App Store Review Analyzer: Feature Roadmap
Pricing
$250.00 / 1,000 evidence reports
App Store Review Analyzer: Feature Roadmap
Collect public Apple App Store review feeds or import reviews, then group feature requests, usability complaints and recurring issues into an evidence-linked roadmap research report.
Pricing
$250.00 / 1,000 evidence reports
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Technical Dost Solutions
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Prioritize recurring review complaints and feature requests before a product-planning session. Each finding retains evidence, review counts and transparent heuristic priority factors.
Import a ready-to-run workflow
Use the tested starter inputs in n8n JSON or the Make blueprint. Setup instructions explain import, credentials, output fields and recovery. Download both platforms as a ZIP.
Select your own Apify credential after importing and run manually first. The templates set a $1 maximum run budget, check run success and preserve useful output. The $1 setting is a ceiling, not a fixed charge. Source availability and provider execution limits still apply. No recurring schedule or external destination is enabled by these files.
First run
Live sample: $0.25 per completed report. Replace the example Apple app ID with your app’s numeric ID, choose its storefront, and run. Leaving the example selected fetches real public reviews for app ID 310633997. The demo checkbox does not make this live sample free.
Native collection is limited to public Apple App Store customer-review feeds. Feed coverage varies by app and storefront.
Analyze your own data
Use a numeric Apple App Store app ID and storefront country, or import app reviews. Public Apple review feeds provide a bounded sample; complete historical coverage and Google Play are not supported.
For an existing dataset, select it with the dataset picker in the input form. The API equivalent is below; replace the placeholder before running:
You can instead paste objects into records. Each record should contain text and its original sourceUrl; optional fields include id, platform, brand, location, rating, date, likes and title. Supplied sources override synthetic demo mode. Dataset reads do not rerun the collector. Any separate upstream collection workflow is outside this report price.
What comes back
One report row in the default dataset contains an insights array, analyzed-record count and finding count. The output links also provide REPORT.md and REPORT.json in the key-value store. Use JSON to preserve nested evidence; use the Markdown report for review.
Each roadmap finding includes evidence, request examples and priority factors such as sample reach, low ratings and matched severity phrases. Acceptance checklists are research templates, not validated product requirements.
Record limits and sample comparison
maxRecords caps raw current records examined at 1–1,000 before deduplication and filtering. maxInsights caps findings at 1–30. A small or unmatched batch can return fewer findings, including zero. The fee is per delivered report, not per finding.
Supply previousDatasetId to compare the current batch with earlier raw review records. This describes differences between supplied samples, not a verified chronological trend. Do not pass a dataset containing completed reports.
Pricing
$0.25 per delivered report, covering at most 1,000 current raw records. This includes analysis of inline records, existing datasets or native Apple reviews. The actor uses one report-delivered event and no additional dataset-row event. See the Pricing tab for the current platform price.
Synthetic previews on the imported-data workflows have no report-event fee. The App Store live sample uses actual reviews and the normal $0.25 report price.
Method and recurring use
Analysis uses deterministic English phrase/rating rules and text grouping; no generative AI model is called. Scores and draft actions are research aids. Sarcasm, negation, uncommon phrasing and multilingual text can be misclassified. Inspect supporting evidence before acting. Topics can overlap, so their percentages must not be added together. Samples do not establish market-wide demand, product capabilities or future business results.
Use the Actor API or Apify MCP with the same input schema. Imported datasets are snapshots; provide fresh source data for repeated analysis. This actor does not directly scrape YouTube, Reddit or Google Maps or start third-party collectors. No external model key is required.
Local run
Requires Node.js 22 or newer. Run npm ci, then npm start. The Dockerfile uses apify/actor-node:22.
Input example
All fields are described in the Input tab, and the same JSON works in the API and for AI agents.
Output example
Each result is one dataset item. A real item from a test run (long fields shortened):
Use App Store Review Analyzer with AI agents (MCP)
Give Claude, ChatGPT, Cursor, VS Code or any MCP client this Actor as a tool through the hosted Apify MCP server. The agent sees the input fields with descriptions and example values, runs the Actor, and reads the results.
MCP server URL (this Actor only):
Claude Code:
Cursor, VS Code, Claude Desktop and other clients (mcp.json or the client's MCP settings):
Clients that support sign-in (such as Claude.ai connectors) can use the URL without a token and log in to Apify instead. Get your token in Apify Console → Settings → API & Integrations, or generate a ready config for your client at mcp.apify.com. In the agent the tool is named technicaldost--app-review-feature-roadmap; ask it, for example, to "run App Store Review Analyzer with {"demoMode": false, "sourceType": "app-store", "appIds": ["310633997"], "country": "us", "maxPages": 1, "maxRecords": 40}".
AI agents without an Apify account can also run and pay for this Actor per request with x402 agentic payments.
Run App Store Review Analyzer via API
Run the Actor and get its results in one call (replace YOUR_APIFY_TOKEN):
Python (pip install apify-client):
JavaScript / TypeScript (npm install apify-client):
Long runs: start them with POST /v2/acts/technicaldost~app-review-feature-roadmap/runs and fetch the dataset when the run finishes, or receive a webhook.
Related Actors
- App Store Review Scraper (Apple iOS) — Scrape Apple App Store reviews and app metadata: rating, title, text, author, version, plus average rating and
- Competitor Review Analyzer: Brand Comparison — Compare imported customer-feedback records by brand to surface recurring complaints, strengths and product gap
- B2B SaaS Review Buyer Intent Monitor — Extract review-site complaints, alternatives, and buyer-intent signals for SaaS competitors
- Trustpilot Company Review Scraper — Scrape Trustpilot company reviews at scale: rating, title, text, author, date and reply. Analyze customer sent
FAQ
Can I use App Store Review Analyzer with AI agents like Claude or ChatGPT?
Yes. Connect the Apify MCP server with ?tools=technicaldost/app-review-feature-roadmap (see above) and the agent can run the Actor and read its results on its own.
Can I call App Store Review Analyzer from my own code?
Yes. Use the REST API, or the official Python and JavaScript clients shown above. Results are available as JSON, CSV, Excel, XML or HTML.
How do I run it on a schedule?
Save your input as a task in Apify Console and add a schedule (for example, daily). Use webhooks or integrations to send each run's results onward.
Is it legal to use this Actor?
It works with publicly available data. Make sure your use complies with the source website's terms and with data protection laws such as GDPR, especially for personal data.
Something doesn't work or I need a feature. What should I do?
Open an issue in the Issues tab on this Actor's page with an example input, and we'll look into it.
