Ecommerce store database: what it is, what is inside, and how to choose one

What is an ecommerce store database?

At its simplest, an ecommerce store database connects a store domain to facts observed on that store's public storefront. It may show that the business sells through Shopify, WooCommerce, Wix, or another platform; which storefront technologies or applications were detected; where the store appears to operate; and whether it publishes a business email address or telephone number. Buyers use those attributes to build a relevant list instead of starting with an undifferentiated list of websites.

A store database differs from a general business directory because the record is based on the storefront itself. A directory entry can survive after a shop closes, changes platform, or stops selling. A well-maintained store database treats the live or most recently observed storefront as evidence and records when that evidence was collected. It also differs from a raw domain list: a domain is only an address until a process establishes that it currently represents a store.

What should each store record contain?

The domain should be the record's stable identifier, accompanied by fields that let a buyer understand and audit the result. Platform identifies the commerce system. Technologies and apps describe detected storefront components. Currency and country provide commercial context. A public business email or store-published business phone can make the record actionable, but contact coverage should be reported separately because not every legitimate store publishes both. Most importantly, the source URL and last-seen date show where the claim came from and how current it is.

OpenData's records follow that evidence-based model. In the warehouse snapshot dated 15 Nov 2026, OpenData reported 1,504,744 verified ecommerce stores among 1,554,041 tracked domains. Exactly 205,297 verified stores had a public business email, and 111,859 had a store-published business phone. Those separate figures are more informative than implying that every store includes every field. Buyers can inspect the model through the free sample or look up an individual domain in the free store checker.

• Domain and canonical store URL
• Ecommerce platform, such as Shopify or WooCommerce
• Detected technologies and storefront applications
• Currency and country fields where observed
• Public business email and store-published phone where available
• Source URL for the evidence behind the record
• Last-seen date and verification status

How verification should work

Verification should mean more than finding a domain that resolves. A sound process fetches the storefront, inspects observable evidence, and requires at least one detected ecommerce technology before classifying the domain as a verified store. That rule excludes many parked domains, publisher sites, corporate pages, and other live websites that are reachable but are not stores. The classification should be repeatable: another reviewer should be able to visit the recorded source and understand why the store qualified.

Fresh verification also matters before records are exported. Stores close, migrate, redirect, and change technology, so an old observation should not be presented as current without a re-check. OpenData defines a verified store as a fetched storefront with at least one detected ecommerce technology, keeps one record for one store, and re-checks stores before packaging. Its database contains 269,688 verified Shopify stores, but the more important quality signal is the method used across every platform: observed evidence, a source URL, and a last-observed date.

Pay-per-row exports versus subscription search tools

Subscription tools are usually designed for continuing search and monitoring. They can suit teams that query changing criteria throughout the year, watch technology adoption, or need repeated exports. The trade-off is recurring cost and, in some products, a distinction between searching the interface and exporting usable data. Store Leads is vendor-reported at $75/mo for search-only access and from $250/mo for export, with 14M+ stores reported (vendor-reported, checked 2026-11-15). BuiltWith Basic is $295/mo and Wappalyzer Pro is $250/mo (vendor-reported, checked 2026-11-15). Product boundaries and prices should always be checked against the current vendor offer before purchase.

A one-off export is better suited to a defined project: a campaign, a market map, a research snapshot, or a fixed segment that a team wants to own and analyse locally. It avoids paying every month for a list that may be used once, but the buyer must pay closer attention to the snapshot date and refresh policy. OpenData charges per row for its records, with no subscriptions. Its index is refreshed monthly, and a paid pull exports CSV plus a formatted Excel file, fingerprinted per buyer under a no-redistribution licence.

How to evaluate database quality

Begin by separating three quantities that are often advertised as one: domains tracked, stores verified, and stores with usable contact fields. A large tracked number may include candidates that have not qualified as stores. The verified count should use a stated rule, while email and phone coverage should be disclosed as subsets. Next, ask how duplicates are handled. Aliases, redirects, and host variants can make one business appear several times unless the database resolves them to a single canonical store record.

Then inspect provenance and freshness on individual rows, not just in marketing copy. Can you see the source URL? Is there a last-seen date? Are stores re-checked before delivery? Does the provider publish counts by platform and segment so the inventory can be reconciled? OpenData's comparison page and reports expose its definitions and counts, while its country catalog shows the country segments. Those checks will not prove every record is perfect, but they make errors easier to detect and compare.

• Ask for tracked, verified, and contactable counts as separate numbers
• Require one canonical record per store, not one per alias or redirect
• Check a sample for source URLs and last-seen dates
• Confirm whether stores are re-checked before packaging
• Compare the snapshot date and refresh policy, not just total size
• Verify that contact fields are public business facts, not personal data

Costs and where OpenData fits

The right price depends on whether the buyer needs continuous software access or a finished dataset. Our separate guide to ecommerce store database cost explains the pricing models in more detail. OpenData is positioned for buyers who want records at a simple per-row price: full records with public contacts cost $4.00 per 1,000 rows, however they are filtered. Platform- or technology-only records cost $1.00 per 1,000 rows, and every filter — platform, app, technology or country — is priced by the same per-row rates. Checkout is through PayPal, the download starts right after payment, and refreshed files can be re-downloaded free for 60 days.

That model will not replace a monitoring platform for every team, and a large headline count should never be the sole reason to buy any database. The practical test is narrower: download the sample, check whether the fields match the intended workflow, verify several records against their source pages, and compare the snapshot and delivered segment with the price. If those tests pass, a per-row database can provide the needed store intelligence without an ongoing subscription.

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