How to Pull a Targeted Ecommerce Lead List in Minutes (from a 52M-Site Database) ¶

Most "lead scrapers" make you wait. You give them a niche, they crawl the web live, and twenty minutes later you get a few hundred rows — some with an email, most without, and no way to know upfront how many you'll actually get. For a one-off that's fine. For building repeatable outreach lists across platforms and countries, it's slow and unpredictable.

There's a faster model: query a database that's already built. That's what our Shopify & Ecommerce Store Finder does — 52 million+ sites already discovered, enriched, and sitting in a live database. You pick platforms, apply filters, choose columns, and export. No crawl, no wait. This post is a practical walkthrough of how to turn that into a clean, targeted lead list.

Database, not a crawler ¶

The distinction matters. This actor doesn't go out and scrape when you press run — it reads from a pre-built, continuously updated dataset of 52M+ sites across 13 ecommerce and CMS platforms. One site = one row. Because the data already exists, results come back instantly and you can preview exactly how many leads match before you spend anything.

New sites are added daily from platform-discovery crawls, and existing records are re-enriched on a rolling schedule (contacts, tech stack, rankings). Every row carries Last Found and Last Indexed so you know how fresh it is.

13 platforms, 52M sites ¶

You can pull from all platforms at once or target specific ones:

Want only Shopify stores? Pick Shopify. Pitching a WooCommerce plugin? Filter to WooCommerce. Hunting merchants on older stacks for a redesign? ASP.NET, Joomla, and Magento are right there.

59 fields per site ¶

Leave the output columns empty and every row comes back fully enriched. The highlights:

• Contact: verified Emails, Telephones (international format), owner/people names.
• Company & vertical: root/primary domain, company name, vertical.
• Socials: Facebook, Instagram, LinkedIn, X, TikTok, YouTube, Pinterest, and more.
• Firmographics: sales revenue band, employee count, SKU count, technology spend, ticker.
• Tech stack: ecommerce platform, CMS, CRM, marketing automation, payment platforms, hosting, AI tools.
• Rankings & performance: Overall Score, Tranco, Page Rank, Majestic, CRuX, Cloudflare Rank, plus performance/SEO/accessibility/best-practices scores.
• Geo: city, state, zip, country.
• Dates: first detected, last found, first/last indexed.

Or select just the columns you need for a lean export.

Filter down to exactly who you want ¶

The point isn't 52M rows — it's the few thousand that fit. Filters narrow results without re-running or paying more:

• Countries — one or more ISO-2 codes (US, DE, GB…).
• Keyword — substring match in domain or company name.
• Only with email / Only with phone — drop rows you can't act on.
• Phone country code — e.g. keep sites with a +44 number.
• Extra filters — JSON conditions on any of the 59 columns, with 9 operators (equals, contains, starts_with, in_list, not_empty…), combined with AND.

A concrete example — Shopify-style stores in Berlin that accept PayPal:

[
  { "column": "Payment Platforms", "operator": "contains", "value": "PayPal" },
  { "column": "City", "operator": "equals", "value": "Berlin" }
]

Then sort by Overall Score, Sales Revenue, Employees, Tranco, or Last Found to get the best leads instead of a random slice — up to 100,000 rows per run, deduplicated by domain.

Preview before you pay ¶

Use Count only to see how many sites match your filters for almost nothing. Dial the filters in, confirm the volume, then run the real export. No more paying to discover that your niche + country combo only had 40 usable rows.

Export anywhere ¶

Every run stores results in a dataset you can download as CSV, JSON, XML, Excel, HTML, or JSONL — no re-run needed. Via API:

https://api.apify.com/v2/datasets/{datasetId}/items?format=csv

Drop it straight into your CRM, cold-email tool, or spreadsheet.

Who uses it ¶

• Web agencies — find businesses on outdated or specific platforms and pitch redesigns or migrations.
• SaaS sales — target stores by tech stack, revenue band, or employee count (e.g. everyone running a competing tool).
• Lead generation — bulk-export emails + phones filtered by country and vertical.
• Market research — measure platform market share, payment adoption, and geographic distribution.
• SEO & marketing — discover sites by performance score, ranking, or ad presence.

At $1.50 per 1,000 leads with no subscription, a few thousand targeted, enriched rows costs a few dollars.

A note on responsible use ¶

These are public business records — company domains, published business emails and phones, tech stack, and firmographics — the same data that powers standard B2B prospecting. Use it for legitimate outreach and research: respect opt-outs and suppression lists, honor Compliance/Exclusion flags in the data, and keep your campaigns within GDPR, CAN-SPAM, and local rules. B2B contact data is a tool for relevant outreach, not spam.

Bottom line ¶

If you need ecommerce and CMS leads — by platform, country, tech, or revenue — Shopify & Ecommerce Store Finder turns a 52M-site database into a targeted, enriched, deduplicated list in minutes: verified emails and phones, 59 fields, previewable counts, and clean exports, for $1.50 per 1,000. Query the database instead of waiting on a crawl.

What filter or field would make your prospecting easier? Tell me and I'll look at adding it.