Shopify Store Intelligence - Competitor Analysis & Tech Stack Detector

Deep dive into any Shopify store: Extract ALL products, detect installed apps and the base theme, analyze pricing strategy, and measure review coverage. One-time comprehensive store analysis for competitor research and market intelligence.

Every number in the output is measured, never modelled. This actor does not publish a sales estimate, because a Shopify storefront does not expose order volume and any "estimated sales" figure would be a guess dressed up as data. Fields that could not be measured are null and status_reason says why.

🎯 What Does This Actor Do?

This Shopify Store Intelligence Scraper provides a comprehensive, one-time deep analysis of any Shopify store - going far beyond simple product scraping.

Unlike basic product scrapers that just list items, this actor delivers complete business intelligence per store:

✅ Complete Product Catalog - All products, variants, pricing, inventory ✅ Tech Stack Detection - Base theme + merchant theme name/version + 20+ app categories ✅ Pricing Intelligence - Min/avg/max prices, discount analysis ✅ Review Signals - Review coverage and average rating, measured on real product pages ✅ Competitive Positioning - Category focus, pricing strategy ✅ Store Age & History - Launch date estimation ✅ Inventory Health - Stock availability percentage ✅ Best-Selling Products - Top 10 by collection position

Output: ONE comprehensive intelligence record per store (not per product!)

💼 Use Cases

🔍 Competitor Research

Understand rivals' product mix, pricing strategy, and technology choices. See what apps successful stores use for reviews, email marketing, upselling, and more.

💰 Dropshipping Intelligence

Find winning products and compare stores by catalogue size, price band, discount depth and how much review social proof they have actually accumulated.

📊 Market Analysis

Benchmark your store against competitors. Compare catalog size, pricing, review coverage, and technology adoption.

🔌 Tech Stack Discovery

See what apps and themes successful stores use. Perfect for agencies prospecting stores that need better tools or optimization.

🎯 E-commerce Market Research

Gather data on pricing strategies, product categories, and business metrics across multiple stores in your industry.

🚀 Features

📦 Complete Product Catalog Analysis

• Scrapes ALL products with variants, pricing, and inventory
• Calculates price ranges (min/avg/max)
• Identifies products on sale with average discount percentage
• Breaks down categories and product types
• Analyzes vendor/brand distribution

🔌 Tech Stack & App Detection

Automatically detects installed Shopify apps via HTML pattern matching:

Reviews: Judge.me, Yotpo, Stamped.io, Loox, Okendo Email Marketing: Klaviyo, Omnisend, Privy Upsell/Cross-sell: Rebuy, Zipify, Bold Upsell Search: Searchanise, Klevu, SearchSpring Loyalty: Smile.io, Yotpo Loyalty Analytics: Google Analytics, Segment, Hotjar Customer Support: Gorgias, Zendesk, Tidio Payments: Afterpay, Affirm, Klarna Personalization: Nosto, Dynamic Yield Social Proof: Fomo, Prove Source

Plus: base theme (theme_base_name, e.g. Dawn, Impulse, Prestige), the merchant's own theme name, theme version and Shopify theme ID. Most merchants rename their theme copy — theme_name comes back as things like (24 Jun-2024) Worked of the American-Art-Decor, so group and filter on theme_base_name.

💰 Measured Store Metrics

• Review coverage percentage — share of the sampled product pages (up to 15) that show reviews
• Average reviews per product and average product rating across those sampled pages
• Inventory health (% variants in stock)
• Store age estimation (from oldest product date)

Review figures are measured on a sample of up to 15 real product pages per store, not on the whole catalogue — the field titles and descriptions say so, and review_coverage_pct is the share of that sample.

📈 Competitive Positioning

• Price strategy analysis (min/avg/max across catalog)
• Category distribution and focus areas
• Discount strategy (% products on sale, average discount)
• SEO quality indicators (images per product, description coverage)
• Collection count and organization

📥 Input

Simple configuration - just add store URLs:

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

Parameters:

• mode - url to analyse the URLs you supply, discovery to pull store domains from a directory first
• storeUrls (required in URL mode) - Array of Shopify store URLs to analyze
• category / maxStores / maxPages - discovery mode only
• proxyConfiguration (optional) - Enable Apify proxies for large stores

📤 Output

Every URL you submit gets exactly one row — nothing is ever dropped silently.

Each row starts with a status field that tells you what happened to that URL:

status	Meaning	Billed?
analyzed	Full intelligence below	Yes
not_shopify	The site is genuinely not a Shopify store	No
rate_limited	The store's product API throttled us even after retries with backoff	No
blocked	It is a Shopify store, but its public product API is closed (password / region / app block)	No
unreachable	DNS, TLS, timeout or server error	No
skipped_run_timeout	The run hit its time limit before reaching this URL	No
budget_exhausted	The run reached its maximum charge before this URL could be delivered	No

status_reason carries the plain-language explanation. On non-analyzed rows every analysis field is null — no placeholder numbers, no "Unknown", no invented dates.

The run also writes an OUTPUT record to the key-value store with per-URL counts and reasons, and the run fails loudly (instead of reporting a green empty result) when none of your URLs could be analysed for a reason on our side.

If you see rate_limited rows, switch Proxy Settings on and pick the RESIDENTIAL group — retries then come from fresh IPs.

An analyzed row carries 41 intelligence fields:

{
  "status": "analyzed",
  "status_reason": null,
  "store_domain": "gymshark.com",
  "shop_name": "gymshark.myshopify.com",
  "country": "US",
  "currency": "USD",
  "locale": "en-US",

  "theme_name": "Gymshark/main",
  "theme_base_name": "Dawn",
  "theme_id": 123456789,
  "theme_version": "1.0.0",
  "apps_installed": ["Judge.me", "Klaviyo", "Rebuy"],
  "apps_by_category": {
    "Reviews": ["Judge.me"],
    "Email Marketing": ["Klaviyo"],
    "Upsell": ["Rebuy"]
  },
  "apps_count": 3,

  "total_products": 1234,
  "total_variants": 5678,
  "collections": ["Men", "Women", "Accessories"],
  "collections_count": 12,
  "categories": ["T-Shirts", "Hoodies", "Leggings"],

  "price_min": 15.00,
  "price_avg": 45.50,
  "price_max": 120.00,
  "products_on_sale": 234,
  "discount_pct_avg": 15.5,

  "products_with_reviews": 11,
  "review_coverage_pct": 73.3,
  "avg_reviews_per_product": 123,
  "avg_rating": 4.5,

  "total_variants_available": 4500,
  "inventory_availability_pct": 79.3,

  "estimated_launch_date": "2019-03-15",
  "oldest_product_date": "2019-03-15",
  "newest_product_date": "2025-10-27",

  "products_with_images": 1234,
  "avg_images_per_product": 4.5,
  "products_with_description": 1200,

  "top_products": [
    {
      "title": "Premium Hoodie",
      "url": "https://gymshark.com/products/premium-hoodie",
      "price": 55,
      "reviews": 1234,
      "rating": 4.8
    }
  ],

  "scraped_at": "2025-10-27T15:30:00Z",
  "scrape_duration_sec": 45
}

products_with_reviews and review_coverage_pct describe the sample of up to 15 product pages, not the full catalogue — a 953-product store with review_coverage_pct: 73.3 means 11 of the 15 sampled pages showed reviews.

Output Format

• Flat structure - Easy to import into spreadsheets, databases, n8n, Zapier
• Predictable fields - Same fields every time (uses null for missing data)
• No nested complexity - Ready for automation tools
• Clean arrays - Collections, categories, vendors, apps
• No modelled figures - every number is measured; nothing is extrapolated or assumed

🔧 How It Works

1. Verifies Shopify store - Checks /products.json endpoint
2. Fetches homepage HTML - Extracts base theme, merchant theme name, shop metadata
3. Detects installed apps - Pattern matching on CDN scripts, HTML comments, known app signatures
4. Scrapes all products - Via Shopify's /products.json pagination API
5. Samples reviews - Up to 15 real product pages per store (Judge.me, Yotpo, Stamped, Loox, Okendo, JSON-LD)
6. Fetches collections - Via /collections.json endpoint
7. Calculates metrics - Aggregates pricing and inventory from the catalogue it actually read
8. Outputs intelligence - ONE comprehensive record per store

Technology: HTTP client only (no browser needed) - fast and efficient!

💲 Pricing

Pay-per-store analyzed (pay-per-event model)

Why This Pricing?

• Traditional product scrapers provide just raw product data
• Store Intelligence provides complete analysis including products, tech stack, apps, business metrics, and insights
• One-time deep analysis (not ongoing monitoring) = Maximum value

See current pricing in the Apify Console when starting a run.

Cost scales with the number of stores analyzed. Perfect for competitor analysis and market research.

⚡ Performance

• Speed: about 5-20 seconds per store, driven by catalogue size (a 2,700-product store takes longest)
• Reliability: HTTP API-based (no browser = no blocking)
• Scalability: Analyze multiple stores in one run
• Data quality: no placeholder values, no "N/A", no invented numbers. A field that could not be measured is null and status_reason explains it.

🆚 Comparison: Why Choose This Over Product Scrapers?

Feature	Product Scrapers	Store Intelligence
Price	Per-product pricing	Per-store pricing
Output	100+ records (per product)	1 record (per store)
Data	Product details only	Products + Apps + Metrics
Tech Stack	❌ Not included	✅ Theme + Apps detected
Business Metrics	❌ Not calculated	✅ Pricing, apps, review coverage
Use Case	Price monitoring	Competitor research
Value	Low (raw data)	5× Higher (insights)
Integration	Complex (many records)	Easy (one flat record)

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📬 Custom Solutions & Enterprise

Need a custom data feed, modified output format, or enterprise integration?

Contact: Furkanc58@gmail.com

I offer:

• Daily/weekly data feeds (Snowflake, S3, BigQuery, Google Sheets)
• Custom scrapers for platforms not yet covered
• White-label solutions for agencies
• Priority support and SLAs

Response within 24-48 hours.

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Legal Disclaimer

This actor is a general-purpose tool for analyzing publicly accessible web data. The user bears sole responsibility for ensuring their specific use complies with:

• Applicable laws (GDPR/DSGVO, copyright law)
• The target website's Terms of Service
• Apify's Terms of Service

The provider (webdatalabs) expressly disclaims liability for any unauthorized or unlawful use. By using this actor, the user agrees to indemnify the provider against any third-party claims arising from their use of the data.

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This tool is not affiliated with Shopify. All trademarks belong to their respective owners.

🔍 SEO Keywords & Searchability

What this actor helps you discover:

• Shopify competitor analysis
• Shopify store analytics
• Shopify app detector
• Shopify theme detector
• E-commerce market research
• Dropshipping product research
• Shopify store scraper
• Competitor pricing intelligence
• Shopify tech stack analysis
• Store performance metrics
• Shopify base theme detector
• E-commerce business intelligence
• Shopify market intelligence
• Store catalog analyzer
• Shopify apps list detector

Industries & Niches: Fashion, Apparel, Shoes, Accessories, Beauty, Cosmetics, Health, Fitness, Sports, Electronics, Home Goods, Pet Supplies, Jewelry, Dropshipping, Print on Demand

📊 Example: Real Store Analysis

Store: Allbirds.com (Sustainable Footwear Brand)

Results:

• 717 products across 6 categories
• Wide price range with competitive average pricing
• 631 products on sale (31% average discount)
• 30 collections, including Men's/Women's shoes
• Theme: merchant copy Allbirds v1.51.1, base theme Dawn
• Review coverage: 9.9% of the sampled product pages (4.4★ average)
• 82% inventory availability
• Store launched: October 2019

Time: 58 seconds

🛠️ Technical Details

Requirements

• Valid Shopify store URL (e.g., https://store.com or https://store.myshopify.com)
• Store must be publicly accessible (not password-protected)

Detection Methods

Apps Detection:

1. HTML comments: <!-- BEGIN app block: shopify://apps/... -->
2. CDN scripts: cdn.shopify.com/s/files/.../[app].js
3. Known signatures: CSS classes, data attributes, script patterns

Theme Detection:

• Shopify.theme JavaScript object
• Theme name, ID, schema version

Shop Metadata:

• Shopify.shop, Shopify.country, Shopify.currency, Shopify.locale

Review Sampling:

• Up to 15 real product pages per store, read directly
• Judge.me, Yotpo, Stamped, Loox, Okendo badges plus JSON-LD aggregateRating

📚 Frequently Asked Questions

Q: Can I use this for ongoing price monitoring? A: This is designed for one-time deep analysis. For ongoing monitoring, use a product scraper scheduled daily.

Q: How many apps can it detect? A: 20+ major app categories with 40+ specific apps. Detection rate varies by store's implementation.

Q: What if a store has 10,000+ products? A: Actor scrapes ALL products. Larger stores take longer (2-5 minutes) but still output one comprehensive record.

Q: Can I integrate with n8n/Zapier/Make? A: Yes! Output is flat JSON, perfect for automation tools. One record per store = easy to map.

Q: Does it work with custom Shopify themes? A: Yes! Works with both Shopify themes and custom implementations as long as the store uses Shopify's product API.

Q: What if a store blocks scraping? A: Enable Apify proxies in settings (free with Apify subscription). HTTP API access is rarely blocked.

Q: Can I get historical data? A: No, this provides current snapshot only. For trends, run periodically and compare results.

Q: Is this legal? A: Yes. All data is publicly accessible via Shopify's standard product JSON feeds and public HTML.

🎓 Examples & Tutorials

Use Case 1: Competitor Analysis

# Analyze top 5 competitors
{
  "storeUrls": [
    { "url": "https://competitor1.com" },
    { "url": "https://competitor2.com" },
    { "url": "https://competitor3.com" },
    { "url": "https://competitor4.com" },
    { "url": "https://competitor5.com" }
  ]
}

Compare:

• Pricing strategies (average prices, discount rates)
• Tech stack adoption (which apps they use)
• Catalog sizes (product/variant counts)
• Review strategies (coverage %, ratings)

Use Case 2: Agency Prospecting

# Find stores missing key apps
# 1. Run actor on target stores
# 2. Filter results where apps_count < 5
# 3. Reach out offering app installation services

Use Case 3: Market Research

# Analyze entire niche (e.g., sustainable fashion)
# 1. Identify 20-50 stores in niche
# 2. Run actor on all stores
# 3. Export to Excel/Google Sheets
# 4. Calculate industry benchmarks:
#    - Average price points
#    - Most common apps
#    - Review coverage standards
#    - Typical catalog sizes

🔗 Integration Examples

Google Sheets

1. Run actor on Apify
2. Download dataset as CSV
3. Import to Google Sheets
4. Use pivot tables for analysis

n8n Workflow

Trigger (Schedule/Webhook)
  → Apify Actor Node (Store Intelligence)
  → Filter Node (apps_count < 3)
  → Email Node (Send opportunities to sales team)

Zapier Integration

1. Trigger: New row in Google Sheets (competitor URLs)
2. Action: Run Apify actor
3. Action: Add results to Airtable
4. Action: Notify Slack channel

📞 Support & Feedback

Questions or issues? Email: via Apify

Feature requests? We're actively improving detection of:

• More Shopify apps (currently 40+, expanding to 100+)
• Additional business metrics
• Historical data comparison
• Multi-currency support

🏷️ Tags

shopify ecommerce competitor-analysis market-research business-intelligence app-detector theme-detector pricing-intelligence base-theme-detector dropshipping store-analytics tech-stack shopify-scraper store-intelligence

📈 Changelog

v1.1 (2026-07-27)

• Removed total_estimated_sales and avg_sales_per_product. Both were extrapolations built on an assumed 2% review rate applied to a top-of-catalogue sample, so they were guesses, not measurements. A Shopify storefront does not publish order volume, so no honest replacement exists.
• Removed discovery_category (it only ever echoed the category you typed into the input) and shopify_theme_directory (a third-party mirror that was empty on every URL-mode row).
• Added theme_base_name: the parent Shopify theme the storefront is built on, read from the storefront itself, in both modes.
• Review coverage, average reviews per product and average rating are now collected on every run. The extractReviews toggle is gone; passing it does nothing.

v1.0.0 (2025-10-27)

• Initial release
• Complete product catalog scraping
• Tech stack detection (themes + apps)
• Pricing and inventory analysis
• Review sampling on real product pages
• Top products identification
• Pay-per-event pricing model

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Built with ❤️ by [WebDataLabs](mailto:via Apify)

Perfect for e-commerce entrepreneurs, dropshippers, market researchers, agencies, and anyone needing competitive intelligence on Shopify stores.