DEV Community: ApiVault Labs

How to Build a Technographic Lead List: Target Companies by the Tech They Run

ApiVault Labs — Wed, 19 Aug 2026 21:51:14 +0000

Most lead lists are built on the wrong axis. You pick an industry, a country, maybe a company-size band, and you end up with thousands of businesses that share a label but nothing else. Half of them will never buy what you sell, because the thing that actually predicts a sale isn't their vertical — it's their tech stack.

If you sell a Klaviyo alternative, your best prospect is a store already paying for Klaviyo. If you sell a Shopify app, you want stores on Shopify, not "ecommerce companies" in general. If you're an agency that migrates brands off aging platforms, you want the sites still running the old stack today. That's technographic prospecting: building a list around the technologies a company already uses, and it turns a vague "total addressable market" into a sharp "these specific companies are a fit right now."

This post is a practical walkthrough of how to build that kind of list.

Why the tech stack is the strongest buying signal

A company's technology choices are visible, current, and predictive in a way that firmographics aren't:

Fit — a business running a competing tool has already decided the category is worth paying for. You're not educating them on why they need it; you're arguing why yours is better.
Timing — a store on an outdated platform, or one that just added a payment provider, is signaling a project in motion.
Message — knowing the exact tool someone runs lets you open with a relevant line instead of a generic pitch. "I saw you're on X" beats "Hi, hope you're well."

The hard part has always been seeing the stack across thousands of companies at once. You can inspect one site's technology by hand, but you can't inspect fifty thousand.

Query a database instead of crawling

Rather than crawl the web live every time you need a list, you can query a pre-built database that already has the tech stack detected and enriched. That's the model behind Shopify & Ecommerce Store Finder on Apify — 52 million+ sites across 14 ecommerce and CMS platforms, each row already carrying its detected technologies, contacts, and firmographics. You pick platforms, filter on the tech you care about, choose columns, and export. Because the data already exists, results come back instantly and you can preview how many companies match before you spend anything.

One site = one row, and new sites are added daily while existing records are re-enriched on a rolling schedule, so the tech data reflects what companies run now, not a stale snapshot.

Start with the platform

The first cut is the platform itself. You can pull from all of them or target the ones that matter to your offer:

Platform	Sites	Platform	Sites
WordPress	12.0M	Squarespace	2.9M
Wix	8.9M	Mailchimp	920K
Shopify	7.0M	Joomla	770K
WooCommerce	6.5M	PrestaShop	174K
ASP.NET	4.6M	Magento	105K
Mastercard (online merchants)	4.6M	BigCommerce	37K
WooCommerce Checkout	3.5M	Angular	Growing

Selling a Shopify app? Pick Shopify. Pitching a WooCommerce plugin? Filter to WooCommerce. Running an agency that migrates brands off legacy stacks? ASP.NET, Joomla, and Magento are the pool of redesign and migration candidates.

Filter on the technologies that matter

Platform is the coarse cut. The tech-stack fields are where the list gets sharp. Every row can carry its detected eCommerce Platform, CMS, CRM, Marketing Automation, Payment Platforms, Hosting Provider, and AI tools — and you can filter on any of them.

The Extra filters accept JSON conditions on any column, with nine operators (equals, contains, starts_with, in_list, not_empty, and more), combined with AND. A few technographic recipes:

Competitor displacement — companies already paying for the tool you compete with:

[
  { "column": "Marketing Automation", "operator": "contains", "value": "Klaviyo" }
]

Stack gap — companies that take card payments but have no CRM detected, a classic "you've outgrown spreadsheets" pitch:

[
  { "column": "Payment Platforms", "operator": "not_empty", "value": "" },
  { "column": "CRM Platform", "operator": "equals", "value": "" }
]

Specific payment ecosystem — stores accepting a given processor, for a payments or finance integration:

[
  { "column": "Payment Platforms", "operator": "contains", "value": "Stripe" },
  { "column": "Country", "operator": "equals", "value": "US" }
]

Because conditions combine with AND, you can stack "on Shopify" + "runs a competitor's email tool" + "no loyalty app" and get exactly the greenfield-or-displacement segment you want, nothing else.

Add the fields that turn a match into an outreach row

A technographic match is only useful if you can act on it. Leave the output columns empty and every row comes back fully enriched across 59 fields; the ones that matter for outreach:

Contact — verified Emails, Telephones in international format, owner/people names.
Company & vertical — root/primary domain, company name, vertical.
Firmographics — sales-revenue band, employee count, SKU count, technology spend.
Socials — Facebook, Instagram, LinkedIn, X, TikTok, YouTube, and more.
Geo — city, state, zip, country.
Dates — first detected, last found, first/last indexed, so you know how current each record is.

Then sort — by Overall Score, Sales Revenue, Employees, or Last Found — to get the best-fit accounts first instead of a random slice, up to 100,000 rows per run, deduplicated by domain.

Preview the count before you pay

Use Count only to see how many companies match your technographic filters for almost nothing. This matters more here than with broad lists: narrow tech segments can be small, and it's better to learn that a segment holds 800 companies before you export than after. Dial the filters in, confirm the volume, then run the real export.

Export straight into your stack

Every run stores results in a dataset you can download as CSV, JSON, XML, Excel, HTML, or JSONL — no re-run needed. Via API:

https://api.apify.com/v2/datasets/{datasetId}/items?format=csv

Drop it into your CRM, cold-email tool, or spreadsheet, and use the detected tool as a merge field so every message references the exact stack the company runs.

Who this is for

SaaS sales — build competitor-displacement lists and target by the tools a company already pays for.
Agencies — find brands on outdated or specific platforms and pitch redesigns, migrations, or a missing capability.
Integrations & platforms — target companies on a given payment processor, CRM, or marketing tool your product plugs into.
Market research — measure real-world adoption of platforms, payment providers, and marketing tools across a market.

At $1.50 per 1,000 leads with no subscription, a few thousand tech-targeted, enriched rows costs a few dollars.

A note on responsible use

These are public business records — company domains, published business emails and phones, detected technologies, and firmographics — the same data that powers standard B2B prospecting. Use it for legitimate outreach and research: respect opt-outs and suppression lists, honor Compliance and Exclusion flags in the data, and keep campaigns within GDPR, CAN-SPAM, and local rules. Technographic data is a tool for relevant outreach, not spam.

Bottom line

Stop building lists by industry label and start building them by the technology companies actually run. Pick the platform, filter on the exact tools that signal fit or timing, sort by the accounts most worth your attention, and export a clean, deduplicated list with contacts attached. If you want the whole thing from one query, Shopify & Ecommerce Store Finder turns a 52M-site database into a technographic prospect list in minutes — detected stacks, verified emails and phones, 59 fields, previewable counts, for $1.50 per 1,000. Query the database instead of guessing at the stack.

What do you prospect on first — a competitor's tool, a payment provider, or a platform gap? Tell me and I'll add a filter recipe for it.

How to Reverse-Engineer Any Shopify Store: Revenue, Traffic, and Tech Stack

ApiVault Labs — Thu, 13 Aug 2026 14:51:17 +0000

Every Shopify storefront leaks more than its owner realizes. The catalog, the scripts in the page source, the sitemap, the SSL history — all of it is public, and together it tells you roughly how much a store makes, where its traffic comes from, and exactly which apps it runs. If you do competitor research, sell to DTC brands, or vet a niche before entering it, knowing how to read those signals is a superpower.

This is a practical walkthrough of the three questions that matter most — how much revenue, how much traffic, what tech stack — how to answer each by hand, and how to do all three at once when you need to profile more than a handful of stores.

Why these three signals

Catalog size and product photos are easy to eyeball. The hard, decision-driving numbers are:

Revenue — is this store worth copying, pitching, or acquiring?
Traffic — is it actually growing, and where does demand come from?
Tech stack — what does it run, and what's it missing that you could sell?

Get those three and you can prioritize. Everything else is detail.

1. Estimating revenue

There's no public "revenue" field, so you model it. The industry-standard formula is the same one SimilarWeb-style tools use:

monthly_revenue ≈ monthly_visits × conversion_rate × average_order_value

Each input is knowable:

Monthly visits — from public web-traffic estimates (more on that below).
Conversion rate — use a category benchmark: fashion ~1.5–2%, electronics ~1%, impulse-buy ~3–4%. A blended 2.5% is a reasonable default.
Average order value — you can't see real orders, but you can approximate AOV from the catalog. Pull /products.json (public on almost every Shopify store), take the median product price, and multiply by ~1.5 to account for multi-item carts.

So for a store doing 200,000 monthly visits, a 2% conversion rate, and a $60 AOV:

200,000 × 0.02 × 60 = $240,000 / month

Treat it as an order-of-magnitude estimate, not accounting data. On stores with 100K+ monthly visits it typically lands within about ±25%. For tiny stores with no traffic data, there's a fallback: count the store's total reviews and multiply by a reviews-to-orders factor (only a fraction of buyers review, so ~30× is a common assumption) to back into an order volume.

Reality check: revenue estimates are for prioritization, not for a term sheet. Use them to sort a list, not to sign a deal.

2. Reading traffic

Traffic is the input revenue depends on, and it's the better growth signal on its own. What to look for:

Monthly visits + 3-month trend — direction matters more than the absolute number. A store climbing month over month is a very different prospect than one sliding.
Global / country / category rank — context for the raw visit count.
Traffic-source mix — the story is in the split. A store that's 70% paid search is buying its growth; one that's 50% direct/brand has real demand. Search-heavy means SEO-dependent; social-heavy means it lives on content or influencers.
Top countries — tells you the real market, which often differs from the currency or the .com domain.
Top keywords — what people actually search to land there, with volume and CPC. This is competitor keyword research for free.
Bounce rate and pages per visit — cheap proxies for how sticky the store is.

The trap here is stopping at the headline visit number. The mix is where the insight lives — it tells you whether growth is bought or earned, and that changes how you'd compete or pitch.

3. Fingerprinting the tech stack

This is the most mechanical of the three and, for many use cases, the most valuable. Every app a Shopify store installs leaves a footprint — a script tag, a DOM element, a CDN reference, a global JS variable. You can read the homepage source and match those footprints to known apps.

What's worth detecting:

Reviews — Judge.me, Yotpo, Loox, Okendo, Junip
Email / SMS — Klaviyo, Attentive, Postscript, Omnisend
Subscriptions & loyalty — ReCharge, Smile.io, LoyaltyLion
Support — Gorgias, Tidio, Zendesk, Re:amaze
Page builders — Shogun, GemPages, PageFly, Rebuy
CRO / analytics — Hotjar, Triple Whale, Northbeam
Dropshipping — Oberlo, DSers, AutoDS, CJ Dropshipping, Spocket (a strong tell that a store isn't a real brand)
Payments / BNPL — Shop Pay, Klarna, Afterpay, Affirm

Two power moves most people miss:

Tracking IDs. The page source usually exposes Google Tag Manager, GA4, Facebook Pixel, TikTok Pixel, and Klaviyo public keys. Because operators reuse the same pixel or tag across their properties, two different domains sharing one Facebook Pixel ID are almost always the same company. That's how you map a brand's hidden network of stores.

The gap, not just the stack. The absence of an app category is a sales signal. No reviews app = a warm lead for a reviews app. No email tool = a warm lead for Klaviyo's competitor. Running a rival's tool = a warm lead for yours. Prospecting by missing category is far sharper than prospecting by name.

Doing all three at once

Any one of these you can do by hand in a few minutes. The problem is volume and consistency. Profiling one store manually is fine; profiling a hundred is a lost week, and you'll read each one slightly differently, so the results aren't comparable.

That's the itch I built the Shopify Store Analyzer on Apify to scratch. It pulls all three signals — plus contacts, brand age, promos, and a dropshipper-risk score — from nine public data sources in a single pass, and returns one structured record per store. It runs pay-per-use rather than a monthly subscription, so it fits research that spikes and goes quiet, and it needs no login, API key, or proxy setup because everything it reads is public.

A minimal run:

{
  "storeUrls": ["https://allbirds.com", "https://gymshark.com"],
  "conversionRate": 2.5
}

An abbreviated record back:

{
  "domain": "gymshark.com",
  "tech_stack": ["Shop Pay", "Klaviyo", "Apple Pay"],
  "tracking_ids": { "facebook_pixel": "1234567890" },
  "avg_order_value": 51.0,
  "traffic": { "monthly_visits": 15000000, "global_rank": 4200 },
  "revenue_estimate": { "monthly_revenue_usd_est": 19125000 },
  "customer_segment": "mid-market",
  "dropshipper_risk_score": 5
}

If you don't even have a list of stores yet, it can also discover them by keyword or niche, and filter by the app gap you sell into — so you get a scored, contactable lead list instead of a pile of URLs. It's priced as pay-per-use per store analyzed, a fraction of the monthly seat you'd pay a StoreLeads or Commerce Inspector, and you're only charged for stores that are actually reachable Shopify stores.

A quick end-to-end example

Say you sell a reviews app and want ten warm leads in the coffee niche:

Discover stores for ["specialty coffee", "coffee subscription"].
Filter to stores whose tech stack has no reviews app (that's your gap).
Read each store's revenue estimate and traffic trend — keep the ones with real, growing demand.
Grab the public owner/marketing email attached to each record.
Pitch the exact gap: "You're doing real traffic but running no social proof — here's what a reviews widget would lift."

Every input there is public data. Automating it just makes it fast and consistent.

Responsible use

Everything covered here reads publicly available storefront data — the same information any visitor's browser loads — and public business contact details. That's standard competitive research and B2B prospecting. Respect rate limits, honor opt-outs and unsubscribe requests, and keep outreach compliant with applicable laws (CAN-SPAM, GDPR) and platform terms.

Bottom line

Revenue, traffic, and tech stack are the three signals that turn a storefront into a decision. Revenue tells you if it's worth your time, traffic tells you if it's growing and how, and the tech stack tells you what it runs and what it's missing. You can read all three by hand from public data — and when you need to do it across a hundred stores at once, the Shopify Store Analyzer returns them as one structured record per store, ready for your spreadsheet or CRM.

What's the first thing you check when you size up a competitor's store — revenue, traffic, or their app stack? Curious what everyone leads with.

How to Pull Thousands of Local Business Leads from Google Maps in Minutes

ApiVault Labs — Tue, 11 Aug 2026 15:01:35 +0000

Google Maps is the best local business database in the world — and the worst one to work with by hand. Every restaurant, dentist, plumber, and agency in a city is right there with a phone number and often a website, but you can only see a handful around one map pin at a time. Scrolling and copy-pasting your way to a lead list is a full day's work for a few hundred rows.

It doesn't have to be. Our Google Maps Scraper searches a whole city by category and returns clean, structured leads — names, addresses, phones, websites, ratings, hours, and (optionally) public emails and social profiles — fast enough that a full city takes minutes, not days. Here's how to use it.

Speed you can actually feel

The whole point is throughput. Measured development benchmarks:

500 businesses with contact enrichment — 11 seconds.
82,002 businesses across a large multi-category city — 243 seconds (~4 minutes).
Contact enrichment on 82,002 websites — 394 seconds.

Actual speed depends on the location, how dense the category is, which filters you enable, and how fast each business's website responds — but the point stands: you're pulling thousands of leads in the time it takes to make coffee. And results stream in progressively, so you see rows appear while the run is still going instead of staring at a spinner.

Whole-city coverage, not one map pin

Point it at a city and one or more of 74 categories and it searches across the entire city, not just the businesses clustered around a single point. You can target by:

City — "city": "Chicago", "country": "us"
Postal code
Coordinates + radius — lat, lng, span
Exact rectangular area — north, south, east, west

Combine multiple categories with custom search terms in one run (e.g. ["dentist", "plumber", "marketing agency"]).

What you get per business

Business: name, categories, description, status, Google Maps URL, place IDs.
Contacts: address, phone, website, domain, public emails, social profiles.
Location: street, city, state, postal code, country, coordinates, timezone.
Reputation: rating, review count, photo, price range.
Availability: open status, weekly hours, closed-business flag.
Booking: availability, provider, and booking links.
Website: page title, HTTPS, certificate status, platform, response time.
Lead intelligence: opportunity signals, a 0–100 priority score, and a recommended offer.

Export to CSV, Excel, JSON, XML, or RSS — and there are ready-made dataset views (Contacts, Prioritized leads, Website profile, Hours & booking) so you can grab exactly the slice you need. No Google API key or Google account required.

Emails and enrichment (optional)

Turn on contact enrichment and it visits each business's public website to pull business emails, social profiles (Facebook, Instagram, LinkedIn, TikTok, YouTube…), and likely Contact/About pages, with email-format, disposable-domain, and DNS checks. Need an email list rather than every business? Use Only leads with an email — just know it can shrink the final count, since many businesses don't publish an address.

Filter down to the leads worth calling

The scraper is built for qualification, not just extraction. Filter by:

with or without a website (perfect for web-design / SEO prospecting)
with a phone or email
open now / not marked closed
without online booking (a ready pitch for booking software)
min/max rating, min/max review count
Google category, exact or partial business-name match
minimum lead priority score
only new or changed businesses

That last one matters: the optional website audit scores each business 0–100 on public signals (HTTPS, certificate, mobile readiness, platform, response time, booking, visible opportunities) and suggests a service offer — so you can sort a city by "who most needs what I sell" instead of cold-calling alphabetically.

Example input

{
  "country": "us",
  "city": "Chicago",
  "categories": ["dentist", "plumber", "marketing agency"],
  "websiteFilter": "with",
  "maxResults": 1000,
  "concurrency": 250,
  "enrich": true,
  "enrichmentMode": "contacts",
  "verifyEmails": true
}

Monitor a territory over time

Run it on a schedule and it labels each business new, changed, or unchanged, with first/last-seen dates. Turn on fresh-only output and each run hands you just the new or updated leads — a standing pipeline of businesses entering your market, not the same list every week.

You only pay for what you enable

Pricing is event-based, so you're not charged for options you don't turn on:

Operation	Price
Delivered business lead	$1.25 / 1K (paid plans)
Website contact enrichment	$0.75
Deep Contact/About page search	$0.50
Email verification	$0.50
Full website audit	$1.00
Change monitoring	$0.25

Filtered-out records aren't charged as delivered leads. A plain name/phone/website pull of a whole city costs about a tenth of a cent per business.

Who uses it

Local lead generation — restaurants, contractors, dentists, lawyers, salons, agencies, with phones, sites, and emails.
Web design & SEO outreach — find businesses without a website or without booking, pre-scored by opportunity.
Sales territory research — map every company across a city, postal code, radius, or bounding box.
Market monitoring — scheduled runs that surface only new or changed businesses.
CRM & AI workflows — pipe structured leads into Sheets, a CRM, or MCP agents; automate via API, webhooks, n8n, Make, or Zapier.

A note on responsible use

The data comes from publicly available business listings. Availability varies by country, category, and what each business publishes. Use the leads in line with applicable privacy, marketing, and data-protection rules (GDPR, CAN-SPAM, local outreach laws) — this is a tool for relevant B2B outreach, not spam.

Bottom line

If you need local business leads, stop scrolling Google Maps by hand. Google Maps Scraper turns a city and a category into thousands of clean, enriched, pre-scored leads in minutes — phones, emails, websites, and a priority score — with no Google API key, and you only pay for the enrichment you actually use. Search a city, grab the list, start calling.

What field or filter would make your lead lists easier to work? Tell me and I'll look at adding it.

How to Check If Your TikTok Videos Are Shadowbanned (and What to Do About It)

ApiVault Labs — Mon, 10 Aug 2026 19:58:50 +0000

You post a video, it does the usual first-hour numbers, and then… nothing. No FYP, no search, no new followers. You didn't get a notification, TikTok didn't tell you anything, but the reach just quietly evaporated. That's what a shadow ban feels like — a soft suppression you're never officially informed about.

The hard part is proving it. "My views dropped" isn't evidence; it could be the algorithm, the topic, or the time of day. What you need is the actual restriction signals on the video itself. That's what our TikTok Shadow Ban Checker reads — and this post explains what a shadow ban actually is, how to check for one, and how to read the result.

What a "shadow ban" really is

There isn't one single switch. TikTok can restrict a video in several ways at once: pull it from the For You feed, hide it from search, force it to private, or disable features like duet, stitch, comments, downloads, or sharing. Any of those quietly caps your reach. So a real check can't rely on one flag — it has to look at all of them.

Historically the giveaway was TikTok's internal indexEnabled property. But throughout 2026 TikTok has been removing that flag from public HTML, which breaks the old single-signal checkers. The reliable approach now is multi-signal.

Multi-signal detection, not one flag

The checker aggregates 15+ signals rather than betting on one:

Hard signals: takeDown, secret, privateItem, divertedToPrivate (TikTok forced the video private — a strong ban indicator), isReviewing, forFriend, plus the legacy indexEnabled, forYouEligible, and searchVisible.
Soft restrictions: duet disabled, stitch disabled, comments disabled, downloads disabled, sharing disabled.

Because it reads many signals, the verdict survives even when TikTok hides the legacy indexEnabled flag. Each video comes back with a banReasonHints[] list (human-readable, every active restriction spelled out) and a restrictionCount.

The numbers it computes

Beyond raw flags, every video gets scored:

videoHealthScore (0–100) — a composite of all ban indicators plus engagement. 80+ healthy, 60–79 slightly restricted, 40–59 restricted, 20–39 heavily restricted, under 20 shadowbanned.
videoHealthStatus — the same thing in words (healthy → shadowbanned).
engagementRate — (likes + comments + shares) / views × 100. Industry benchmark is roughly 3–6%; unusually low engagement (under ~0.5%) can itself signal soft suppression.
viralPotentialScore (0–100) — based on view velocity and engagement.

Advice, not just flags

The part that actually helps: every result includes a recommendations[] array in plain English, tied to the signals it found. Instead of a wall of booleans, you get lines like:

🟠 Restricted. Several signals flagged — consider re-uploading.
💬 Comments are disabled. Re-enable them in Privacy settings — engagement loops drive FYP eligibility.
📉 Engagement rate 0.4% is well below average for 50,000 views — possible soft suppression. Hook the first 3 seconds harder.

And when a video is clean:

✅ Video looks healthy. No shadow-ban action needed.
🔥 Engagement rate 4.23% is excellent — keep this format in rotation.

A 30-second check

Paste any number of video URLs (one per line, or the urls array):

{
  "urls": [
    "https://www.tiktok.com/@username/video/1234567890",
    "https://vm.tiktok.com/shortlink"
  ],
  "includeRecommendations": true
}

An abridged result:

{
  "shadowbanned": false,
  "videoHealthScore": 95,
  "videoHealthStatus": "healthy",
  "engagementRate": 4.23,
  "viralPotentialScore": 72,
  "restrictionCount": 0,
  "recommendations": ["✅ Video looks healthy. No shadow-ban action needed."]
}

Check a batch and you also get a summary record — totalChecked, shadowbannedPct, avgHealthScore, avgEngagementRate, and the most common restrictions across the set — so an agency can hand a client one clean health report instead of raw JSON.

Built to survive TikTok's defenses

TikTok actively fights scrapers, so a naive fetch fails constantly. The checker uses a multi-strategy fetcher that tries embed/v2 → embed → desktop UA → mobile UA → m.tiktok.com → Googlebot in sequence, extracts data from the embedded JSON, and falls back to oEmbed metadata if the full HTML fails — so you still get title, author, and thumbnail rather than an empty error. It runs through residential proxies (pin a country with proxyCountry, or bring your own via customProxyUrls).

How people use it

Creators — monitor your own videos and catch a restriction early, before you've wasted a week wondering.
Agencies — bulk-check a client's recent uploads and generate a health report.
Competitor research — see whether a rival's videos are quietly restricted.
Automation — wire it into your content workflow via the API, the official n8n community node (n8n-nodes-apivault-tiktok-shadowban), or the Python SDK (check_one(), check_urls()).

At $0.01 per video ($10 per 1,000), auditing a whole content calendar costs pennies.

A note on interpreting results

Shadow-ban detection reads the public signals TikTok exposes, and TikTok changes what it exposes over time. indexEnabled=false is the most reliable single signal because it's TikTok's own internal flag; others (For You eligibility, search visibility) are less consistently present in the HTML. For catch-all situations the checker reports its best signal rather than a false "healthy," so you get an honest read instead of a misleading one. Treat it as strong evidence to act on, not a courtroom verdict.

Bottom line

If your reach dropped and you can't tell whether it's you or the algorithm, stop guessing. TikTok Shadow Ban Checker reads 15+ restriction signals per video, scores each one 0–100, and tells you in plain English what's wrong and what to do next — in bulk, for a cent a video. Check first, then fix.

What signal or recommendation would make your TikTok audits more useful? Tell me and I'll look at adding it.

How to Clean an Email List Before You Hit Send

ApiVault Labs — Sun, 09 Aug 2026 15:52:59 +0000

Every cold email to a dead address costs you twice. You pay to send it, and you pay again in sender reputation — bounces pile up, mailbox providers take note, and eventually your good emails start landing in spam. Most senders don't notice until their open rates quietly fall off a cliff.

The fix is unglamorous but decisive: verify the list before you send. Not just "does this look like an email" — actually ask the mail server whether the inbox exists. That's what our Fast Email Validator & Verifier does, and it can also find the addresses you're missing. Here's how to use both.

Past the surface: three layers of checking

Plenty of "validators" stop at a regex. They'll happily wave through a perfectly-formatted address to a mailbox that was deleted two years ago. This one checks in layers:

Syntax — RFC-aware validation, so malformed addresses are caught immediately.
Domain (MX) — confirms the domain can actually receive mail and reveals the provider (Google Workspace, Microsoft 365, Zoho, Yandex…). Fast and cheap at any list size.
Live SMTP mailbox check (optional) — connects to the destination mail server (RCPT TO) and confirms whether that specific inbox exists, and detects catch-all domains that accept everything.

The mailbox check is opt-in, so you run the fast syntax + domain pass on everything and spend the extra time only on the lists that need mailbox-level certainty.

What you get for every address

status — valid / risky / invalid / unknown
deliverabilityScore — a transparent 0–100 confidence score, with a plain-English reasons array
syntaxValid, hasMx, mxProvider
mailboxExists and catchAll (when the live check is on), plus smtpConnected
freeProvider (Gmail, Outlook…), roleBased (info@, sales@, support@), disposable (18,000+ throwaway domains)
suggestedCorrection — typo rescue, e.g. john@gmial.con → john@gmail.com
normalizedEmail — a de-duplication key that treats john.doe+news@gmail.com and johndoe@gmail.com as the same inbox

A sample row:

{
  "input": "John.Doe+news@Gmail.com",
  "email": "john.doe+news@gmail.com",
  "status": "valid",
  "deliverabilityScore": 95,
  "normalizedEmail": "johndoe@gmail.com",
  "mxProvider": "Google Workspace",
  "mailboxExists": true,
  "catchAll": false,
  "reasons": ["domain can receive mail", "mailbox confirmed to exist"]
}

Typo rescue saves real leads

Most validators silently drop gmial.com, yaho.com, or outlook.con as invalid — and you lose a lead who just fat-fingered their address. This one recovers the intended domain and hands it back as suggestedCorrection, so a typo becomes a usable contact instead of a deletion.

Built-in Email Finder

Don't have the address yet? Give the finder a name and a company domain. It builds the common corporate patterns (john.doe@, jdoe@, john@, jsmith@…) and, with the SMTP check on, verifies which one actually exists — skipping catch-all domains so you don't get false positives.

Input, one person per line:

Elon Musk, tesla.com
Bill Gates @ microsoft.com

Output per person:

{
  "mode": "finder",
  "name": "Elon Musk",
  "domain": "tesla.com",
  "candidates": ["elon.musk@tesla.com", "elonmusk@tesla.com", "emusk@tesla.com", "elon@tesla.com"],
  "mostLikely": "elon.musk@tesla.com",
  "email": "emusk@tesla.com",
  "found": true,
  "verified": true,
  "reason": "mailbox verified via live SMTP check"
}

If the SMTP check is off or a server is unreachable, it still returns the ranked candidates and mostLikely (with verified: null), so the run is always useful.

Honest about the hard cases

Email verification has genuine edge cases, and this actor handles them without lying to you. Some mail servers greylist or rate-limit verification; catch-all domains accept every address. In those cases it returns its best signal from the domain/MX checks and marks the result risky/unknown instead of a false valid — and the run never fails. For maximum certainty on big lists, enable a proxy (auto for Apify Residential, or your own).

Inputs and filters

Emails — one per line, or paste a whole CSV column; Name <john@acme.com> pastes are handled.
Toggles — domain (MX) check on/off, live SMTP mailbox check on/off.
Filters — return only valid / only deliverable, drop disposable, drop role-based, de-duplicate.
Export — full JSON, CSV-friendly flat columns, or both. Dataset views include a ready-made "Deliverable (clean list)" table.

Turn on "return only deliverable" and you export just the addresses that are safe to send to — nothing else to clean up.

Who uses it

Clean a lead list before a cold campaign — drop invalid and risky, keep your bounce rate low.
Scrub a purchased or scraped list before importing to a CRM.
Validate sign-up form input in bulk to stop fake and disposable signups.
Find a decision-maker's email from a name and company domain.
Protect sender reputation by removing role and catch-all addresses that are prone to spam traps.

Pricing

$1 per 1,000 results — you pay only for the addresses the actor returns, with no monthly subscription, no seat fees, and no API key. Use the only-valid / only-deliverable filters to return fewer records and pay for just the addresses you keep.

A note on responsible use

This tool checks whether addresses are valid and deliverable — it never sends mail, never logs in to any account, and never returns passwords or mailbox contents. Use it for legitimate list hygiene and B2B verification on addresses you have a lawful basis to process, and keep your sending compliant with GDPR, CAN-SPAM, and CCPA.

Bottom line

A clean list is the cheapest deliverability upgrade you'll ever make. Fast Email Validator & Verifier checks syntax, domain, and — when you want it — the live mailbox, rescues typos, finds missing addresses, and hands you a deliverable-only export, all for $1 per 1,000 with no API key. Verify before you send, and let the bounces be someone else's problem.

What signal or output field would make your pre-send checks easier? Tell me and I'll look at adding it.

How to Pull a Targeted Ecommerce Lead List in Minutes (from a 52M-Site Database)

ApiVault Labs — Sat, 08 Aug 2026 19:59:29 +0000

Most "lead scrapers" make you wait. You give them a niche, they crawl the web live, and twenty minutes later you get a few hundred rows — some with an email, most without, and no way to know upfront how many you'll actually get. For a one-off that's fine. For building repeatable outreach lists across platforms and countries, it's slow and unpredictable.

There's a faster model: query a database that's already built. That's what our Shopify & Ecommerce Store Finder does — 52 million+ sites already discovered, enriched, and sitting in a live database. You pick platforms, apply filters, choose columns, and export. No crawl, no wait. This post is a practical walkthrough of how to turn that into a clean, targeted lead list.

Database, not a crawler

The distinction matters. This actor doesn't go out and scrape when you press run — it reads from a pre-built, continuously updated dataset of 52M+ sites across 13 ecommerce and CMS platforms. One site = one row. Because the data already exists, results come back instantly and you can preview exactly how many leads match before you spend anything.

New sites are added daily from platform-discovery crawls, and existing records are re-enriched on a rolling schedule (contacts, tech stack, rankings). Every row carries Last Found and Last Indexed so you know how fresh it is.

13 platforms, 52M sites

You can pull from all platforms at once or target specific ones:

Platform	Sites	Platform	Sites
WordPress	12.0M	Squarespace	2.9M
Wix	8.9M	Mailchimp	920K
Shopify	7.0M	Joomla	770K
WooCommerce	6.5M	PrestaShop	174K
ASP.NET	4.6M	Magento	105K
Mastercard (online merchants)	4.6M	BigCommerce	37K
WooCommerce Checkout	3.5M

Want only Shopify stores? Pick Shopify. Pitching a WooCommerce plugin? Filter to WooCommerce. Hunting merchants on older stacks for a redesign? ASP.NET, Joomla, and Magento are right there.

59 fields per site

Leave the output columns empty and every row comes back fully enriched. The highlights:

Contact: verified Emails, Telephones (international format), owner/people names.
Company & vertical: root/primary domain, company name, vertical.
Socials: Facebook, Instagram, LinkedIn, X, TikTok, YouTube, Pinterest, and more.
Firmographics: sales revenue band, employee count, SKU count, technology spend, ticker.
Tech stack: ecommerce platform, CMS, CRM, marketing automation, payment platforms, hosting, AI tools.
Rankings & performance: Overall Score, Tranco, Page Rank, Majestic, CRuX, Cloudflare Rank, plus performance/SEO/accessibility/best-practices scores.
Geo: city, state, zip, country.
Dates: first detected, last found, first/last indexed.

Or select just the columns you need for a lean export.

Filter down to exactly who you want

The point isn't 52M rows — it's the few thousand that fit. Filters narrow results without re-running or paying more:

Countries — one or more ISO-2 codes (US, DE, GB…).
Keyword — substring match in domain or company name.
Only with email / Only with phone — drop rows you can't act on.
Phone country code — e.g. keep sites with a +44 number.
Extra filters — JSON conditions on any of the 59 columns, with 9 operators (equals, contains, starts_with, in_list, not_empty…), combined with AND.

A concrete example — Shopify-style stores in Berlin that accept PayPal:

[
  { "column": "Payment Platforms", "operator": "contains", "value": "PayPal" },
  { "column": "City", "operator": "equals", "value": "Berlin" }
]

Then sort by Overall Score, Sales Revenue, Employees, Tranco, or Last Found to get the best leads instead of a random slice — up to 100,000 rows per run, deduplicated by domain.

Preview before you pay

Use Count only to see how many sites match your filters for almost nothing. Dial the filters in, confirm the volume, then run the real export. No more paying to discover that your niche + country combo only had 40 usable rows.

Export anywhere

Every run stores results in a dataset you can download as CSV, JSON, XML, Excel, HTML, or JSONL — no re-run needed. Via API:

https://api.apify.com/v2/datasets/{datasetId}/items?format=csv

Drop it straight into your CRM, cold-email tool, or spreadsheet.

Who uses it

Web agencies — find businesses on outdated or specific platforms and pitch redesigns or migrations.
SaaS sales — target stores by tech stack, revenue band, or employee count (e.g. everyone running a competing tool).
Lead generation — bulk-export emails + phones filtered by country and vertical.
Market research — measure platform market share, payment adoption, and geographic distribution.
SEO & marketing — discover sites by performance score, ranking, or ad presence.

At $1.50 per 1,000 leads with no subscription, a few thousand targeted, enriched rows costs a few dollars.

A note on responsible use

These are public business records — company domains, published business emails and phones, tech stack, and firmographics — the same data that powers standard B2B prospecting. Use it for legitimate outreach and research: respect opt-outs and suppression lists, honor Compliance/Exclusion flags in the data, and keep your campaigns within GDPR, CAN-SPAM, and local rules. B2B contact data is a tool for relevant outreach, not spam.

Bottom line

If you need ecommerce and CMS leads — by platform, country, tech, or revenue — Shopify & Ecommerce Store Finder turns a 52M-site database into a targeted, enriched, deduplicated list in minutes: verified emails and phones, 59 fields, previewable counts, and clean exports, for $1.50 per 1,000. Query the database instead of waiting on a crawl.

What filter or field would make your prospecting easier? Tell me and I'll look at adding it.

How to Build a List of Websites by Traffic Volume (Bulk Site Discovery)

ApiVault Labs — Fri, 07 Aug 2026 14:13:28 +0000

Most traffic-intelligence tools work the same way: you type in a domain, they hand back the numbers. One site in, one report out. That's perfect when you already know which site you care about. It's useless when the thing you're trying to find is the list of sites.

"Give me every website in the health niche doing 10K–50K visits a month in the US." "Show me the top 1,000 sites in the world by traffic." "Which sites rank for crypto wallet?" None of those start with a domain — they start with criteria. That's the gap our Websites by Traffic Volume actor fills. This post walks through how to use it as a discovery engine, not a checker.

The core idea: numbers → domains, not domain → numbers

Traditional tools go domain → metrics. This one runs in reverse: criteria → domains. You set the filters — a traffic range, a category, a country, a keyword they rank for, a growth threshold — and get back every website that matches, in bulk, from a database of 40 million+ sites.

	Traditional tool	This actor
Direction	one domain → its metrics	criteria → list of domains
Best for	analyzing a site you know	discovering sites you don't
Bulk output	limited / expensive	up to 1,000,000 rows per run
Reverse keyword lookup	rare / pricey	built in
Price	$100–500+/mo subscription	$5 per 1,000 results, no subscription

It isn't a live per-URL scraper — it's a searchable database of pre-collected traffic estimates. That's exactly what makes cheap bulk lookups (criteria → list of sites) possible in the first place. Treat the numbers as directional estimates (every record carries a dataDate so you know how fresh each one is), the same way you'd treat any traffic-intelligence source.

Three ways to use it

1. Filter the database (the main mode). Pick one way to select sites, then stack optional filters on top:

By traffic volume — a min/max monthly-visits range, e.g. sites doing 10K–50K/month.
By top global rank — enter 1000 and get exactly the sites ranked 1–1000 worldwide, ordered from rank 1.
Then narrow with category (health, finance, games, e-commerce…), country (#1 visitor country), traffic channel + minimum share (search, direct, social, referral, email, paid ads, AI/LLM), engagement (max bounce, min pages/visit, min time on site), growth (only rising sites, or a minimum month-over-month %), or a keyword they rank for.

2. Look up specific domains. Already have a list? Pass the domains and get their full traffic profile back (for domains tracked in the dataset).

3. Find competitors. Give one domain, get its rivals — other sites in the same category with comparable traffic. Instant look-alike prospecting.

What you get for every site

Each matching website comes back as a structured record:

Traffic: latestMonthVisits, totalVisits, a month-by-month monthlyVisits history, and monthlyGrowthPercent.
Rank: globalRank, category + categoryRank, country + countryRank.
Engagement: bounceRate, pagesPerVisit, avgTimeOnSite.
Channels: trafficSources (share by search, direct, social, referral, mail, ads, AI) plus aiVisits for LLM-referred traffic.
Intent & audience: topKeywords with volume, topCountries with share.
Freshness: dataDate.

Export the whole thing as JSON, CSV, or Excel — up to a million rows per run, streamed for the big pulls.

Real queries

Mid-traffic health sites in the US for a lead list:

{ "minVisits": 10000, "maxVisits": 50000, "category": "health", "country": "US", "maxResults": 2000 }

The world's top 1,000 sites — just one field:

{ "topGlobalRank": 1000 }

Every site ranking for a keyword (reverse keyword lookup):

{ "keyword": "crypto wallet", "maxResults": 500 }

Fast-growing sites to catch a trend early:

{ "minGrowthPercent": 50, "minVisits": 100000, "sortBy": "visits" }

Sticky, social-driven sites:

{ "trafficSource": "social", "minSourceShare": 40, "maxBounce": 35, "minPagesPerVisit": 4 }

Competitors of one domain:

{ "similarTo": "stripe.com", "maxResults": 50 }

Who this is for

Lead generation — build lists of sites in your niche at a target traffic level, ready for outreach.
Media buying & partnerships — shortlist sites for ads, sponsorships, or guest posts by real audience size.
Competitor and market research — map an industry by traffic and category instead of guessing.
SEO & keyword research — find every site ranking for a term, or the most search-dependent sites in a space.
Trend spotting — surface fast-growing sites before they peak.

A note on the data

The traffic figures, keywords, and channel shares are third-party estimates, not the site owners' own analytics — directional, like any traffic-intelligence tool. Use them to discover and rank at scale, then verify the individual sites that matter with a per-domain tool. Category and country are known for a subset of sites, so combining fewer filters returns broader lists.

Bottom line

If your question starts with a domain, a normal traffic checker is fine. If it starts with "which websites…", you need discovery. Websites by Traffic Volume turns a set of criteria into a clean, deduplicated list of matching sites — with visits, rank, engagement, channels, and keywords — in bulk, for $5 per 1,000 results and no subscription. Find the sites here, then deep-dive the winners wherever you like.

What filter combination would make your prospecting easier? Tell me and I'll see about adding it.

Inside Our Facebook Profile Scraper: Emails, Contacts, and Lead Scoring (No Login)

ApiVault Labs — Thu, 06 Aug 2026 22:30:28 +0000

Facebook business Pages are one of the richest public sources of B2B contact data on the web. A single Page often exposes an email, a phone number, a website, opening hours, a rating, and a category — all sitting in public view. The hard part isn't that the data is hidden; it's pulling it consistently, at scale, without logging in or getting blocked.

That's what our Facebook Profile Scraper is built for. This post is a walkthrough of what it actually does, field by field, and how people use it.

The core principle: public data, no login

The scraper never logs in and never uses cookies. It reads only what any logged-out visitor's browser can see — the public metadata of a profile or business Page. That's a deliberate design choice:

No account, no cookies, no ban risk on your side. You don't hand over credentials or a session.
Real-time. Each run fetches live data, not a stale cached database, so what you get reflects the Page as it is right now.
Fast and cheap. A desktop-first fetch strategy with retries and deduplication keeps runs quick and the cost per profile a fraction of a cent.

The trade-off is honest: without login you get profile and Page metadata, not private feeds or a full post history. For lead generation and business research — which is what this tool is for — that metadata is exactly what you need.

What you get back: 30+ fields per profile

Feed it one or many profile/Page URLs (or even a numeric Facebook ID) and each result comes back as a structured record. Here's what's in it.

Core identity

username, fullName, bio, category, locale, verified, profileType, profileUrl, avatarUrl, coverPhoto, facebookId. You always know exactly who the record is, whether it's a verified Page, and what kind of entity it is.

Audience

followerCount (pulled as an exact number, not a rounded guess), plus audienceSize bucketing from micro to mega and an engagementRatio. Useful for quickly sorting a list by reach.

Contact data — the lead-gen payload

This is the part most people come for:

emails[] + primaryEmail
phones[] + primaryPhone
websites[] + primaryWebsite
bestContact: { channel, value, label } — the single best way to reach them, pre-picked

And a genuine differentiator: website enrichment. When a Page lists a business website but no email on Facebook itself, the scraper visits that site (homepage, /contact, /about) and pulls the email and phone from there. You get contact data even when Facebook alone doesn't have it.

Local business details

When the Page is a local business, you also get aggregateRating (value + count), a structured address (street, city, region, postcode, country), and openingHours[]. That's enough to build a local-lead list or a competitor map without touching another tool.

Lead intelligence

Beyond raw fields, each record carries a scored assessment:

activityScore (0–100) with activityScoreReasons[]
activityTier — small / growing / established / major
recommendations[] and an outreachPitch

Instead of "here's a Page," you get "here's a Page, here's how active it is, and here's an angle to open with." Sort by activityScore and you've got a priority queue for outreach.

Cross-platform reach

socialSearchUrls{} gives you deep links to find the same brand on Instagram, LinkedIn, TikTok, X, YouTube, and Google — handy for enriching a profile across channels.

Run summary

Every run also emits an aggregate _summary record: total scraped, verified count, how many had an email, average activity score, and the tier breakdown. Good for a quick read on list quality before you export.

Built for bulk

Single-profile lookups are trivial; the scraper is built for lists. Practical features that matter at scale:

Deduplication — input URLs are normalized (vanity vs numeric, m. vs www, locale variants) and output is deduped by facebookId, so you never scrape or pay for the same Page twice.
Retries — failed fetches get a fresh proxy IP and backoff.
Concurrency — runs many profiles in parallel; 25 mixed Pages finish in seconds.
Webhook — optionally POST each profile in real time to your own endpoint as it's scraped.
Non-existent Pages aren't billed — if a Page doesn't resolve, you don't pay for it.

Pricing is pay-per-result at $4 per 1,000 profiles, so a few thousand leads costs a few dollars, with no subscription.

How people use it

B2B lead lists — feed a list of business Page URLs, get back emails, phones, and websites ready for outreach, pre-scored by activity.
Local prospecting — pull ratings, addresses, and hours for businesses in a category or area.
Influencer and brand research — exact follower counts, verification status, and cross-platform links for a shortlist.
CRM enrichment — resolve a Facebook URL into a full contact record, including data pulled from the business's own website.

There's also a Python SDK (scrape(), scrape_one(), filter_by_tier(), filter_with_email(), and more) if you'd rather call it from code than run it from the console.

A note on responsible use

The scraper reads publicly available business and Page metadata — the same information any visitor sees — and public business contact details. That's standard B2B research. Use it for legitimate outreach and market research, respect opt-outs, don't target private individuals for surveillance, and keep your outreach compliant with applicable laws (GDPR, CAN-SPAM) and Facebook's terms.

Bottom line

If you need Facebook Page contact data for leads, the Facebook Profile Scraper turns a URL (or a whole list) into a clean, scored record — emails, phones, websites, ratings, and a ready outreach angle — with no login, in real time, for well under a cent per profile. Public data, structured and ready to work.

What field would make your Facebook lead workflow easier? Tell me and I'll look at adding it.

Best Shopify Store Analyzer Tools in 2026 (Tested & Compared)

ApiVault Labs — Mon, 03 Aug 2026 21:15:22 +0000

If you sell to Shopify brands — as an agency, a SaaS vendor, a dropshipper, or an investor — you already know the hard part isn't finding stores. It's understanding them. How much revenue does that store actually pull? What apps run on it? Is it growing or stalling? And crucially: is there a gap you can pitch?

A good Shopify store analyzer answers those questions in seconds instead of hours of manual digging. I spent a week testing the analyzers currently available and ranked them by data depth, accuracy, and how useful the output actually is for real outreach and research.

Here's the honest breakdown.

What a Shopify store analyzer should actually do

Before the list, it helps to know what separates a real analyzer from a plain product scraper. A product scraper dumps a catalog. An analyzer turns a storefront into intelligence:

Revenue and traffic estimates — the single most requested data point
Tech stack detection — which apps the store runs (Klaviyo, Recharge, Yotpo, Gorgias, etc.)
Contact data — emails and phones, so you can actually reach the store
Growth signals — product velocity, discounting, international expansion
Opportunity scoring — where the store is weak, so you know what to sell

Keep those five in mind as you read. Most tools do one or two well. Very few do all five.

The 7 best Shopify store analyzers

1. Shopify Store Analyzer by ApiVault Labs

This is the most complete analyzer I tested, and it's the one I'd reach for first. A single run returns an unusually rich record per store:

Estimated monthly and annualized revenue
Real traffic metrics — monthly visits, bounce rate, pages per visit, global/country rank, traffic-source mix, and top keywords
Tech stack across 60+ apps (reviews, email/SMS, subscriptions, loyalty, CRO, payments, helpdesk)
Contact extraction — emails (with role tagging), phones, and social links
Ad intelligence — detected ad platforms plus a direct link to the store's Meta Ad Library
Brand age (via Wayback + SSL), international expansion signals, and a dropshipper risk score
An opportunity score with ready-made pitch angles — e.g. "no reviews app detected → pitch social proof"
Change tracking between runs (new/removed products, traffic and revenue deltas)

It's positioned as a StoreLeads alternative at roughly $10 per 1,000 stores, and it works from either a domain list or keyword discovery. If you run outbound to Shopify brands, the pitch-angle output alone saves real time.

👉 Shopify Store Analyzer on Apify

2. Shopify DTC Brand Discovery + Tech Stack Filter (george.the.developer)

A solid pick if your whole workflow is agency outbound. It finds Shopify stores by niche, detects installed apps, and — its best feature — computes an opportunity score based on missing app categories, then suggests pitch angles. Priced higher (around $50/1K), so it fits teams that value the pre-built pitch logic over raw volume.

3. Shopify Store Discovery (seibs.co)

Strong on filtering. It detects 30+ apps with per-app confidence scores and lets you apply server-side filters — return only stores that run (or don't run) specific apps, or fall within a revenue band. Great for building a tightly targeted prospect list. Around $8/1K.

4. Shopify Store Audit & Tech-Stack Lead Scorer (fried_calamaries)

Built for monitoring. Beyond the standard app/theme/catalog audit, it diffs current and previous snapshots so you can catch when a store installs or removes an app — a genuine buying signal. Around $10/1K.

5. Shopify Revenue Estimator (nexgendata)

Focused squarely on revenue estimation, themes, and traffic sources. Positioned as a SimilarWeb/Semrush/Store Leads alternative for strategists and investors doing competitive due diligence. Around $20/1K.

6. Shopify Store Opportunity Intelligence (thescrapelab)

A budget option (around $2/1K) that covers catalog, pricing, tech stack, public contacts, and agency-fit opportunity scores. Good value if you mainly need catalog and CRO signals and can live with lighter revenue/traffic data.

7. Shopify Store Intelligence (constructive_calm)

A premium catalog/price/stock tracker aimed at monitoring specific stores over time. Priced for enterprise use cases; overkill for most prospecting but useful if you're tracking a handful of competitors closely.

Quick comparison

Tool	Revenue	Traffic	Tech stack	Contacts	Pitch/opportunity
ApiVault Labs	✅	✅	60+ apps	✅	✅
DTC Brand Discovery	estimate	–	✅	✅	✅
Store Discovery	band	–	30+ apps	✅	filter
Audit & Lead Scorer	–	–	✅	–	✅
Revenue Estimator	✅	✅	✅	–	–

How to choose the right one

Running outbound to Shopify brands? Prioritize contact data + pitch angles. ApiVault Labs and DTC Brand Discovery lead here.
Building a filtered target list? Store Discovery's server-side filters are the fastest path.
Watching competitors over time? Pick a tool with snapshot diffing (Audit & Lead Scorer, or ApiVault Labs' change tracking).
Investor / market research? Revenue and traffic depth matter most — ApiVault Labs or the Revenue Estimator.

Frequently asked questions

How do you estimate a Shopify store's revenue?
Analyzers combine traffic estimates with observed order signals, average order value from the catalog, and a modeled conversion rate. It's an estimate, not accounting data — but for prospecting and prioritization it's more than accurate enough.

Can you detect which apps a Shopify store uses?
Yes. Installed apps leave footprints in the storefront's script tags, DOM, and CDN references. The better tools map 30–60+ apps and, in some cases, attach a confidence score per detection.

Is it legal to analyze Shopify stores?
These tools read publicly available storefront data — the same information any visitor's browser loads. That's standard competitive research. As always, use the data responsibly and in line with applicable laws and platform terms.

What's the difference between a Shopify scraper and an analyzer?
A scraper extracts raw data (usually the product catalog). An analyzer interprets a storefront into intelligence: revenue, traffic, tech stack, contacts, and opportunity signals you can act on.

Bottom line

If you only try one, start with the Shopify Store Analyzer by ApiVault Labs — it's the most complete on data depth and the only one that hands you pitch angles and change tracking out of the box. For specialized needs (tight filtering, pure revenue research, or long-term monitoring), the alternatives above each have a clear edge worth considering.

Whatever you pick, the workflow is the same: analyze first, prioritize by opportunity, and reach out with a pitch tied to what the store is actually missing.

Have a Shopify prospecting workflow you'd add? Drop it in the comments.