About EcomScout

We track over a million Shopify stores so you don't have to guess what works in ecommerce. Built in Vilnius by people who spent a decade collecting web data at scale.

Why We Built This

EcomScout started in July 2025 with a simple frustration: serious ecommerce data was locked behind tools charging hundreds of dollars a month, while the "free" alternatives showed numbers nobody could trace. We had spent years building web-scale data collection products for other companies — so we knew the underlying data wasn't hard to get. It was just hard to get honestly, at a fair price, with the methodology written down.

So that's what we built. Today our pipeline monitors over a million Shopify storefronts, detects which of ~10,000 apps each store runs, tracks product catalogs twice a day through our ClickHouse cluster, and cross-references it all with Trustpilot reviews, SpyFu marketing data, and traffic estimates. Every score on this site links back to a published methodology — if you disagree with a number, you can see exactly how we got it.

What We Offer

Our Methodology

Transparency is core to what we do. Below is a detailed breakdown of how we score and rank stores, apps, and trending products. All scores are computed from publicly available data combined with our proprietary models.

EcomScout Store Rating (0–100)

Every store in our directory receives a composite score from 0 to 100 based on five weighted dimensions. The score determines a letter grade from S (top tier) to F.

DimensionWeightWhat It Measures
Revenue Performance40%Estimated monthly revenue, revenue growth, average order value, and performance relative to vertical benchmarks
Traffic & Growth23%Yearly visit volume, traffic growth rate, and estimated conversion rate
Customer Trust15%Trustpilot rating and review volume — reflects real customer sentiment
Marketing Efficiency12%Ad spend, SEO keyword rankings, organic vs paid traffic balance, and ROI indicators (powered by SpyFu data)
Business Quality10%Business model assessment (e.g. brand vs dropshipping), category positioning, and operational indicators

A sixth dimension — Product Catalog Quality (catalog size, pricing, and product completeness) — is displayed in individual store breakdowns for informational purposes but is not factored into the headline score.

Letter Grades

Grades are assigned using fixed score thresholds calibrated against the full distribution of stores in our database:

S 59+A+ 54–58A 50–53B+ 45–49B 40–44C+ 35–39C 30–34D 20–29F <20

Data Sources

Store scores are built from a combination of traffic and revenue estimates (from proprietary models and third-party data providers), Trustpilot public reviews, SpyFu marketing data, and product catalog analysis from each store's publicly accessible pages.

App Popularity Score (0–100)

Every Shopify app in our leaderboard receives a popularity score computed from five signals:

SignalMax PointsHow It's Calculated
Category Rank27Percentile position within the app's Shopify category — higher rank = more points
Install Adoption25Detected installs across our store corpus (logarithmic scale) — measures real-world usage, not Shopify's public counts
User Rating22Average star rating on the Shopify App Store (out of 5), scaled to points
Badges & Recognition15Built for Shopify (6 pts), Featured (4 pts), Editor's Choice (3 pts), Staff Pick (2 pts)
Review Volume11Total number of reviews (logarithmic scale) — more reviews = more confidence

Install counts are derived from our own store corpus — we detect which apps each store in our database is running and aggregate those counts per app. This gives a ground-truth adoption metric independent of what's publicly reported.

Trending Product Scores

We track trending products from two separate ecosystems, each with its own scoring model:

Shopify Trending Products

Powered by our ClickHouse analytics pipeline, we monitor product-level signals across the stores in our database, aggregated twice daily:

  • Best-seller exposure — how often a product appears on best-seller lists across stores
  • Position improvements — products moving up in store rankings
  • Price movements — significant price drops signalling promotional momentum
  • New listings — freshly added products gaining traction
  • Multi-store presence — products appearing across multiple stores (wider adoption)

These signals are combined with position multipliers (products in top-5 positions score higher) and growth bonuses (week-over-week improvement) to produce a final trend score.

Amazon Trending Products

Amazon trending data is sourced from Amazon Movers & Shakers and Google Trends via SerpAPI. The trend score weighs:

  • Google Trends value (35%) — search interest over time
  • Growth rate (25%) — trajectory of demand
  • Amazon rank (20%) — position in Movers & Shakers
  • Review volume (10%) — social proof and market validation
  • Data quality bonus (10%) — verified, high-confidence data points score higher

Data Freshness & Updates

  • Store ratings are recalculated periodically as new traffic, revenue, and review data becomes available.
  • App popularity scores are updated with each full scraping cycle of the Shopify App Store.
  • Shopify trending products are aggregated twice daily from our ClickHouse pipeline.
  • Amazon trends are refreshed daily from SerpAPI.
  • Market reports are regenerated weekly or monthly depending on the report type.

Our Data Sources

EcomScout combines multiple data sources to provide comprehensive ecommerce intelligence:

  • Publicly accessible store data — product catalogs, themes, and technology stacks from Shopify stores
  • Third-party analytics providers — traffic estimates, revenue models, and marketing data (including SpyFu for SEO/SEM analysis)
  • Shopify App Store — ratings, reviews, categories, badges, and pricing from public app listings
  • Trustpilot — public customer reviews and ratings
  • Amazon & Google Trends — product demand signals via SerpAPI
  • Our proprietary store corpus — app adoption detection, product tracking, and cross-store analytics powered by ClickHouse

Who's Behind EcomScout

Germanas Latvaitis, co-founder of EcomScout

Germanas Latvaitis

Co-Founder · Vilnius, Lithuania

Before starting EcomScout, Germanas spent four years leading product at Oxylabs and Smartproxy — two of the largest web data collection platforms in the world — followed by a year as Head of Product at Geonode. Earlier he built real-time data products at Genius Sports and co-founded Wellbee, a Lithuanian mental-health platform. He currently also serves as CTO of RatePunk, a travel-tech product with millions of users.

That background is the reason EcomScout exists: scraping a million storefronts reliably, deduplicating messy product data, and turning it into scores you can audit is exactly the kind of problem he's worked on for a decade.

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Company Information

Legal Name
Ecomscout, MB
Company Code
307547929
VAT Number
LT100019793112
Registered Address
Girulių g. 20, LT-12123 Vilnius, Lithuania
General Enquiries
[email protected]

Store Owner Requests

If you are a store owner and would like your store removed from our directory, please contact us at [email protected]. We honour all removal requests promptly — your store will be delisted from public listings, removed from search results, and blocked from API access. See our Terms and Conditions for details.

Get in Touch

Have a question, partnership idea, or data request? We'd love to hear from you.