You can identify Shopify stores with public signs of Klaviyo by screening storefront pages or using a technology lookup service—but a match is only a lead, not proof of a current Klaviyo account. A practical low-cost approach is to start with domains you have a legitimate reason to inspect, check for Shopify and Klaviyo-related signals in their public pages, save dated evidence, and manually verify promising candidates.
What a storefront match can—and cannot—tell you
Klaviyo’s Shopify integration can sync customer profiles, orders, and consent data. Klaviyo also documents onsite tracking and signup forms that can use its Shopify app embed. Those features create public implementation clues, but a visible script or form does not establish that a store is currently paying for or actively using Klaviyo. Nor can a storefront scan reveal the merchant’s plan or how extensively the platform is used. Klaviyo’s Shopify setup documentation explains the integration and app embed.
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Keep the two identification tasks separate: first determine whether a domain appears to be a Shopify storefront; then look for Klaviyo-related evidence. The official sources cited here do not document a complete, free public registry of Shopify stores connected to Klaviyo.
Which signals are worth checking?
Shopify evidence
Look for Shopify-related page or storefront patterns as one part of the assessment. A technology lookup service can also classify a URL. Do not treat a single platform clue as conclusive: storefronts can be customized, and the visible page may not expose every part of their commerce setup.
Klaviyo evidence
Inspect the public page source and loaded page for Klaviyo-associated scripts, endpoints, or form references. An embedded signup form or onsite tracking code can be a useful clue because Klaviyo documents those storefront behaviors. Klaviyo’s Shopify integration guide and its instructions for adding an embed form to a Shopify site describe the relevant implementation context.
Do not make a decision from one substring alone. A tag may be conditional, stale, or associated with a third-party implementation; conversely, consent choices or a storefront that loads behavior differently can hide a signal. Treat each clue as evidence to review, not a definitive customer-status check.
Screen candidate domains with Python
A small Python script can fetch public pages and flag possible clues for manual review. The example below uses broad text checks rather than a validated detection rule. It does not establish accuracy, and it is not a substitute for a live technology scan or human inspection. Run it only against domains you have a legitimate reason to inspect, respect site terms and applicable rules, and keep request rates low.
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import time
from datetime import datetime, timezone
from urllib.parse import urlparse
import requests
USER_AGENT = "ExampleTechnologyCheck/1.0 (contact: [email protected])"
DELAY_SECONDS = 3
TIMEOUT_SECONDS = 15
# Supply domains from an authorized source; one domain per line.
with open("domains.txt", encoding="utf-8") as f:
domains = [line.strip() for line in f if line.strip()]
rows = []
for domain in domains:
host = domain.removeprefix("https://").removeprefix("http://").strip("/")
url = f"https://{host}/"
observed_at = datetime.now(timezone.utc).isoformat()
result = {
"input_domain": domain,
"page_url": url,
"observed_at_utc": observed_at,
"http_status": "",
"shopify_clue": False,
"klaviyo_clue": False,
"status": "needs verification",
"error": "",
}
try:
response = requests.get(
url,
headers={"User-Agent": USER_AGENT},
timeout=TIMEOUT_SECONDS,
allow_redirects=True,
)
result["page_url"] = response.url
result["http_status"] = response.status_code
page = response.text.lower()
result["shopify_clue"] = any(
clue in page for clue in ("cdn.shopify.com", "myshopify.com")
)
result["klaviyo_clue"] = any(
clue in page for clue in ("klaviyo", "klaviyo.com")
)
if response.ok and result["shopify_clue"] and result["klaviyo_clue"]:
result["status"] = "candidate"
except requests.RequestException as exc:
result["error"] = type(exc).__name__
rows.append(result)
time.sleep(DELAY_SECONDS)
with open("screened_stores.csv", "w", newline="", encoding="utf-8") as f:
writer = csv.DictWriter(f, fieldnames=rows[0].keys() if rows else [])
if rows:
writer.writeheader()
writer.writerows(rows)Use domains.txt for a domain list you are permitted to use. The example checks a handful of broad page-source strings; customize it only with signals you can explain and verify. A no-match result means the script did not see those strings in that response, not that the store does not use Klaviyo. A positive result is marked “candidate” only when the response succeeds and both broad checks appear.
Review the output before relying on it
- Keep the input domain, final page URL after redirects, HTTP status, observation time, and the exact evidence you saw.
- Inspect candidate pages manually and confirm that the clues appear in relevant storefront behavior rather than relying on a raw text hit.
- Recheck important candidates later; pages and technology records can change.
- Handle timeouts, blocked requests, and non-success responses as unknowns, not as evidence that a technology is absent.
When to use a lookup service instead
Managed lookup services can save time when you already have candidate domains. Compare what each route actually returns: a cached database record is not the same as a fresh scan, and the cited sources do not provide a head-to-head accuracy benchmark.
| Route | What it supports | Important limit |
|---|---|---|
| Manual page inspection | Review the public page and source for Shopify and Klaviyo-related clues. | Signals may be conditional, missing, stale, or ambiguous. |
| Wappalyzer technology lookup | Its API documentation describes URL technology lookups, including cached and live options. | API access requires an eligible plan; a standard lookup costs 1 credit per URL, while a live recursive lookup costs 5 credits per URL and can complete asynchronously. Wappalyzer API documentation |
| BuiltWith Free API | The documented free API provides technology-group or category counts and last-updated information. | It requires an API key and is limited to 1 request per second; the documentation does not establish it as a free bulk exporter of all domains matching a technology. BuiltWith Free API documentation |
| Custom Python screen | Combine page requests with rules and retain dated, auditable observations. | The example is an implementation approach, not tested code or a measured detector. |
Wappalyzer limits and pricing
On its pricing page accessed October 7, 2026, Wappalyzer listed 50 free technology lookups per month for free accounts and a Pro plan at $250 per month in USD. These are changeable terms; check the current Wappalyzer plans and pricing before budgeting. Wappalyzer also distinguishes cached technology data from live scanning in its FAQ; a live result can be more current, but it consumes more credits under the documented lookup options.
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How to qualify a lead responsibly
- Start with a legitimate domain source. Record where each domain came from and when it was acquired; this workflow does not make every source or use of prospect data permissible.
- Normalize and request the storefront carefully. Use HTTPS where available, follow redirects, set a timeout and identifiable user agent, and rate-limit requests. The code above waits three seconds between requests as a conservative example, not a universal safe rate.
- Collect more than one independent clue. Assess Shopify evidence separately from Klaviyo-associated scripts, endpoints, or forms.
- Save the evidence and date. Preserve the page URL, HTTP result, observation timestamp, and reason for classifying a domain as a candidate.
- Verify before acting. Revisit the public page or run an appropriate live lookup, then manually inspect the storefront before using the result for outreach or qualification.
Currentness is a separate question from detection. A record may be cached, and visible page behavior may differ from server-side commerce data. Klaviyo’s Hydrogen integration documentation distinguishes synced commerce data from onsite website activity. That is another reason a browser-visible signal cannot describe the full integration.
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