Open data for ecommerce: how online sellers and agencies actually use it

What counts as open data for an online store

A storefront publishes more than it realises. Its domain, the platform it runs on, the apps and widgets embedded in its pages, its currency and country signals, and the business email and phone it prints for customers are all public facts. Open data for ecommerce means collecting those facts at scale and organising them so they can be filtered by the questions a seller actually asks. On OpenData, a store counts as verified only when we fetched the storefront ourselves and detected at least one ecommerce technology on it; every record keeps its source URL and the date it was last observed. Contact data follows the same public-only rule — business emails a store publishes for contact, such as sales@ or support@ — never private addresses — and removal requests are honoured within 48 hours.

Workflow 1: competitor store discovery

A dropshipper or online seller entering a niche needs to know who already sells there and how those stores are set up. Start from a country or platform slice, then narrow by the technology that signals a direct competitor, such as a reviews app or a specific payment gateway. Open the surviving domains, note their pricing, shipping promises and product range, and you have a competitor map built from real storefronts instead of guesswork. The free store checker lets you inspect individual records in full before you commit to a complete list.

• Filter by country and platform first — broad slices hide the competitors that actually matter.
• Use technology signals (apps, gateways, analytics) to find stores at your size, not just the biggest names.
• Re-check a shortlist in the checker before building your offer around it.

Workflow 2: lead generation for agencies and freelancers

The most common buyers of store lists are not big companies. They are individual web designers, Shopify freelancers, email marketers and solo lead-gen operators who need a prospect list they can actually work. The workflow is simple: pick a slice that matches your service — Wix stores if you migrate websites, stores without a reviews app if you sell social proof — then work the list with a specific, relevant pitch. 205,297 stores in the database (205,297 exactly) publish a business email, and 111,859 publish a business phone, so a filtered list doubles as a contact list with no scraping of your own.

• Match the slice to the service: your pitch should be true for every store on the list.
• Start with the free sample file for your industry to test the record format before buying a slice.

Workflow 3: market sizing by country and platform

Before opening a store or launching a service in a new country, count what is actually there. Country slices run from 139,254 verified stores in the United States down to 723 in Thailand, with the United Kingdom at 5,957, Brazil at 5,675 and Germany at 3,332 — the full set is on the stores by country pages. Platform cuts answer a different question: Shopify is the largest single platform slice, at 269,688 verified stores. A big count means proven demand and proven competition; a small count can mean an open niche or a market that is not there yet. The count starts the investigation — it does not finish it.

Workflow 4: find stores using a specific app or technology

If you sell an app, a plugin or a migration service, your best prospects are stores already paying for something adjacent — or stuck on a tool they have outgrown. Technology slices make that targeting mechanical: the Cloudflare slice covers 141,204 stores and the React slice 81,205, and the stores by technology pages narrow further to stores running one named tool. A switching campaign built on a tech slice speaks to a problem the store demonstrably has today, which is why these lists outperform generic store directories for outreach.

Workflow 5: suppliers, partners and the Southeast Asia blind spot

Store data also works in reverse: sellers find suppliers, print-on-demand partners and local fulfilment options by looking at which stores operate in a target country and what they run. One coverage gap matters here. Most store databases are built with US- and EU-centric detection and miss the payment gateways that signal a real Southeast Asian store — GCash in the Philippines, QRIS in Indonesia and PromptPay in Thailand. OpenData detects these gateways, so its slices for markets like Indonesia (2,868 verified stores) and Thailand (723) include the local stores that gateway-blind databases skip entirely.

What open store data cannot do

Store data is a map, not the territory. A verified count tells you a store exists and what it runs; it says nothing about that store's revenue, traffic or how long it will stay open. Treat every figure as a dated snapshot — OpenData's current one is 15 Nov 2026 — and spot-check important records before acting on them. If you are comparing sources, the comparison page shows how OpenData's verification standard differs from other store databases.

Questions, answered straight

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