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 (): 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//reviews?page=1&sort_by=newest&ratings[]=1&ratings[]=2 Enter fullscreen mode Exit fullscreen mode 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"]}' Enter fullscreen mode Exit fullscreen mode 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..." } Enter fullscreen mode Exit fullscreen mode 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.