DEV Community: StoreSignal

Build a targeted list of Shopify stores by country, niche and installed apps

StoreSignal — Fri, 13 Nov 2026 19:52:43 +0000

If you sell an app, a service or a wholesale product to online stores, the useful list is rarely "all Shopify stores". It is something like "UK skincare stores that already send email with Klaviyo but have no subscription app". This post builds exactly that list in two API calls, with real numbers from a run made on 13 November 2026.

Both steps use public data only: no login, no browser, no bot-protection tricks.

Step 1: pick stores by country and niche

Shopify Store Finder searches a database of 57,202 Shopify stores that appear in two open popularity lists (Chrome UX Report and Majestic Million). You filter by country, currency, keywords, domain extension, traffic rank and catalog size. Results come back ordered by popularity, so the first rows are the stores with the most traffic.

curl -X POST "https://api.apify.com/v2/acts/storesignal~shopify-store-finder/run-sync-get-dataset-items?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "maxResults": 40,
    "countries": ["GB"],
    "keywords": ["skincare", "skin care"],
    "minProducts": 20,
    "maxProducts": 2000
  }'

The database has 4,296 stores registered in the United Kingdom. 96 of them mention skincare in their name, description or domain, and 83 fit the catalog range above. The run returns the top 40 in about two seconds, because the Actor filters a bundled file and makes no network requests.

One record from that run:

{
  "domain": "upcirclebeauty.com",
  "url": "https://upcirclebeauty.com",
  "trafficRank": 500000,
  "name": "UpCircle Beauty",
  "country": "GB",
  "currency": "GBP",
  "city": "London",
  "productCount": 134,
  "dataUpdatedAt": "2026-11-13"
}

trafficRank is the Chrome UX Report bucket: 500000 means the store is among the 500,000 most visited sites. productCount lets you drop both the tiny shops and the giant catalogs before you spend anything on enrichment.

Step 2: add installed apps and contacts

Pass the domain values to Shopify Store Intelligence. It reads each store's public pages and returns detected apps, public contact addresses and social profiles.

from apify_client import ApifyClient

client = ApifyClient("<APIFY_TOKEN>")

found = client.actor("storesignal/shopify-store-finder").call(run_input={
    "maxResults": 40,
    "countries": ["GB"],
    "keywords": ["skincare", "skin care"],
    "minProducts": 20,
    "maxProducts": 2000,
})
domains = [s["domain"] for s in client.dataset(found["defaultDatasetId"]).iterate_items()]

analyzed = client.actor("storesignal/shopify-store-intelligence").call(run_input={
    "storeUrls": domains,
    "maxProductsPerStore": 0,
    "proxyConfiguration": {"useApifyProxy": True},
})
stores = [
    s for s in client.dataset(analyzed["defaultDatasetId"]).iterate_items()
    if s["status"] == "ok"
]

maxProductsPerStore: 0 skips the catalog statistics, which this segment does not need. In our run 39 of the 40 stores returned ok and one was blocked (blocked stores are not charged).

Step 3: cut the segment

What the 39 stores run:

App	Stores
Klaviyo	32
Gorgias	16
Judge.me	14
Trustpilot	14
Yotpo	13
Triple Whale	13
LoyaltyLion	10
Recharge	9

The segment is a few lines of plain Python:

SUBSCRIPTIONS = {"Recharge", "Skio", "Ordergroove", "Bold", "Smartrr"}

segment = [
    s for s in stores
    if "Klaviyo" in s["apps"] and not SUBSCRIPTIONS & set(s["apps"])
]
with_email = [s for s in segment if s["contacts"]["emails"]]

In this run the segment is 16 stores, and 12 of them publish a contact email. Swap the two conditions for your own case. "Has a reviews app but no loyalty program" or "uses Gorgias, so support volume is real" are the same two lines.

A trimmed record from the segment:

{
  "domain": "upcirclebeauty.com",
  "storeName": "UpCircle Beauty",
  "status": "ok",
  "contacts": { "emails": ["hello@upcirclebeauty.com"] },
  "apps": ["GemPages", "Google Tag Manager", "Klaviyo", "Microsoft Clarity", "Shopify Shop App", "Triple Whale", "Zipify"]
}

What this run cost

Both Actors are pay per result: $0.003 per store returned by the finder and $0.008 per successfully analyzed store. 40 stores listed and 39 analyzed is $0.43. The Apify free plan includes $5 of monthly credit, which covers about 450 stores through both steps.

Limits worth knowing

The database is not every Shopify store. It holds stores that appear in the two popularity lists, so it leans toward stores with real traffic. Stores behind Cloudflare or another proxy are not detected.
App detection reads the homepage HTML. An app that loads only at checkout or after a click is missed, so "no subscription app" means "none visible on the homepage". Check the store before you write to it.
Keywords are substrings, so a store that mentions skincare once in its description also matches. Read the names before you send anything.
The addresses returned are public company contacts. Whether you may email them depends on the rules that apply to you and the recipient (GDPR and ePrivacy in the EU and UK, CAN-SPAM in the US).

Have a list of domains instead of a niche? The first guide covers that direction: any domain list in, contacts and tech stack out.

Turn a list of domains into qualified e-commerce leads (emails, platform, installed apps)

StoreSignal — Fri, 13 Nov 2026 14:21:51 +0000

Most outreach lists start the same way: a spreadsheet of company domains and nothing else. Before anyone can write a relevant first line, somebody has to open each site and answer three questions:

Is there a public contact address, and which one is the general inbox?
What does the site run on (Shopify, WooCommerce, Webflow, something custom)?
Which tools are already installed (reviews, subscriptions, email, chat, analytics)?

Doing this by hand takes two to three minutes per site. This post shows how to do it for a few thousand domains with two small Apify Actors and about ten lines of code. Both read only public pages, with no browser and no login.

Step 1: enrich any domain list

Website Contact & Tech Stack Finder takes domains in any form and returns one record per site.

curl -X POST "https://api.apify.com/v2/acts/storesignal~website-contact-tech-finder/run-sync-get-dataset-items?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "urls": ["www.beardbrand.com", "basecamp.com", "www.deathwishcoffee.com"],
    "proxyConfiguration": { "useApifyProxy": true }
  }'

A trimmed record from a real run:

{
  "domain": "www.beardbrand.com",
  "status": "ok",
  "primaryEmail": "support@beardbrand.com",
  "socials": { "instagram": "https://www.instagram.com/beardbrand" },
  "contactPageUrl": "https://www.beardbrand.com/pages/contact",
  "platform": "Shopify",
  "technologies": ["Cloudflare", "Judge.me", "Klaviyo", "Recharge", "Shopify Shop App"]
}

Two fields do most of the work for segmentation: platform and technologies. A list of 2,000 domains becomes "the 310 Shopify stores that use Klaviyo but have no reviews app", which is a list you can write one honest, specific message for.

What to expect: in our test on 19 well-known sites, 18 returned a record and 8 of those exposed a public email. Large brands often hide addresses behind forms; small and mid-size businesses publish them far more often. Sites that render everything in JavaScript may return no email.

Step 2: go deeper on the Shopify stores

Filter the output of step 1 to platform == "Shopify" and pass those domains to Shopify Store Intelligence:

from apify_client import ApifyClient

client = ApifyClient("<APIFY_TOKEN>")

sites = client.actor("storesignal/website-contact-tech-finder").call(
    run_input={"urls": domains, "proxyConfiguration": {"useApifyProxy": True}}
)
shopify = [
    item["domain"]
    for item in client.dataset(sites["defaultDatasetId"]).iterate_items()
    if item.get("platform") == "Shopify"
]

stores = client.actor("storesignal/shopify-store-intelligence").call(
    run_input={"storeUrls": shopify, "proxyConfiguration": {"useApifyProxy": True}}
)
for store in client.dataset(stores["defaultDatasetId"]).iterate_items():
    print(store["inputUrl"], store["status"], store.get("appCount"))

For each store you get the store name, country, currency, product count, public contacts and the detected apps. That is enough to sort a list by store size and to skip stores that already use your competitor.

No domain list yet? Start from a ranked list of Shopify stores

If you do not have domains to begin with, Shopify Store Finder returns popular Shopify stores filtered by country, currency, keyword and catalog size, ordered by traffic rank:

{
  "maxResults": 500,
  "countries": ["GB"],
  "keywords": ["skincare", "beauty"],
  "minProducts": 20
}

Each record carries the domain, store name, country, currency and product count. Feed the domain values into step 2 to add contacts and installed apps. A second guide walks through that direction with real numbers.

What this costs

The Actors are pay per result: $0.004 per analyzed site, $0.008 per analyzed Shopify store and $0.003 per store returned by the finder. Blocked or unreachable sites are returned with a status and are not charged. A list of 1,000 domains with 200 Shopify stores in it costs about $5.60, and the Apify free plan includes $5 of monthly credit.

Limits worth knowing

Only public pages are read. If a site publishes no email, there is no email.
Some large stores refuse requests from cloud servers. Those come back as blocked, not as wrong data.
Contact addresses of companies are fine to collect; before you send anything, check the rules that apply to you and your recipients (GDPR and ePrivacy in the EU, CAN-SPAM in the US).