Whether you're a marketing agency looking for new clients, a SaaS company building a prospect list, or a supplier wanting to reach store owners, finding the right contact information is step one. The tools range from free browser extensions to enterprise sales intelligence platforms, each with different strengths.
This guide covers the most useful options, organized by use case. All of these work for finding Shopify store owner emails specifically, not just general B2B contacts.
Best for: Finding emails by domain name
Hunter takes a domain (like storename.com) and finds all email addresses associated with it. It also provides email pattern guessing — if you know the company's email format, you can find addresses for specific people.
Pricing: Free tier: 25 searches/month. Pro starts at $49/month for 1,000 searches.
Pros: Simple interface, fast results, Chrome extension for quick lookups. Email verification built in.
Cons: Limited free tier. Only works if you already have the domain name. Doesn't discover stores — it finds contacts for stores you already know about.
Best for: Email finding + LinkedIn integration
Snov.io combines email finding with LinkedIn prospecting. You can extract emails from LinkedIn profiles of Shopify store owners and managers, then verify them within the same platform.
Pricing: Free tier: 50 credits/month. Paid plans start at $39/month.
Pros: LinkedIn integration is valuable since many store owners have profiles. Includes email verification, drip campaigns, and CRM features.
Cons: LinkedIn features require a LinkedIn account. Email finding from domains is less comprehensive than Hunter.
Best for: Full sales intelligence workflow
Apollo is more than an email finder. It's a complete sales platform with contact database, enrichment, sequencing, and analytics. You can filter by "uses Shopify" or "e-commerce" to find relevant prospects.
Pricing: Free tier: limited credits. Basic starts at $49/month. Professional at $99/month unlocks better data.
Pros: Massive database, good filtering, built-in outreach tools. Integrates with major CRMs.
Cons: Data accuracy for niche segments varies. The Shopify-specific filtering isn't as granular as dedicated tools. Higher learning curve.
Best for: Building lists at scale
Apify's Shopify scrapers extract store data directly from Shopify's storefronts. The Email Leads Scraper specifically targets stores with published email addresses, giving you both the contact info and the store URL in one pass.
Pricing: Pay-per-use. Around $0.60 per 1,000 stores scraped, plus Apify platform fees.
Pros: Scalable, automated, no manual effort. Captures store metadata (products, categories, location) alongside contact info.
Cons: Requires a technical mindset to configure. No built-in email verification. Results need cleaning and verification after export.
Best for: Social media + e-commerce hybrid scraping
Phantombuster automates data extraction from social platforms and websites. You can set up workflows that find Shopify stores through Facebook or Instagram, then scrape their contact pages for email addresses.
Pricing: Free trial: 1 hour of usage. Paid plans start at $69/month.
Pros: Combines social discovery with contact extraction. Good for finding stores that are active on social media.
Cons: Complex setup for multi-step workflows. Pricing adds up for high-volume use. Less reliable for stores without social media presence.
Best for: Quick list building without technical skills
D7 Lead Finder offers a simple search interface. Type in criteria like "Shopify store in USA" and get a list of contacts. No scraping configuration required.
Pricing: Credits starting at $29/month.
Pros: Very easy to use. Good for non-technical users who need a list quickly. Includes phone numbers alongside emails.
Cons: Data freshness is inconsistent. Limited customization of search filters. Some users report outdated records.
Best for: E-commerce specific intelligence
BrandNav claims 14.5M+ e-commerce stores in their database. Their Screener tool lets you filter by platform, revenue, traffic, and niche. It's purpose-built for e-commerce prospecting.
Pricing: Contact for quote. Likely enterprise pricing given the data scope.
Pros: E-commerce focused, rich filtering, includes revenue and traffic estimates. Great for finding stores by performance metrics.
Cons: No self-serve pricing. May be overkill for small campaigns. Contact data quality depends on the store's public presence.
Best for: Identifying which stores use Shopify
Wappalyzer is a browser extension and API that detects the technology stack of any website. Use it to confirm a store runs on Shopify and to discover stores through Google searches.
Pricing: Browser extension: free. API starts at $99/month.
Pros: Accurate technology detection. Useful when combined with other tools for a two-step discovery process.
Cons: Doesn't provide contact information. Only identifies the platform.
Best for: Verifying found emails
Not a discovery tool, but essential infrastructure. ZeroBounce verifies emails found through other methods, catching bounces before they damage your sender reputation.
Pricing: Pay-per-use, starting at $0.003 per email.
Pros: High accuracy (99%+), fast processing, good API. Catches catch-all domains and disposable emails.
Cons: It's an additional cost on top of discovery tools. Free tier is limited.
Best for: Getting started immediately
If you need contacts now and don't want to learn multiple tools, buying a pre-built list is the fastest path. We offer 1,000 verified US Shopify store owner emails for $29. The data is already scraped, cleaned, and verified. Download and start sending.
Pros: No technical setup, no monthly subscription, ready to use immediately. Cost per contact is competitive with tool subscriptions.
Cons: Less customizable than building your own list. You get what the provider scraped, not specific filters.
If you're just getting started, buy a pre-built list to test your outreach message. If you're running ongoing campaigns at scale, invest in Apollo.io or Apify. If you're on a tight budget, combine Hunter.io's free tier with manual Google research.
The best approach for most teams is a combination: use a pre-built list for the initial campaign to validate your messaging, then invest in tools for ongoing list building once you've proven the model works.