How to find Shopify stores to sell to, and the 15 tools that do it

Every list of Shopify stores is too long. The useful question is not who runs Shopify, it is who runs Shopify and something that proves they buy things.

StackScan answers that in one query. Pick the platform, add the app or technology, add a country and a TLD, and export what comes back. Its free Shopify usage page will give you the headline counts before you spend anything.

Stores running Klaviyo in Germany. Stores running a subscription app but no loyalty app. Stores on Shopify Plus in the United Kingdom. Those are lists you can sell against, and none of them are things a plain store directory can produce.

The app filter is the part that changes outreach. A merchant already paying monthly for three apps has demonstrated willingness to pay. A merchant with a bare theme and no apps has demonstrated the opposite, and no subject line fixes that.

At $39 a month it is a fraction of what the dedicated ecommerce databases charge, and the index is far broader than Shopify, so the same subscription covers WooCommerce, BigCommerce and the rest.

Delivery is not a bottleneck either. REST API, CRM integrations, Zapier and n8n, an MCP server, or plain CSV with up to 20 fields per domain.

What it does not give you is merchant revenue or a named contact. Pair it with a revenue estimator from further down, and a contact tool at the end.

Charm's argument is that a list of stores is not the hard part. Knowing which are growing is.

It scores ecommerce brands on growth trajectory and operational health, then lets you filter on those scores rather than on static attributes.

For a service business that is a genuinely different filter. A store growing 40% year on year has budget and problems. A flat store has neither.

The data goes deeper than technology detection, pulling in marketplace presence, social signals and retail distribution.

Pricing is quote only, which puts it out of reach for anyone testing a market rather than committed to one.

It is also brand focused rather than long tail, so the smallest stores are thin or absent.

BuiltWith holds lists of every site running Shopify, and lists of every site running each Shopify app, going back years.

The history is what you cannot get elsewhere. Stores that installed a competing app last quarter, or dropped one, are a far warmer list than stores that merely exist.

Its free Trends pages will tell you adoption counts per technology without an account, which is often enough to size an opportunity before spending anything.

Paid access opens at $295 a month on annual billing, which is a lot for ecommerce prospecting specifically when narrower tools cost a quarter of that.

The lists are also raw. You get domains and technologies, not merchant revenue, product counts or contacts.

SimilarTech covers similar ground to BuiltWith with more emphasis on trend than on archive.

For ecommerce that means you can see which apps and platforms are gaining share in a market before you commit to building for them.

It will also produce lists of sites using a given technology, filtered by country and category, which is the part relevant here.

Nothing is published on pricing, and the product now presents itself as part of Similarweb, so ask about roadmap before signing a term.

Coverage of the very long tail of small Shopify stores is weaker than the ecommerce specialists.

The tools above return domains. PipeCandy returns companies.

It profiles ecommerce and direct to consumer brands with the kind of firmographic detail a domain list cannot carry: estimated GMV band, category, fulfilment model, headcount and growth signals.

That matters when your product only suits merchants of a certain size. A filter on estimated GMV removes the hobby stores before you ever look at them.

It also publishes market research on how D2C brands are performing, which is useful context if you sell into the category rather than just to individual stores.

Pricing is quote only, and it is aimed at teams with a real ecommerce go to market rather than at someone testing an idea.

Coverage skews to established brands, so the long tail of small Shopify stores is thinner here than in a raw technology index.

On data quality alone this would rank second. It sits at six because of how the plans are drawn.

That specialism buys you fields nothing general purpose has. Estimated monthly sales, product count, installed apps, technologies, social followings and merchant contact details, across 13.7 million active stores on 405 platforms.

For anyone selling to merchants the estimated revenue field alone is worth the subscription, because it separates the hobby stores from the businesses.

Access starts at $75 a month, but that tier is the interface only. CSV export and API sit on the $250 Pro plan, so the entry price is not the price of a list you can work.

That is why five cheaper tools rank above it. Every one of them will hand you an exportable list for less than Store Leads charges to let you download yours.

Outside ecommerce it does nothing at all, which is either fine or a dealbreaker depending on what else you sell.

Wappalyzer detects around 8,000 technologies with accuracy that is consistently among the best.

It will produce lists of sites using a given technology, and its API is the nicest to build against in this category.

For ecommerce prospecting specifically the maths is hard to justify. $250 a month buys a smaller ecommerce list than $39 does elsewhere, because the catalogue is not built around merchant apps.

Where it earns its place is verification. If you have a list from a cheaper source and need to confirm what each store actually runs, this is the most reliable checker.

The free extension and roughly 50 free monthly lookups make that verification workflow cheap to test.

Open any Shopify store, click the extension, and see the theme, the apps and often the products.

For qualifying a specific prospect before you write to them, that is exactly the right amount of information and it costs nothing.

Knowing a store runs a competing app changes your first sentence. Knowing it runs none changes whether you write at all.

It builds no list. One store at a time, by hand, forever.

Used alongside a list builder it is close to ideal. Used alone it is a research toy.

PPSPY estimates how much a Shopify store is selling, which is the number that decides whether a prospect is worth your time.

It reads public signals to model sales volume per store and per product, and tracks that over time.

For anyone selling a percentage-of-revenue service, or simply trying to avoid pitching hobby stores, that estimate is the qualifying filter.

Entry pricing sits around $20 a month, which is cheap for what it does.

The numbers are estimates from outside the store. Treat them as bands rather than facts, and never quote them back to a merchant who knows their real figures.

ShopHunter overlaps heavily with PPSPY and costs more, so the question is what the extra buys.

Mostly it buys tracking. You add stores to a watchlist and it follows their sales over time rather than giving you a single reading.

A store growing month on month is a very different prospect from one shrinking, and a snapshot cannot tell you which you are looking at.

Around $50 a month, roughly double PPSPY.

Same caveat applies. These are external estimates, and the smaller the store the less you should trust them.

Dropship.io has a store database you can filter, which nudges it into the list building category, just.

You can search stores by revenue band, by product category and by platform, and get sales estimates alongside.

The framing is squarely aimed at people who want to copy what is selling, not at people who want to sell to the merchant. That colours every filter.

For B2B prospecting it still works, you just have to read past the language.

From about $29 a month with a trial, which is reasonable for a partial list builder with revenue data attached.

A store with no traffic has no revenue, whatever its theme looks like.

Similarweb models traffic per domain and breaks it down by channel, which for merchant prospecting doubles as a crude revenue proxy and a pitch angle.

A store pulling most of its visits from paid is spending money to acquire customers, which tells you something useful before you write to them.

It will do limited list building by category and traffic band, though that is not its strength.

Estimates degrade badly on small sites, which is most Shopify stores. Quote based pricing, and expensive.

Use it to rank the top of a list rather than to build one.

ECDB covers the ecommerce market rather than individual stores you can sell to.

Country level revenue, category growth, top retailer rankings, the kind of figures that go in a strategy document.

That makes it useful for deciding which market to enter and useless for deciding who to email once you have.

It is on this page because market selection genuinely precedes list building, and choosing the wrong country is an expensive mistake to make with a good list.

Quote only pricing with teaser figures visible free.

Grips sits alongside ECDB with a finer grain, reporting demand and share at product and category level rather than by country alone.

For someone selling into a specific vertical, knowing which product categories are growing tells you which merchants to prioritise.

It is analysis rather than prospecting. There is no export of stores to contact.

Quote only, aimed at brands and investors rather than at agencies building a pipeline.

Included here so the top of the list has a ceiling to be measured against, and because market data is genuinely a step people skip.

Every tool above this line gives you domains. At some point you need a human being to write to.

Apollo takes a domain list and returns named contacts with emails, titles and often phone numbers, then sequences the outreach if you want it to.

For small merchants the founder is usually the only contact and easy to find. For larger brands you need to identify the ecommerce manager rather than the CEO, and that is where a contact database earns its money.

Around $49 per seat per month for the whole outbound stack, which is good value if you use all of it.

Contact accuracy on small ecommerce businesses is noticeably worse than on traditional B2B, because merchants often run on personal or generic addresses.