  "marketplace": "example-marketplace.com",
  "country": "de",
  "listing_url": "https://www.example-marketplace.com/item/88213470",
  "listing_title": "ACME Pro trail shoe, original, fast shipping",
  "seller": {
    "name": "Example Trading Co",
    "seller_id": "A1B2C3D4",
    "on_authorized_list": false
  },
  "price": 39.9,
  "currency": "EUR",
  "brand_terms_matched": ["ACME", "ACME Pro"],
  "official_images_reused": true,
  "violation": "unauthorized_seller_trademark_in_title",
  "evidence": {
    "captured_at": "2026-10-14T06:12:09Z",
    "screenshot": "evidence/BP-2026-10-0417.png",
    "html_sha256": "9f2c4e1a7b...e81d"
  },
  "status": "ready_for_notice"
}
```

An illustrative case record. Zenrows supplies the page, its HTML and the screenshot; the matching against your authorized seller list and the case status come from your own rules.

## What brand protection teams collect

A brand protection program watches the places where buyers meet a brand they think is real. Most of the work is search: the same brand terms, run against many sites, every day.

| Job | Data you collect | Typical sources |
|---|---|---|
| Unauthorized seller discovery | Seller name and ID, storefront URL, offer price, ship-from country | Amazon, eBay, Walmart Marketplace, Allegro, Mercado Livre |
| Trademark misuse in listings | Brand terms in titles, bullet points, keywords and image text | Etsy, AliExpress, Temu, Shopee, Wish |
| Impersonation storefronts | Domain, page title, logo use, checkout provider, contact details | Lookalike Shopify and WooCommerce stores found through Google Search |
| Copied product content | Official photos and descriptions reused on third-party pages | Retailer sites, comparison sites, marketplace listings |
| Grey market and parallel imports | Regional listings, currency, packaging language, seller location | Country marketplace domains such as amazon.de and amazon.co.jp |
| Takedown evidence | Screenshot, HTML, timestamp and final URL for each violation | Every confirmed listing, captured before the notice goes out |

Fake goods are one slice of this. For the listing-level signals that separate a counterfeit from a genuine offer, see counterfeit detection.

## Why online brand protection is hard

Infringing sellers depend on not being seen by the brand, and the marketplaces they use are some of the most protected sites on the web. Both work against a homegrown crawler.

| What breaks | What it looks like | How Zenrows handles it |
|---|---|---|
| Marketplace bot protection | CAPTCHAs, 403s or login redirects after a few hundred searches on a large marketplace | Zenrows' stealth and automatic retries get through PerimeterX, Akamai and similar protections, so a search returns the results page |
| Regional storefronts | A seller hides offers from your home country, or the grey-market listing only appears on the local domain | Geotargeting runs the search from the market where the offer is sold |
| Evidence that holds up | A listing is edited or deleted between detection and the notice, and the record is gone | One request returns the rendered HTML and a screenshot from the same visit, so you can hash and store both |
| Volume of brand terms | Hundreds of terms times dozens of marketplaces times several countries, every day | Batch takes the whole URL list as one job and posts results to your webhook |
| Impersonation sites spread out | One operator runs many lookalike domains with the same template | Crawl maps every product and checkout page on a suspect domain |

## How to run brand protection monitoring with Zenrows

1. **Turn brand terms into search URLs.** Build one search URL per term, marketplace and country, plus Google Search queries for lookalike stores.
2. **Sweep them daily with Batch.** Submit the list as one job with Adaptive Stealth Mode, and parse sellers, titles and prices from each results page.
3. **Filter against your rules.** Compare sellers with your authorized list and titles with your trademarks. Keep only the listings that break a rule.
4. **Capture evidence for each hit.** Fetch the listing again in a full browser render with a screenshot, then store the screenshot, the HTML and a hash with a timestamp.
5. **Map suspect stores.** When a lookalike domain turns up, crawl it to list every product page before you file the complaint.

```python Python
# pip install requests
import base64, hashlib, json, requests
from datetime import datetime, timezone

params = {
    "apikey": "YOUR_ZENROWS_API_KEY",
    "url": LISTING_URL,  # a listing that broke one of your rules
    "js_render": "true",  # screenshots need a full browser render
    "premium_proxy": "true",
    "screenshot": "true",
    "json_response": "true",  # HTML and screenshot in one response
}
data = requests.get("https://api.zenrows.com/v1/", params=params).json()

html = data["html"]
png = base64.b64decode(data["screenshot"]["data"])
record = {
    "listing_url": LISTING_URL,
    "captured_at": datetime.now(timezone.utc).isoformat(),
    "html_sha256": hashlib.sha256(html.encode()).hexdigest(),
}
open("evidence/case.png", "wb").write(png)
open("evidence/case.json", "w").write(json.dumps(record))
```

```bash cURL
curl -X POST "https://async.api.zenrows.com/v1/jobs" \
  -H "X-API-Key: YOUR_ZENROWS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "type": "regular",
    "status": "closed",
    "zenrows_params": { "mode": "auto" },
    "tasks": [
      { "url": "MARKETPLACE_SEARCH_URL_ACME_DE", "external_id": "acme-amazon-de" },
      { "url": "MARKETPLACE_SEARCH_URL_ACME_PRO_US", "external_id": "acme-pro-ebay-us" },
      { "url": "GOOGLE_SEARCH_URL_ACME_OUTLET", "external_id": "acme-outlet-google" }
    ]
  }'
```

```bash CLI
npx -y @zenrows/cli init
# sweep.jsonl: one {"url": "..."} per brand search, marketplace and country
zenrows batch create sweep.jsonl --wait
zenrows batch results JOB_ID --out sweep-results.jsonl
zenrows fetch LISTING_URL --screenshot --out evidence/case.png
```

```text Claude Code (MCP)
# Connect once
claude mcp add zenrows -e ZENROWS_API_KEY=YOUR_ZENROWS_API_KEY -- npx -y @zenrows/mcp

# Then ask:
Use Zenrows to search amazon.de and ebay.de for "Acme Pro headphones", list every seller with their seller ID, price and listing URL, and flag any seller not in authorized-sellers.csv. Take a screenshot of each flagged listing.
```

## Which Zenrows product fits

| If you need to | Use | Why |
|---|---|---|
| Capture one listing with HTML and screenshot as evidence | [Fetch](/products/fetch) | Screenshots and the rendered HTML come back from the same page load |
| Run every brand search across every marketplace each day | [Batch](/products/batch) | One job for thousands of search URLs, results by webhook or download |
| List every page on a suspected impersonation store | [Crawl](/products/crawl) | Follows links on the domain and keeps the URLs that match your patterns |
| Search a marketplace that needs clicks, filters or scrolling | [Browser](/products/browser) | A hosted browser you drive with Playwright or Puppeteer |

## What success looks like

| Measure | What good looks like | How to check it |
|---|---|---|
| Marketplace coverage | Every marketplace and country on your enforcement list returns results on every run | Count successful searches per marketplace and country after each Batch job |
| Detection lead time | A new unauthorized seller is in your queue within one sweep of listing | Compare the first-seen date in your data with the listing date shown on the page |
| Evidence completeness | Every case has a screenshot, HTML, hash, timestamp and final URL | Reject cases in your queue that miss any of the five |
| Takedown rate | Most notices you send result in removal | Recheck each reported URL a week later and record whether it still loads |
| Repeat offenders | Sellers who reappear under new names are linked to their old cases | Match seller IDs, images and contact details across cases |
| Time to evidence | Every confirmed violation has its evidence captured on the day it is found | Compare the detection date with the evidence capture date for each case |

## Brand protection FAQ

### What is online brand protection?

Online brand protection is the ongoing work of finding and removing listings, websites and accounts that misuse a brand: unauthorized resellers, trademark use in product titles, stores that copy the official site, reused product photos and counterfeit goods. It combines monitoring, which finds the violations, with enforcement, which uses marketplace reporting tools, notices to hosts and legal action to remove them.

### Is Zenrows brand protection software?

Zenrows is the data layer underneath it. It fetches the marketplace searches, listings and suspect sites your system needs, from protected sites, and returns HTML, Markdown, JSON or screenshots. Your team, your brand protection services provider or your own software decides what counts as a violation and files the takedowns.

### Which marketplaces and sites can I monitor?

Any page a buyer can open without logging in: Amazon, eBay, Walmart Marketplace, Etsy, AliExpress, Temu, Shein, Shopee, Mercado Livre, Allegro and regional sites, plus Google Search results for lookalike stores. Use geotargeting to see each country's version of a marketplace the way local buyers do.

### How do I capture evidence for a takedown notice?

Fetch the listing in a full browser render with a screenshot, returned together in one JSON response. The response holds the rendered HTML and a base64 screenshot from the same visit. Store both with a UTC timestamp and a SHA-256 hash of the HTML, so you can show what the page said at the moment you captured it, even if the seller edits it later.

### How do I find unauthorized sellers on Amazon or eBay?

Search each marketplace for your brand terms and product identifiers, collect the seller on every offer, and compare that list with your authorized resellers. Anyone not on the list goes into review. Run the searches daily, since a short-lived seller can list, sell and leave between two weekly checks.

### How does trademark monitoring work on marketplaces and the open web?

On marketplaces, search for your brand terms, common misspellings and product model numbers, then check titles, bullet points and seller names for unauthorized use. On the open web, run Google Search queries that pair your brand with words like outlet, sale or official store, and check new domains that combine your mark with other words. Keep each match with its URL, screenshot and capture date for the notice.

### Is it legal to monitor marketplaces for brand abuse?

Brands commonly review public marketplace listings to enforce their trademarks, and marketplaces run their own reporting programs for this purpose. Collect only what you need for enforcement, respect each site's terms, and handle any personal data about individual sellers under the privacy law that applies. Your legal team should confirm the approach for your markets.

## Related use cases

- [MAP monitoring](https://www.zenrows.com/use-cases/map-monitoring.md): Advertised prices and sellers across marketplaces and resellers, with screenshot evidence of every MAP violation.
- [Brand monitoring](https://www.zenrows.com/use-cases/brand-monitoring.md): Every mention of your brand in news, forums, reviews and search, as clean text for sentiment and alerts.
- [Threat intelligence data](https://www.zenrows.com/use-cases/threat-intelligence-data.md): Phishing pages, lookalike domains and scam stores captured as HTML, screenshots and final URLs for security teams.
- [Digital shelf analytics](https://www.zenrows.com/use-cases/digital-shelf-analytics.md): Search rank, share of shelf, content compliance, ratings and availability for CPG brands, per retailer and region.
- [Ecommerce web scraping](https://www.zenrows.com/use-cases/ecommerce-web-scraping.md): Product, price, stock, review and seller data from Amazon, Walmart, eBay and Shopify stores, as JSON.

Start free: https://app.zenrows.com/register. Docs: https://docs.zenrows.com