Ecommerce Platform Detector | Shopify & More

Classify a list of ecommerce websites

Ecommerce Platform Detector: Identify exposed Shopify, WooCommerce and other ecommerce platform signals from supplied websites. Export platform, payment and analytics columns for integration research. A missing signal does not prove there is no store.

Start with a small example

1. Open Input, switch to JSON and paste the example below. Replace the example startUrls with your own research targets when ready.
2. Check the live Pricing tab and set a run spending limit.
3. Run the Actor, then open the Workflow output view. Inspect url, ecommerce, paymentProcessors, analytics, matchesFilter, error.
4. Export JSON for nested data or CSV for a spreadsheet. Review a small sample before increasing the scope.

{
  "startUrls": [
    "https://www.allbirds.com",
    "https://www.patagonia.com",
    "https://www.squarespace.com"
  ],
  "filterTechnologies": [
    "Shopify",
    "WooCommerce",
    "Magento",
    "BigCommerce",
    "Salesforce Commerce Cloud",
    "Squarespace Commerce",
    "Wix eCommerce",
    "PrestaShop",
    "OpenCart",
    "Shopware",
    "commercetools",
    "Fourthwall",
    "Ecwid",
    "Volusion"
  ],
  "onlyMatching": false
}

At the rate checked September 9, 2026, 3 saved rows cost $0.009 in Actor event fees ($3.00 per 1,000 rows). Actual returned rows determine the event fee. Taxes and other account services may be separate.

What can I use it for?

• Platform-specific prospecting. Apps, payment providers, 3PLs, agencies and SaaS tools that integrate with one platform can turn any merchant list into the subset they can actually serve.
• Migration and replatforming sales. Find every store on a platform you migrate away from, or on an ageing platform version, and approach them with a concrete offer.
• Market sizing and share. Measure how a category, region or keyword set splits across Shopify, WooCommerce, Magento and the rest.
• Competitive intelligence. See which payment, review, email and analytics tools stores on each platform tend to pair it with.
• Enriching merchant data. Add a platform column and the surrounding stack to a spreadsheet of accounts in a single run.

What data does it return?

One record per store:

Field	Meaning
url, finalUrl, statusCode, title	What you asked for, where it resolved to, and the page title
ecommerce	The platform detected, e.g. Shopify, WooCommerce, Salesforce Commerce Cloud — null if none was found
matchesFilter	true when the platform is one of those you listed under Platforms to look for
matchedTechnologies	Which listed platforms were found
paymentProcessors	Shop Pay, Stripe, PayPal, Klarna, Adyen and similar
analytics, tagManager, advertising	Google Analytics, GTM, Meta Pixel, TikTok Pixel and others
marketingAutomation, liveChat	Klaviyo, Mailchimp, Gorgias, Zendesk and similar
cms, javascriptFrameworks	The CMS and front-end framework, useful for headless setups
cdn, hosting, webServer	Infrastructure behind the store
technologyCount	Total technologies detected
technologies	The complete list with categories, versions and confidence scores
error	null on success, otherwise why the site could not be checked

Stores that time out or refuse the connection are still returned with an error, so your input and output line up one to one.

Illustrative output

Values explain the output shape; they are not a live result or guaranteed field coverage.

{
  "url": "https://www.patagonia.com",
  "finalUrl": "https://www.patagonia.com/home/",
  "statusCode": 200,
  "title": "Outdoor Clothing & Gear | Patagonia",
  "ecommerce": "Salesforce Commerce Cloud",
  "matchesFilter": true,
  "matchedTechnologies": ["Salesforce Commerce Cloud"],
  "paymentProcessors": null,
  "cdn": "Cloudflare, Akamai",
  "technologyCount": 12,
  "technologies": [
    { "name": "Salesforce Commerce Cloud", "categories": ["Ecommerce"], "version": null, "confidence": 100, "website": "https://www.salesforce.com/products/commerce-cloud/overview/" },
    { "name": "Cloudflare", "categories": ["CDN"], "version": null, "confidence": 100, "website": "https://www.cloudflare.com" }
  ],
  "error": null
}

How to use it

1. Paste your store domains under Online stores to identify, upload a file, or pass them through the API as startUrls.
2. Leave Platforms to look for at its defaults to classify across all major platforms, or narrow it to one platform to build a targeted list.
3. Switch on Store only matching websites to pay only for stores on the platforms you listed and discard everything else.
4. Click Start and download the results as JSON, CSV or Excel from the Output tab, or read them from the dataset via the API.

Input example

{
  "startUrls": [
    "https://www.allbirds.com",
    "patagonia.com",
    "https://www.squarespace.com"
  ],
  "filterTechnologies": ["Shopify", "WooCommerce", "Magento", "BigCommerce"],
  "onlyMatching": false
}

Raise Max concurrency for large lists. Turn on Proxy configuration only if some stores block the platform's IP range — most do not.

How detection works

Every hosted or self-hosted ecommerce platform leaves fingerprints on the first response: Shopify's X-ShopId header and cdn.shopify.com assets, WooCommerce's wc- scripts and cart cookies, Magento's X-Magento-Vary cookie and static/version paths, Salesforce Commerce Cloud's demandware.store URLs, BigCommerce's bigcommerce.com CDN, and so on. The detector fetches each homepage once and checks the headers, cookies, HTML, script sources and meta tags against more than 7,600 technology fingerprints, adding implied technologies — a WooCommerce hit implies WordPress — at a lower confidence so you can tell direct evidence from inference. Because it never executes the page's JavaScript, signals that only appear after scripts run are not evaluated; for platform identification the first response is almost always enough, and the trade-off buys speed and a much lower cost per store.

The fingerprint database is the open-source webappanalyzer technology dataset, used under the GNU GPL v3. The matching engine in this actor is original code.

Pricing

You pay per store saved to the dataset. With Store only matching websites on, that means per store found on the platforms you listed; with it off, per store checked. There are no subscriptions, seats or minimums, and you can cap the maximum spend of a run before it starts.

Scope and responsible use

The actor requests only the publicly served homepage of each URL you provide, exactly as a browser would. It requires no login, no cookies and no credentials, does not crawl beyond the page you give it, does not add anything to a cart or touch a checkout, and does not attempt to bypass any access control or bot-protection challenge. You are responsible for using the results in accordance with the laws that apply to you and the terms of the websites you check.

Limitations

• Very large retail homepages that deliberately stall automated traffic can exceed the request timeout; they come back as rows with an error rather than being dropped.
• Headless storefronts that put a custom front end in front of the platform (Shopify Hydrogen, commercetools, a Next.js front on Magento) expose fewer platform signals and may be reported with lower confidence or as the front-end framework only.
• A marketing site and its store sometimes live on different domains; only the URL you supply is fetched.
• Marketplaces and custom-built stores that use no recognised platform return ecommerce: null with the rest of their stack intact.
• Version numbers are reported only where a store publishes them.

Something not detected?

Open an issue on this actor's Issues tab with the URL and the platform you expected. Include a reproducible input and the expected signal so the report can be investigated.

Interpret empty results and errors

A null technology field means no recognized signal was found in the inspected response; it does not prove absence. A returned error means that URL was not successfully analyzed. With onlyMatching: true, non-matches and errors are omitted from the dataset, so a zero-row result cannot distinguish an unavailable site from no matches. Start with onlyMatching: false when checking coverage.

Every saved row, including an error row, incurs the configured per-row event fee. Proxy selection and run duration can affect operating costs. Do not increase concurrency or enable a more expensive proxy merely to work around an unclear result.

For a reproducible problem, open this Actor’s Issues tab with a small public input, expected behavior and relevant error text. Remove tokens and confidential information. No response-time or uptime guarantee is offered.

Use the result in an automation

In Make or n8n, use the Apify integration to run this Actor with the same JSON input, wait for completion, then retrieve its default dataset. Route failed runs and error rows to a review step before sending valid results to your spreadsheet or CRM. Scheduling does not make these Actors emit only new records: deduplicate downstream using the source URL or record ID.

For Node.js, install the official apify-client package and set your own APIFY_TOKEN environment variable. The following example starts a paid run with a small spending limit.

import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const input = {
  "startUrls": [
    "https://www.allbirds.com",
    "https://www.patagonia.com",
    "https://www.squarespace.com"
  ],
  "filterTechnologies": [
    "Shopify",
    "WooCommerce",
    "Magento",
    "BigCommerce",
    "Salesforce Commerce Cloud",
    "Squarespace Commerce",
    "Wix eCommerce",
    "PrestaShop",
    "OpenCart",
    "Shopware",
    "commercetools",
    "Fourthwall",
    "Ecwid",
    "Volusion"
  ],
  "onlyMatching": false
};
const run = await client.actor('keystonelabs/ecommerce-platform-detector').call(input, {
  memory: 512, timeout: 180, maxTotalChargeUsd: 0.10
});
if (run.status !== 'SUCCEEDED') throw new Error('Run did not succeed: ' + run.id);
const { items } = await client.dataset(run.defaultDatasetId).listItems({ limit: 100 });
console.log(items);

The first 100 rows are retrieved in this example. Use the dataset API pagination for a larger result. Keep credentials out of shared inputs and source files.

A useful next step

• Website Tech Stack Detector | Bulk URL Lookup: qualify a website list by its technology. This is a separate Actor with its own input and price.

Understand the run at a glance

Open Run summary in Output for counts of unique URLs, analyzed responses, failures, matches and saved rows. It remains useful when onlyMatching produces an empty dataset. The summary is written at normal completion and does not add a billable dataset row. Failed or aborted runs may not have a final summary; check run status first. The summary contains counts only, not a second copy of scraped content.