How to Scrape Shopify App Store Reviews (and Track Competitor Churn) in 2026

Note: this uses a tool I built, but the parsing approach works for any Shopify App Store scraping project.

If you build Shopify apps — or invest in them — the App Store is a goldmine of competitive intelligence. The trick most people miss: reviews come with the reviewing merchant's store name, country, and how long they used the app. That turns "3 new 1-star reviews" into "here are the named stores leaving my competitor this week, and why."

The good news: it's server-rendered HTML

Unlike a lot of modern sites, Shopify App Store pages render on the server. No headless browser needed.

• App details live in a JSON-LD block (<script type="application/ld+json">): name, aggregateRating, category. Developer, launch date, and pricing plans are in the surrounding markup.
• Reviews are chunked by a data-merchant-review marker. Each chunk has the star rating (aria-label="X out of 5 stars"), date, review text, and — three lines above the "using the app" tenure string — the store name and country.

The reviews endpoint even supports filtering:

https://apps.shopify.com/<slug>/reviews?page=1&sort_by=newest&ratings[]=1&ratings[]=2

That ratings[] filter plus sort_by=newest is the whole churn-monitoring machine: only new 1–2 star reviews, newest first.

The monitoring pattern

Scrape newest-first, stop when reviews get older than your last run's date, and you have a cheap weekly "who's unhappy with competitor X" pipeline. Pair it with star filters and you're watching exactly the merchants most likely to switch.

The no-code way

I published a Shopify App Store Scraper on Apify that returns flat JSON: one item per app, one per review, with store/country/tenure attached.

curl -X POST "https://api.apify.com/v2/acts/ethanteague~shopify-app-store-scraper/run-sync-get-dataset-items?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"appUrls": ["stockie"], "maxReviewsPerApp": 200, "reviewsNewerThan": "2026-01-01", "starFilters": ["1","2"]}'

Example review item:

{
  "appSlug": "stockie",
  "store": "Woolyn",
  "country": "United States",
  "tenure": "4 days using the app",
  "stars": 4,
  "dateIso": "2026-07-15",
  "text": "Pros: The most robust PO ordering system I've found..."
}

Pricing is $2 per 1,000 reviews, $5 per 1,000 apps — schedule it weekly and you've got a standing competitive-intel feed.

What to build

• Churn-watch on competitors (the reviewers are named stores → warm-lead signal)
• App-idea validation: mine every complaint in a category before writing code
• Portfolio monitoring: ratings and pricing across dozens of apps
• A market-data feed for an AI agent via MCP

Comments open for edge cases.