Find your competitor's weak spots: export App Store and Google Play reviews and let AI sort the complaints

A developer built an Apify scraper that exports App Store and Google Play reviews into a uniform schema, letting teams filter 1-3 star complaints and feed them to Claude or ChatGPT for thematic ranking. The workflow surfaces competitor weak spots by grouping complaints, weighting them by thumbsUpCount, and flagging themes that began after a specific appVersion. Apple caps exports at the latest 500 reviews per app, country and sort order, while Google Play has no fixed limit; the Actor is priced at $0.25 per 1,000 reviews.

Before you build an app, the most useful research is free and public: what people hate about the apps that already exist. It sits in the 1- and 2-star reviews on the App Store and Google Play. The problem is getting it out. The stores show a few reviews at a time, Apple and Google format them differently, and copy-pasting 2,000 reviews into a chat window is not a plan. This post shows a simple workflow: I'll use the App Store & Google Play Reviews Scraper https://apify.com/trendcraft/app-reviews-scraper on Apify. Disclosure: I built it. It takes App Store and Google Play links side by side and returns the same fields for both, which is what makes the AI step easy. https://apps.apple.com/us/app/.../id1234567890 https://play.google.com/store/apps/details?id=com.example.app Plain IDs work too 1234567890 , com.example.app . us , gb , de ... Reviews are collected separately for each country. 500 . newest to see what people complain about Every row looks the same, whichever store it came from: { "store": "googlePlay", "appName": "ChatGPT", "country": "us", "rating": 2, "title": null, "text": "Keeps logging me out after the last update...", "date": "2026-10-19T14:18:56", "appVersion": "1.2026.279", "thumbsUpCount": 14 } rating , appVersion and thumbsUpCount are the useful columns: you can filter to low ratings, see which version broke something, and weight complaints by how many people agreed. Limits to know: Apple only shares the latest 500 reviews per app, country and sort order. To get more from the App Store, add countries or run both sort orders. Google Play has no fixed limit. You don't need 5-star "great app " reviews for this. In Google Sheets or Excel, filter rating to 1, 2 and 3 . For a popular app, that's usually a few hundred rows, small enough for any AI chat. Or in Python: python import pandas as pd df = pd.read csv "dataset.csv" bad = df df "rating" <= 3 .sort values "thumbsUpCount", ascending=False bad "store", "rating", "appVersion", "thumbsUpCount", "title", "text" .to csv "complaints.csv", index=False print len bad , "complaints saved" Upload complaints.csv to Claude or ChatGPT and use a prompt like this: These are 1-3 star reviews of APP NAME from the App Store and Google Play. 1. Group them into themes bugs, pricing, missing features, UX, support... . 2. For each theme give: number of reviews, total thumbsUpCount, and 2 short quotes. 3. Rank themes by how painful and how common they are. 4. List the top 5 feature requests people explicitly ask for. 5. Note any theme that started after a specific appVersion. Be concrete. Do not invent reviews that are not in the file. What you get back is a ranked map of the competitor's weak spots, with real quotes as evidence. The "started after version X" question often finds the best opportunities: users who were happy until an update broke something are actively looking for an alternative. A few patterns that come up again and again: Run the same export for 3-5 competing apps and compare the themes side by side. That's an afternoon of work instead of weeks of guessing. The official apify-client package runs the same scraper from Python: python from apify client import ApifyClient client = ApifyClient "YOUR APIFY TOKEN" run = client.actor "trendcraft/app-reviews-scraper" .call run input={ "apps": "389801252", "com.instagram.android" , "countries": "us", "gb" , "maxReviewsPerApp": 500, "sort": "newest", } reviews = list client.dataset run "defaultDatasetId" .iterate items print len reviews , "reviews" Results are priced per review; the current price is on the Actor's Pricing tab. At the time of writing it is $0.25 per 1,000 reviews, so a 2,000-review study costs about fifty cents. If you only need Google Play reviews and are comfortable with Python, the free google-play-scraper library works well. The Actor is useful when you want both stores in one format , several countries in one run, a scheduled daily export, or no code at all. Questions or a missing field? Open an issue on the Actor's Issues tab. This post was drafted with AI assistance; input names and output fields were checked against the Actor itself.