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You can find Shopify storefronts with signs of Klaviyo by inspecting public pages, checking technology lookup services, or screening a list of domains with Python. These methods identify candidates, not confirmed customers: a storefront signal cannot prove that a business currently pays for Klaviyo, which plan it has, or how extensively it uses the platform.
What counts as evidence of Klaviyo on a Shopify store?
Klaviyo documents a Shopify integration that syncs customer profiles, orders, and consent data. Its storefront setup can also use onsite tracking and sign-up forms through the Klaviyo app embed. Those features suggest clues to look for in a public page’s rendered content or source code, such as Klaviyo-associated scripts, endpoints, or form references. Klaviyo’s Shopify setup documentation and its instructions for adding an embed form describe these implementation paths.
A clue is not proof of an active commercial relationship. A script may be conditional, left behind after a configuration change, or supplied by a third party. A form may only appear after a visitor’s consent or an interaction. Conversely, a storefront may use Klaviyo without exposing an obvious clue on the page you inspect. Treat each match as a lead to verify.
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| Route | Useful for | Important limit |
|---|---|---|
| Manual page or source inspection | Checking a small number of stores and understanding the specific clue. | Signals may be absent, conditional, stale, or ambiguous. Klaviyo documents the embed and onsite JavaScript behavior, but not a universal signature that proves use. |
| Wappalyzer lookup | Checking a domain against a technology detection service, with cached or live lookup options. | The lookup API requires an eligible plan. Live recursive checks use more credits than standard lookups and may finish asynchronously. |
| BuiltWith Free API | Querying documented technology-group or category counts and last-updated information. | It is not documented as a free bulk exporter of every domain matching a technology. |
| Custom Python screening | Applying repeatable checks to a domain list and saving dated evidence for review. | The example below is an implementation approach, not a tested detector or an accuracy benchmark. |
Can you get a complete list for free?
The official sources cited here do not establish a complete, free public registry of Shopify stores connected to Klaviyo. Store discovery and app detection are separate tasks: first obtain candidate domains from a source you are permitted to use, then determine whether each appears to be a Shopify storefront and whether it shows Klaviyo-associated clues.
Wappalyzer’s pricing page, accessed October 7, 2026, listed 50 free technology lookups per month for free accounts and a Pro plan at $250 per month, with prices labeled USD. Its terms can change, so check the current plans and pricing before budgeting. The API documentation, also accessed October 7, 2026, lists one credit per URL for a standard technology lookup and five credits per URL for a live recursive lookup; the latter may complete asynchronously. See Wappalyzer’s technology lookup API documentation for current details.
BuiltWith’s Free API documentation describes technology-group or category counts and last-updated information. It requires an API key and documents a limit of one request per second. That scope does not support treating it as a free, complete domain-list export.
Screen a domain list with Python
The following example fetches a public homepage and checks for independent clues. It labels hits as candidates, saves the observed page URL and time, and does not claim that the store is a current Klaviyo customer. The indicator strings are deliberately illustrative: the official setup documentation establishes relevant integration behavior, not a complete set of signatures. Review and adapt the clues to the pages you are entitled to inspect.
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Use domains with a legitimate source and keep that source and acquisition date alongside your input list. This is a small screening example, not a production crawler: it has a per-request timeout and a delay between requests, but it does not implement a comprehensive crawl policy, robots handling, persistent retry queue, or robust domain normalization.
import csv
import time
from datetime import datetime, timezone
from urllib.parse import urlparse
import requests
INPUT_CSV = "domains.csv" # One domain per row, in a column named domain
OUTPUT_CSV = "screened.csv"
DELAY_SECONDS = 2
TIMEOUT_SECONDS = 15
# Illustrative clues only; not a validated or exhaustive signature list.
SHOPIFY_CLUES = ("cdn.shopify.com", "myshopify.com")
KLAVIYO_CLUES = ("klaviyo", "static.klaviyo.com")
def homepage_url(value):
value = value.strip()
if not value:
return None
if "://" not in value:
value = "https://" + value
parsed = urlparse(value)
if parsed.scheme not in {"http", "https"} or not parsed.netloc:
return None
return value
results = []
with open(INPUT_CSV, newline="", encoding="utf-8") as source:
for row in csv.DictReader(source):
requested_url = homepage_url(row.get("domain", ""))
if not requested_url:
continue
observed_at = datetime.now(timezone.utc).isoformat()
try:
response = requests.get(
requested_url,
timeout=TIMEOUT_SECONDS,
headers={"User-Agent": "StorefrontSignalCheck/1.0 (contact: you@example.com)"},
)
page = response.text.lower()
shopify_hits = [clue for clue in SHOPIFY_CLUES if clue in page]
klaviyo_hits = [clue for clue in KLAVIYO_CLUES if clue in page]
status = "candidate" if shopify_hits and klaviyo_hits else "no clear match"
results.append({
"input_domain": row.get("domain", ""),
"final_url": response.url,
"http_status": response.status_code,
"observed_at_utc": observed_at,
"shopify_clues": ";".join(shopify_hits),
"klaviyo_clues": ";".join(klaviyo_hits),
"result": status,
"error": "",
})
except requests.RequestException as exc:
results.append({
"input_domain": row.get("domain", ""),
"final_url": "",
"http_status": "",
"observed_at_utc": observed_at,
"shopify_clues": "",
"klaviyo_clues": "",
"result": "needs review",
"error": type(exc).__name__,
})
time.sleep(DELAY_SECONDS)
with open(OUTPUT_CSV, "w", newline="", encoding="utf-8") as destination:
fields = [
"input_domain", "final_url", "http_status", "observed_at_utc",
"shopify_clues", "klaviyo_clues", "result", "error",
]
writer = csv.DictWriter(destination, fieldnames=fields)
writer.writeheader()
writer.writerows(results)Install the dependency with python -m pip install requests. Save the input as domains.csv with a domain header, then run the script. The output is an auditable screening record, not a definitive classification. The placeholder contact in the user agent should be replaced with a real contact method before using the script beyond a personal example.
Improve the screening before using it at scale
- Use more than one independent clue for each platform. A substring match alone can be triggered by unrelated content, and a page can omit scripts that load only after consent or interaction.
- Keep the exact clue, final page URL, HTTP status, observation time, source of the domain, and acquisition date. Do not convert a timeout or blocked request into a negative match.
- Set a request pace appropriate to the site and stop if a host signals that requests are unwelcome. The two-second pause is an example, not a universal safe rate.
- Before contacting a business, revisit the page or use an appropriate live lookup, then manually inspect the relevant page and confirm that the evidence is meaningful.
How to judge whether a match is current
Technology lookup records and storefront code can both lag reality. Wappalyzer distinguishes cached technology lookups from live scans; its FAQ explains that lookup results may differ in freshness. A live scan is a newer observation, not proof of account status.
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Storefront architecture also affects what a page reveals. Klaviyo’s Hydrogen integration documentation distinguishes commerce data synchronized server-side from onsite website activity. A page-only check can therefore miss relevant implementation, while a visible trace can remain after use has changed. Record when each signal was observed and recheck promising leads close to the time you act on them.
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- Freshness: Is the result a cached record or a live observation, and when was it last updated?
- Coverage: Does the service identify the storefront platform, the marketing technology, or both? What does the specific endpoint return?
- Usage limits and cost: Compare lookup credits, monthly allowances, rate limits, and plan requirements against the size and frequency of your checks.
- Evidence confidence: Can you see the underlying clue and recheck it yourself, or are you relying on a technology label without context?
The cited sources do not establish a head-to-head accuracy result for these approaches. Pick the route that gives you enough dated evidence to qualify a lead, then verify it manually rather than treating a vendor label or script hit as certainty.
What a Klaviyo signal can—and cannot—tell you
A storefront clue can help prioritize a prospect for human review. It cannot establish whether the business currently has an active account, what plan it uses, whether it sends campaigns, or how much of the platform it has configured. Use “candidate” or “needs verification” in your records, and base any outreach on independently confirmed, current evidence.
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