Apify app store optimization reviews

App Store Optimization From Reviews: Complaints, Ratings, and Version Trends

Work the review side of app store optimization. One Actor call pulls the most critical or most recent Apple App Store reviews for your app and its competitors, and the rows carry everything an ASO audit needs: rating, full review text, reviewed version, country store, and dates.

When to use this skill

• The user wants ASO insight from reviews: what users complain about, what they praise, what to fix before the next listing update.
• They want a competitor review audit: pull the most critical reviews for rival apps and compare themes.
• They want to know how an update landed: group ratings by reviewed_version before and after a release.
• They want per-country feedback to guide localized listings, using the same app across different stores.
• They ask about "review sentiment", "why are we getting 1-star reviews", or "what do reviews say about app X".

Not for: App Store keyword rankings or search visibility (use the Apple App Store Search Actor), listing metadata such as title, price, or screenshots (use the Apple App Store Product API Actor), or Google Play reviews.

What each review row carries

review_id, review_title, review_text, rating (integer 1 to 5), review_date plus review_date_iso, reviewed_version (the version trend axis), author_name, author_id, product_id, app_platform, app_country, sort_order, page_number, position_on_page, position_global, total_page_count, reviews_for_current_version, fetch_timestamp. When a review carries helpfulness votes and parse_helpfulness is on, helpful_count and total_helpful_count are added; most rows do not have them.

Prerequisites

• Apify account (sign up at https://apify.com?fpr=9n7kx3&fp_sid=skillrepo).
• Authentication via apify login, or an APIFY_TOKEN environment variable (Apify Console, Settings, Integrations).

The Actor

• Store page: https://apify.com/johnvc/apple-app-store-reviews-api?fpr=9n7kx3&fp_sid=skillrepo
• Actor ID: johnvc/apple-app-store-reviews-api
• Pricing: pay per review returned, plus a small per-run setup fee (see references/gotchas.md).

Run it with the Apify CLI

Pull the most critical reviews for your app plus two competitors in one run:

apify actors call "johnvc/apple-app-store-reviews-api" -i '{"product_ids":["534220544","363590051","324684580"],"country":"us","sort":"mostcritical","max_reviews":100}' \
  --json \
  --user-agent apify-awesome-skills/apify-app-store-optimization-reviews \
  2>/dev/null

Pull recent reviews for the same app from a non-US store for localization feedback:

apify actors call "johnvc/apple-app-store-reviews-api" -i '{"product_ids":["534220544"],"country":"de","sort":"mostrecent","max_reviews":100}' \
  --json \
  --user-agent apify-awesome-skills/apify-app-store-optimization-reviews \
  2>/dev/null

Every call carries the three flags this repo expects: --json, --user-agent apify-awesome-skills/apify-app-store-optimization-reviews, and 2>/dev/null.

Run it from Claude or another AI agent (MCP)

The Actor is MCP-ready. Add the hosted server URL:

https://mcp.apify.com/?tools=actors,docs,johnvc/apple-app-store-reviews-api

Then ask, for example: "Pull the 100 most critical App Store reviews for Netflix in the US, group the complaints into themes, and tell me the top three." MCP setup docs: https://docs.apify.com/platform/integrations/mcp

Workflow

1. Define the audit set. Collect the numeric product_ids for the user's app and each competitor (the number after id in an App Store URL). One run handles the whole set.
2. Pick the lens. Complaint mining: sort: "mostcritical". Fresh pulse and update reaction: sort: "mostrecent". What fans value: sort: "mostfavorable". Note sort applies to iOS only.
3. Pick the stores. Run once per country that matters; the same app can read very differently in us, de, and jp. Review text and dates come back in the store locale.
4. Bound the volume and estimate cost. max_reviews of 100 to 200 per app is enough for themes; confirm with the user if the estimate is large. See references/gotchas.md.
5. Analyze the rows. Group complaints into themes from review_text and review_title on 1-star and 2-star rows; chart rating by reviewed_version to see how releases landed; compare theme mix across apps and across app_country.
6. Deliver findings the user can act on: the top complaint themes with example quotes, the version where a regression appeared, and per-country issues worth a localized listing or release-note fix.

Inputs

• product_ids (array of strings): the audit set, fetched sequentially in one run
• app_name (string): auto-resolve alternative when only a name is known (one of the two is required)
• country (enum, 52 codes, default us): the store under audit
• sort (enum, default mostrecent): the audit lens; mostcritical for complaint mining (iOS only)
• max_reviews (integer, default 100): per-app cap; 0 = unlimited up to 50 pages
• include_macos (boolean, default true), normalize_dates (boolean, default true), parse_helpfulness (boolean, default true)
• start_page (integer, default 1): resume a long pull

Cost

Billing is per review returned plus a small one-time setup fee per run, so a three-app audit at 100 reviews each costs well under a dollar. Repeating it across five country stores stays in the low single dollars. Batch all product_ids for a store into one run so the setup fee is paid once. Live prices and estimates are in references/gotchas.md.

Honest limits

• The Actor returns review rows, not scores: sentiment, themes, and trends are your analysis on top of review_text, rating, and reviewed_version.
• sort works on iOS apps only; macOS apps always return most recent.
• Apple serves a bounded review window per app (about 50 pages), so this is a strong sample of recent and voted reviews, not the full historical archive.
• reviewed_version reflects the version the reviewer had; users on old versions still post, so version grouping has noise.
• No keyword-ranking data in the output; that is the Search Actor's job.

Troubleshooting

• Complaint set looks thin: raise max_reviews, or the app simply has few critical reviews in that store (check total_page_count).
• Reviews in the wrong language: country sets the store and locale; run per store rather than expecting one global feed.
• Same rows across audit runs: expected for slow-moving apps; dedupe on review_id and diff against the previous audit.
• Wrong app in the set: app_name matched a different app; use exact product_ids for audits.

See references/gotchas.md for cost guardrails and error recovery, and references/actor-index.md for the Actor routing table.

Related Apple App Store Actors

• Apple App Store Search (keyword rankings, the other half of ASO): https://apify.com/johnvc/apple-app-store-search?fpr=9n7kx3&fp_sid=skillrepo
• Apple App Store Product API (listing metadata for the same app IDs): https://apify.com/johnvc/apple-app-store-product-api?fpr=9n7kx3&fp_sid=skillrepo