Define your ideal customer profile - platform, merchant size, category, region, tech stack, carrier/PSP usage, or even a single example store - and get a fully qualified list of merchants that match your ICP at scale.
INPUT ICP:
"Mid-size Shopify beauty stores in DE/AT
using Klarna or Stripe, shipping cross-border,
with modern marketing stack"
OUTPUT:
=== 312 high-fit merchants found ===
1. glowhaus.de
Merchant size: Top segment
Platform: Shopify | PSP: Stripe, Klarna
Cross-border: DE → AT, CH
Tech: Klaviyo, Meta Ads, GA4
2. pureblossom.at
Merchant size: Top segment
Platform: Shopify | PSP: Stripe
Cross-border: AT → DE/IT
Carriers: DHL, DPD
3. skincare-luxe.de
Merchant size: Top segment
Platform: Shopify | PSP: Klarna, PayPal
Tech: Klaviyo, Trustpilot, Hotjar
→ All fully enriched & ICP-alignedWhy ICP Targeting Is Broken Today
Raw account lists ignore merchant size, tech stack, cross-border capability, and semantic fit. Many accounts do not match the real ICP.
Standard account lists ignore merchant size, carrier/PSP usage, cross-border capability, tech stack, and semantic fit. Result: many accounts waste SDR time.
ICP isn't just category + platform. ICP is: "Stores that feel our pain and have money to pay." Generic lists from the internet don't understand this.
Two stores in "Beauty" can sell supplements, dermocosmetics, perfumes, or razors - completely different ICPs. Standard tools can't distinguish semantic clusters.
ShopRank ICP-Based Lead Lists
Not a list of stores. A list of stores perfectly matched to your product - semantically similar, merchant-size-qualified, tech-aligned, and cross-border ready.
What You Get
From ICP definition to qualified list - end weeks of manual research. Get actionable pipeline immediately.
Target only stores similar to your best customers, in your merchant size range, with your tech stack, solving your problem. Waste less time on bad fits.
Save SDR budget - stop chasing wrong stores. One ICP list = pipeline for months. Accelerate sales team velocity immediately.
Example: SaaS Provider Expanding in DACH
A SaaS provider offering review automation wanted to find high-fit brands for expansion in Germany, Austria, and Switzerland.
Example anonymized. Results based on realistic aggregated scenarios from the SaaS industry.
| Metric | Before ShopRank | With ShopRank | Impact |
|---|---|---|---|
| Time to build ICP list | 2-3 weeks | 48 hours | faster |
| High-fit merchants identified | ~50 | 420 | more relevant pipeline |
| Account fit | Mixed | Focused | clearer targeting |
| Outreach focus | Manual research | Pre-qualified list | stronger fit |
"We were spending weeks manually researching DACH merchants that might fit our review automation platform. ShopRank delivered a pre-qualified merchant list filtered by platform, merchant size band, and marketing stack. The team focused outreach on better-fit accounts and improved campaign quality."
- VP of Sales, European Review Automation SaaS
What's Included in Your ICP Lead List
Contact Enrichment
Verified emails & phone numbers
Social Profiles
LinkedIn & social media links
Custom Filtering
Tailored to your exact ICP
CSV, XLSX, JSON
48 hours – 7 days depending on complexity
From ICP to Qualified Leads in 3 Steps
Examples: "Mid-size Shopify beauty stores with Stripe", "High-potential fashion brands shipping abroad", "Shopify stores like Patagonia but in Europe", "WooCommerce electronics stores with DHL + PayPal".
Our engine creates semantic profile of your ICP and filters millions of stores by merchant size, platform, PSP, carriers, tech stack, cross-border capability, and category fit.
Prioritized by merchant size, platform, PSP, carriers, and semantic similarity. Delivered as CSV/XLSX/JSON with 40+ fields per merchant, ready for immediate outreach.
Perfect For Go-To-Market Teams
Reviews, email marketing, personalization, analytics, CRO, WMS, ERP. Find merchants matching your exact platform, merchant size, and tech maturity requirements.
ICP example: "Stores shipping OOH in DE/AT/CH with top merchant size band". Perfect for entering new segments or geographic markets with qualified pipeline.
ICP example: "Shopify + Klarna/Stripe merchants top merchant size band". Target high-value merchants with compatible tech stack and budget for PSP upgrades.
ICP example: "Spanish sellers shipping fashion abroad". Onboard merchants at scale with cross-border capability and category alignment.
ICP example: "High-potential brands in X category using modern marketing stack". Build targeted outbound campaigns with perfect-fit prospects.
Find merchants using complementary tools, similar platforms, or lacking critical integrations. Perfect for partnership and integration plays.
Everything You Need to Know
Yes - one example store is enough to generate semantic similarity clusters. We'll find 100-500 stores that match your example's product theme, merchant size range, and business model.
Yes - combine platform + merchant size + PSP + semantic similarity + category + country + cross-border + tech stack. The more specific your ICP, the better the match quality.
From 50 to 5,000+ depending on ICP specificity and market size. Narrow ICPs (e.g., "Shopify beauty DE/AT, top merchant size band") yield ~200-500 accounts. Broader ICPs can yield thousands.
Contact enrichment (emails, phone numbers, LinkedIn profiles) is available as an optional add-on. Pricing depends on volume and coverage rate.
Yes - recurring ICP list updates are available as a subscription. Get monthly refreshes with new merchants entering your ICP or existing merchants changing tech stack.
Standard delivery: 48 hours to 7 days depending on ICP complexity and customization requirements. Simple ICPs (single platform, single country) typically within 48-72 hours.
Full-Market gives you ALL merchants in a country (e.g., all 58k stores in Spain). ICP Lead Generator filters to ONLY stores matching your specific profile (e.g., 312 Shopify beauty stores with Klarna in DE/AT). Much higher fit, lower volume.
Yes - during scoping we can help refine your ICP based on your best customers, product capabilities, and market opportunity. We often start broad and narrow iteratively.